State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design.multi-agent learning; auction markets; design economics; agent-based computational economics

Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated

The More Cooperation, the More Competition? A Cournot Analysis of the Benefits of Electric Market Coupling

Market coupling in Belgian and Dutch markets would permit more efficient use of intercountry transmission, 1) by counting only net flows against transmission limits, 2) by improving access to the Belgian market, and 3) by eliminating the mismatch in timing between interface auctions and the energy spot market. A Cournot market model that accounts for the region’s transmission pricing rules and limitations is used to simulate market outcomes with and without market coupling. This accounts for 1) and 2). Market coupling improves social surplus in the order of 108 €/year, unless it encourages the largest producer in the region to switch from a price-taking strategy in Belgium to a Cournot strategy due to a perceived diminishment of the threat of regulatory intervention. Benefit to Dutch consumers depends on the behavior of this company. The results illustrate how large-scale oligopoly models can be useful for assessing market integration