# Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
## 1 Introduction
Similarly, the work in [27] considers the optimization of power source capacity in a microgrid with various distributed generators, including wind turbines, and energy storage devices, using a coordinated planning strategy. The authors in [28] examine the problem of optimally sizing battery banks in microgrids with wind power systems, while the study in [29] addresses the problem of optimal placement of energy storage devices in a microgrid to improve the system transient stability. The work in [30] analyzes the performance of a laboratory-scale microgrid with battery banks and hydrogen storage. In [31], a hierarchical approach is introduced for the management of cooperative microgrids and the interaction with the macrogrid. Moreover, the authors in [32] examine the economic impact of integrating PHEVs in microgrids, using a radial basis function network approach to forecast the photovoltaic power output, as well as Monte Carlo simulation to tackle the uncertainties of input parameters. In [33], a stochastic programming formulation is described for the 24-h scheduling problem in a microgrid using combined heat and power, where wind speed forecasting is based on autoregressive-moving-average models. When combining all of the above, it becomes clear that accurate wind power forecasts are particularly important not only for reducing energy storage requirements and operation costs, but also for participating in the short-term electricity market. Moreover, the adoption of market-based electricity prices provides new opportunities to recoup losses of net revenue and introduces new financial incentives, taking advantage of the electricity storage during peak demand when prices rise and buying electricity from the grid during off-peak periods. The present work examines the impact of energy management decisions on the performance of a grid-connected microgrid, consisting of a residential building, three wind turbines, and a storage unit, under different short-term wind power forecasting techniques. Specifically, three scenarios are considered with respect to the wind power forecasts: (a) a naive approach based on the previous value of produced wind power; (b) an ANN clustering approach using statistical feature parametersin the input vector, as a means of striking a reasonable balance between forecasting accuracy, data requirements, and computational complexity [34; 35]; and, (c) the previous ANN approach, adjusted by the probability of lower misclassification (PLM) method in order to provide a more realistic value of the expected wind power, and thus reduce the negative effects of forecast errors over electricity utilization, battery storage, and trading with the electricity market [36; 37]. In each case, the Monte Carlo simulation is employed to assess the impact of energy management decisions in the residential microgrid, with the aim to reduce the cost of electricity for the building by optimizing the electricity exchanges with the grid and the energy stored in the battery bank. In this context, the main contribution of this work is the introduction of the misclassification probability as a control method in order to reduce the wind power forecasting errors, and by extension, to enhance the energy management decisions for more cost-effective operation of the microgrid. The rest of this paper is organized as follows: Section 2 describes the methodological approach to model the system components; Section 3 presents the simulation study and discusses the results obtained, while the last section concludes this work.