Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Evolving game theory based decision making systems for NETA power market modelling, analysis and trading strategy development

In this thesis, current work carried out on analyzing the strategic behaviours in electricity trading is first reviewed. An intelligent decision-making and support technique, game theory, is often used in the market practice. Game theory is a discipline concerned with how individuals make decisions when they are partly aware of how their action might affect each other and when each individual might take this into account. Deficiencies and limitations of traditional game theory based methods developed for decision-making in electricity trading are also investigated. This research then explores to discover the impact of intelligent systems based trading strategies in the UK power markets. To model these behaviours and the New Electricity Trading Arrangements (NETA) system of the UK, traditional competitive and cooperative game theory strategies are taken into account in the work reported in this thesis. An improved methodology, “trigger price strategy”, is introduced to simulate power generation companies’ enhanced gaming strategies. Such modelling problem is, however, intractable and hence an extra-numerical search technique, Evolutionary Computation, is employed to solve the game theory based system modelling problem. An encoded Genetic Algorithm based technique is developed to search for an effective model for the complex decision-making process and to help decision-makers evaluate their strategies and bidding parameters. A novel and effective electricity trading simulation model is thus developed, where its design features are close to the NETA. The model scale is as close as possible to NETA. A complex and more realistic two-sided transaction mechanism with demand fully incorporated is incorporated in this model. These are a world first in this research area

MASCEM: Optimizing the performance of a multi-agent system

The electricity market sector has suffered massive changes in the last few decades. The worldwide electricity market restructuring has been conducted to potentiate the increase in competitiveness and thus decrease electricity prices. However, the complexity in this sector has grown significantly as well, with the emergence of several new types of players, interacting in a constantly changing environment. Several electricity market simulators have been introduced in recent years with the purpose of sup-porting operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents a new, enhanced version of MASCEM (Multi-Agent System for Competitive Electricity Markets), an electricity market simulator with over ten years of existence, which had to be restructured in order to be able to face the highly demanding requirements that the decision support in this field requires. This restructuring optimizes the performance of MASCEM, both in results and execution time.info:eu-repo/semantics/publishedVersio

Multi-energy retail market simulation with autonomous intelligent agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

Classification of local energy trading negotiation profiles using artificial neural networks

Electricity markets are evolving into a local trading setting, which makes it for unexperienced players to achieve good agreements and obtain profits. One of the solutions to deal with this issue is to provide players with decision support solutions capable of identifying opponents' negotiation profiles, so that negotiation strategies can be adapted to those profiles in order to reach the best possible results from negotiations. This paper presents an approach that classifies opponents' proposals during a negotiation, to determine which is the typical negotiation profile in which the opponent most relates. The classification process is performed using an artificial neural network approach, and it is able to adapt at each new proposal during the negotiation process, by re-classifying the opponents' negotiation profile according to the most recent actions. In this way, effective decision support is provided to market players, enabling them to adapt the negotiation strategy throughout the negotiations.This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017info:eu-repo/semantics/publishedVersio

MODELING OF BIDDING PRICES IN POWER MARKETS USING CLUSTERING AND FUZZY ASSOCIATION RULES MODELAMIENTO DE PRECIOS DE OFERTA EN MERCADOS DE ELECTRICIDAD USANDO ALGORITMOS DE AGRUPAMIENTO Y REGLAS DE ASOCIACION DIFUSAS

ABSTRACT: In this paper, a strategy for discovering patterns over the continuous domain of bidding prices is proposed. In particular, the proposed method represents bidding functions as points in a multidimensional space where a clustering algorithm is applied. Also, as a result of this method, a dramatic reduction over the search space of bidding strategies is achieved. In addition, some relations of dominance over bidding strategies are found, improving the pattern recognition process of agents' bidding behavior. This method is applied on the bidding prices database for some GENCOs of the Colombian power (electricity) market. Furthermore, an application of some data mining algorithms is presented with the purpose of quantifying some hypothesis formulated on the effect of hydrology over both spot and bidding prices. KEYWORDS: Bidding prices, electricity market, pattern recognition. RESUMEN: Este artículo propone una metodología para el descubrimiento de patrones sobre el dominio continuo de los precios de oferta en mercados de electricidad. El método propuesto representa las funciones de oferta como puntos en un espacio multidimensional donde un algoritmo de agrupación es aplicado. Como resultado se obtiene una gran reducción sobre el espacio de búsqueda de estrategias al igual que algunas relaciones de dominancia sobre las mismas, mejorando el reconocimiento de patrones de comportamiento de la oferta. Este método es aplicado para los 10 agentes generadores más grandes del mercado eléctrico colombiano. Adicionalmente, se presenta la aplicación de algoritmos de minería de datos con el propósito de cuantifi car la veracidad de algunas hipótesis tradicionalmente formuladas sobre el efecto de la hidrología tanto en los precios de oferta como en los precios de bolsa

Modeling of bidding prices in power markets using clustering and fuzzy association rules

In this paper, a strategy for discovering patterns over the continuous domain of bidding prices is proposed. In particular, the proposed method represents bidding functions as points in a multidimensional space where a clustering algorithm is applied. Also, as a result of this method, a dramatic reduction over the search space of bidding strategies is achieved. In addition, some relations of dominance over bidding strategies are found, improving the pattern recognition process of agents’ bidding behavior. This method is applied on the bidding prices database for some GENCOs of the Colombian power (electricity) market. Furthermore, an application of some data mining algorithms is presented with the purpose of quantifying some hypothesis formulated on the effect of hydrology over both spot and bidding prices

Grid Scale Battery Energy Storage Investment Potential - Analysis and Simulations of Frequency Control Markets in Germany and the UK

The need for energy storages in future power systems is acknowledged both in literature and in industry. Simultaneously battery energy storage technologies, especially Lithium-ion, are seen technologically relatively mature with favorable cost development. Whereas frequency control markets provide exploitable commercial and technical framework for battery investment. Nevertheless, true commercial viability is still uncertain in leading European markets in Germany and the UK. The purpose of the study was to provide complete and comparative market analysis and demonstrated prospective investment profitability outcomes for grid scale battery energy storages in Germany and the UK. In addition, the study aimed to show required conditions for desired investment performances. The study explored investment potential in primary frequency control market in Germany and enhanced frequency response market in the UK by analyzing market attractiveness from multiple aspects. The countries were ranked based on the analyzed aspects by Analytical Hierarchy Process. Finally, financial Monte Carlo investment simulations with revenue and cost uncertainties were performed. Simulations also provided required conditions for profitability. Analyzed data was based on historical market data, performed online market research and literature. Key findings of the study revealed that the chosen markets form suitable commercial framework for battery investments, but Germany shows clearly higher potential. However, the potential was questionable since both markets face significant challenges especially in financial sense. The concerns were confirmed by the simulations which suggested around 1–5 % and -3–3 % internal rate of return levels for Germany and the UK respectively. In addition, reaching 6 % return was seen very challenging whilst over 10 % return levels seemed unrealistic in the UK and extremely optimistic for Germany. The overall conclusion was that battery energy storage investment in either of the markets cannot currently be justified primarily by financial returns but needs strategic support.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format