MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in

Strategic Structural Reorganization in Multi-agent Systems Inspired by Social Organization Theory

Autonomic systems, capable of adaptive behavior, are envisioned as a solution for maintaining large, complex, real-time computing systems that are situated in dynamic and open environments. These systems are subject to uncertainties in their perceptual, computational, and communication loads. As a result, the individual system components find the need to cooperate with each other to acquire more information and accomplish complex tasks. Critical to the effective performance of these systems, is the effectiveness of communication and coordination methods. In many practical applications of distributed and multi-agent systems, the problem of communication and coordination becomes even more complicated because of the geographic disparity of tasks and/or agents that are performing the tasks. Experience with even small systems has shown that lack of an effective communication and coordination strategy leads the system to no-answer, or sub-optimal answer situations. To address this problem, many large-scale systems employ an additional layer of structuring, known as organizational structure, which governs assignment of roles to individual agents, existence of relations between the agents , and any authority structures in between. Applying different organizational structures to the same problem will lead to different performance characteristics. As the system and environment conditions change, it becomes important to reorganize to a more effective organization. Due to the costs associated with reorganization, finding a balance in how often or when a reorganization is performed becomes necessary. In multi-agent systems community, not a lot of attention has been paid to reorganizing a system to a different organizational structure. Most systems reorganize within the same structure, for example reorganizing in a hierarchy by changing the width or depth of the hierarchy. To approach this problem, we looked into adaptation of concepts and theories from social organization theory. In particular, we got insights from Schwaninger's model of Intelligent Human Organizations. We introduced a strategic reorganization model which enables the system to reorganize to a different type of organizational structure at run time. The proposed model employs different levels of organizational control for making organizational change decisions. We study the performance trade-offs and the efficacy of the proposed approach by running experiments using two instances of cooperative distributed problem solving applications. The results indicate that the proposed reorganization model results in performance improvements when task complexity increases

CO-EVOLUTIONARY BIDDING AND COOPERATION STRATEGIES FOR BUYERS IN POWER MARKETS

Master'sMASTER OF ENGINEERIN

Ontologies for the interoperability of multiagent electricity markets simulation platforms

Electricity markets worldwide are complex and dynamic environments with very particular characteristics. These are the result of electricity markets’ restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. The rising complexity and unpredictability in electricity markets has increased the need for the intervenient entities in foreseeing market behaviour. Market players and regulators are very interested in predicting the market’s behaviour. Market players need to understand the market behaviour and operation in order to maximize their profits, while market regulators need to test new rules and detect market inefficiencies before they are implemented. The growth of usage of simulation tools was driven by the need for understanding those mechanisms and how the involved players' interactions affect the markets' outcomes. Multi-agent based software is particularly well fitted to analyse dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Still, they have a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow market players from different systems to interact in common market environments. This dissertation proposes the development and implementation of ontologies for semantic interoperability between multi-agent simulation platforms in the scope of electricity markets. The added value provided to these platforms is given by enabling them sharing their knowledge and market models with other agent societies, which provides the means for an actual improvement in current electricity markets studies and development. The proposed ontologies are implemented in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) and tested through the interaction between MASCEM agents and agents from other multi-agent based simulators. The implementation of the proposed ontologies has also required a complete restructuring of MASCEM’s architecture and multi-agent model, which is also presented in this dissertation. The results achieved in the case studies allow identifying the advantages of the novel architecture of MASCEM, and most importantly, the added value of using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from the various systems.Os mercados de energia elétrica são ambientes complexos e dinâmicos que possuem características particulares. Tais características são resultado da sua reestruturação e evolução a escalas regionais e, por vezes, até continentais. A crescente necessidade de adaptação dos mecanismos existentes para que possam fazer face à integração adequada de fontes de energia renováveis também contribui para a peculiaridade destes mercados. A constante complexidade e imprevisibilidade nos mercados de eletricidade aumentou a necessidade das entidades neles intervenientes preverem o seu comportamento. Os reguladores precisam testar e detetar ineficiências nos algoritmos do mercado antes de serem implementados. Por outro lado, os agentes compradores e vendedores têm a necessidade de compreender o comportamento do mercado e o seu modo de operação, de modo a maximizarem os seus lucros ou minimizarem os seus custos. O crescimento do uso de ferramentas de simulação foi motivado pela necessidade de compreensão destes mecanismos e de como as interações entre as entidades intervenientes afetam os resultados dos mercados. Software baseado em tecnologia multiagente é particularmente adequado para estudar e analisar sistemas dinâmicos e adaptativos com interações complexas entre os seus constituintes, tais como os mercados de energia elétrica. Diversas ferramentas de modelação direcionadas ao estudo dos mercados reestruturados da eletricidade foram surgindo, como por exemplo o MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). No entanto, estas ferramentas de simulação partilham uma limitação comum: a falta de interoperabilidade entre os vários sistemas, que permita o intercâmbio de modelos e conhecimento, e ainda o teste e estudo de diferentes modelos de mercado. O MASCEM é um simulador multiagente de mercados competitivos de energia elétrica, que tem vindo a ser desenvolvido desde 2003. Inclui os principais modelos de mercado e as principais entidades que nele participam, permitindo o estudo dos modelos e comportamento do mercado e de cada um dos respetivos participantes. No entanto, com as constantes atualizações que o MASCEM tem acomodado, o seu ambiente tornou-se excessivamente complexo, revelando a fragilidade da sua arquitetura e da plataforma de comunicação dos agentes. Deste modo, tornou-se essencial reestruturar o sistema por completo, definindo uma nova arquitetura, um novo modelo multiagente, o uso de mecanismos adequados para lidar com os requisitos de tempos de execução, e, para facilitar a interoperabilidade com sistemas externos, o uso de semântica nas mensagens trocadas entre os principais intervenientes do mercado. Esta dissertação propõe, além da reestruturação completa da arquitetura e modelo multiagente do simulador MASCEM, o desenvolvimento e implementação de ontologias para a interoperabilidade semântica entre plataformas multiagente no âmbito dos mercados de energia elétrica. O valor acrescentado a estas ferramentas é dado através da partilha do seu conhecimento e modelos de mercado com outras sociedades de agentes, dispondo assim dos meios para uma efetiva melhoria nos estudos e desenvolvimento dos atuais mercados de eletricidade. Os resultados obtidos nos casos de estudo permitem identificar a adequação da nova arquitetura do simulador MASCEM, bem como as vantagens do uso das ontologias propostas. O uso destas ontologias facilita a integração de simuladores multiagente independentes, disponibilizando um modo para a compreensão das mensagens trocadas entre os agentes de sistemas heterogéneos

Housing Justice in Unequal Cities

Housing Justice in Unequal Cities is a global research network funded by the National Science Foundation (BCS 1758774) and housed at the Institute on Inequality and Democracy at UCLA Luskin. This open-access volume, co-edited by Ananya Roy and Hilary Malson, brings together movement-based and university-based scholars to build a shared field of inquiry focused on housing justice. Based on a convening that took place in Los Angeles in January 2019, at the LA Community Action Network and at the University of California, Los Angeles, the essays and interventions situate housing justice in the long struggle for freedom on stolen land. Embedded in the stark inequalities of Los Angeles, our work is necessarily global, connecting the city’s Skid Row to the indebted and evicted in Spain and Greece, to black women’s resistance in Brazil, to the rights asserted by squatters in India and South Africa. Learning from radical social movements, we argue that housing justice also requires a commitment to research justice. With this in mind, our effort to build a field of inquiry is also necessarily an endeavor to build epistemologies and methodologies that are accountable to communities that are on the frontlines of banishment and displacement