研究目的
To present a novel energy-management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads, addressing various uncertainties with a risk-constrained scenario-based stochastic programming framework.
研究成果
The proposed energy management system is effective in engineering practice and beneficial for both the microgrid and the customers. Future work will study the energy management system for the microgrid aggregator and virtual power plant under the electricity market environment.
研究不足
The forecasting errors of the wind speed, PV power, loads, and electricity prices are assumed to follow normal distributions, which may not capture all real-world uncertainties. The computational complexity increases with the number of generated scenarios.
1：Experimental Design and Method Selection:
A double-layer scenario-based stochastic optimization approach is proposed. The first layer obtains an economic operation scheme based on forecasting data, while the second layer provides the power to controllable units based on real-time data.
2：Sample Selection and Data Sources:
Forecast data of uncertainties such as wind speed, PV power, loads, and electricity prices are obtained by traditional forecasting techniques.
3：List of Experimental Equipment and Materials:
The microgrid includes renewable energy, diesel generators, battery storage, and various loads.
4：Experimental Procedures and Operational Workflow:
A Monte Carlo simulation with the Latin hypercube sampling technique is implemented to generate scenarios representing values of the uncertain parameters.
5：Data Analysis Methods:
The conditional value at risk method is used for risk management in the objective function.
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