Agent-based simulation: an application to the new electricity trading arrangements of England and Wales.

This paper presents a large-scale application of multiagent evolutionary modeling to the proposed new electricity trading arrangements (NETA) in the U.K. This is a detailed plant-by-plant model with an active specification of the demand side of the market. NETA involves a bilateral forward market followed by a balancing mechanism and then an imbalance settlement process. This agent-based simulation model was able to provide pricing and strategic insights, ahead of NETA's actual introduction

Simulation of electricity markets using agent-based computational learning

The purpose of this research is to conduct an analysis of how agent-based computational learning may contribute to a better understanding of pricing policies and strategic management of plant portfolio in electricity markets. The contributions of this thesis are methodological and theoretical with applications in policy analysis for electricity markets. At a policy level, this thesis applies agent-based simulation to the analysis of the impact of market design on the players' strategies and on the industry's performance as a whole. This represents the first detailed study of the New Electricity Trading Arrangements (NETA) in the England and Wales (E&W) electricity market, giving insights into the implications of NETA before its introduction. Further, this thesis addresses the issue of dominant position abuse by individual players in electricity markets. The context is a real application to the E&W electricity market as part of a Competition Commission Inquiry. The research contributions are in the areas of both market power and market design policy issues. At a methodological level, this thesis presents two contributions: the Finite Automata Dynamic Game (FADG) and the Plant Trading Game. The FADG models learning and adaptation in N-player extensive form games of incomplete information, where co-evolutionary automata learn and adapt together. The plant trading game is a large coordination game, simulating how players optimally learn and adapt in order to trade electricity plants. At a theoretical level, this thesis presents three contributions. First, it develops a stylised model for conduct-evaluation in electricity markets, which is applied to the analysis of market power abuse and regulatory policy. Second, it studies plant trading within the context of a Cournot game. Third, it shows that, in an FADG, best response is a necessary but not a sufficient condition for rational behaviour

Agent-based simulation of electricity markets: a literature review

Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets. --

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

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.

Modeling the strategic trading of electricity assets

We analyze how strategic asset trading can be used to gain competitive advantage. In the case of electricity markets, companies seek to improve the value of their generating portfolios by acquiring, or selling, power plants. Accordingly, we derive the basic determinants of plant value, explaining how a particular productive asset may have different values for different firms. From this, we develop an evolutionary model to understand how market structure interacts with strategic asset trading to increase the competitive advantage of firms, and furthermore, how this depends upon the actual price-setting microstructure in the wholesale market itselfCompetitive advantage, computational learning, auctions, asset trading, simulation, electricity markets

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

Behavioural simulations in spot electricity markets

We study the consistency of behavioural simulation methods used to model the operations of wholesale electricity markets. We include different supply and demand representations and propose the Experience-Weighted Attractions method (Camerer and Ho, 1999) to encompass several behavioural paradigms. We compare the results across assumptions and to standard economic theory predictions. The match is good under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The simulations perform best under reinforcement learning, less well under best-response and especially poorly under fictitious play. The overall conclusion is that simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model liberalised electricity markets

The Italian Electricity Prices in Year 2025: an Agent-Based Simulation

In this paper, we build a realistic large-scale agent-based model of the Italian dayahead-electricity market based on a genetic algorithm and validated over several weeks of 2010, on the basis of exact historical data about supply, demand and network characteristics. A statistical analysis confirms that the simulator well replicates the observed prices. A future scenario for the year 2025 is then simulated, which takes into account market’s evolution and energy vectors’ price dynamics. The future electricity prices are contrasted with the ones that might arise considering also the possible (yet unlikely) construction of new nuclear power (NP) plants. It is shown that future prices will be higher than the actual ones. NP production can reduce the prices and their volatility, but the size of the impact depends on the pattern of the expected demand load, and can be negligible.Electricity market, PUN, Agent-based computational economics, Nuclear power.