Optimal two-stage arbitraging and scheduling strategy for distributed energy resource aggregators

Area of Science:

• Energy Systems Engineering
• Optimization Theory
• Renewable Energy Integration

Background:

• The increasing penetration of distributed energy resources (DERs) presents challenges for wholesale market participation due to inherent uncertainties.
• Integrating DERs effectively is crucial for maximizing economic benefits while managing risks associated with renewable generation and market price volatility.

Purpose of the Study:

• To propose an energy trading and optimal offering strategy for DER aggregators to participate in wholesale energy markets.
• To address the uncertainties in renewable generation and market prices using a novel self-adaptive minimax regret (MMR) approach.
• To develop an efficient and communication-free solution methodology for the formulated optimization problem.

Main Methods:

• Development of an arbitraging strategy for DER aggregators to facilitate energy trading between DERs and the wholesale market.
• Formulation of a self-adaptive minimax regret (MMR)-based optimal offering model to manage dual uncertainties.
• Implementation of an exact and communication-free solution methodology for high computational efficiency.

Main Results:

• The proposed strategy and model achieve near-to-optimal profits for DER aggregators.
• The approach demonstrates robust performance and satisfactory outcomes even in highly volatile market environments.
• The solution methodology ensures high computational efficiency in resolving the complex optimization problem.

Conclusions:

• The proposed arbitraging strategy and MMR-based offering model effectively enable DER aggregators to achieve economic benefits in wholesale markets.
• The developed solution methodology is computationally efficient and reliable for practical implementation.
• This work provides a valuable framework for enhancing DER integration and profitability under market uncertainties.