Voltage Regulation For Residential Prosumers Using a Set of Scalable Power Storage

Abstract

Among the electrical problems observed from the solar irradiation variability, the electrical energy quality and the energetic dispatch guarantee stand out. The great revolution in batteries technologies has fostered its usage with the installation of photovoltaic system (PVS). This work presents a proposition for voltage regulation for residential prosumers using a set of scalable power batteries in passive mode, operating as a consumer device. The mitigation strategy makes decisions acting directly on the demand, for a storage bank, and the power of the storage element is selected in consequence of the results obtained from the power flow calculation step combined with the prediction of the solar radiation calculated by a recurrent neural network Long Short-Term Memory (LSTM) type. The results from the solar radiation predictions are used as subsidies to estimate, the state of the power grid, solving the power flow and evidencing the values of the electrical voltages 1-min enabling the entry of the storage device. In this stage, the OpenDSS (Open distribution system simulator) software is used, to perform the complete modeling of the power grid where the study will be developed, as well as simulating the effect of the overvoltages mitigation system. The clear sky day stored 9111 Wh/day of electricity to mitigate overvoltages at the supply point; when compared to other days, the clear sky day needed to store less electricity. On days of high variability, the energy stored to regulate overvoltages was 84% more compared to a clear day. In order to maintain a constant state of charge (SoC), it is necessary that the capacity of the battery bank be increased to meet the condition of maximum accumulated energy. Regarding the total loading of the storage system, the days of low variability consumed approximately 12% of the available capacity of the battery, considering the SoC of 70% of the capacity of each power level.

Flexible Energy Storage for Sustainable Load Leveling in Low-Voltage Electricity Distribution Grids with Prosumers

“…These are modeled from generic electrical networks [11,12] or real distribution systems [13]. The impact of using storage is discussed from the technical standpoint, where parameters such as the analysis of transient states [13], voltage regulation [14], energy loss [15], or network security [16] are evaluated, but also from the economic perspective, modeled with indices such as the cost of electricity [8]. If the prosumer + ESS behavior is studied at the community level, often the study involves the creation and operation of a local electricity trading market.…”

“…As the challenges encountered in integrating renewables and storage in electricity distribution systems can be often formulated as optimization problems, the tools used in solving them are diverse. They range from exact optimization techniques such as the gradient descent [17], mixed-integer linear programming [18], or the alternating direction method of multipliers [22], to simulation tools like MATLAB-Simulink [14], game theory [30,31], several types of artificial neural networks (ANNs) such as complex-valued ANNs [32], Radial Basis Function (RBF) [33], Long Short-Term Memory Networks (LSTM) [34], or layerrecurrent networks [27], or metaheuristic methods such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) [11], Coyote Search [12], and Whale Algorithm [15], often using multiple stages of optimization for complex problems. For instance, paper [35] optimizes the microgrid planning for a scenario involving smart prosumers, electric vehicles, and storage optimization in three stages considering also a stochastic component, while a metaheuristic algorithm is used in [24] to find the number and power of charging points, the installed area of the PV panels, the size of required storage, and the power requirement from the grid in a fast-charging station for electric vehicles.…”

Optimal Individual Phase Voltage Regulation Strategies in Active Distribution Networks with High PV Penetration Using the Sparrow Search Algorithm

“…In 2021, PV installation capacity accounted for around 40% of the total 49.77 GW installed capacity. Consequently, the intermittence of PV effects of DNs cannot be ignored because it results in voltage quality problems [2][3][4][5][6], such as voltage fluctuation, overvoltage, voltage sag, voltage dip, and unbalanced voltage. Related studies have proposed feasible techniques and solutions to overcome these problems [7][8][9][10][11][12][13].…”