Low-Carbon Robust Predictive Dispatch Strategy of Photovoltaic Microgrids in Industrial Parks

With the flexible integration of local renewable energy with the smart distribution network system, the problems of high operating costs and power shortage can be effectively solved. However, taking the industrial park microgrid with high penetration photovoltaic as an example, due to the uncertainties and fluctuations arising from the meteorological conditions and the load demands, the safe and reliable operation of the microgrid system has been threatened significantly. Operators often need to pay additional unnecessary costs to maintain stable operations of the microgrid. Therefore, in this study, a dispatch strategy based on robust model predictive control considering low-carbon cost is designed to reduce the adverse effects of uncertainties. First, a low-carbon energy management scheme is formulated based on short-term source and load forecast information in which a two-stage robust optimization solution method is used to generate the optimal dispatch scheme under the worst scenario. Then, an intraday real-time strategy with a closed-loop feedback mechanism is formed based on the model predictive control. Finally, the feasibility of the proposed strategy is simulated and analyzed based on the measured data of the photovoltaic microgrid in the industrial park. The results show that compared with the general intraday scheduling strategy and the day-ahead robust strategy, the proposed strategy can effectively get low-carbon scheduling plans considering the uncertainty of source and load while efficiently balancing the robustness and economy of the grid-connected industrial park photovoltaic microgrid system operation.