# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 1 Introduction  
Microgrids are typically regarded as key building blocks of future power grids, enabling wider deployment of distributed energy resources (DERs) and their effective integration into the main grid [1]. To this end, the Smart Grid concept envisages the use of energy storage for load balancing of decentralized generation units, combined with advanced control and communication technologies, as well as management strategies to improve efficiency, reliability, and the safety of power supply [2]. In this new paradigm, currently passive electricity consumers, such as households and small businesses, can potentially become electricity market agents, also referred to as prosumers, with the ability to produce, consume, and store electricity [3]. Hence, the forecasting accuracy of local production and consumption plays an essential role for successful trading in electricity markets [4]. In this context, prosumers can benefit from real-time monitoring and control over electricity usage in residential or small commercial buildings, where electricity is generated from local micro-renewable energy sources (microRES) and are stored in fixed batteries and/or batteries of plug-in electric vehicles (PHEVs) [5; 6]. Despite the challenges for the energy management problem with respect to determining the most beneficial time instance to charge/discharge the fixed and/or PHEV batteries, as well asbuy/sell electricity from/to the grid, forecasting the power output from microRES within a certain time frame enables more informed decisions in order to maximize the profit for the prosumer [7]. A typical approach to estimate the power output of a wind turbine consists in matching forecasted wind speed values on the wind turbine power curve. The relevant literature in the field classifies the wind speed forecasting methods into two broad categories, namely physics-based numerical weather prediction models and data-driven approaches [8]. More specifically, the former rely on the physics of the lower atmospheric boundary layer to produce wind flow information, and are thus characterized by high computational complexity [9]. The latter are based on historical wind speed data and employ either statistics-based methods (e.g., time series [10], Kalman filtering [11], Markov chain models [12], and Bayesian methods [13]) or artificial intelligence-based (e.g., artificial neural networks (ANNs) [14], fuzzy systems [15], and support vector machines [16]), or hybrid approaches that combine both techniques to produce wind speed forecasts [17; 18; 19]. Depending on the intended application, the time scale of wind forecasting can be divided into ultra-short-term (minutes to 1 h ahead), short-term (1 h to several hours ahead), medium-term (several hours to one week ahead), and long-term (one week to more than six months ahead), with respect to the time horizon of the forecasts [20; 21]. In general, microgrids can operate either in grid-connected or islanded mode (e.g., in autonomous applications or due to faults in the upstream network), incorporating appropriate control strategies to ensure that the techno-economic requirements for an enhanced energy utilization rate and reduced operating costs are met [22; 23]. Given that wind energy is one of the most attractive renewable energy sources (RES) in terms of efficiency and cost-competitiveness, yet it is inherently intermittent and highly variable in nature, the integration of wind turbines in microgrids can be facilitated by the use of energy storage devices in order to counterbalance the potential problems in the reliability and quality of power supply [24]. In [25], an affine projection-like algorithm is proposed to control a wind-diesel microgrid with a battery storage system. In this direction, the work in [26] presents a modified chaos particle swarm optimization approach for the case of a stand-alone microgrid, consisting of photovoltaic, wind turbine, fuel cell, diesel engine, micro-gas turbine, and energy storage.