Photovoltaic Curve Management Using Demand Response with Long and Short-Term Memory

Abstract Photovoltaic power is considered a promising power generation candidate in dealing with climate change, as a green and renewable resource, the scale of solar energy is increasing. Because of the strong randomness, volatility, and intermittence, 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, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy,so, 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, the proposed DR procedure is used for load management, and linear integer programming (LIP) is used to implement load scheduling. Here, a novel dynamic power flow is carried out to check limit violation of the voltage, also, scheduling is performed for two types of loads namely deferrable with interruptible and deferrable with uninterruptable, when either of maximum demand or voltage limit violation occurs. Finally, the suggested model is validated on a IEEE-12 bus radial distribution system. The result shows that the suggested mothed minimizes the forecast error and demand response program schedules household appliances without a demand limit violation and ensures the prevention of voltage collapse. This paper solves the dual objectives that is flattening the duck curve and effective use of solar energy.

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