This study is aimed at determining the optimal energy storage system (ESS) operation schedule to minimize the peak load on the feeder of a distribution network. To reduce the peak load, the feeder load profile needs to be predicted. A deterministic prediction is not reliable, however, because it may contain errors. This study proposes the use of prediction intervals (PIs) of estimated error based on prior predictions. The proposed algorithm is intended for the determination of an optimal ESS schedule using the PIs. To demonstrate the method’s validity, a case study is presented where a proposed optimal ESS schedule determined from PIs reduces the peak load during network operations over a one-year period. The performance of the proposed method is superior to that of the conventional method which uses deterministic load prediction.

Internode line impedance values are essential information for achieving coordinated voltage regulation with distributed energy devices. However, in a low-voltage distribution system, these values are difficult to acquire, especially when the devices are installed in an ad hoc manner by individual users. This paper proposes a novel method for automated impedance estimation based on practically available parameters at the terminal nodes. The distributed energy devices measure and transmit the terminal parameters to a host device. Then, the host device estimates the line impedances based on its impedance model and the collected parameters. For practical implementation, the proposed method is built under several assumptions. For example, it is assumed that the phases of terminal voltages are not synchronized owing to communication latency. Instead, only the magnitude value is incorporated during the impedance estimation. The proposed method is verified via a simulation that assumes a realistic environment.

A demand side management technique is deployed along with battery energy-storage systems (BESS) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study, we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand.

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy.

Smart meters often fail to measure or transmit the data they record when measuring energy consumption, known as meter readings, owing to faulty measuring equipment or unreliable communication modules. Existing studies do not address successive and non-periodical missing meter readings. This paper proposes a method whereby missing readings observed at a node are estimated by using circuit theory principles that leverage the voltage and current data from adjacent nodes. A case study is used to demonstrate the ability of the proposed method to successfully estimate the missing readings over an entire day during which outages and unpredictable perturbations occurred.