A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a multi-layer optimization model for cooperative multi-Microgrids (MMGs) using a Demand Response Program (DRP). The model enhances techno-economic-environmental energy management, reducing emissions and improving reliability.
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.
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