A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with...

Area of Science:

• Energy Systems Engineering
• Optimization Theory
• Environmental Science

Background:

• Microgrids (MMGs) require sophisticated energy management to balance economic, environmental, and reliability objectives.
• Integrating Demand Response Programs (DRP) offers a pathway to optimize MMG operations.
• Existing optimization models often struggle with multi-objective complexities and uncertainty.

Purpose of the Study:

• To develop a multi-layer, multi-objective (MLMO) optimization model for cooperative MMG energy management.
• To simultaneously optimize operating costs, operator benefits, environmental emissions, and MMG dependency.
• To address uncertainties in renewable energy generation, load demand, and energy prices.

Main Methods:

• A novel hybrid ε-lexicography-weighted-sum method was proposed for objective handling.
• A three-layer optimization structure was implemented: Layer 1 for economic scheduling with DRP, Layer 2 for environmental operation, and Layer 3 for reliability maximization.
• The Enhanced Equilibrium Optimizer (EEO) algorithm was applied to solve the MLMO problem.
• The 2m+1 Point Estimation Method (PEM) was used to model uncertainties.

Main Results:

• The MLMO approach demonstrated a reduction in environmental emissions (2.45%–3.5%) and an enhancement in the independence index (2.49%–4.8%).
• Case studies confirmed the model's effectiveness in deterministic and probabilistic scenarios.
• Probabilistic simulations showed a slight increase in mean cost (approx. 2.6%) due to uncertainty, validating the robustness of the approach.

Conclusions:

• The proposed MLMO optimization model effectively balances techno-economic-environmental objectives in cooperative MMGs.
• The integration of DRP and the EEO algorithm provides a robust solution for complex MMG energy management.
• The model successfully accounts for uncertainties, offering improved reliability and reduced environmental impact.