Semi-decentralized and fully decentralized multiarea economic dispatch considering participation of local private aggregators using meta-heuristic method

This paper presents a semi-decentralized (SD) and a fully decentralized (FD) multiarea economic dispatch (MAED) model based on meta-heuristic optimization (MO) for optimal operation of transmission system operators (TSOs) and private aggregators (PAs). The existing MO-based MAED studies are limited to either using centralized models or not considering multiple autonomous participants in their decision-making framework. The proposed models allow to determine the effective integration of autonomous PAs in the transmission systems (TS) and their co-operation with the TSOs. The objective of both models is to minimize the total operation cost of the system by effectively coordinating the TSOs and PAs operations. The TSOs and PAs evaluate their operational uncertainties and determine the power reserves considering the best and worst-case scenarios of the uncertain variables, thus enabling the resulting models to be solved in three stages using a robust Real-Coded Elitism Genetic Algorithm (RCEGA). To preserve the ownership of TSOs and PAs, the RCEGA efficiently utilizes separate population sets to solve the operations of the areas in parallel in a two-layer operation approach, allowing the TSOs and PAs to achieve optimal operations, independently. Case studies are performed on a modified Nigerian 330 kV 39-bus transmission systems having three TSOs each with three PAs to demonstrate the effectiveness of the proposed models.

Automated summary

This paper presents semi-decentralized and fully decentralized multiarea economic dispatch models based on meta-heuristic optimization for optimal operation of transmission system operators and private aggregators. The models aim to minimize total operation cost by effectively coordinating TSOs and PAs operations. Utilizing a Real-Coded Elitism Genetic Algorithm, the models can be solved in three stages, allowing TSOs and PAs to achieve optimal operations independently.