Research On Power Forecasting And Energy Management For Photovoltaic Microgrid

A typical microgrid (MG) is composed of distributed energy sources, such as wind turbines, photovoltaic (PV) arrays, biomass etc. Because of the impact of geography, climate, weather and other external factors, the output energy of renewable energy sources is intermittent and unpredictable, which will cause the complexity of energy exchange between the distributed source and load in the microgrid and the large power system. The massive microgrid accession to the large power system will affect the security and stability of the power system, if these sources are not under proper control. Further, microgrid access will have a profound impact on the electricity market. On one hand, the user can buy electricity from the power company and can also sold surplus energy the of the own distributed generation (DG) to power companies or provide emergency power support services; on the other hand, the participation of microgrid in the main grid competition will have an impact on the existing power market trading patterns. The basic analysis theory and design methods of energy management of photovoltaic microgrid are studied, including control structure, power generation forecasting, optimal allocation of energy storage, energy management of photovoltaic microgrid.With the increase of the capacity of photovoltaic generated systems, power forecasting of photovoltaic system plays a very important role in optimal combination of distributed generation, economic dispatch, and optimal power flow and power market transactions. And the high prediction accuracy of PV system is benefit to the utilization of distributed generation and effectiveness of economic dispatch. In this paper, prediction models and design methods of the photovoltaic power generation based on neural network is proposed, the meteorological factors which influence power output of photovoltaic power generation system is analyzed and the measures to treat and identify these factors are given. An off-line prediction model of photovoltaic power generation based on BP neural network is presented, the design method of input layer, hidden layer and output layer of BP neural network is given and the prediction accuracy of the off-line model is evaluated. In addition, an on-line prediction model based on fuzzy recognition is proposed and the selection of the neutral network structure and the spread value are discussed. The power output of some distributed resources will vary with external conditions so that they can not satisfy the load independently and additional power supply or energy storage device are required to provide support and backup. In addition, the grid-connected of distributed resource changes the power flow of the distribution power system. Energy management system of distributed generation becomes significant for the stability and economic operation of the DG system as the power flow between generator units, energy storage units, the grid and the load need to be optimally controlled and managed. This paper introduces a basic structure of DG system and illustrates the principle of power forecasting using neutral network. Finally a novel energy management system of DG is proposed to realize the economic operation. The proposed system determines the operation mode according to the forecasted output power and the state of the battery. The fuzzy control is adopted to control the power flow to reduce the operation costs.DG supplies users with green power from various locally available renewable energy resources, but large scale inter-connected DG in the distribution system may give rise to security and operation problems. Microgrid technology provides an interface to the interconnection of various DGs by means of structures at different levels, which is the most efficient way for the operation of DG. The energy management system makes it possible to realize flexible access to DG and safe, reliable, and optimal operation of the entire power system by effectively managing the microgrids. This paper presents a smart energy management system to optimize the operation of the microgrid. The functions of the controllers of energy management system are presented. The definition, architecture and object of energy management system for photovoltaic microgrid are discussed. The basic principle of energy management strategy and energy balance control of MGs under the stand-alone and grid-connection modes are analyzed, and then the energy management model of generation units and storage units for photovoltaic microgrid system is researched, finally its economical analysis is performed.As the performance of a MG strongly depends on the allocation and arrangement of its ESS, comprehensive design aspects of the energy storage system (ESS) are required for the MG, such as the operational and economical interdependence between various energy storage devices and charge/discharge control systems, the maximum/minimum storable energy of energy storage installations, the peak power and the equality constraint of the periodical behavior, the evolution of weather conditions and fuel prices. The paper presents a methodology for the optimal allocation and economic analysis of ESS in microgrids on the basis of Net Present Value (NPV). A unique feature of the method is that following the Matrix Real-Coded Genetic Algorithm (MRCGA) optimization process, it provides a quality estimation of the economic operation that is subsequently used to improve the economic performance of microgrids. A comparative evaluation between Vanadium Redox Battery (VRB) and Lead-acid Battery (LAB) is performed to evaluate economical performances of energy storage devices. Finally, some computational simulation results in different conditions (various energy storage devices, capacity allocation methods, weather conditions, etc.) are presented to verify the effectiveness of the proposed ESSs.