Modeling Electricity Wholesale Markets With Model Predictive and Profit Maximizing Agents

Authored by Matthias D. Galus, Goeran Andersson, Lukas A. Wehinger, Gabriela Hug-Glanzmann

Date Published: 2013-05

DOI: 10.1109/tpwrs.2012.2213277

Sponsors: No sponsors listed

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Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

In this paper, a new agent-based electricity market model is presented in which participants correspond to generation plants as well as storage power plants. In contrast to agent-based models where agents use learning heuristics and trial-and-error approaches to maximize their profits, the proposed model predictive bidding uses multi-step optimization to find bidding curves which maximize the expected discounted profit over a time horizon in the future. The profit is calculated based on an hourly price forward curve (HPFC), whereby the HPFC is constructed taking several factors into account. In addition, a price adjuster is used in these calculations which allows the participant to take into account his market power. The resulting optimization problem for each agent is solved using dynamic programming. A case study is carried out in which the proposed agent-based market model is applied to the four countries Switzerland, Germany, Italy, and France to study the effects of constrained cross-border capacities. The simulations show that the transmission system operators as well as the power generating units have no incentive to build additional cross-border capacity, since it lowers their profits.

Tags

Agent-based modeling multi-agent model electricity markets European electricity market German electricity market hourly price forward curve implicit cross-border allocation model predictive control