Optimal Selection of Distributed Energy Resources Under Uncertainty and Risk Aversion

Abstract

The adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. In order to overcome this barrier to investment, we develop a mean-risk optimisation model for the long-term risk management problem of an energy consumer using stochastic programming. The consumer can invest in a number of generation technologies and also has access to electricity and gas futures to reduce its risk. We examine the role of on-site generation in the consumer's risk management strategy as well as interactions between on-site generation and financial hedges. Our study shows that by swapping electricity (with high price volatility) for gas (with low price volatility), even relatively inefficient technologies reduce risk exposure and CO 2 emissions. The capability of on-site generation is enhanced through the use of combined heat and power (CHP) applications. In essence, by investing in a CHP unit, a consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing its financial risk. Finally, in contrast to the extant literature, we demonstrate that on-site generation affects the consumer's decision to purchase financial hedges. In particular, while on-site generation and electricity futures may act as substitutes, on-site generation and gas futures can function as complements.

Optimal Risk Adoption and Capacity Investment in Technological Innovations

“…Hence, measures to reduce the overall risk are crucial to induce investments in RE capacity. For example, adequate financial markets to hedge price risk can limit the effect of risk aversion [29].…”

“…Hence, the value of the option to invest in Regime 1 is obtained by solving (28) and (29) and is described as follows:…”

A New Pythagorean Fuzzy Based Decision Framework for Assessing Healthcare Waste Treatment

“…Next, several MCDM models have been developed by numerous authors for different purposes. Some noteworthy models are presented by Maurovich-Horvat et al [37], Bai et al [38], Lima et al [39], Raghunathan et al [40] and Dowd et al [41]. In the procedure of MCDM, the criteria weights are significant concerns for DEs.…”

Optimal operation of combined heat and power under uncertainty and risk aversion

“…[27] tackle the day-ahead CHP scheduling problem of a risk-averse consumer facing uncertainty in electricity prices and demand with a deterministic self-generation cost. In contrast to the numerous deterministic and real options papers on CHP investment, [28] apply stochastic programming to examine a consumer's prospects to invest in CHP.…”

“…Analogous to [28], our paper provides insights into the interaction of financial hedges and on-site generation, but we focus on a consumer's risk management in the medium term instead of its long-term investment decisions. We also report on how different technologies can contribute to reaching the 2020 CO 2 emissions targets.…”