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• Analyze the correlation between power load data and relevant search engine indices. • Take search engine indices as input variables to forecast electricity load. • Use Hodrick-Prescott filter to decompose the time series of search engine indices. • Propose a LSTM model based on quantum-behaved particle swarm optimization algorithm. Accurate electricity load forecast plays an important role in the operation of a power system. Many factors influence the electricity load data such as air temperature, humidity and holidays, and they are taken as the explanatory variables in load forecasting cases traditionally. Search engine indices, a variable may be related to load data which has been never considered before in load forecasting, is discussed and utilized to increase the accuracy of power load prediction in this paper. Spearman's correlation coefficients and Granger test results verify the correlation between Google Trends (GT) and electricity load data. A methodology for processing GT time series with Hodrick-Prescott filter is proposed. To forecast electricity load with an adaptive network model in such a novel situation, we propose a long short-term memory neural network model based on quantum particle swarm algorithm. The performance of load forecast for Long Island region taking GT and weather data as input variables is compared with that taking only weather data as input variables, which shows that the introduction of GT improves short-term forecasting effectiveness significantly. [ABSTRACT FROM AUTHOR]

• To reduce the power curtailment, a new PCA is used to realize coordinated RPC-APC in an optimal order. • To improve the voltage regulation ability, the PRN with the maximum voltage rise is always regulated. • To reduce the number of power control, the most sensitive PV is preferred for the RPC-APC for PRN. • To achieve an accurate RPC-APC, the real-time PRN update and exact power calculation are carried out. Increasing photovoltaic (PV) penetration has caused overvoltage problem in distribution systems. Power curtailment that includes power absorption of PV inverter and charge of battery energy storage is usually used to improve the voltage profile. Most methods prefer to use an excessive local power curtailment to regulate voltage. This inevitably reduces PV generation economy. Hence, a coordinated power compensation algorithm (PCA) is proposed in this paper to regulate bus voltage, aimed at reducing the total power curtailment while serving requisite load demand. Selecting primary control nodes and optimal PV compensation orders based on sensitivity sequence, our PCA achieves an effective voltage regulation with the minimal power curtailment. Final results on a tested PV system model verify the effectiveness. [ABSTRACT FROM AUTHOR]