Institute of Engineering Thermodynamics
Modeling Electricity Markets as Complex Adaptive Systems - An Agent-Based Approach Martin Klein (
[email protected]) Department of Systems Analysis and Technology Assessment
RESEARCH RATIONALE
RESEARCH HYPOTHESIS
“Complex systems consist of interacting, autonomous components; complex adaptive systems have the additional capability for agents to adapt at the individual or population levels.” [1]
Electricity markets can be considered as both complex and adaptive. Agent-based models can unravel unknown dynamic properties of current and future energy systems.
METHOD - Agent Based Modeling
APPLICATION – Investment - the agent‘s perspective
Bottom-up energy system models can be categorzied into two main branches: simulation and optimization models:
Concerning investment choices, the net present value (NPV) is often used as decision rule whether to accept (NPV > 0) or reject (NPV < 0) a project. Via a Monte-Carlo method, we systematically vary the economic input parameters for the NPV calculation which are different among individual agents. The derivative of this calculation in respect to the discount rate r yields a probability density function of internal rates of return (IRR) for all projects considered in the simulation. To explain the absolute value of investment per time, we propose a normal probability density function with mean µh(t) and standard deviation σh(t) which represents individual hurdle rates of agents concerning the adoption of a technology. If and only if the specific drawn rate rIRR,i from the IRR-distribution is higher than the individual hurdle rate rh,i, then the agent decides to invest, as this corresponds to a NPV > 0 (a).
Simulation
Optimization
System Dynamics
Energy System Models
Agent Based Models
Game Theory
Categorization of bottum-up energy models, adapted from [2]
Agent based models (ABM) consist of a set of interacting agents embedded into an environment to which they can adapt to. In an energy system modeling context, ABMs are typically used to examine the economic success or failure of agents in an environment which is characterized through different technologies, markets and policy measures. The agent’s heterogenous behaviors may not purely be based on rational economic decisions. Thus, it is possible to analyze different market designs and emergent phenomena which may result from the interactions between agents. The electricity market model AMIRIS [3] is being developed in order to gain insights about the integration of renewable energy sources (RES) into the energy system. Special focus is laid on a detailed representation of RES and flexibility options like storage technologies.
The resulting distribution in (b) has the same sigmoid shape as logit and probit representations known from discrete choice models [4]. The research results will be used to expand the AMIRIS model in order to depict the deployment of renewable energy sources endogenously. .
REFERENCES [1] C. M. Macal and M. J. North, “Tutorial on agent-based modelling and simulation,” Journal of Simulation, vol. 4, no. 3, pp. 151–162, Sep. 2010. [2] D. Möst, Energiesystemanalyse: Tagungsband des Workshops “Energiesystemanalyse” vom 27. November 2008 am KIT Zentrum Energie, Karlsruhe. Universitätsverlag Karlsruhe, 2009. [3] M. Reeg, K. Nienhaus, N. Roloff, U. Pfenning, M. Deissenroth, S. Wassermann, W. Hauser, W. Weimer-Jehle, T. Kast, and U. Klann, “Weiterentwicklung eines agentenbasierten Simulationsmodells (AMIRIS) zur Untersuchung des Akteursverhaltens bei der Marktintegration von Strom aus Erneuerbaren Energien unter verschiedenen Fördermechanismen - Kurzfassung,” Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Apr. 2013. [4] K. E. Train, Discrete choice methods with simulation, 2nd ed. Cambridge University Press, 2009.
Knowledge for Tomorrow
Wissen für Morgen
