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The Photovoltaic (PV) plants are significantly different from the conventional synchronous generators in terms of physical and electrical characteristics, as it connects to the power grid through the voltage-source converters. High penetration PV in power system will bring several critical challenges to the safe operation of power grid including transient stability. To address this problem, the paper proposes a control strategy to help the PVs work like a synchronous generator with variable inertia by energy storage system (ESS). First, the overall control strategy of the PV-based virtual synchronous generator (PV-VSG) is illustrated. Then the control strategies for the variable inertia of the PV-VSG are designed to attenuate the transient energy of the power system after the fault. Simulation results of a simple power system show that the PV-VSG could utilize the energy preserved in the ESS to balance the transient energy variation of power grid after fault and improve the transient stability of the power system.

Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.

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