研究目的
To address the operational problem of managing excess energy in the deployment of renewable power, specifically solar power, in Africa, using stochastic dual dynamical programming and comparing it to a greedy policy.
研究成果
The SDDP algorithm results in average cost savings of 30.9% relative to a greedy storage dispatch policy in the case study of Burkina Faso. The stochastic solution is effective in dealing with the trade-off between using energy generated by PV power during the day and storing power for use at night to avoid operating high-cost domestic thermal generators.
研究不足
The proposed problem does not represent some significant properties of the economic dispatch model, such as transmission constraints or ramping constraints.
1：Experimental Design and Method Selection:
The study employs Stochastic Dual Dynamical Programming (SDDP) for optimizing the management of storage in a system with adequacy challenges. The methodology includes the development of an open-source MATLAB toolbox for multistage stochastic programming.
2：Sample Selection and Data Sources:
The case study focuses on storage management in Burkina Faso, using one year of data for calibrating the stochastic model.
3：List of Experimental Equipment and Materials:
The study uses a MATLAB toolbox (FAST) for implementing the SDDP algorithm and Gurobi for solving the optimization problems.
4：Experimental Procedures and Operational Workflow:
The study involves generating a lattice of net load using samples of PV power and load, and comparing the SDDP solution with a greedy policy.
5：Data Analysis Methods:
The performance of the different policies is assessed through computational results, including average total cost and confidence intervals.
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