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. --

Higher-Order Simulations: Strategic Investment Under Model-Induced Price Patterns

The trading and investment decision processes in financial markets become ever more dependent on the use of valuation and risk models. In the case of risk management for instance, modelling practice has become quite homogeneous and the question arises as to the effect this has on the price formation process. Furthermore, sophisticated investors who have private information about the use and characteristics of these models might be able to make superior gains in such an environment. The aim of this article is to test this hypothesis in a stylised market, where a strategic investor trades on information about the valuation and risk management models used by other market participants. Simulation results show that under certain market conditions, such a \'higher-order\' strategy generates higher profits than standard fundamental and momentum strategies that do not draw on information about model use.Financial Markets, Multi-Agent Simulation, Performativity, Higher-Order Strategies

Learning by Doing vs Learning by Researching in a Model of Climate Change Policy Analysis

Many predictions and conclusions in climate change literature have been made on the basis of theoretical analyses and quantitative models that assume exogenous technological change. One may wonder if those policy prescriptions hold in the more realistic case of endogenously evolving technologies. In previous work we modified a popular integrated assessment model to allow for an explicit role of the stock of knowledge which accumulates through R&D investment. In our formulation knowledge affects the output production technology and the emission-output ratio. In this paper we make progress in our efforts aimed to model the process of technological change. In keeping with recent theories of endogenous growth, we specify two ways in which knowledge accumulates: via a deliberate, optimally selected R&D decision or via experience, giving rise to Learning by Doing. We simulate the model under the two versions of endogenous technical change and look at the dynamics of a number of relevant variables.Climate Policy, Environmental Modeling, Integrated Assessment, Technical Change

Prospects for large-scale financial systems simulation

As the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research

Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade

In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model

Trade in bilateral oligopoly with endogenous market formation

We study a strategic market game in which traders are endowed with both a good and money and can choose whether to buy or sell the good. We derive conditions under which a non-autarkic equilibrium exists and when the only equilibrium is autarky. Autarky is ‘nice’ (robust to small perturbations in the game) when it is the only equilibrium, and ‘very nice’ (robust to large perturbations) when no gains from trade exist. We characterize economies where autarky is nice but not very nice; that is, when gains from trade exist and yet no trade takes place

The Complexities of Financial Risk Management and Systemic Risks

Risk-management systems in financial institutions have come under increasing scrutiny in light of the current financial crisis, resulting in calls for improvements and an increased role for regulators. Yet such objectives miss the intricacy at the heart of the risk-management process. This article outlines the complexity inherent in any modern risk-management system, which arises because there are shortcuts in the theoretical models that risk managers need to be aware of, as well as the difficulties in sensible calibration of model parameters. The author suggests that prudential regulation of such systems should focus on failures within the financial firm and in the market interactions between firms and reviews possible strategies that can improve the performance of risk management and microprudential regulatory practice.