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Option value of electricity demand response

Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.
- Lawrence Berkeley National Laboratory United States
- Lawrence Berkeley National Laboratory United States
- Citigroup United States
- Citigroup United States
- University of California System United States
policy and economy, Energy conservation, Energy planning, Environmental Energy Technologies, Energy conservation, consumption, and utilization, consumption, Energy planning, policy and economy, and utilization
policy and economy, Energy conservation, Energy planning, Environmental Energy Technologies, Energy conservation, consumption, and utilization, consumption, Energy planning, policy and economy, and utilization
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).103 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
