Short-Term Predictive Power Management of PV-Powered Nanogrids

Abstract

Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level.

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

“…Moreover, with respect to the 24 hours ahead prediction, the overall dispersion around the mean value is lowered as well, as detailed in Table 6 for a single forecast horizon (H1). Worth highlighting is that all the performance metrics reported show the same improving pattern I, evaluated according to (7), almost halving both the standard deviation and the mean value.…”

“…In fact, when PV modules are part of the generation units of a complex energy system, an integrated forecasting approach is useful to address several scopes. From the Energy Management System (EMS) point of view, the 24 hours ahead forecast is needed to initially perform a strategic optimization of the system management, with the goal of, for example, minimizing the operating cost, reducing the fuel consumption over the following 24 hours and properly manage and size the battery energy storage system (BESS) [7], [8]. Approaching the operating time, additional refinements are useful to further correct the original forecast and tune the dispatching schedule in the so called intraday or very-short term forecast [9].…”

A Multi-Criteria Computer Package-Based Energy Management System for a Grid-Connected AC Nanogrid

“…Concurrently, microgrids (MG) are intended as the essential building blocks of smart grids, being the latter defined as "electricity network that can intelligently integrate the actions of all users connected to it (generators, consumers and those that do both) to efficiently deliver sustainable, economic and secure electricity supplies" [2]. At the same time, nanogrids (NGs) with various electric appliances were defined as kW scale smart grids that can combine different power sources with the help of information technology [3]. In the context of this paper, a NG is understood as a single-end user with embedded generation, storage and loads.…”

Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

“…However, under abrupt weather conditions, the prediction results of this model are not ideal. In literature [6], deep learning model was adopted to predict photovoltaic output, but the factors affecting photovoltaic output were not further explored.…”