The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis

被引:8
作者
Lynch, Muireann A. [1 ]
Curtis, John [1 ]
机构
[1] Econ & Social Res Inst, Dublin, Ireland
关键词
Electricity; Monte Carlo analysis; wind generation; utility framework; conditional value-at-risk; simulation; POWER-GENERATION; ENERGY-POLICY; MARKET; IMPACT; RISK; PORTFOLIO; VARIABILITY; INCENTIVES; DISPATCH; MODEL;
D O I
10.1080/00036846.2015.1076145
中图分类号
F [经济];
学科分类号
02 ;
摘要
We use Monte Carlo analysis to examine the potential of increased renewable generation to provide a hedge against variability in energy prices and costs. Fuel costs, electricity demand and wind generation are allowed to vary and a unit commitment and economic dispatch algorithm is employed to produce cost-minimizing generation schedules under different levels of installed wind capacity. Increased wind capacity reduces the mean and the variance of production costs but only the variance of electricity prices. Wind generators see their market revenues increase while consumer payments and fossil generator profits do not considerably vary as wind capacity increases. Risk aversion is captured by considering the conditional value-at-risk for both consumers and producers. The optimal level of wind generation increases as risk aversion increases due to the potential of wind to act as a hedge against very high electricity prices in high fuel price scenarios.
引用
收藏
页码:133 / 151
页数:19
相关论文
共 66 条
  • [1] Alexander C., 2009, Market Risk Analysis
  • [2] [Anonymous], ECNC05100
  • [3] Coherent measures of risk
    Artzner, P
    Delbaen, F
    Eber, JM
    Heath, D
    [J]. MATHEMATICAL FINANCE, 1999, 9 (03) : 203 - 228
  • [4] Investing in photovoltaics: risk, accounting and the value of new technology
    Awerbuch, S
    [J]. ENERGY POLICY, 2000, 28 (14) : 1023 - 1035
  • [5] Awerbuch S., 2003, IEA EET WORKING PAPE
  • [6] Awerbuch S., 2005, INTERMITTENCY BRIEFI
  • [7] A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data
    Carapellucci, Roberto
    Giordano, Lorena
    [J]. APPLIED ENERGY, 2013, 101 : 541 - 550
  • [8] Risk Management in the Oil Supply Chain: A CVaR Approach
    Carneiro, Maria C.
    Ribas, Gabriela P.
    Hamacher, Silvio
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2010, 49 (07) : 3286 - 3294
  • [9] A stochastic programming approach to electric energy procurement for large consumers
    Carrion, Miguel
    Philpott, Andy B.
    Conejo, Antonio J.
    Arroyo, Jose M.
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (02) : 744 - 754
  • [10] CER and NIAUR, 2013, ROUND 5 R5 SEM PLEX