Clean Energy Technologies
Energy effi ciency versus renewable energy technologies
Clean energy technologies encompass energy-efficient solutions and renewable energy technologies (RETs), both of which aim to decrease reliance on conventional energy sources like fossil fuels While they share the common goal of reducing energy consumption, they differ significantly in their approaches and applications.
Energy efficiency measures are strategies aimed at reducing energy consumption in providing goods or services, particularly in heating, cooling, or electricity generation For instance, efficient refrigeration systems that incorporate waste heat recovery can deliver the same cooling performance as traditional systems while consuming considerably less energy These energy-saving techniques can be implemented across diverse sectors and applications, enhancing overall energy efficiency.
Clean energy technologies focused on energy efficiency encompass a range of solutions such as combined heat and power systems, advanced refrigeration technologies, energy-efficient lighting, ventilation heat recovery systems, variable speed motors for compressors and fans, enhanced insulation, and high-performance building envelopes and windows, along with other innovative and emerging technologies.
Renewable energy technologies convert renewable resources into usable forms of energy, such as heat, cooling, electricity, or mechanical power Unlike fossil fuels, which deplete with use, renewable resources like solar energy remain abundant and sustainable for future generations For instance, utilizing solar energy for heating does not diminish the availability of sunlight, whereas overharvesting biomass, such as trees, can lead to deforestation and render the resource non-renewable Proper management is essential to maintain the sustainability of renewable energy sources.
3 See Appendix A for a detailed list of experts involved in RETScreen International.
Worldwide Energy Consumption by Sector [adapted from World Resources Institute, 2003]
Energy Consumption in Commercial Buildings in the United States [adapted from Swenson, 1998]
Renewable energy technologies (RETs) encompass systems that transform sunlight into electricity, heating, and cooling They also harness energy from wind, hydroelectric sources, ocean waves, and tidal movements to generate electricity Additionally, RETs can extract heat from the ground or provide cooling by transferring heat back into the ground.
Project planners should prioritize cost-effective energy efficiency measures before exploring renewable energy technologies (RETs) Often, simple adjustments can significantly lower energy consumption with minimal investment, making it more economical than achieving similar reductions solely through RETs By decreasing the energy demand that RETs need to meet, these efficiency measures allow for the implementation of smaller, more affordable renewable energy systems Since RETs usually involve high upfront costs, investing in energy efficiency can enhance the financial viability of renewable energy solutions.
To reduce conventional energy consumption in a cold climate, focus on enhancing the building envelope with high insulation levels, minimal thermal bridging, and airtight construction to minimize heat loss during winter Next, design heating and cooling systems and select appliances that prioritize energy efficiency Finally, explore renewable energy technologies, such as solar water heating and photovoltaics, to harness electricity directly from sunlight.
Installing a photovoltaic system on this house's roof may attract more attention from neighbors compared to enhancing the building envelope; however, it would be less effective in reducing energy consumption and come with a significantly higher cost.
Many projects can achieve a significant reduction in energy consumption—up to 50%—by implementing commercially available efficiency measures compared to standard practices Additionally, integrating cost-effective renewable energy technologies can further decrease or even completely eliminate the remaining reliance on conventional energy sources.
Effi ciency Measures, Passive Solar Design and a Solar Water Heating System Combined in a Residential Application in Canada
The line between energy-efficient technologies and renewable energy technologies (RETs) can often be unclear For instance, high-performance windows that minimize heat loss can enhance a home's energy efficiency, but when strategically oriented and shaded, they also facilitate passive solar heating in winter, qualifying them as RETs Likewise, ground-source heat pumps efficiently utilize electricity to heat homes, while the heat they extract from the ground ultimately originates from solar energy Regardless of these distinctions, the primary objective remains consistent: to save money and reduce reliance on conventional energy sources through clean energy technologies.
Reasons for the growing interest in clean energy technologies
Clean energy technologies are receiving increasing attention from governments, industry, and consumers This interest reflects a growing awareness of the environmental, economic, and social benefits that these technologies offer.
Environmental concerns regarding global warming and local pollution drive the development of clean energy technologies in the 21st century Global warming refers to the rising average temperatures observed globally, primarily due to increased emissions of greenhouse gases such as carbon dioxide, methane, and nitrous oxide These gases allow sunlight to enter the atmosphere but hinder the escape of heat, leading to a warming effect similar to that of a greenhouse.
Global warming poses a significant threat to both ecosystems and human populations, leading to potential extinction of numerous species due to rapid climate changes As ice caps continue to melt, rising sea levels will flood low-lying regions worldwide While average temperatures increase, the frequency of extreme weather events, including severe winter storms, is also expected to rise This shifting climate will result in some areas facing increased flooding, while others will experience drought and desertification, putting additional pressure on agricultural resources Furthermore, changing climatic conditions may allow tropical diseases, such as malaria, to spread into temperate regions like Europe and North America, disproportionately impacting communities, particularly those like Aboriginal peoples, whose livelihoods are closely linked to specific ecosystems.
Scientists widely agree that human activities, particularly the burning of fossil fuels, are the primary cause of observed global warming The combustion of oil, gas, and coal for transportation, electricity, and heating releases greenhouse gases such as carbon dioxide, nitrous oxide, and methane, significantly contributing to environmental issues Clean energy technologies help mitigate this problem by decreasing fossil fuel consumption The RETScreen Clean Energy Project Analysis Software enables users to estimate the reduction in greenhouse gas emissions when clean energy technologies replace conventional energy systems.
Global warming is just one of the many environmental issues prompting the rise of clean energy technologies Traditional energy systems contribute to both local and global pollution, with combustion processes releasing harmful compounds that worsen respiratory ailments and create urban smog Additionally, burning sulphur-rich coal leads to acid rain, while small energy systems can cause significant noise and visual pollution, impacting nearby residents For instance, a diesel generator used in a remote park would generate noise and present visual clutter from fuel containers, alongside the risk of diesel spills harming the environment These detrimental effects can be greatly mitigated by adopting clean energy solutions like photovoltaic and wind power.
Absorption of solar energy heats up the earth
NASA Goddard Space Flight Center (NASA-GSFC)
Recent growth in clean energy technology sales is largely attributed to customers who prioritize low life-cycle costs over environmental concerns Many are choosing clean energy solutions because, in the long run, these technologies can be cost-competitive or even cheaper than traditional energy options.
The unpredictability of conventional energy costs, alongside their inherent expenses, renders these systems less appealing Prices for electricity, natural gas, and oil fluctuate based on local, national, and global supply and demand dynamics Over the past decade, unexpected surges in energy prices have led to significant financial challenges for individuals, families, industries, and utilities This volatility not only impacts consumers but also raises concerns for governments, which are often held responsible for economic stability.
Conventional energy costs are expected to rise in the coming decades due to declining discovery rates of new oil reserves and increasing global demand While vast conventional reserves exist, they are concentrated in a limited number of countries Although large unconventional reserves, such as oil sands in Canada and Venezuela, are available, their production into usable fuel is costlier and results in higher greenhouse gas emissions Consequently, escalating energy prices and the potential for price shocks make clean energy technologies increasingly appealing.
RETScreen Software features advanced yet user-friendly financial analysis and sensitivity & risk analysis tools that assess the financial viability and risks associated with clean energy projects Users can explore the impact of various financial parameters, such as potential increases in energy prices, to make informed decisions.
Clean energy technologies offer significant social benefits that attract government interest, primarily through job creation, as they typically require more labor per unit of energy produced compared to conventional methods Unlike traditional energy technologies, which rely on capital-intensive, concentrated resources and constant exploration for new sources, clean energy focuses on maximizing existing resources and utilizing renewable energy technologies (RETs) that tap into more dispersed energy sources This approach necessitates greater human involvement in technology application, manufacturing, and servicing Although clean energy technologies incur higher labor costs, these are balanced by the lower costs of energy inputs, particularly evident in solar and wind energy, where the energy source itself is free.
Importing fossil fuels drains local economies, while energy efficiency measures and renewable energy technologies (RETs) leverage local resources, fostering transactions among local organizations This retention of money within the community enhances its "multiplier effect." For instance, a biomass combustion system utilizing waste woodchips supports local companies for collection, quality assurance, and delivery, thereby creating jobs and encouraging spending at local businesses In contrast, oil-fired boilers send funds to distant oil companies, limiting local economic benefits Although the global implications may vary, local governments are increasingly motivated to invest in clean energy technologies due to these advantages.
The increasing global demand for energy is a significant social and economic driver behind the interest in clean energy technologies According to the International Energy Agency (IEA), worldwide energy demand is projected to triple by 2050 due to historical trends and economic growth This anticipated expansion has prompted industries to recognize the potential opportunities and governments to seek new technologies and fuels to address this growing need, thereby fueling the interest in clean energy solutions.
Common characteristics of clean energy technologies
Several characteristics shared by clean energy technologies become apparent when they are compared to conventional energy technologies; these have already been mentioned in passing, but deserve further emphasis
Clean energy technologies are generally more environmentally friendly than traditional methods, although they still have some environmental impact that cannot be completely eliminated It is crucial to use these technologies responsibly to minimize their ecological footprint Designed to tackle significant environmental challenges, clean energy solutions offer substantial energy benefits while maintaining a much lower environmental cost compared to conventional technologies, particularly those that depend on fossil fuels.
Clean energy technologies often face higher upfront costs compared to traditional energy sources, leading some to perceive them as too expensive However, this perspective overlooks the significant operational and maintenance costs associated with all energy systems, regardless of their cleanliness.
Clean energy technologies generally offer lower operating costs compared to conventional technologies This is primarily due to efficiency measures that minimize energy requirements and the utilization of renewable energy resources, which are often accessible at minimal or no additional cost.
When evaluating clean energy technologies, it is essential to compare their high initial costs and low operating expenses with the low initial costs and high operating expenses of conventional technologies by analyzing the total lifetime costs of the project This comprehensive assessment encompasses not only initial expenditures such as feasibility studies, engineering, development, equipment procurement, and installation but also ongoing operational costs.
Annual costs for fuel and operation and maintenance;
Costs for major overhauls or replacement of equipment;
Costs for decommissioning of the project (which can be very signifi cant for technologies that pollute a site, through fuel spills, for example); and
The costs of fi nancing the project, such as interest charges.
All these costs must then be summed, taking into account the time value of money, to determine the overall “lifecycle cost” of the project
Clean energy technologies, while having higher initial costs, are often more cost-effective over their lifecycle compared to conventional technologies, particularly for specific applications The RETScreen Clean Energy Project Analysis Software is designed to help identify and tabulate all associated costs, enabling a comprehensive lifecycle analysis that allows for an accurate comparison to determine the viability of clean energy technologies for particular projects.
Renewable energy electricity generating technologies
RETScreen International focuses on various renewable energy technologies for electricity generation, with the four most commonly used being wind energy, photovoltaics, small hydro, and biomass combustion power The initial sections provide a brief overview of the first three technologies, while biomass combustion is discussed later within the combined heat and power technology section For more comprehensive details, readers can refer to the dedicated chapters for each technology.
Wind energy systems harness the kinetic energy of moving air to generate electricity or mechanical power, serving various applications such as central and isolated grids, remote power supply, and water pumping Available in a wide range of sizes, small wind turbines for off-grid battery charging and water pumping typically range from 50 W to 10 kW, while larger turbines for isolated grid applications vary from 10 to 200 kW As of 2005, the largest turbines installed on central grids are rated between 1 and 2 MW, with prototypes for shallow offshore waters reaching capacities of up to 5 MW.
A strong wind resource is essential for the success of commercial wind energy projects, as the energy generated from wind increases with the cube of wind speed, which generally rises with altitude For a viable wind energy project, the annual average wind speed must exceed 4 m/s at a height of 10 meters Additionally, specific topographical features can enhance wind speeds, making them ideal locations for wind turbine installation.
Wind resources are often found at the crests of long, gradual slopes, mountain passes, and valleys that direct winds Additionally, regions with minimal obstructions, like the sea surface near coastlines and expansive flat grassy plains, can also provide significant wind energy potential.
Since the early 1990s, wind energy has become the fastest-growing electricity generation technology globally, driven by a consistent decrease in production costs as the industry matures In areas with strong wind resources connected to the central grid, wind energy often emerges as one of the most cost-effective electricity sources, comparable to natural gas combined-cycle generation.
Small hydro systems efficiently convert the potential and kinetic energy of flowing water into electricity through turbines connected to generators By harnessing the energy from water moving from higher to lower elevations, such as in rivers and waterfalls, these systems have been a reliable technology for over a century They can supply power to central grids, isolated grids, or off-grid loads and can operate as either run-of-river systems or with water storage reservoirs.
Hydroelectricity primarily originates from large hydro projects, often exceeding several gigawatts (GW), which typically involve significant water storage behind dams In contrast, small hydro projects, with installed capacities under 50 megawatts (MW), leverage insights from larger projects but operate on a much smaller scale These smaller systems rarely necessitate the construction of extensive dams, except in certain isolated areas where electricity demand is high due to limited competition Additionally, small hydro projects can have capacities as low as 1 kilowatt (kW) for off-grid applications.
A reliable and consistent water flow is essential for the success of a commercial small hydro project, as the energy produced by a hydro turbine depends on both the flow rate and the vertical head difference Most of the costs associated with small hydro projects arise from initial construction and equipment purchases, allowing these projects to generate substantial electricity with minimal operating costs and low maintenance for over 50 years.
As opportunities for large hydroelectric developments diminish globally, the focus is shifting towards smaller sites, which present significant growth potential for the small hydro market, particularly in regions like China.
Photovoltaic systems harness solar energy to produce electricity through photovoltaic cells, which are typically made from thin semiconductor wafers When sunlight hits these cells, they generate a small electric current These cells can be combined into modules and arranged in various sizes to form larger arrays, ranging from small units used in devices like wristwatches and calculators to extensive systems with capacities exceeding 5 MW.
Photovoltaic systems offer a cost-effective solution for small off-grid applications, supplying power to rural homes in developing countries, off-grid cottages, motor homes, and remote telecommunications systems globally They play a crucial role in water pumping for domestic supplies and agriculture, particularly in developing regions where they provide essential water access to villages These systems are known for their simplicity, modularity, and high reliability, as they have no moving parts Additionally, photovoltaic systems can be integrated with fossil fuel generators to enhance energy supply.
4 In reality, this must be adjusted for various losses.
SNC-Lavalin. applications having higher energy demands or in climates characterized by extend- ed periods of little sunshine (e.g winter at high latitudes) to form hybrid systems.
Photovoltaic systems can connect to isolated or central grids through specialized inverters, but without subsidies, on-grid applications are often not cost-effective due to the high price of photovoltaic modules, despite their steady decline since 1985 Distributed generation is emerging as the preferred approach for cost-effective on-grid applications, as it involves small photovoltaic systems installed on buildings and structures, eliminating land costs Advances in technology have enabled the integration of photovoltaic systems into building designs, which helps offset conventional material and labor expenses.
Photovoltaic systems have experienced remarkable growth similar to that of wind turbines, albeit from a smaller initial capacity In 2003, the global installed photovoltaic capacity was approximately 3,000 MW, which was less than one-tenth of the wind capacity at that time Despite this smaller base, the photovoltaic industry is expanding rapidly and making significant strides in renewable energy.
Renewable energy heating and cooling technologies
RETScreen International explores various renewable energy heating and cooling technologies that can greatly lessen the world's dependence on traditional energy sources These established, cost-effective technologies have significant growth potential Key technologies discussed include biomass heating, solar air heating, solar water heating, passive solar heating, and ground-source heat pumps Each technology is briefly introduced, with detailed information available in dedicated chapters.
Photovoltaic System at Oberlin College’s Adam Joseph Lewis Center for Environmental studies (USA); the panels cover 4,682 square feet on the buildings south-facing curved roof
Biomass heating systems utilize organic materials like wood chips, agricultural residues, and municipal waste to produce heat for buildings, communities, and industrial processes These advanced systems surpass traditional woodstoves in efficiency, achieving nearly complete combustion of biomass fuel by precisely managing fuel and air supply, and often featuring automated fuel handling mechanisms.
Biomass heating systems are composed of a heating plant, a heat distribution system, and a fuel supply operation The heating plant utilizes various heat sources, including waste heat recovery, biomass combustion, peak load heating, and backup heating systems The heat distribution system transports hot water or steam from the heating plant to the intended loads, which can be within a single institutional or industrial building, or part of a district heating system serving multiple nearby buildings.
Biomass fuels encompass a diverse array of materials such as wood residues, agricultural byproducts, and municipal solid waste, exhibiting greater variability in quality and consistency than liquid fossil fuels This variability necessitates a more complex fuel supply operation for biomass plants, making it a crucial component of the biomass heating system While biomass heating systems typically involve higher capital costs compared to conventional boilers and require careful management, they can deliver substantial heat on demand at significantly lower fuel costs, influenced by the source of the biomass.
As of today, biomass combustion contributes 11% to the global Total Primary Energy Supply (TPES), translating to more than 20,000 MW of installed capacity worldwide This energy source plays a crucial role in developing countries, where it accounts for approximately 50% of Africa's TPES, primarily serving for cooking and heating needs.
5 A measure of the total energy used by humans.
Solar air heating systems harness solar energy to warm air for building ventilation and industrial applications like drying These systems can increase the temperature of incoming air by approximately 5 to 15ºC (41 to 59ºF) on average, providing a significant portion of the necessary heat while conventional heaters supply the remainder.
A solar air heating system, as analyzed by RETScreen, features a transpired collector made of steel or aluminum with numerous small perforations that allow outside air to flow through Positioned on a building wall facing the equator, this collector captures sunlight and heats the air layer in contact with it.
A fan draws sun-warmed air through perforations into the air space behind the collector and distributes it via ducting throughout the building or industrial processes Temperature controls adjust the mix of recirculated and fresh air or modulate a conventional heater's output During summer, a damper bypasses the collector when heat is unnecessary This system also acts as insulation, recuperates heat lost through walls, and reduces hot air stratification near ceilings in large spaces Overall, it offers a cost-effective, durable solution with minimal maintenance and efficiency rates reaching up to 80%.
Solar air heating systems are particularly cost-effective in new construction, as the installation costs of transpired collectors can be offset by savings from traditional weather cladding Additionally, new builds allow for better integration of the collector into the building's ventilation system and design Furthermore, replacing aging weather cladding with a transpired collector is a sensible option for improving energy efficiency.
The use of perforated collectors for solar air heating presents significant potential due to the substantial energy required for heating ventilation air This technology is most viable in regions with extended heating seasons, high ventilation needs, and expensive conventional heating fuels Consequently, industrial buildings represent the largest market, followed by commercial and institutional structures, multi-unit residential buildings, and schools Additionally, solar air heating holds considerable promise for industrial processes that demand large quantities of heated air, such as agricultural product drying.
Solar water heating systems harness solar energy to heat water, with temperatures ranging from tepid to nearly boiling based on the type of solar collector, weather conditions, and hot water demand Typically, these systems can fulfill 20 to 85% of annual hot water needs, while conventional heating sources supplement the remainder by either increasing water temperature or providing hot water during times when solar systems are insufficient, such as at night.
Solar systems are ideal for generating moderately hot water in various settings, from residential bathrooms and kitchens to larger applications like multi-unit apartments, restaurants, hotels, motels, hospitals, and sports facilities Off-the-shelf packages cater to typical household needs, while custom systems are tailored for higher demands Additionally, solar water heating plays a significant role in industrial and commercial processes, including car washes and laundries.
Globally, millions of solar collectors are in use, primarily in China and Europe While the North American solar water heating market has faced challenges due to low conventional energy prices, the demand for swimming pool heating has propelled unglazed technology to a leading position in sales Additionally, many developing countries with abundant solar resources and expensive or unreliable conventional energy sources have adopted solar water heating technology.
Passive solar heating harnesses solar energy for space heating in buildings by utilizing strategically oriented, high-performance windows and specific interior materials that absorb and retain heat during the day, releasing it at night This approach significantly decreases the reliance on conventional energy sources for heating, maintaining a comfortable indoor climate throughout the year By implementing passive solar heating, buildings can achieve a reduction in space heating needs of 20 to 50%.
Advancements in commercial window technologies have enhanced passive solar heating by minimizing heat loss while allowing ample solar radiation to enter High-performance windows, known for their excellent thermal properties, enable building designers to optimize daylight usage, offering greater flexibility in size and placement compared to traditional windows As a result, the adoption of high-performance windows is increasingly becoming the norm in the construction industry.
Combined Heat and Power (CHP) technologies
Combined heat and power (CHP), also known as cogeneration, focuses on recovering waste heat produced during fuel combustion in electricity generation systems Typically, this heat is released into the environment, resulting in a significant energy loss However, by utilizing this waste heat for applications such as space heating, water heating, and industrial processes, the overall efficiency of the system can be significantly improved Depending on the equipment and application, cogeneration can enhance efficiency levels from 25-55% up to 60-90%.
6 Such as fossil fuels (e.g natural gas, diesel, coal, etc.), renewable fuels (wood residue, biogas, agricultural byproducts, bagasse, landfi ll gas (LFG), etc.), hydrogen, etc.
Combined heat and power (CHP) systems can be tailored to various scales, provided there is an adequate thermal load Large-scale CHP systems, suitable for community energy initiatives and industrial complexes, can utilize gas turbines, steam turbines, and reciprocating engines with generating capacities up to 500 MW Independent energy sources, like those for hospitals and universities, typically range around 10 MW, while small-scale CHP systems often employ reciprocating engines to supply heat for individual buildings with lower demands Additionally, CHP systems with electrical capacities under 1 kW are available for remote, off-grid applications, such as on sailboats When significant cooling needs are present near the power plant, integrating a cooling system into the CHP project can be beneficial, addressing industrial process cooling or space cooling and dehumidification for buildings.
Combined Heat and Power (CHP) systems generate electricity that can be utilized for nearby loads or fed into the electric grid Unlike electricity, heat is less efficient to transport over long distances, so it is typically used for heating needs within the same building or supplied to a local district heating network This decentralized energy model allows for electricity production closer to the demand, minimizing transmission losses and enabling the establishment of geographically dispersed generating plants.
& process heating and/or cooling for single or multiple buildings ( Figure 14 ).
A Combined Heat and Power (CHP) installation consists of four key subsystems: the power plant, the heat recovery and distribution system, an optional heating and/or cooling system, and a control system The power plant can utilize various types of equipment, provided that the power equipment rejects heat at sufficiently high temperatures.
7 In such case, the CHP project becomes a “combined cooling, heating and power project”.
8 Heating equipment such as waste heat recovery, boiler, furnace, heater, heat pump, etc.
9 Cooling equipment such as compressor, absorption chiller, heat pump, etc.
10 Power equipment such as gas turbine, steam turbine, gas turbine-combined cycle, reciprocating engine, fuel cell, etc.
Combined Heat & Power Kitchener’s City Hall, Ontario, Canada
Urban Ziegler from NRCan highlights the efficiency of Combined Heat and Power (CHP) systems in managing thermal loads These systems can recover heat and distribute it either as steam for high-temperature industrial processes or as hot water for lower temperature applications, such as domestic hot water and space heating.
Globally, combined heat and power (CHP) systems boast an impressive electrical capacity of approximately 240 GW, highlighting their significant role in the energy supply Notably, CHP plants produce substantially more heat than electricity, showcasing their efficiency As most electricity is generated through fuel combustion in rotating machinery, CHP systems present vast potential for expansion Future growth is likely to shift from large industrial setups to numerous small CHP projects, particularly with the increasing adoption of decentralized energy approaches and the rise of commercial products aimed at this sector.
Other commercial and emerging technologies
RETScreen International highlights various clean energy technologies that are either commercially available or in different stages of development This section provides a brief overview of these existing and emerging technologies Additionally, ongoing and upcoming developments for several technologies not yet included in the software are also in progress.
Many other commercial clean energy technologies and fuels are presently available Some are described here.
Biofuels, including ethanol and biodiesel, are produced through the fermentation of agricultural products like corn and sugar cane Ethanol, a type of alcohol, is widely used as a transportation fuel, particularly in Brazil, where it is often blended with conventional gasoline for regular car engines This process allows biomass fuel to replace fossil fuels Additionally, researchers are exploring methods to produce ethanol from cellulose, aiming to convert wood waste into a viable liquid fuel.
Biofuel - Agriculture Waste Fuel Supply
David and Associates DOE/NREL highlight that plant and animal oils, including soybean oil and used cooking grease, can serve as fuel for diesel engines When blended with fossil fuels, these biomass oils contribute to reduced air pollution compared to conventional diesel However, it's important to note that biomass oils may solidify in low temperatures.
The use of waste oils is common in biofuel production; however, when crops are specifically cultivated for their oils or alcohols, it is essential that sustainable agricultural practices are employed to classify them as renewable energy sources To ensure the effective implementation of new biofuel technologies, it is crucial to first secure a consistent supply of regular biofuels, making them more accessible.
Ventilation heat recovery and efficient refrigeration systems play a crucial role in reducing energy consumption associated with heating, cooling, and ventilation in large buildings, such as supermarkets and industrial complexes These systems can simultaneously address heating and cooling loads by transferring heat from cooling areas to heating zones For instance, absorption cooling systems and desiccant dehumidifiers utilize waste heat to power cooling equipment, enhancing energy efficiency Additionally, ventilation heat recovery systems can reclaim up to 50% of sensible heat lost through exhausted air, with advancements in technology enabling the recovery of latent heat as well, all while ensuring high air quality.
Variable speed motors play a crucial role in energy efficiency, as they account for approximately 65% of total industrial electricity consumption in Europe Unlike traditional motors, which operate at a fixed speed determined by the electric grid frequency, variable speed drives integrate power electronics to optimize motor performance These electronics assess the load and adjust the motor speed to meet specific application needs For instance, in ventilation systems, reducing the motor speed when lower airflow is needed leads to enhanced efficiency and energy savings.
Secondary loop pumping system for recovery of heat rejected by the refrigeration systems in a supermarket
Daylighting and efficient lighting systems play a crucial role in reducing electricity consumption in commercial buildings Advances in technology, such as high-intensity discharge (HID) lamps, fluorescent tubes, and electronic ballasts, have significantly improved lighting efficiency More efficient lighting not only lowers energy usage but also reduces cooling loads in buildings that tend to overheat Additionally, enhanced windows and transparent insulation enable designers to maximize natural daylight, further decreasing reliance on artificial lighting This approach is particularly beneficial for office buildings, where working hours align with daylight availability, and is most effective in new constructions and retrofitting existing structures.
Global concerns regarding energy security and climate change, along with the anticipated depletion of fossil fuels and their rising prices, have accelerated the development of innovative energy technologies Several of these technologies are currently in the prototype or pilot phases and have the potential to become commercially viable in the future.
Solar-thermal power has been harnessed for over two decades through large-scale projects that convert solar energy into electricity using mechanical processes Notably, arrays of mirrored parabolic troughs have been instrumental in this technology Throughout the 1980s, nine commercial solar thermal systems were established in California's Mohave Desert These parabolic troughs concentrate sunlight onto a collector tube, heating a heat transfer fluid to temperatures of 390ºC (734ºF) This heated fluid is then utilized to generate steam that drives a turbine, contributing to a combined electric capacity of the nine plants.
350 MW, and their average output is over 100 MW The systems have functioned reliably and the most recently constructed plants generate power at a cost of around
$0.10/kWh Several studies have identified possible cost reductions
A solar thermal power system utilizes a large array of small mirrors that track the sun, concentrating its rays onto a central receiver tower This concentrated sunlight can heat the receiver to temperatures as high as 1,000ºC (1,800ºF), generating steam to drive a turbine Prototype plants with electrical capacities reaching up to 10 MW have been successfully constructed in various countries, including the United States, Ukraine, Israel, Spain, Italy, and France.
A third solar thermal power technology integrates a Stirling cycle heat engine with a parabolic dish, effectively harnessing solar energy The parabolic dish concentrates sunlight to provide heat to the engine at approximately 600ºC, resulting in impressive efficiency levels demonstrated by prototype systems.
Parabolic-Trough Solar Power Plant
Central Receiver Solar Power Plant
Sandia National Laboratories DOE/NREL
The ability to co-fire with natural gas or other fossil fuels enhances the firm capacity of these technologies, allowing them to serve as reliable peak power providers This reliability makes them more appealing to utilities compared to photovoltaics, which may not consistently deliver power on demand.
Solar thermal power systems harness only direct sunlight and necessitate significant land use Currently, this technology is still in the development phase, where reducing costs and associated risks is essential Gaining experience in real operating conditions will further enhance its viability and effectiveness.
Ocean-thermal power harnesses electricity from the ocean through various methods, notably ocean thermal energy conversion (OTEC), which utilizes the temperature gradient between warm surface water and cooler water found deeper in tropical oceans This temperature difference, often exceeding 20ºC, is capable of generating low-pressure steam to drive a turbine Pilot plants in Hawaii and Japan have demonstrated this technology with a net power output of up to 50 kW However, high production costs, potential negative impacts on marine ecosystems, and a limited number of suitable locations have hindered its widespread adoption, necessitating further demonstration before it can be commercially deployed.
Tidal power harnesses energy from high tides by damming narrow basins, forcing water through turbines during tidal changes This technology has been successfully implemented in regions like eastern Canada, Russia, and France, where a 240 MW project has been operational since 1966 Despite its technical feasibility, tidal power faces high initial costs and potential environmental impacts, such as sedimentation, coastal flooding, and unpredictable ecosystem changes Additionally, challenges related to configuration, reliability, safe deployment, grid connection, and maintenance pose significant market barriers to the widespread adoption of tidal energy technology.
Preliminary Feasibility Studies
Favourable project conditions
Decision-makers frequently lack familiarity with clean energy technologies, which hinders their ability to recognize promising opportunities for these innovations in pre-feasibility studies Key indicators of favorable conditions for successful clean energy project implementation include various factors that signal potential viability.
The necessity for an energy system becomes evident when addressing energy demands, serving as a crucial foundation for any energy initiative This is particularly true for clean energy projects, where overcoming awareness barriers frequently poses significant challenges.
Incorporating clean energy technologies during new construction or renovations is typically more cost-effective, as it allows for the offsetting of initial costs with the savings from traditional equipment and materials By planning early, the integration of clean energy solutions into the facility becomes smoother and more efficient.
High conventional energy costs make clean energy technologies more appealing, as their typically higher initial investments can be offset by significantly lower fuel expenses compared to traditional energy sources.
Interest by key stakeholders: Seeing a project through to completion can be a protracted, arduous affair involving a number of key stakeholders
If even just one key stakeholder is opposing the project, even the most fi nancially and environmentally attractive projects could be prevented from moving to successful implementation
A streamlined approvals process significantly reduces development costs by facilitating easy access to necessary permits However, existing local, regional, and national regulations often fail to recognize the unique characteristics of clean energy technologies, potentially placing them at a disadvantage compared to conventional energy sources.
Easy access to funding and fi nancing: With access to fi nancing, subsidies, and grants, the higher initial costs of clean energy technologies need not present a major hurdle.
Adequate local clean energy resources: A plentiful resource (e.g wind) will make clean energy technologies much more fi nancially attractive.
Evaluating favorable conditions is essential for identifying opportunities in clean energy project implementation This initial assessment can effectively filter and prioritize potential projects, guiding investment decisions during the pre-feasibility analysis stage.
Project viability factors
Evaluating the essential factors that contribute to the financial viability of clean energy projects is crucial for saving time and costs for stakeholders Key viability elements include project location, resource availability, regulatory support, and financing options, which are particularly relevant for wind energy initiatives.
Energy resource available at project site
(e.g diesel generators for remote sites)
On-going and periodic project costs
(e.g cleaning of wind turbine blades)
(e.g debt ratio & term, interest rate)
Taxes on equipment & income (or savings)
Environmental characteristics of energy displaced
(e.g coal, natural gas, oil, large hydro, nuclear)
Environmental credits and/or subsidies
(e.g greenpower rates, GHG credits, grants)
Decision-maker’s defi nition of cost-effective
(e.g payback period, IRR, NPV, Energy production costs)
The RETScreen Clean Energy Project Analysis Software, as described in the next section, has a number of features to make this focus on key factors relatively straight-forward.
2 RETSCREEN CLEAN ENERGY PROJECT ANALYSIS SOFTWARE
The RETScreen International Clean Energy Project Analysis Software is a global tool designed to assess energy production, life-cycle costs, and greenhouse gas emission reductions for a variety of renewable energy technologies (RETs) and energy-efficient solutions.
RETScreen Software addresses barriers to implementing clean energy technologies during the preliminary feasibility stage by providing a reliable methodology for comparing conventional and clean energy options This allows analysts to concentrate on pre-feasibility studies without the need to develop complex methodologies With minimal data input requirements and integrated weather and product databases, RETScreen enables quick and accurate analyses at a fraction of the cost of custom pre-feasibility studies Consequently, it facilitates the screening of multiple potential projects, helping to identify and prioritize the most promising initiatives for implementation.
It also facilitates the sharing of information by way of a standardised, internationally ac- cepted platform.
RETScreen Software features a uniform design across all clean energy technology models, promoting efficient decision-making with dependable outcomes Each model is equipped with comprehensive databases for products, costs, and weather, along with an extensive online user manual, significantly lowering the time and expenses involved in preparing pre-feasibility studies.
RETScreen is a powerful tool designed to facilitate project analysis while offering valuable insights into clean energy technologies By enhancing users' understanding of these technologies and their applications, RETScreen helps identify when specific solutions should be implemented Additionally, it serves as an excellent resource for education and information sharing in the field of clean energy.
This section introduces RETScreen Software, highlighting its project analysis approach along with various clean energy technology models, databases, and additional resources included in the software While detailed methodologies and algorithms for specific RETScreen Clean Energy Technology Models are covered in their respective chapters, this section focuses on common methodologies applicable to all models, including greenhouse gas analysis, financial analysis, and sensitivity and risk analysis methodologies.
RETScreen Software Overview
Five step standard project analysis
RETScreen employs a standardized five-step analysis procedure across all clean energy technologies, allowing users to easily transition between different technologies once they are familiar with the software Developed in Microsoft® Excel, each step corresponds to specific Excel worksheets, facilitating user interaction The RETScreen Software Model Flow Chart visually represents this five-step standard project analysis, which is detailed further in the accompanying descriptions.
RETScreen Software Model Flow Chart: A Five Step Standard Analysis
STEP 1 - Energy Model (and sub-worksheet(s)): In this worksheet, the user specifies pa- rameters describing the location of the energy project, the type of system used in the base case, the technology for the proposed case, the loads (where applicable), and the renewable energy resource (for RETs) In turn, the RETScreen Software calculates the annual energy production or energy savings Often a resource worksheet (such as the “Solar Resource” or the “Hydrology and Load” worksheet) or an “Equipment Data” worksheet—or both—ac- companies the Energy Model worksheet as sub-worksheet(s) The algorithms used in each technology’s Energy Model worksheet along with their validations can be found in the respective chapters of this textbook.
STEP 2 - Cost Analysis: In this worksheet, the user enters the initial, annual, and periodic costs for the proposed case system as well as credits for any base case costs that are avoided in the proposed case (alternatively, the user can enter the incremental costs directly) The user has the choice between performing a pre-feasibility or a feasibility study For a
In a pre-feasibility analysis, less detailed and accurate information is needed, whereas a feasibility analysis requires more comprehensive and precise data The calculations in the RETScreen Software for this phase are straightforward, primarily involving addition and multiplication, making the online manual for each input and output cell adequate for a thorough understanding of the worksheet.
Step 3 of the process involves an optional Greenhouse Gas (GHG) Analysis, which evaluates the annual reduction in greenhouse gas emissions achieved by implementing the proposed technology compared to the base case Users can choose from a simplified, standard, or custom analysis and can specify if the project is to be considered for the Clean Development Mechanism (CDM) RETScreen facilitates this by automatically determining if the project qualifies as a small-scale CDM initiative, allowing access to simplified baseline methods and other relevant procedures Detailed methodologies and algorithms for this analysis are outlined in Section 2.2 of the RETScreen Software documentation.
STEP 4 - Financial Summary: In this worksheet, the user specifies financial parameters related to the avoided cost of energy, production credits, GHG emission reduction credits, incentives, inflation, discount rate, debt, and taxes From this, RETScreen calculates a vari- ety of financial indicators (e.g net preset value, etc.) to evaluate the viability of the project
The financial summary worksheet includes a cumulative cash flow graph, with detailed descriptions of the methodology and algorithms utilized in the RETScreen Software provided in Section 2.3.
STEP 5 - Sensitivity & Risk Analysis (optional): This optional worksheet assists the user in determining how uncertainty in the estimates of various key parameters may affect the financial viability of the project The user can perform either a sensitivity analysis or a risk analysis, or both The methodology and algorithms used in the RETScreen Software for this step are described in detail in Section 2.4.
12 The Kyoto Protocol has established three mechanisms (the Clean Development Mechanism (CDM), Joint Implementation (JI), and Emissions Trading) which allow Parties to pursue opportunities to cut emissions, or enhance carbon sinks, abroad.
Common platform for project evaluation & development
RETScreen Software streamlines project implementation by offering a unified evaluation and development platform for diverse stakeholders It is utilized globally for various applications, such as conducting feasibility studies, performing lender due diligence, executing market studies, analyzing policies, disseminating information, providing training, and facilitating the sale of products and services, as well as aiding in project development.
The RETScreen Software facilitates electronic sharing of project files among stakeholders, enhancing collaboration and efficiency For instance, a consultant may create a RETScreen study for an independent power producer (IPP), who may then modify input values for sensitivity analysis on key parameters like return on investment Subsequently, the IPP might share the updated file with potential lenders for due diligence reviews, while utility regulators may request access to verify greenhouse gas emission reduction estimates.
Common Platform for Project Evaluation & Development
The RETScreen Software enhances decision-making by providing comprehensive reporting capabilities that consolidate all key information for project studies This functionality simplifies due diligence and facilitates comparisons among various energy project options, benefiting all stakeholders involved Additionally, it significantly lowers study costs by minimizing the time and effort required to produce project assessment reports Notably, the output from a RETScreen study often serves as a sufficient report during the early stages of project implementation, as demonstrated in the section on "Reducing the Cost of Pre-feasibility Studies," which highlights its effectiveness in project identification initiatives.
The language switch feature in RETScreen 13 enhances communication among stakeholders by enabling analysis in multiple languages This functionality allows partners who speak different languages to evaluate projects seamlessly, eliminating the need for manual translation of reports and results For instance, a project proponent from France can effortlessly prepare a RETScreen analysis in French for a potential clean energy initiative, as the language switch automatically translates the entire analysis.
China, which might result in GHG production credits as a clean devel- opment mechanism (CDM) project as defined in the Kyoto Protocol
The RETScreen language switch allows for automatic translation of analyses initially prepared in French into Simplified Chinese for potential Chinese partners, as well as into English for broader accessibility.
RETScreen significantly contributes to the acceleration of clean energy project implementation and market expansion by providing substantial time and cost savings An independent impact assessment revealed that between 1998 and 2004, users of RETScreen Software saved an estimated $600 million globally, with projections indicating that these savings could soar to $7.9 billion by 2012.
13 As of September, 1 st 2005, the languages available include: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hindi, Italian, Japanese, Korean, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, and Telugu Additional product translations for these languages and additional translations in other languages are expected to be available in RETScreen.
14 Graham, Stephen and Steve Higgins, SGA Energy Ltd., An Impact Assessment of RETScreen ® International 1998-2012, Final Report to NRCan’s CETC-Varennes, April 2004.
RETScreen was instrumental in helping Natural Resources Canada’s (NRCan) CETC-Varennes and a team of eleven consulting firms prepare studies for
A recent study identified 56 potential renewable energy technology (RET) projects in 300 remote communities across Canada, each costing under $2,000 Notably, 27 of these projects demonstrated commercial viability without requiring government incentives This cost-effective approach saved significant funds, estimated to be 5 to 10 times less than traditional studies, allowing for the development of several projects Among these is the 35 m² solar air heating collector, which has already been constructed [Alward, 1999].
Solar Air Heating Technology, Yellowknife, NWT, Canada.
Clean energy technology models
The RETScreen Software can be used to evaluate industrial, commercial, institutional, community, residential and utility applications Some of the RETScreen clean energy tech- nology models are as follows 15 :
The Wind Energy Project Model encompasses both central-grid and isolated-grid connected projects, featuring a diverse range of systems from large-scale multi-turbine wind farms to small-scale single-turbine wind-diesel hybrid setups.
The Small Hydro Project Model encompasses both central-grid and isolated-grid connected projects, featuring a diverse range of installations from multi-turbine small and mini hydro systems to single-turbine micro hydro systems.
This article explores various photovoltaic project models, including on-grid systems that cater to both central and isolated grids, as well as off-grid solutions such as stand-alone PV-battery systems and hybrid configurations that combine PV, batteries, and generators Additionally, it addresses the application of photovoltaic technology in water pumping systems, highlighting the versatility and efficiency of solar energy in diverse settings.
The Biomass Heating Project Model is designed for evaluating heating projects that utilize biomass and waste heat recovery (WHR), applicable to both large-scale developments for clusters of buildings and individual building applications This model assesses three fundamental heating systems: those that rely solely on waste heat recovery, those that utilize biomass, and those that integrate both biomass and waste heat recovery for enhanced efficiency.
The Solar Air Heating Project Model utilizes transpired-plate solar collectors for effective ventilation air heating and process air heating applications This model is suitable for a range of settings, from small residential systems to larger commercial and industrial-scale ventilation solutions Additionally, it plays a crucial role in air-drying processes for various crops, enhancing energy efficiency and sustainability in agricultural practices.
The Solar Water Heating Project Model offers efficient solutions for domestic hot water, industrial process heating, and swimming pool heating, catering to a wide range of applications This model encompasses systems of varying sizes, from small residential setups to large-scale commercial, institutional, and industrial installations, ensuring versatility and effectiveness in harnessing solar energy.
15 NRCan continues to develop the RETScreen Software, including new energy effi ciency models See the RETScreen Website (www.retscreen.net) for the latest developments.
The Passive Solar Heating Project focuses on innovative passive solar designs and energy-efficient window applications tailored for low-rise residential and small commercial buildings This model is suitable for both new construction and retrofit projects, promoting sustainable energy solutions in architecture.
The Ground-Source Heat Pump Project Model offers efficient heating and cooling solutions for residential, commercial, institutional, and industrial buildings This model is applicable for both retrofit and new construction projects, utilizing ground-coupled systems—such as horizontal and vertical closed loops—or groundwater heat pumps.
The Combined Heat & Power (CHP) Project Model is versatile, applicable to power generation, heating, and cooling for single or multiple buildings, industrial processes, communities, and district systems It utilizes a diverse range of renewable and non-renewable fuels, such as landfill gas, biomass, biodiesel, hydrogen, natural gas, oil, coal, and municipal waste The system can incorporate various types of equipment, including reciprocating engines, gas turbines, steam turbines, geothermal systems, fuel cells, wind turbines, hydro turbines, photovoltaic modules, boilers, heat pumps, and absorption chillers, all designed to operate efficiently under different load conditions, including base load, intermediate load, and peak load.
This textbook provides a comprehensive overview of the algorithms utilized in RETScreen Software for various clean energy technologies, with detailed descriptions available in the corresponding chapters.
Clean energy related international databases
RETScreen Software leverages meteorological and product performance data to assess the energy output or savings of clean energy projects, as well as to calculate essential parameters like heating loads To evaluate the financial aspects of a project, it also requires additional cost and financial data Collecting this information can be both time-consuming and costly for individual projects To address this challenge, RETScreen integrates various databases, streamlining the implementation of clean energy initiatives globally Users also have the flexibility to input data from external sources as needed.
This section outlines the sources of meteorological data utilized in RETScreen, highlighting both ground-based meteorological data and NASA's satellite-derived datasets, which collectively offer comprehensive climate information for the Earth's surface Additionally, it provides an overview of the hydrology, product, and cost data integrated within the RETScreen software.
Worldwide ground-based meteorological data
The RETScreen Software now features integrated worldwide ground-based meteorological data, utilizing an extensive International Online Weather Database This database comprises average observations from over 4,700 weather stations globally, gathered from more than 20 diverse sources covering the period from 1961 to 1990 A comprehensive map illustrating all ground-based weather stations utilized in RETScreen is also available.
Figure 25 , and an example of the integrated weather database in the Solar Water
Heating Project Model is presented in Figure 26
16 The RETScreen Combined Heat & Power Model Version 3.2 and subsequent versions of the RETScreen Software integrate data for over 4,720 ground-monitoring stations Earlier version have 1,000 weather stations.
Worldwide location of ground-based weather stations in RETScreen
Example of the Integrated Weather Database to the RETScreen Solar Water Heating Project Model
The data presented in this repository is compiled from various sources, ensuring a cohesive and standardized format To maintain consistency, all measurements are converted to SI units, regardless of their original units Additionally, certain variables, such as relative humidity, are derived from other measurements, like minimum and maximum humidity levels, depending on the station.
Over 20 different sources were used to compile the database However, not all sources contributed equally For example, some sources had limited spatial cover- age (i.e covered only one country), or proved less reliable than other sources for the same location and were thus used only as a last resort in the absence of other, more reliable data The most significant sources were:
1 Environment Canada (1993) Canadian Climate Normals, 1961-1990 Ottawa: Minister of Supply and Services Canada This six-volume book includes a wealth of meteorological information for Canada and was used for most Canadian stations (except for solar radiation and wind data, see below).
2 Environment Canada (1998) The Canadian renewable energy wind and solar resource (CERES) Ottawa: Minister of Supply and Services Canada This CD-ROM contains wind and solar radiation information for all available Canadian sites
3 Numerical Logics Inc (1998) Monthly averages of solar radiation and sunshine derived from data from the World Radiation Data Centre (WRDC) Online Archive (1964-1993) Averages for solar radiation were calculated from data stored at the
WRDC; only stations having more than fi ve years of data were included in the RETScreen database.
4 National Climatic Data Center and National Renewable Energy Laboratory
The Solar and Meteorological Surface Observation Network (SAMSON) dataset, covering the years 1961 to 1990, serves as a crucial resource for climate data, particularly solar radiation, across various locations in the United States This data, available in version 1.0 on three CD-ROMs, includes monthly averages derived from hourly measurements.
5 World Meteorological Organization (1996) Climatological Normals (CLINO) for the period 1961-1990 WMO/OMM-No.847 Geneva: Secretariat of the World
The Meteorological Organization has compiled a comprehensive document featuring climatological data contributed by member countries The extent of the reported parameters varies by country, with less developed nations often providing only a single parameter, while more developed countries typically report a full range of values necessary for the RETScreen database.
A detailed description of the meteorological variables used in the RETScreen Soft- ware is found in the Online Manual.
NASA’s satellite-derived meteorological data set
NASA offers satellite-derived meteorological data for any location on Earth through the NASA Surface Meteorology and Solar Energy (SSE) Data Set, which is accessible via RETScreen Software This dataset, created in partnership with RETScreen International, serves as a valuable alternative when ground-based data or detailed resource maps are unavailable for a specific project site Users can easily access the NASA website through the RETScreen Software, allowing them to copy the necessary data and paste it directly into the appropriate worksheets within the software.
The SSE data set is primarily sourced from various NASA-developed data sets, including the Goddard Earth Observing Systems Version 1 (GEOS-1) and solar radiation data from the International Satellite Cloud Climatology Project Version D (ISCCP D-1) These data sets utilize an atmospheric model that incorporates satellite and sounding observations, which are based on analyses from earth-orbiting satellites such as the Geostationary Operational Environmental Satellites (GOES) and Polar-Orbiting Environmental Satellites (POES).
US National Oceanic & Atmospheric Administration (NOAA), the Meteorological Satellites (Meteosat) operated by the European Space Agency, and the Geostationary Meteorological Satellites (GMS) operated by the Japan Meteorological Agency.
Satellite-derived data offer extensive coverage compared to ground-based measurements, making the SSE a valuable resource for climatic variables globally, especially in isolated areas lacking measurement stations Unlike the RETScreen meteorological database, the SSE is not restricted to specific locations, which is crucial for remote regions However, the SSE's grid resolution may overlook local climate nuances, such as microclimates influenced by natural or urban factors, and may not adequately represent areas with significant topographic features While some climate parameters, like wind speed, may be sensitive to variations within the grid, others, such as insolation, are well-suited to this resolution, indicating that higher resolution data may not significantly impact energy analysis Maps illustrating average SSE data from 1983-1992 for July, including insolation, wind speed, and earth skin temperature, highlight these findings.
The NASA SSE dataset, compiled from data collected over a decade from July 1983 to June 1993, employs a 1-degree grid size that encompasses the entire globe, divided into 64,800 regions At mid-latitudes of 45°, each cell measures approximately 80×110 km A detailed sample of this grid, specifically covering the United Kingdom and Ireland, is illustrated in Figure 28 The 1-degree data is produced using the NASA Goddard Earth Observing System - Version 1 (GEOS-1) Multiyear Assimilation Timeseries Data.
This textbook does not delve into the algorithms used to derive the Solar Surface Energy (SSE) However, a comprehensive overview of the Staylor algorithm, which is employed to calculate solar insolation, can be found in the Surface section.
Radiation Budget (SRB) Langley DAAC
Data Set Document, available on-line 17
NASA’s methodology and other relevant additional information can also be found on the NASA’s Surface meteorology and
17 http://charm.larc.nasa.gov/GUIDE/dataset_documents/srb.html
Example of global maps derived from average NASA SSE data for the month of July
Example of the grid covering the United Kingdom and Ireland, used by NASA
NASA’s Surface Meteorology and Solar Energy (release 5.1) Website.
Online manual and training material
A number of additional resources have been developed to help users learn how to use the RETScreen Software quickly, effectively, and accurately These resources are:
RETScreen Software features a comprehensive Online User Manual that serves as a valuable resource for both new and experienced users Each cell that displays an output or requires input is linked to a dedicated page in the manual, providing clear explanations and guidance New users can navigate the spreadsheet with confidence, while expert users can refer to the manual for clarification on conventions and details such as pricing and sizing Additionally, the manual offers insights into clean energy technologies and the RETScreen methodology, enhancing its educational value For convenience, the manual is also available for download in PDF format from the RETScreen website, allowing users to print it if desired.
Example of the Integrated Cost Data in the RETScreen Software
Example of the integrated online manual in the RETScreen
Training material: Training material for a modular case study-based
The Clean Energy Project Analysis Course is designed for educational institutions and training organizations worldwide, catering to professionals and college/university students in a self-study format Each module can function as an independent seminar or workshop for professionals or be integrated into a college/university curriculum When combined, the modules can be delivered as an intensive one to two-week course for professionals or a one to two-semester program for students Additionally, course materials, including slide presentations and an instructor’s voice-over, are available for free download on the RETScreen Website.
Engineering textbook: the electronic textbook Clean Energy Project
The article "Analysis: RETScreen Engineering & Cases" is designed for professionals and university students seeking to enhance their skills in assessing the technical and financial feasibility of clean energy projects It provides an overview of the technologies featured in the RETScreen Software, along with a comprehensive explanation of the algorithms utilized in various clean energy technology models within the software Additionally, this textbook is available for free download on the RETScreen Website.
The RETScreen website offers a free collection of clean energy project case studies designed to enhance training and facilitate the use of RETScreen Software These case studies include assignments, detailed solutions, and insights into the real-world performance of various projects, providing valuable resources for users.
Example of the complementary training course material (Slides) available with the RETScreen Software
The following section provides a detailed overview of the methodologies and algorithms utilized across all models, encompassing greenhouse gas analysis, financial analysis, and sensitivity and risk analysis techniques.
Greenhouse Gas (GHG) Emission Reduction Analysis Model
GHG for electricity generating technology models
The method described in this section applies to technologies that produce electricity.
GHG emission reduction summary - electricity
The annual reduction of greenhouse gas (GHG) emissions is assessed using the GHG Emission Reduction Analysis worksheet This reduction, denoted as Δ GHG, is determined by comparing the base case GHG emission factor (e base) with the proposed case GHG emission factor (e prop), while also factoring in the annual electricity produced (E prop) and the fraction of electricity lost during transmission and distribution (λ prop) for the proposed scenario Additionally, the GHG emission reduction credit transaction fee (e cr) is taken into account in this calculation.
In both the base case and the proposed case systems, on-site generation, such as off-grid and water-pumping photovoltaic (PV) applications, is assumed to have zero transmission and distribution losses.
GHG emission factor – base case electricity system
To calculate the greenhouse gas (GHG) emission factors, which represent the mass of greenhouse gases emitted per unit of energy produced, Equation (1) is utilized For a specific fuel type or source, the base case electricity system GHG emission factor, denoted as e base, is determined using the formula e CO.
2 are respectively the CO 2 , CH 4 and N 2 O emission fac- tors for the fuel/source considered, GWP CO
2 are the global warming potentials for CO 2 , CH 4 and N 2 O, η is the fuel conversion efficiency, and λ is the fraction of electricity lost in transmission and distribution.
The Global Warming Potential (GWP) of a greenhouse gas (GHG) measures its effectiveness in trapping heat in the atmosphere relative to carbon dioxide, which has a GWP of 1 For instance, nitrous oxide (N2O) has a GWP of 310, meaning that one tonne of nitrous oxide has a warming effect equivalent to 310 tonnes of carbon dioxide.
Methane and nitrous oxide have a global warming potential (GWP) that is significantly higher than carbon dioxide, with methane being 310 times more impactful per tonne Users can define GWP values for these gases through custom analyses, while standard analyses utilize preset values from the software.
(2) used by RETScreen are shown in Table 2 ; these values can be found in the Revised Intergovernmental Panel on Climate Change (IPCC) Guidelines for Greenhouse Gas Inventories, 1996.
The greenhouse gas (GHG) emission factor is influenced by the fuel type and quality, as well as the power plant's type and size Emission factors can be specified by the user for custom analyses or determined by the software for standard analyses.
Table 2: Global warming potentials of greenhouse gases.
To calculate the greenhouse gas (GHG) emission factor for an electricity mix with multiple fuel sources, the base emission factor is determined as a weighted sum of the individual emission factors for each fuel type This involves considering the number of fuels in the mix, the fraction of electricity generated from each fuel, and the specific emission factors for each source The emission factor for each fuel is derived from the CO2, CH4, and N2O emission factors, alongside the fuel conversion efficiency and the fraction of electricity lost during transmission and distribution.
Alternatively, the GHG emission factor for the electricity mix, before transmission and distribution losses are applied, can be entered directly by the user, in case of a
The greenhouse gas (GHG) emission factor for the electricity mix will remain in effect from the project's inception until the specified year of baseline change, unless stated otherwise; in such cases, the emission factor will be applicable for the entire project duration When a change in the baseline emission factor occurs, the new factor for the year of change and subsequent years will be calculated based on the percentage change in the baseline GHG emission factor.
GHG emission factor – proposed case electricity system
The calculation of the proposed case electricity system GHG emission factor, denoted as e prop, mirrors that of the base case GHG emission factor However, for off-grid systems, the transmission and distribution losses are considered to be zero Consequently, e prop is determined using equation (2) with λ set to zero for a single fuel/source, or through equations (3) and (4) with all λ i equal to zero in the case of a mixed fuel/source scenario.
Alternatively, the proposed case GHG emission factor, before transmission and dis- tribution losses are applied, can be entered directly by the user, in case of a “user- defined” analysis.
GHG for heating and cooling technology models
The method described in this section applies to heating and cooling technologies.
GHG emission reduction summary – heating and cooling
The annual reduction in greenhouse gas (GHG) emissions is assessed using the GHG Emission Reduction Analysis Worksheet The overall reduction, denoted as Δ GHG hc, is derived from the combined annual reductions in heating (Δ GHG heat) and cooling (Δ GHG cool) emissions.
The base case greenhouse gas (GHG) emission factors for heating and cooling are represented as e base heat and e base cool, while the proposed case factors are denoted as e prop heat and e prop cool Additionally, E prop heat refers to the annual heating energy delivered in the proposed case, and E prop cool indicates the annual cooling energy delivered in the proposed case.
GHG emission factor – base case electricity system
Certain applications necessitate establishing a baseline electricity system to evaluate greenhouse gas (GHG) emissions related to heating, air conditioning, and the operation of auxiliary equipment like fans and pumps For instance, a solar water heating system may depend on an electric pump to circulate water through its collectors The GHG emission factor is determined using equation (2) for a single fuel or source, while equations (3) and (4) are employed for a combination of fuels or sources.
GHG emission factor – base case and proposed case heating and cooling systems
The GHG emission factor for a single fuel type is calculated using a formula similar to equation (2), excluding transmission and distribution losses, as heating or air-conditioning systems are considered to operate at the site of use In this equation, η represents the fuel conversion efficiency, while other variables maintain their definitions from equation (2) When multiple fuel types are involved, the overall GHG emission factor is determined by the weighted sum of the emission factors for each individual fuel source.
In the analysis of energy sources, the total emissions can be calculated using the formula that incorporates the number of fuels in the mix (n), the fraction of end-use energy derived from each fuel (f i), and the respective emission factors (e i) for each fuel This calculation also considers the fuel conversion efficiency (η i) for accurate assessment of emissions associated with different energy sources.
Calculating the emission factor for heating systems requires careful consideration of parasitic electric energy, as it can significantly impact greenhouse gas (GHG) emissions For instance, the electricity needed to operate a solar collector pump does not contribute to the system's clean energy output but increases its GHG emissions To accurately reflect this, the parasitic electrical energy, denoted as E prop para, is incorporated into the GHG emission factor, which is derived from the base case electricity system's emission factor (e elec) and the annual heating energy delivered in the proposed case (E prop heat).
Financial Analysis Model
Debt payments
Debt payments consist of regular installments made over a specified period, referred to as the debt term The annual debt payment, denoted as D, is determined using a specific formula.
The total initial cost of the project, denoted as C, is influenced by the debt ratio (f d), the effective annual debt interest rate (i d), and the debt term in years (N ′) According to equation (13), the annual debt payment can be divided into two components: the principal payment (D p n) and the interest payment (D i n).
Both D p n , and D i n , vary from year to year; they are calculated by standard functions built into Microsoft ® Excel.
Pre-tax cash fl ows
The annual calculation of cash flows for a clean energy project involves tracking all expenses (outflows) and revenues (inflows) This section outlines the formulas utilized in RETScreen to assess the project's cash flows prior to tax considerations.
In year zero, the pre-tax cash outflow (C out,0) corresponds to the project equity, representing the segment of the total investment needed to finance the project that is directly funded and not included in the financial leverage, such as debt.
For subsequent years, the pre-tax cash outflow C out n , is calculated as:
In the context of clean energy projects, the annual operation and maintenance costs (C O & M) are influenced by various factors, including the inflation rate (r i) and the annual fuel or electricity costs (C fuel) Additionally, the energy cost escalation rate (r e) and annual debt payments (D) play a crucial role in determining the overall financial performance Periodic costs or credits (C per) associated with the system also contribute to the project's economic viability.
For year zero, the pre-tax cash inflow C in,0 is simply equal to the incentives and grants IG :
For subsequent years, the pre-tax cash inflow C in n , is calculated as:
In equation (18), variables are defined as follows: n represents the year, C ener denotes annual energy savings or income, C capa indicates annual capacity savings or income, and C RE signifies annual renewable energy production credit income Additionally, r RE refers to the renewable energy credit escalation rate, while C GHG represents greenhouse gas reduction income, and r GHG is the greenhouse gas credit escalation rate At the project's conclusion, the end-of-project life credit, adjusted for inflation, is incorporated into the equation.
Pre-tax cash fl ows
The pre-tax cash flow C n for year n is simply the difference between the pre-tax cash inflow and the pre-tax cash outflow:
Asset depreciation
The calculation of asset depreciation (or capital cost allowance) depends on the deprecia- tion method chosen by the user in the Financial Summary Worksheet: choices are “None,”
When calculating income taxes and after-tax financial indicators, it's essential to choose between the "Declining balance" and "Straight-line" depreciation methods Users should select the method that aligns most closely with the practices of tax authorities in their project's jurisdiction Ultimately, this choice will impact the financial outcomes at the end of the project's life.
“End of project life” value and its undepreciated capital costs is treated as income if posi- tive and as a loss if negative.
In a no-depreciation scenario, the project is fully capitalized at inception, retaining its full value throughout its lifespan without any depreciation At the project's conclusion, the total depreciation matches the undepreciated value of the assets The model also assumes that for both declining balance and straight-line depreciation methods, the maximum allowable depreciation for each year is consistently utilized.
The declining balance depreciation method accelerates asset depreciation in the initial years, resulting in higher deductions early in the asset's useful life In the first year, known as year zero, the capital cost allowance (CCA 0) is determined by the portion of the initial costs that can be fully expensed during the construction year.
The depreciation tax basis, denoted as δ, determines the capitalized portion of initial costs eligible for tax depreciation, while the remaining costs are fully expensed in the construction year (year 0) The undepreciated capital cost at the conclusion of year zero, referred to as UCC 0, is calculated accordingly.
For subsequent years, the capital cost allowance is given by:
(22) where d is the depreciation rate, and UCC n 1 is the undepreciated capital cost at the end of the ( n 1 )-th period, given as:
At the conclusion of the project in year N, the remaining undepreciated capital cost is fully expensed, resulting in the capital cost allowance for the final year being equal to the undepreciated capital cost.
(24) so that the undepreciated capital cost at the end of that year becomes zero:
The straight-line depreciation method assumes that capitalized project costs are depreciated at a constant rate throughout the depreciation period, as defined by the depreciation tax basis Any initial costs that are not capitalized are expensed in the construction year, referred to as year 0 This method utilizes specific formulas to calculate depreciation.
(26) for year zero, and for subsequent years within the depreciation period:
(27) where N d is the user-defined depreciation period in years.
Income tax
The income tax analysis within the financial analysis model enables the calculation of after-tax cash flows and financial indicators The effective equivalent tax rate, specified by the user in the RETScreen Financial Summary worksheet, determines how the project's net income is taxed The model operates under the assumption of a consistent single income tax rate that remains constant throughout the project's lifespan, applied to net income.
Net taxable income is calculated based on project cash inflows and outflows, with the assumption that all revenues and expenses are settled at the end of the year they are earned or incurred The tax amount for year n, denoted as Tn, is determined by multiplying the effective income tax rate (t), as specified by the user, by the net income for that year (In).
The net income for years one and beyond is calculated as:
The net income for year 0 is calculated using the pre-tax annual cash flow (C n), the principal payment (D p n), and the capital cost allowance (CCA n), which is determined by the chosen asset depreciation method.
(30) where IG is the value of incentives and grants.
Loss carry forward
A financial loss, such as a negative net income, can impact tax obligations in various ways Under certain taxation rules, these losses may reduce taxes owed for the same year Alternatively, some regulations allow for the deferral of losses to counterbalance profits in future years However, there are also circumstances where the loss cannot be utilized either in the current year or in subsequent years, resulting in a permanent loss from a tax perspective.
The Carry Forward option in the Financial Summary worksheet enables users to choose from three rules applicable to the project under analysis Selecting the Loss Carry Forward option allows losses to be carried forward and offset against net income in subsequent years, effectively lowering taxes Conversely, if this option is not chosen, the losses are forfeited as a tax offset and cannot be applied to other income Alternatively, opting for the Flow-through option means that losses will not be carried forward but will instead generate a refundable tax credit in the year the loss is incurred.
After-tax cash fl ow
The after-tax cash flow, denoted as C~ n, is determined by analyzing pre-tax cash flows, asset depreciation, income tax implications, and the application of loss carry forwards.
(31) where C n is net cash flow (equation 19) and T n the yearly taxes (equation 28).
Financial feasibility indicators
This section highlights key financial feasibility indicators generated automatically by the RETScreen Software within the Financial Summary worksheet These indicators, derived from user-entered data, enhance the project evaluation process, aiding planners and decision-makers in their analysis.
Internal rate of return (IRR) and return on investment (ROI)
The internal rate of return (IRR) is the discount rate at which the net present value (NPV) of a project equals zero To determine the IRR, one must solve the specific formula that defines this relationship.
The project life, denoted as N in years, influences the calculation of cash flows, where C_n represents the cash flow for year n, and C_0 indicates the project's equity minus incentives and grants for year zero The pre-tax Internal Rate of Return (IRR) is derived from pre-tax cash flows, while the after-tax IRR is based on after-tax cash flows It's important to note that the IRR may be undefined in specific scenarios, particularly when the project generates immediate positive cash flow in year zero.
The simple payback period (SP) refers to the duration in years required for the cash flow, excluding debt payments, to match the total investment, which encompasses both debt and equity contributions.
(33) where all variables were previously defined.
Year-to-positive cash fl ow (also Equity payback)
The year-to-positive cash flow (YPCF) marks the initial year when the cumulative cash flows for a project turn positive This metric is determined by solving a specific equation to identify the exact year in which the project begins to generate a net positive cash flow.
(34) where C~ n is the after-tax cash flow in year n
The net present value (NPV) of a project represents the value of future cash flows adjusted to today's currency using a discount rate It is determined by discounting all projected cash flows according to a specific formula.
(35) where r is the discount rate.
The Annual Life Cycle Savings (ALCS) represents the levelized nominal yearly savings that match the project’s lifespan and net present value It is determined using a specific formula designed for this purpose.
The benefit-cost ratio (BCR) measures a project's relative profitability by comparing the present value of its annual revenues, including income and savings, to its annual costs and project equity.
The Debt Service Coverage (DSC) ratio measures a project's operating income against its debt payments, indicating its ability to generate sufficient cash flow to meet these obligations This metric is crucial for assessing the financial health and liquidity of a project in relation to its debt responsibilities.
DSC n for year n is calculated by dividing net operation income (net cash flows before depreciation, debt payments and income taxes) by debt payments (principal and interest):
(38) where COI n is the cumulative operating income for year n , defined as:
The Financial Analysis model calculates the debt service coverage for each year of the project and reports the lowest ratio encountered throughout the term of debt
The energy production cost represents the avoided cost of energy that results in a net present value of zero This crucial parameter is excluded from the Combined Heat & Power Model due to the variety of energy types produced, each with its own unique production cost Consequently, the energy production cost, denoted as C prod, is determined by solving for this variable.
The GHG Emission reduction cost GRC represents the levelised nominal cost to be incurred for each tonne of GHG avoided It is calculated by:
(44) where ALCS is the annual life cycle savings calculated in equation 36, and GHG is the annual GHG emission reduction, calculated in the GHG Analysis worksheet (equation 1).
Sensitivity and Risk Analysis Models
Monte Carlo simulation
The Risk Analysis Model in RETScreen utilizes a Monte Carlo simulation, a method that generates a distribution of potential financial outcomes by using randomly selected input parameters within a specified range to simulate various scenarios.
The Monte Carlo simulation in RETScreen Software utilizes pre-selected technical and financial input parameters to generate key financial output indicators This simulation process involves two distinct steps, ensuring a comprehensive analysis of the financial metrics.
Avoided cost of energy ($/kWh)
The sensitivity analysis chart from the Wind Energy Project Model showcases the after-tax Internal Rate of Return (IRR) and Return on Investment (ROI) with a sensitivity range of 20% and a threshold set at 15% The analysis highlights three original values, which are emphasized in bold, providing a clear view of the project's financial performance under varying conditions.
1 For each input parameter, 500 random values are generated using a normal (Guassian) distribution with a mean of 0 and a standard deviation of 0.33 using the Random Number Generation function in Microsoft ® Excel’s Data Analysis ToolPack Once generated, these random numbers are fi xed.
2 Each random value is then multiplied by the related percentage of variability (range) specifi ed by the user in the Sensitivity and Risk Analysis worksheet The result is a 500 x 9 matrix containing percentages of variation that will be applied to input parameters’ initial value in order to obtain 500 results for the output fi nancial indicators.
The RETScreen Clean Energy Project Model produces consistent results in its Risk Analysis Model due to a fixed set of random numbers When identical input parameters and variability ranges are applied, users can expect to receive the same outcomes every time.
Net GHG reduction – credit duration
RE production credit (CE production credit)
The after-tax internal rate of return (IRR) is a crucial metric for evaluating the profitability of investments, both in terms of equity and assets It provides insights into the potential returns after accounting for taxes Additionally, understanding the year-to-positive cash flow, or equity payback period, is essential for assessing how quickly an investment can start generating profits Furthermore, net present value (NPV) serves as a vital tool in determining the value of future cash flows in today's terms, helping investors make informed decisions.
Table 3: Input Parameters and Output Indicators associated with the Monte Carlo simulation performed in the RETScreen Risk Analysis Model.
Impact graph
The influence of each input parameter on a financial indicator is determined through standardized multiple linear regression analysis The coefficients for the input parameters, derived using the least squares method, are represented on the impact graph (refer to Figure 38) This multiple linear regression is illustrated using the Wind Energy Project Model as a case study.
Let Y , the dependent variable, be a financial indicator, and the independent variables X be the input parameters as follows:
X 1 be the avoided cost of energy;
X 6 be the debt interest rate;
X 8 be the GHG emission reduction credit; and
X 9 be the RE production credit.
Then the multiple linear regression model is:
The model is constructed using data generated from a Monte Carlo simulation, which produces 500 values In this context, β k represents the coefficients for each parameter k, while ε denotes the model error.
Y associated to 500 values for each X The Microsoft ® Excel function LINEREG, applied to the
Y vector and the X matrix, calculates the coefficients using the method of least squares
These coefficients are then standardised by applying the following formula:
(46) where s k is the standard deviation of the 500 X k values and s Y is the standard deviation of the 500 Y values The b k values are then plotted on the impact graph.
19 See Neter, Wasserman, Kutner Applied Linear Statistical Models 3 rd edition Homewood, IL: Irwin, 1990.
Median & confi dence interval
The median of a financial indicator, representing the 50th percentile of 500 values produced by a Monte Carlo simulation, is determined using the MEDIAN function in Microsoft® Excel To calculate the median, the 500 financial indicator values are first arranged in ascending order, and the median is then found by averaging the 250th and 251st values in this ordered list.
A confidence interval represents the range of values produced by a Monte Carlo simulation, indicating where the results are likely to fall For instance, a 90% confidence interval signifies that 90% of the 500 financial indicator values will be contained within a specific range Users can define their desired level of risk, which reflects the percentage of values expected to lie outside the confidence interval; in this case, a 90% confidence interval corresponds to a 10% level of risk.
The minimum confidence level for a financial indicator is determined by the percentile that corresponds to half the user's defined risk level, calculated using the PERCENTILE function in Microsoft Excel For instance, with a risk level of 10%, the minimum confidence level is the 5th percentile of 500 values generated through Monte Carlo simulation, which is found by sorting the values in ascending order and averaging the 25th and 26th values Conversely, the maximum confidence level corresponds to one minus half the risk level; in this example, it would be the 95th percentile, calculated by averaging the 475th and 476th values.
Impact on After-tax IRR and ROI
Effect of increasing the value of the parameter
RE delivered Debt interest rate Debt ratio Avoided cost of energy
RE production credit Annual costs GHG emission reduction credit Dept term
The Impact Chart, also known as the Tornado Graph, illustrates the influence of parameter variations on After-Tax IRR and ROI within the Risk Analysis Model This example is derived from the default Wind Energy Project Model found in the Sensitivity and Risk Analysis Worksheet.
Risk analysis model validation
A validation of the Risk Analysis Model was conducted to evaluate the accuracy of impact statistics, including median values and confidence levels This validation also examined how the number of observations in the Monte Carlo simulation affects the precision of the results Statistical outcomes from RETScreen were compared with those from JMP, a statistical software by SAS, using the default example of the RETScreen Wind Energy Project Model as the test case.
The results in the Risk Analysis section are derived from a Monte Carlo simulation utilizing 500 randomly generated observations It is established that a higher number of observations enhances the precision of the simulation estimates; however, this also leads to increased computational time To evaluate the impact of the number of observations on the accuracy of the results, further calculations were conducted.
A multiple linear regression analysis was conducted for each financial indicator using subsets derived from 500 values generated through Monte Carlo simulation These subsets included the last 50, 100, up to 450 observations, as well as the complete set of 500 observations For each subset, the multiple linear regression coefficients and their estimation errors were utilized as input parameters in JMP statistical software, with the estimation errors subsequently standardized based on their standard deviation.
In the analysis of input parameters, Z p i represents the standardized error for parameter p within a specific subset i, such as the last 50 or 100 observations The term Q p i denotes the error in estimating parameter p using subset i, while Q p signifies the average error across all subsets Additionally, σ p indicates the standard deviation of the errors Q p i for parameter p across all subsets considered.
The values of Z p i are illustrated in Figures 39, 40, and 41 It is important to note that a negative standardised error does not indicate underestimation; instead, it signifies that the error is below the average As the Monte Carlo simulation's number of observations increases, the standardised error of the regression coefficients tends to decrease Typically, the slope of the standardised error flattens as the number of observations approaches 500, with this trend being more pronounced for NPV, after-tax IRR, and ROI compared to the year-to-positive cash flow.
NPV - Variability of input parameters’ regression coefficient estimates
Standardised error avoided cost RE delivered Initial costs
Annual costs Debt ratio Interest rate
Debt term GHG credit RE production credit
Standardised Error for Net Present Value as a Function of the Number of Observations
IRR - Variability of input parameters’ regression coefficient estimates
Standardised error avoided cost RE delivered Initial costs
Annual costs Debt ratio Interest rate
Debt term GHG credit RE production credit
Standardised Error for the Internal Rate of Return as a Function of the Number of Observations
In the Wind Energy Project Model, three distinct risk analysis scenarios were developed using the default values from the test case The accuracy of the impact, along with the median, maximum, and minimum levels of confidence, was validated using JMP statistical software For the after-tax internal rate of return (IRR), return on investment (ROI), and the time to achieve positive cash flow, results were presented with three decimal places of precision, while the net present value (NPV) was rounded to the nearest whole number.
In all three scenarios analyzed, the RETScreen Risk Analysis Model produced impact values and medians that were identical to those generated by the JMP software The maximum and minimum values within the level of confidence from RETScreen never deviated by more than 0.7% from JMP's results, as detailed in Table 4 Furthermore, the average difference ratio between RETScreen and JMP across financial output indicators for all scenarios was 0.24% for the minimum level of confidence and -0.30% for the maximum level of confidence.
The RETScreen Risk Analysis Model demonstrates a narrower confidence interval compared to JMP software, offering a higher minimum level of confidence and a lower maximum level of confidence Additionally, the disparity between the financial indicators of RETScreen and JMP widens as the range of these indicators increases, highlighting a significant difference in their analytical outputs.
500 calculations in the Monte Carlo simulation) Overall, the differences are insig- nificant and illustrate the adequacy of the RETScreen Sensitivity and Risk Analysis Model for pre-feasibility studies.
Year - Variability of input parameters’ regression coefficient estimates
3.0 avoided cost RE delivered Initial costs
Annual costs Debt ratio Interest rate
Debt term GHG credit RE production credit
Standardised Error for Year-to-Positive Cash Flow as a Function of the Number of Observations
Average differences (RETScreen vs JMP)
Ratio of average differences over results range
Within level of confi dence Within level of confi dence Financial output Results range Minimum Maximum Minimum Maximum
Table 4: Comparison of RETScreen and JMP for Minimum and Maximum within Level of Confi dence.
Summary
This introductory chapter highlights the growing interest in clean energy technologies, outlining their operations, applications, and market potential It emphasizes the critical role of pre-feasibility analysis in the project implementation cycle Additionally, it details the common methods employed in RETScreen Clean Energy Technology Models, including the utilization of climate and renewable energy resource data, greenhouse gas emission reduction calculations, financial analysis, and sensitivity and risk assessments.
Clean energy technologies have gained significant attention in the past decade as a solution to global warming, rising energy demands, high fuel costs, and local pollution issues The market for commercial power, heating, and cooling technologies is robust, presenting substantial opportunities for global expansion To leverage these technologies effectively, energy project proponents and stakeholders must evaluate proposed projects based on life cycle costs This involves efficiently screening competing energy options early in the project planning process to identify the most financially viable solutions.
The RETScreen International Clean Energy Project Analysis Software is a global tool designed to assess energy production, life-cycle costs, and greenhouse gas emissions for various renewable energy technologies (RETs) By utilizing this software, users can significantly lower costs and enhance the accuracy of pre-feasibility studies, leading to more informed decision-making before project execution RETScreen not only improves access to clean energy technologies but also raises awareness, builds capacity, and identifies opportunities that promote the implementation of cost-saving energy projects while minimizing greenhouse gas emissions.
Alward, R., Remote Community Renewable Energy Technology Project Identifi cation
Initiative (RETPII) , Natural Resources Canada’s CANMET Energy Diversifi cation Research
Laboratory (CEDRL), Varennes, QC, Canada, Initiative Summary (Abstract), 6 pp, 1999.
American Wind Energy Association (AWEA), Global Wind Power Growth Continues to Strengthen , 2005.
ASHRAE, Commercial/Institutional Ground-Source Heat Pump Engineering Manual , American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 1791 Tullie Circle, N.E., Atlanta, GA 30329, USA, 1995.
ASHRAE, Handbook of Fundamentals, SI Edition, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 1791 Tullie Circle, N.E., Atlanta, GA 30329, USA, 1997.
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Chandler, W.S., Brown, D.E., Whitlock, C.H., and Stackhouse, P.W., NASA Climatological
Data for Renewable Energy Assessment , Submitted to ISEC 2003 International Solar Energy
Conference, 16-18 March 2003, Mauna Kea Resort, HI, USA, 2003.
Eliasson, B., Renewable Energy: Status and Prospects 1998 , Baden-Dọttwil, Switzerland: ABB Corporate Research, Ltd., 1998.
Environment Canada, Canadian Weather Energy and Engineering Data Sets (CWEEDS) , 1993. European Commission, European Network of Energy Agencies ATLAS, 2005.
European Wind Energy Association (EWEA), Wind Power Installed In Europe by end of 2003 , 2005.
ESTIF, A Solar Thermal Strategy: Sun in Action II , Renewable Energy World, pp 200-209, July-August 2003.
Gipe, P., The BTM Wind Report: World Market Update , Renewable Energy World, pp 66-83, July-August 2003.
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International Small Hydro Atlas, Small-Scale Hydro Annex of the International Energy
Agency (IEA)’s Implementing Agreement for Hydropower Technologies & Programmes , 2004.
IPCC, IPCC Third Assessment Report—Climate Change 2001: Summary for Policy Makers , Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 2001.
Langcake, P., Getting a Clear View: Strategic Perspectives for Renewable Energy Companies , Renewable Energy World, pp 29-37, March-April 2003.
Leng, G., Monarque, A., Graham, S., Higgins, S., and Cleghorn, H., RETScreen ®
International: Results and Impacts 1996-2012 , Natural Resources Canada’s CETC-Varennes,
Lund, J., Ground-source Heat Pumps: A World Overview , Renewable Energy World, pp 218-227, July-August 2003.
Marine Current Turbines, Bristol, UK, 2005.
Maycock, P., PV Market Update , Renewable Energy World, pp 84-101, July-August 2003. Microsoft Online Help and Support, Microsoft, 2005.
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National Renewable Energy Laboratory (NREL), Golden, CO, USA, 2005.
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International Locations: Part I, Algorithms, and Part II: Production, ASHRAE Transactions,
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APPENDIX A - RETSCREEN DEVELOPMENT TEAM & EXPERTS
The CETC-Varennes core team oversees the technical management of RETScreen International, supported by a vast network of industry, government, and academic experts who offer specialized technical assistance on a contractual or collaborative basis This strategy enables RETScreen International to leverage a diverse range of expert skills essential for specific tasks.
Over 221 individuals have contributed to the development and support of RETScreen International, with an annual core team of 20 to 50 professionals This diverse group includes staff from partner organizations such as UNEP, NASA, the World Bank, and various Government of Canada programs, along with experts from private-sector firms like GPCo, Enermodal Engineering, Numerical Logics, TN Conseil, Ottawa Engineering, and Econoler International.
IT Power India, Umen, Cybercat and Projet Bleu, to name but a few.
Our core team comprises energy modeling specialists who create technology simulation models, cost engineering experts with practical installation experience, greenhouse gas modeling and baseline specialists skilled in economic and environmental analysis, and financial and risk analysis professionals adept at evaluating and financing projects.
The project involves a diverse team of experts, including those responsible for developing ground stations and satellite weather databases, alongside product databases Additional specialists validate the core team's work, while others focus on testing and debugging the final products They also prepare case studies, e-textbook chapters, and training materials for the course.
The team also includes numerous people involved in the overall software completion and website development and a dedicated group involved in customer support and outreach.
Numerous individuals continuously contribute valuable feedback and suggestions for enhancing the RETScreen software, while an expanding international network of RETScreen trainers offers localized training and technical assistance to users worldwide.
The following is an alphabetical listing of the people who have been directly involved in the development and support of RETScreen International to-date:
Asian Institute of Technology (AIT)
Polish Foundation for Energy Effi ciency (FEWE)
Global Village Energy Partnership (GVEP)
Atlantic Wind Test Site Ève-Line Brouillard
Science Applications International Corporation (SAIC)
Science Applications International Corporation (SAIC)
Caribbean Renewable Energy Development Programme
Bengt Degerman ệsterlens Kraft AB
Instituto de Investigaciones Electricas (IIE)
Science Applications International Corporation (SAIC)
The Energy and Resources Institute (TERI)
National Technical University of Athens
SC ECO-ERG Technologie Service S.R.L
World Bank Prototype Carbon Fund (PCF)
World Bank Prototype Carbon Fund (PCF)
Organización Latinoamericana de Energía (OLADE)
Barbados Ministry of Energy and Public Utilities (MEPU)
National Technical University of Athens
Indian Institute of Technology (IIT-Delhi)
Technological Educational Institute of Patras
The Energy and Resources Institute (TERI)
World Bank Prototype Carbon Fund (PCF)
Korea Institute of Energy Research (KIER)
Polish Foundation for Energy Effi ciency (FEWE)
Kumasi Institute of Technology and Environment (KITE)
Eduardo Antunez de Mayolo Ramis
Finnish District Heating Association (FDHA)
University of Northern British Columbia (UNBC)
Korea Institute of Energy Research (KIER)
Kogen Polska Polski Klub Kogeneracji
Renewable Energy and Energy Effi ciency Partnership (REEEP)
Paulo da Silva Filho Pedro
Société Tunisienne de Gérance de L’Énergie (STGE)
Research Institute for Systems Technology
Research Institute for Systems Technology
World Bank Prototype Carbon Fund (PCF)
Science Applications International Corporation (SAIC)
We extend our heartfelt gratitude to the many individuals from government, industry, academia, and NGOs who contributed to the development of RETScreen International We apologize to anyone not explicitly acknowledged for their invaluable support and guidance throughout this process.
As part of the various alpha and beta tests conducted since 1997, numerous other organisations provided comments and suggestions for improvements to the RETScreen International Their efforts are gratefully acknowledged.
RETS CREEN ® ENGINEERING & CASES TEXTBOOK
This publication is intended solely for informational purposes and does not necessarily represent the views of the Government of Canada, nor does it endorse any commercial products or individuals The Government of Canada, including its ministers, officers, employees, and agents, does not provide any warranty regarding this publication and assumes no liability arising from its content © Minister of Natural Resources.
Resources Canada 2001 - 2004. www.retscreen.net
Clean Energy Decision Support Centre © Minister of Natural Resources Canada 2001 - 2004.
1 WIND ENERGY BACKGROUND 5 1.1 Description of Wind Turbines 7 1.2 Wind Energy Application Markets 8 1.2.1 Off-grid applications 8 1.2.2 On-grid applications 9
The RETScreen Wind Energy Project Model provides a comprehensive analysis of wind energy production, beginning with unadjusted energy production metrics that include wind speed distribution and energy curves It further explores gross energy production and the renewable energy delivered, detailing aspects such as renewable energy collection, absorption rates, and excess renewable energy availability The model also examines specific yield and the capacity factor of wind plants Validation processes are crucial, comparing the wind energy model against both hourly models and monitored data to ensure accuracy The section concludes with a summary that encapsulates the key findings and insights from the analysis.
WIND ENERGY PROJECT ANALYSIS CHAPTER
The "Clean Energy Project Analysis: RETScreen® Engineering & Cases" serves as an essential electronic textbook for professionals and university students, focusing on the evaluation of potential wind energy projects through the RETScreen® International Clean Energy Project Analysis Software This chapter provides a comprehensive overview of the underlying technology and detailed insights into the algorithms utilized in the RETScreen® Software Additionally, it features a collection of real-world project case studies, complete with assignments and worked-out solutions, which can be accessed at the RETScreen® International Clean Energy Decision Support Centre Website, www.retscreen.net.
Wind energy, harnessed through wind turbines, is a promising renewable energy source with substantial potential worldwide The effectiveness of capturing this energy largely relies on the local average wind speed, making coastal regions, open inland areas, and locations near bodies of water particularly favorable Additionally, certain mountainous regions also exhibit good potential for wind energy projects Despite geographical limitations, there remains sufficient terrain in many areas to significantly meet local electricity demands through wind energy initiatives.
1 Some of the text in this “Background” description comes from the following two CANMET supported reports: Wind
Energy Basic Information, Backgrounder published by the Canadian Wind Energy Association (CanWEA), and, Rangi,
R., Templin, J., Carpentier, M and Argue, D., Canadian Wind Energy Technical and Market Potential, EAETB, Energy, Mines and Resources Canada (CANMET), ON, Canada, October 1992.
39.6 MW Central-Grid Windfarm in Spain.
The global demand for wind turbines has surged significantly over the past 15 years, with the wind energy industry installing nearly 5,500 MW of new capacity in 2001 alone Currently, over 24,000 MW of wind energy capacity is operational worldwide, driven largely by the increasing need for electric power plants utilizing cleaner fuels Wind farms featuring multiple turbines are being developed in the multi-megawatt range, reflecting the industry's growth Additionally, the average size of individual turbines has expanded from approximately 100 kW over the last decade.
Wind energy projects, including those developed offshore, now boast a generation capacity of 1 MW or more, as illustrated in Figure 2 This advancement has led to large-scale wind energy initiatives in certain regions producing electricity at costs that compete with traditional power plants, such as nuclear, oil, and coal.