Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market. (15th March 2015)

• Record Type: Journal Article
• Authors: Fotouhi Ghazvini, Mohammad Ali; Faria, Pedro; Ramos, Sergio; Morais, Hugo; Vale, Zita
• Is Part Of: Energy. Volume 82(2015)
• Page Start: 786
• Page End: 799
• Publication Date: 2015-03-15
• Subjects: Demand response -- Electricity market -- Financial risk -- Market power -- Retail market -- Stochastic programming; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.01.090
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Following the deregulation experience of retail electricity markets in most countries, the majority of the new entrants of the liberalized retail market were pure REP (retail electricity providers). These entities were subject to financial risks because of the unexpected price variations, price spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand. Highlights: Asset-light electricity retail providers subject to financial risks. Incentive-based demand response program to manage the financial risks. Maximizing the payoff of electricity retail providers in day-ahead market. Mixed integer nonlinear programming to manage the risks.