Multi-agent electricity markets: Retailer portfolio optimization using Markowitz theory

• Autor(a) principal: Algarvio, H.
• Data de Publicação: 2017
• Outros Autores: Lopes, F., de Sousa, Jorge Mendes, Lagarto, João
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.21/7580
• topic: Multi-agent electricity markets; Forward bilateral contracts; Electricity retailers; Risk attitude; Portfolio of customers; Markowitz theory

The major electricity market models include: pools, bilateral contracts and hybrid models. Pool prices tend to change quickly and variations are usually highly unpredictable. In this way, market participants can enter into bilateral contracts to hedge against pool price volatility. In bilateral contracts, market participants can set the terms and conditions of agreements independent of the market operator. The hybrid model combines features of both pools and bilateral contracts. This paper is devoted to risk management and the optimization of the portfolios of retailers operating in liberalized electricity markets. It introduces a model for optimizing portfolios composed by end-use consumers using the Markowitz theory. It also presents an overview of a multi-agent system for electricity markets. The system simulates the behavior of various markets entities, including generating companies, retailers and consumers. The final part of the paper presents three case studies on portfolio optimization involving risk management: a retailer (a software agent) optimizes its portfolio by taking into account the attitude towards risk and the offer of a 3-rate tariff to five different types of consumers: industrial, large and small commercial, residential and street lightning. The results show that the retailer, by being more realistic in choosing consumers to its portfolio, can offer more competitive tariffs to key consumers and keep the portfolio optimal and stable in relation to the risk return ratio.