# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 1 Introduction  
With the rapid development of renewable energy, interest in wind power has drawn more attention, as it possesses advantages such as free energy resources, non-greenhouse gas emission, and the ability of supporting rural areas. However, a high penetration of wind power raises a problem of system instability, caused by the nature of wind uncertainty. The integration of ESS is one of the best solutions to guarantee a stable power system with distributed wind resources [1]. An optimal allocation of ESSs in power systems can reduce total costs, enhance reliability and power quality, and, by determining the best locations and sizes of ESSs, improve voltage profiles [2]. Studies [3;4] show that an optimal planning of locations and sizes of ESSs in power systems can reduce a power system's costs and enhance a its reliability and power quality. A novel method has been presented in [5] for designing an energy storage system dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. [PERSON] et al. in [6] proposed a determination methodology for optimizing the capacity of an ESS that enables a wind power generator to meet the requirements of grid integration. To improve regulation effects, the segmentation method and automatic segmentationmethod are also applied to the proposed algorithm. An improved genetic algorithm is utilized in [7] to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. In [8], [PERSON] et al. proposed a capacity optimization method for a hybrid energy storage system taking SOC and efficiency into account. They used the maximal cumulative capacity and SOC constraint to calculate ESS capacity. [PERSON] et al. [9] performed optimal sizing for a hybrid power system with wind and energy storage sources based on stochastic modeling of historical wind speed and load demand. Since the uncertainties of wind power will lead to large errors in the results of optimally allocating ESSs, a power forecasting method is always used to predict power demand, spot price, and outputs of renewable energy and helps decision-makers determine an ESS's capacity more accurately. A large amount of literature [10; 11; 12; 13] related to power prediction has been developed in recent years. The authors of [14; 15; 16] utilized artificial neural network (ANN) approaches for the optimum estimation and forecasting of renewable energy consumption by considering environmental and economic factors. Physical models were set up to predict solar irradiance and the output power of photovoltaic (PV) generation, which are based on numerical weather predictions and satellite images [17; 18]. [PERSON] et al. developed a statistical approach based on a data-driven formulation using historical measured data to forecast renewable energy time series [19]. In addition to the determination of ESS capacity, it is also necessary to design the placement and rated power of energy storing devices, which is a complex parameter optimization problem. A multi-objective particle swarm optimization (MOPSO) algorithm has been widely utilized to solve the nonlinear, non-differentiable, multidimensional optimization problems [20; 21]. [PERSON] in [22] made use of MOPSO to plan the reactive power compensation of radial distribution networks with a unified power quality conditioner. [PERSON] et al. [23] adopted the MOPSO technique to find the best capacitors in distribution systems that are connected to wind energy generations. The focus of this paper is to optimally allocate ESSs in a power system integrated with wind power taking system costs, carbon dioxide emissions, and voltage fluctuations into account. In order to mitigate the influence of the uncertainties of wind power, a back-propagation neural network technique is utilized to forecast the power gap of the load and wind power using historical data.