Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

A methodology for energy prediction and optimization of a system based on the Energy Hub Concept using Particle Swarms

In this paper, a methodology for the energy prediction for the different consumptions of a system based in the Energy Hub concept is presented. The methodology that has been used for the energy prediction is based on an Adaptive Neuro-Fuzzy Inference System. An optimization method based on Particle Swarms has been used to minimize the energy cost of a system with multiple sources such as, photovoltaic, electrical grid and natural gas.Peer ReviewedPostprint (author's final draft

Smart Grid for the Smart City

Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested