Dynamic sale prices for load serving entity's risk based profit maximization

Abstract

In any retail market, Load Serving Entities (LSE) and consumers can benefit from an effective dynamic pricing mechanism. Consumer's price responsiveness and uncertainty of Wholesale Electricity Market (WEM) prices can offer unexpected profits for LSE. LSEs are incentivised to procure a portion of their energy demand from Energy Storage Systems (ESS) due to increasing renewable penetration at the distribution end. LSE's decisions become complex while handling uncertainty arising from such constantly varying conditions. Sale price dynamics optimization could minimize undesirable impact of these uncertainties and maximize LSE's profit. However, this optimization is subject to severe WEM price uncertainty. In this perspective, this paper proposes a sale price dynamics optimization model to maximize LSE's profit while immunizing LSE against severe uncertainty of WEM prices. Information Gap Decision Theory (IGDT) framework is considered to explicitly model severe price uncertainty and determine robust and opportunistic decisions. Impact of Renewable Energy (RE) availability along with Battery Energy Storage (BES) operations on procurement decisions is analyzed. Results highlight that the proposed model ensures consumer benefit and increases LSE's profit. Correlation variation between demand and WEM price profile establishes effectiveness of the proposed model. RE and BES enhance robustness of procurement decisions.