Authors:
Silvano Cincotti
and
Giulia Gallo
Affiliation:
University of Genoa, Italy
Keyword(s):
Agent-based computational economics, Electricity markets, Reinforcement learning, Multi-agent systems.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Economic Agent Models
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
The paper presents an agent-based framework for modeling and simulating power exchanges, the Genoa Artificial
Power Exchange (GAPEX). The framework is implemented in MATLAB using the OOP paradigm,
which allows one to define classes using a Java/C++ like syntax. GAPEX allows creation of artificial power
exchanges where what-if analysis can be performed. GAPEX also reproduces exactly the market clearing
procedure (e.g. by calculating Locational Marginal Prices based on the Italian high-voltage transmission network
with its zonal subdivision) and the generation plants modeled are in direct correspondence with the real
ones. Moreover, the presence of affine total cost functions for the generation plants results in payoff either
positive, negative and null. This has major implications as negative reward are not generally considered by reinforcement
learning algorithms. In order to overcome such limitation, an enhanced version of the Roth-Erev
algorithm (i.e., that takes into account also
negative payoffs) is presented and discussed. Results point out
effectiveness of the proposed enhanced learning algorithm. Moreover, computational experiments performed
within GAPEX point out a close agreement with historical real market data during both peak- and off-peak
load hours thus confirming the direct applicability of GAPEX to model and to simulate power exchanges.
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