Summary
Retailers in electricity markets face challenges of hourly varying energy prices and consumers' elastic demand. Besides these challenges, utilization of high renewable energy (RE) self‐generation such as solar energy could significantly affect energy procurement and sale prices decisions of a retailer. A retailer with RE self‐generation makes decisions targeting best utilization of RE availability, to maximize its profit. However, a retailer without RE makes decisions considering energy prices and elastic demand. In this perspective, this paper determines optimal Time of Use (ToU) sale prices to be offered and optimal energy procurement portfolio, for a retailer with and without RE, considering elastic demand and wholesale market price uncertainty, aiming to maximize retailer's profit. Case study considers three consumer classes to examine risk neutral and risk averse behavior of a retailer. Results illustrate impact of multiple consumer class behavior on ToU price, procurement strategy, and risk preferences of a retailer.
Load serving entities (LSEs) maximise their profit by increasing the difference between revenue from electricity sale to consumers and the cost of procuring that electricity. Considering the offered sale prices, consumers minimise energy bills by scheduling their energy consumption. Varying spot market prices and consumer behaviour impact LSE's electricity procurement and sale price decisions. The two conflicting objectives of LSE profit maximization and consumer cost minimization can be modelled effectively by hierarchical bi-level programming. Additionally, LSE has to consider spot market price uncertainty and renewable availability during different hours of the day. This paper considers these issues for LSE's risk-based profit maximization decision making model under RE availability by proposing a bi-level framework to determine optimal dynamic sale prices and energy procurement decisions. The upper level considers risk-based profit maximization for LSE and the lower level addresses the consumer's objective of cost minimization. This work considers Conditional Value at Risk (CVaR) to model spot market price risk for pragmatic characterisation of LSE's risk-averse behaviour. A case study on the PJM market shows the effectiveness of the proposed approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.