Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

Abstract

To alleviate the issues of global warming and energy crisis, countries are vigorously developing renewable energy technology. The integration of large-scale renewable energy, including wind energy, hydropower, and photovoltaic (PV), has a great impact on system operation scheduling and economic dispatch. This paper presents an economic dispatching method of wind-PV-CSP-hydro-battery system with wind and photovoltaic power generation as the main energy sources. The long short-term memory (LSTM) neural network is applied to predict wind and PV power, besides, the Latin Hypercube Sampling (LHS) method and the synchronous reduction algorithm are used to obtain 10 typical wind and PV power scenarios. A day-ahead economic dispatch model of wind-PV-CSP-hydro-battery mathematical model is established, and relevant constraints are considered. Concentrated solar power (CSP), hydropower stations, batteries, and transferable loads are used as flexible resources to increase the penetration rate of wind and photovoltaic power generation. Finally, three cases are tested to demonstrate the feasibility of the proposed model. The results show that: (1) LSTM neural network can well predict the output power of wind and photovoltaic power generation with a small root mean square error (RMSE). (2) The introduction of transferable loads and CSP power station into the renewable energy power system can effectively reduce the fluctuation and curtailment rates of wind power and PV power generation.