Prenegotiation and actual negotiation in electricity markets

Software agents have been successfully used in a vast range of applications. Agents have been gradually designed to act in open environments and to manage their cooperative and competitive interactions with other agents present in their environment. In a Multi-agent System (MAS), involving different agents operating individually to meet their design goals, conflict will be inevitable — it is not necessarily bad or good, but it is inevitable. Conflict is the focal point of interaction, i.e. the driving force of negotiation. Furthermore, conflict is the element that connects the individual and social behavior of agents. Software tools based on intelligent agents with negotiating capabilities have became important and pervasive. Particularly, there is a growing demand to develop MAS featuring bilateral contracts in liberalized Electricity Markets (EMs). This dissertation addresses, at least in part, this challenge by presenting the computational tool NSEM – Negotiation Simulator for Electricity Markets. NSEM features Belief-Desire-Intention agents able to effectively plan actions, manage conflicts, and trade proposals to reach mutually beneficial agreements. NSEM focuses on the preliminary activities that should come before negotiation, usually referred to as prenegotiation. These activities include the definition of the issues at stake, their prioritization, and the selection of an appropriate protocol and effective strategies. This dissertation presents details of NSEM’s implementation and test. NSEM was developed with the JAVA programming language and the JADE platform. Its test was performed by using a case study, featuring prenegotiation and actual negotiation of bilateral contracts in liberalized EMs.A tecnologia baseada em agentes computacionais autónomos tem vindo a ser utilizada com sucesso numa vasta gama de aplicações. Num Sistema Multi-agente (SMA), composto por diversos agentes atuando individualmente para alcançarr os seus objectivos de projecto, os conflitos são inevitáveis. Os conflitos constituem o elemento que liga o comportamento individual e social dos agentes, sendo normalmente a força motriz da negociação. O desenvolvimento de agentes com capacidade negocial sofreu avanços significativos ao longo dos últimos anos. Estes agentes apresentam diversas vantagens relativamente aos negociadores humanos, sendo de realçar a capacidade de obterem acordos benéficos para todas as partes envolvidas na negociação. Nesta perspectiva, salienta-se a procura crescente de SMAs para simular a contratação bilateral de energia em mercados liberalizados. Esta dissertação tenta responder a este desafio através do desenvolvimento da ferramenta computacional NSEM: “Negotiation Simulator for Electricity Markets”. NSEM permite criar agentes constituídos pelas atitudes mentais de crença, desejo e intenção, capazes de planear ações de forma efectiva, gerir conflitos, e negociar acordos mutualmente beneficiáveis. NSEM coloca a ênfase no conjunto de atividades preliminares a realizar antes da negociação, referido usualmente como pré-negociação. Estas atividades incluem a definição dos itens a negociar, as suas prioridades, a escolha de um protocolo apropriado, e a seleção de estratégias efetivas. Esta dissertação ao apresenta detalhes da implementação e teste do NSEM. A implementação foi efetuada através do Java e do JADE. O teste foi realizado através do desenvolvimento de um caso de estudo referente à contratação bilateral de energia em mercados de eletricidade liberalizados

Oligopolistic and oligopsonistic bilateral electricity market modeling using hierarchical conjectural variation equilibrium method

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAn electricity market is very complex and different in its nature, when compared to other commodity markets. The introduction of competition and restructuring in global electricity markets brought more complexity and major changes in terms of governance, ownership and technical and market operations. In a liberalized electricity market, all market participants are responsible for their own decisions; therefore, all the participants are trying to make profit by participating in electricity trading. There are different types of electricity market, and in this research a bilateral electricity market has been specifically considered. This thesis not only contributes with regard to the reviewing UK electricity market as an example of a bilateral electricity market with more than 97% of long-term bilateral trading, but also proposes a dual aspect point of view with regard to the bilateral electricity market by splitting the generation and supply sides of the wholesale market. This research aims at maximizing the market participants’ profits and finds the equilibrium point of the bilateral market; hence, various methods such as equilibrium models have been reviewed with regard to management of the risks (e.g. technical and financial risks) of participating in the electricity market. This research proposes a novel Conjectural Variation Equilibrium (CVE) model for bilateral electricity markets, to reduce the market participants’ exposure to risks and maximize the profits. Hence, generation companies’ behaviors and strategies in an imperfect bilateral market environment, oligopoly, have been investigated by applying the CVE method. By looking at the bilateral market from an alternative aspect, the supply companies’ behaviors in an oligopsony environment have also been taken into consideration. At the final stage of this research, the ‘matching’ of both quantity and price between oligopolistic and oligopsonistic markets has been obtained through a novel-coordinating algorithm that includes CVE model iterations of both markets. Such matching can be achieved by adopting a hierarchical optimization approach, using the Matlab Patternsearch optimization algorithm, which acts as a virtual broker to find the equilibrium point of both markets. Index Terms-- Bilateral electricity market, Oligopolistic market, Oligopsonistic market, Conjectural Variation Equilibrium method, Patternsearch optimization, Game theory, Hierarchical optimization metho