scholarly journals Modeling the use of energy storage systems to transfer excess electricity from a solar power

2021 ◽  
Vol 2021 (1) ◽  
pp. 38-44
Author(s):  
I.M. Buratynskyi ◽  

The peculiarity of the operation of solar photovoltaic power plants is the dependence of the generation power on weather conditions, which leads to the maximum production of electrical energy at noon hours of the day. Due to a decrease in electricity consumption, insufficient unloading capacity of pumped storage power plants in the integrated energy system of Ukraine and the specifics of electricity production at solar photovoltaic power plants, dispatching restrictions on the level of generation power are already taking place. To transfer volumes of electrical energy in the world, electrical energy storage systems are used, which operate based on lithium-ion storage batteries. Such systems have a number of advantages over other battery energy systems, which allows their implementation in almost any power generation facility. With the help of energy storage systems, it is possible to make a profit through the purchase of electric energy during a period of low prices and its release during a period of high prices, allowing consumers to save money on its payment. In this paper, we simulate the use of a battery energy storage system for storing electrical energy to transfer excess electrical energy from a solar photovoltaic power plant. To conduct a study and identify excess capacity of a solar photovoltaic power plant, the daily schedule of electrical load is equalized to the capacity of a separate power plant Because of the study, the optimal time for charging and discharging the battery was determined, from which it can be seen that the need to transfer excess electricity to a solar photovoltaic power plant occurs at lunchtime, and their discharge at the peak is the graph of the electrical load of the power system. The aggregate operation of a solar power plant with a total installed capacity of photovoltaic power at the level of 10 MW (DC) and a battery energy storage system for accumulating electric energy with a capacity of 3.75 MWh was simulated. For the study day, the required capacity of a battery system for accumulating electric energy at the level of 1.58 MW was determined. Using the methodology of the levelized cost of electricity and storage, a technical and economic assessment of the transfer of excess capacity of a solar photovoltaic power plant using a battery system for storing electrical energy was carried out. When calculating the cost of storage, the cost of the transferred electrical energy from the solar power plant was taken into account. From the results of technical and economic calculations, it can be seen that, in terms of the cost of equipment, as of 2020, the cost of supplying excess electrical energy from the battery energy storage system is growing when compared with the supply from a solar photovoltaic power plant. Taking into account some forecast assumptions, the cost of electricity supply from the battery energy storage system was calculated for the mode of transferring excess capacity of a solar photovoltaic power plant for 2025 and 2030 years. Keywords: modeling, power system, load demand curve, solar photovoltaic power plant, electric energy storage system, cost

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2326 ◽  
Author(s):  
Yuqing Yang ◽  
Stephen Bremner ◽  
Chris Menictas ◽  
Merlinde Kay

This paper presents a mixed receding horizon control (RHC) strategy for the optimal scheduling of a battery energy storage system (BESS) in a hybrid PV and wind power plant while satisfying multiple operational constraints. The overall optimisation problem was reformulated as a mixed-integer linear programming (MILP) problem, aimed at minimising the total operating cost of the entire system. The cost function of this MILP is composed of the profits of selling electricity, the cost of purchasing ancillary services for undersupply and oversupply, and the operation and maintenance cost of each component. To investigate the impacts of day-ahead and hour-ahead forecasting for battery optimisation, four forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA), were applied for both day-ahead and hour-ahead forecasting. Numerical simulations demonstrated the significant increased efficiency of the proposed mixed RHC strategy, which improved the total operation profit by almost 29% in one year, in contrast to the day-ahead RHC strategy. Moreover, the simulation results also verified the significance of using more accurate forecasting techniques, where ARIMA can reduce the total operation cost by almost 5% during the whole year operation when compared to the persistence method as the benchmark.


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