A Two-Tier Matrix Game Approach for Obtaining Joint Bidding Strategies in FTR and Energy Markets

Abstract

Transmission congestion results in revenue collected from the loads that often exceeds the payment made to the generators. The financial transmission right (FTR) is an instrument that allows the independent system operator (ISO) to redistribute the excess revenue (congestion charge) among the market participants. Generators and loads hold FTRs to hedge against congestion charges. Since the revenues from the FTR and energy markets are interdependent, each generator and load must have a joint bidding strategy for FTR and energy markets to maximize its total payoff. This paper presents a two-tier matrix game model that allows the generators and loads to obtain joint bidding strategies. The model also allows the ISOs and the market designers to assess the impact of various network configurations and rules on market performance (e.g., market power). The two-tier model considers multiple participants, multidimensional bid vectors, network contingencies, and varying demand. We conducted a numerical validation of our model by analyzing the three-node network studied by Joskow and Tirole (2000). Additional numerical studies were conducted on a five-node network to examine the impact of holding FTRs on the bidding strategies in the energy market and joint payoffs of the participants.Index Terms-Energy settlement, financial transmission rights (FTRs), FTR settlement, matrix game, Nash equilibrium, restructured electricity markets, value iteration based reinforcement learning.

A Relevância Da Informação Contábil Para a Tomada De Decisão Nas Empresas De Francisco Beltrão.

“…Segundo a hipótese de Gauss-Markov, se a regressão segue as premissas do modelo de regressão linear, os estimadores do método de mínimos quadrados Tsang, 2009); resolução de matrizes multijogadores no estabelecimento de licitações (Babayig, Rocha, Das, 2010); para o estabelecimento de regras precisas em sistemas de comercialização automática de ações (Krejník, Tyutin, 2012); previsão de demanda energética de modo a evitar desperdício no investimento em infraestrutura de geração de energia (Hyndman, Shu Fan, 2010); análise de dados financeiros de receita de geradores de energia eólica (Gomez-Quiles, Gil, 2011); estudo de dados financeiros com frequências diferentes pois são importantes no estudo de uma variedade de processos de negociação de ações e microestrutura de mercado (Zhang, Liu, Yu, 2012); previsão de valor de opções financeiras baseadas no ciclo de vida de um sistema (Haddad, Sandborn, Pecht, 2012); previsão de custo financeiro modelando a interrupção de serviços de transmissão de energia (Milanovic, Vegunta, 2011).…”

Portfolio management of battery storages in multiple electricity markets

“…A few papers have investigated bidding strategies with consideration of FTR [17], [18]. Joint bidding strategies in the FTR and energy markets are investigated in [19]. An optimisation method for FTR portfolio selection is proposed in [20] that involves the fewest node pairs.…”

Strategic CBDR bidding considering FTR and wind power

“…In [3], the FTR portfolio optimisation based on identification of congested network elements is proposed which overcomes the data handling and heaving computing burden of LMP difference based methods. In [4], a joint bidding model considering both FTR and energy market is proposed which is based on a two‐tier matrix game approach. These papers focus on FTR portfolio optimisation while DR and wind power uncertainty are not considered.…”

“…With both physical and financial instruments in hand, there is a growing need for LSEs to make sound decisions with the help of a framework that can help investigate the coupling effects among various assets to achieve the maximisation of its revenue or in another word, minimising customers' cost through strategic participation in power market. The abovementioned papers focus on FTR portfolio optimisation [3, 4] or DR optimisation [5–17] but did not provide a comprehensive framework to analyse the optimal bidding strategy (i.e. providing the best coupon to consumers to achieve the best financial benefit to a LSE) over an array of the LSE's physical and financial assets under current market clearing methodology.…”