Decentralized multi-area multi-agent economic dispatch model using select meta-heuristic optimization algorithms

Abstract

This paper presents a decentralized multiarea multiagent economic dispatch (DMMED) model using select meta-heuristic optimization (MO) algorithms. The previous multiarea economic dispatch (MAED) studies that used MO algorithms were centrally planned and did not include multiple independent local agents in their decision-making framework. The proposed model allows to optimize the operation of the power systems involving the system operators (TSOs) and multiple independent local aggregators (LAs) in a decentralized manner. The objective of the model is to minimize the total operation cost of the system in a time-efficient manner by effectively coordinating the operation of the TSOs and LAs. Each area accounts for its operational uncertainties and power reserves considering the worst-case scenarios of the uncertainty sets of the individual agent in the area. To respect each area ownership, the solution algorithm utilizes separate population sets and a dependent bi-level operation approach to solve the DMMED model for each area in parallel, allowing the areas to achieve optimal operation, independently. Case studies with select MO algorithms are performed on a modified Nigerian 330 kV 39-bus transmission systems having three areas each with one TSO and three LAs to demonstrate the effectiveness of the proposed model.