Short-Term Solar Power Forecasting and Uncertainty Analysis Using Long and Short-Term Memory

As a green and renewable resource, the scale of solar energy is increasing. Huge amount of solar energy penetration in a distribution system causes drastic changes in the load forming a duck shape load curve which may cause stability problems. Accurate load forecasting and demand response procedures are the key tasks of power distribution system management. For this reason, this paper proposes a new mechanism for optimized operation of solar microgrid based on deep learning long and short-term memory (LSTM) and demand response (DR). LSTM is used for load prediction, and linear integer programming is used to implement load scheduling. The proposed DR procedure is used for load management, the proposed method is implemented in a modified IEEE-12 bus radial distribution network. In a dynamic pricing environment, not only the stability of the voltage is guaranteed, but the electricity cost is minimized. This paper solves the dual objectives that is flattening the duck curve and electricity cost minimization.