Cost and Performance Assumptions for Modeling Electricity Generation Technologies

Tidball, Rick; Bluestein, J.; Rodriguez, Nick; Knoke, Stu

doi:10.2172/1219277

Cited by 55 publications

(26 citation statements)

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“…A.2 and are compared to those of 2010. The error bars are used to compare the results of LCOE from this study to those calculated in the literature [68,[71][72][73][74][75][76][77][78].…”

Section: A2 Cost Modulementioning

confidence: 99%

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

2016

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“…A.2 and are compared to those of 2010. The error bars are used to compare the results of LCOE from this study to those calculated in the literature [68,[71][72][73][74][75][76][77][78].…”

Section: A2 Cost Modulementioning

confidence: 99%

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

2016

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“…Where TCa is the total annual cost, MC is the annual maintenance cost, OC is the annual operating cost, IC is the annual investment cost, and n is the number of renewable generators. The annual maintenance and operation cost of solar energy was assumed to be 21 $/kW-year, and the total annual maintenance and operation cost of solar energy was assumed to be 28 $/kW-year [19], [20]. The renewable energy investment cost IC was obtained using (7) [18].…”

Section: Total Annual Costmentioning

confidence: 99%

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Candelo-Becerra

Riaño

Santander-Mercado

2015

TecnoL.

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Electric power losses have a significant impact on the total costs of distribution networks. The use of renewable energy sources is a major alternative to improve power losses and costs, although other important issues are also enhanced such as voltage magnitudes and network congestion. However, determining the best location and size of renewable energy generators can be sometimes a challenging task due to a large number of possible combinations in the search space. Furthermore, the multiobjective functions increase the complexity of the problem and metaheuristics are preferred to find solutions in a relatively short time. This paper evaluates the performance of the cuckoo search (CS), harmony search (HS), and bat-inspired (BA) algorithms for the location and size of renewable distributed generation (RDG) in radial distribution networks using a multiobjective function defined as minimizing the energy losses and the RDG costs. The metaheuristic algorithms were programmed in Matlab and tested using the 33-node radial distribution network. The three algorithms obtained similar results for the two objectives evaluated, finding points close to the best solutions in the Pareto front. Comparisons showed that the CS obtained the minimum results for most points evaluated, but the BA and the HS were close to the best solution. KeywordsCuckoo search, bat-inspired algorithm, renewable distributed generation, harmony search, photovoltaic energy systems, wind energy systems. ResumenLas pérdidas eléctricas tienen un impacto significativo en los costos totales de las redes de distribución. El uso de las energías renovables es una gran alternativa para mejorar las pérdidas y los costos, aunque también otros problemas en las magnitudes de las tensiones y la congestión de la red pueden ser mejorados. Sin embargo, determinar la mejor localización y dimensionamiento de generadores eléctricos renovables puede ser a veces una tarea difícil debido al gran número de combinaciones posibles existentes en el espacio de búsqueda. Además, el uso de funciones multiobjetivo incrementa la complejidad del problema y se prefiere usar las metaheurísticas para encontrar soluciones en un tiempo relativamente corto. En este trabajo se evalúa el desempeño de los algoritmos inspirados en búsqueda cuco, búsqueda armónica y murciélagos para la localización y dimensionamiento de la generación distribuida renovable en redes de distribución radiales, usando funciones como la minimización de las pérdidas de energía y los costos de la generación distribuida renovable. Las metaheurísticas fueron programadas en Matlab y evaluadas usando la instancia denominada red de distribución radial de 33 nodos. Los tres algoritmos evaluados obtuvieron resultados similares para los dos objetivos evaluados, encontrando Frentes de Pareto cercanos a las mejores soluciones. La comparación realizada mostró que la búsqueda cuco obtiene los mejores resultados, pero los algoritmos inspirados en murciélagos y búsqueda armónica obtuvieron resultados cercanos a la mejor soluci...

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“…Many of these studies utilize learning curves in combination with expert elicitation, engineering models, and near-term market analysis (e.g., EWEA 2009, U.S. DOE 2008, GWEC/GPI 2010, and Lemming et al 2009). Sources: EREC/ GPI 2010, Tidball et al 2010 (includes modeling scenarios from multiple other sources), U.S. DOE 2008, EIA 2011, Lemming et al 2009, EWEA 2011, EPRI 2010, Peter and Lehmann 2008, GWEC/GPI 2010, IEA 2009, and European Commission 2007 The data presented in Figure ES-3 suggest an approximate 0%-40% reduction in LCOE through 2030. The single scenario anticipating no further cost reductions assumes that the upward price pressures observed between 2004 and 2009 are moderated but remain significant enough to prevent future reductions in LCOE.…”

Section: Long-term Trends In Wind Energy Lcoementioning

confidence: 99%

“…20 Many of these studies utilize learning curves in combination with expert elicitation, engineering models, and near-term market analysis (e.g., EWEA 2009, U.S. DOE 2008, GWEC/GPI 2010, and Lemming et al 2009). Some of the more extreme results are generated from comparably conservative assumptions (e.g., Tidball et al 2010) or from advanced scenarios with the most optimistic assumptions (e.g., EREC/GPI 2010, GWEC/GPI 2010, and Peter and Lehmann 2008).…”

Section: Quantitative Summary Of Future Cost Estimatesmentioning

confidence: 99%

IEA Wind Task 26: The Past and Future Cost of Wind Energy, Work Package 2

Lantz¹,

Wiser²,

Hand³

2012

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Executive SummaryOver the past 30 years, wind power has become a mainstream source of electricity generation around the world. However, the future of wind power will depend a great deal on the ability of the industry to continue to achieve cost of energy reductions. This summary report, developed as part of the International Energy Agency (IEA) Wind Implementing Agreement Task 26, The Cost of Wind Energy, provides a review of historical costs, evaluates near-term market trends, reviews the methods used to estimate long-term cost trajectories, and summarizes the range of costs projected for onshore wind energy across an array of forward-looking studies and scenarios. It also highlights high-level market variables that have influenced wind energy costs in the past and are expected to do so into the future. Historical and Near-Term Trends in the Levelized Cost of Wind EnergyBetween 1980 and the early 2000s, significant reductions in capital cost and increases in performance had the combined effect of dramatically reducing the levelized cost of energy (LCOE) for onshore wind energy. Data from three different historical evaluations, including internal analysis by the Lawrence Berkley National Laboratory (LBNL) and the National Renewable Energy Laboratory (NREL) as well as published estimates from Lemming et al. (2009) and the Danish Energy Agency (DEA) (1999), illustrate that the LCOE of wind power declined by a factor of more than three, from more than $150/MWh to approximately $50/MWh between 1980s and the early 2000s ( Figure ES-1). However, beginning in about 2003 and continuing through the latter half of the past decade, wind power capital costs increased-driven by rising commodity and raw materials prices, increased labor costs, improved manufacturer profitability, and turbine upscaling-thus pushing wind's LCOE upward in spite of continued performance improvements ( Figure ES-1). Sources: LBNL/NREL (internal analysis), Lemming et al. 2009, and DEA 1999 [2012][2013] suggests that the LCOE of onshore wind energy is now at an all-time low within fixed wind resource classes, and particularly in low and medium wind speed areas ( Figure ES-2). Moreover, the fact that capital costs remain higher than in the early 2000s but that those increased costs are rewarded by improved performance and a lower LCOE demonstrates the fundamental interdependence of capital cost and performance in wind turbine and project design. Long-term Trends in Wind Energy LCOEFurther into the future, the LCOE of wind energy is expected to continue to fall, at least on a global basis and within fixed wind resource classes. Performance improvements associated with continued turbine upscaling and design advancements are anticipated, and lower capital costs may also be achievable. The magnitude of future cost reductions, however, is highly uncertain.Estimates of the future cost of onshore wind energy conducted to date have often been the result of an iterative process that incorporates some combination of historical trends, learning curve anal...

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Cost and Performance Assumptions for Modeling Electricity Generation Technologies

Abstract: UL 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON a. REPORT

Cited by 55 publications

References 0 publications

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

IEA Wind Task 26: The Past and Future Cost of Wind Energy, Work Package 2

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