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 through 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 focus on the energy management system for the microgrid aggregator and virtual power plant under the electricity market environment.
研究不足
The study assumes forecast errors follow normal distributions and does not consider the start-up times of small DG units, simplifying them as constants. The computational complexity increases with the number of scenarios.
1：Experimental Design and Method Selection:
A two-stage scenario-based stochastic programming approach is proposed to address the uncertainties in the microgrid. The first stage uses forecast data, and the second stage uses scenario-based stochastic data.
2：Sample Selection and Data Sources:
Forecast data for wind speed, PV power, loads, and electricity prices are obtained by traditional forecasting techniques. Monte Carlo simulation with Latin hypercube sampling is used to generate scenarios.
3：List of Experimental Equipment and Materials:
The microgrid includes diesel generators, storage batteries, wind turbines, PV panels, and controllable loads.
4：Experimental Procedures and Operational Workflow:
The model is solved by two levels of stochastic optimization methods to maximize the profit of the microgrid.
5：Data Analysis Methods:
The conditional value at risk method is used for risk management.
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