Retailer's risk‐aware trading framework with demand response aggregators in short‐term electricity markets. Issue 13 (14th June 2019)

• Record Type: Journal Article
• Authors: Mohammad, Nur; Mishra, Yateendra
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 13(2019)
• Page Start: 2611
• Page End: 2618
• Publication Date: 2019-06-14
• Subjects: supply and demand -- optimisation -- stochastic processes -- pricing -- power markets -- retailing; aggregator -- DR transaction cost -- DR offer setting -- market model -- demand response aggregators -- short‐term electricity markets -- risk‐aware electricity retailer -- wholesale pool‐price volatility -- coordinated demand response trading -- end‐users -- market operator -- bi‐level optimisation framework -- upper‐level -- expected cost -- wholesale energy pool -- DR aggregators -- evolving mean reverting volatility -- pool electricity prices -- stochastic jump‐diffusion process -- value‐at‐risk measure -- payoff loss -- lower‐level problem; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6294
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : A risk‐aware electricity retailer may alleviate concern about wholesale pool‐price volatility through coordinated demand response (DR) trading with aggregators who act as intermediaries between end‐users and the market operator (MO). This article proposes cost‐efficient integration of DR into electricity markets using a bi‐level optimisation framework. In the upper‐level, the retailer's problem is to maximise expected payoff, i.e. revenues earned by selling energy to end‐users minus the expected cost of purchasing from the wholesale energy pool and the DR aggregators. The evolving mean reverting volatility in pool electricity prices is captured as a stochastic jump‐diffusion process. The conditional value‐at‐risk (CVaR) measure is explicitly incorporated into the problem to limit the risk of payoff loss due to the price volatility. The lower‐level problem involves the aggregator's strategic bidding offer in which the primary objective of the MO is to minimise the DR transaction cost. In the DR offer setting, the conflicting economic interest to increase the aggregator's payoff is captured. A Lagrangian relaxation method with associated Karush Kuhn Tucker (KKT) optimality is used to solve these problems. The simulation results consider plausible case studies and provide the effectiveness of the proposed market model.