Abstract
The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.
Highlights
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]
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 and are stored in fixed batteries and/or batteries of plug-in electric vehicles (PHEVs) [5,6]
Scenario (a): naive approach
Summary
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]. 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
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