Utilization Of Battery Storage Research Articles

Impact on battery performance with hybrid energy storage: An investigation of rate limiter application

AbstractIntegrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%.

A Feasibility Study of Profiting from System Imbalance Using Residential Electric Vehicle Charging Infrastructure

Residential chargers are going to become the standard in the near future. Their operational cycles are closely tied to users’ daily routines, and the power consumption fluctuates between zero and peak levels. These types of installations are particularly challenging for the grid, especially concerning the balance of electricity production and consumption. Using battery storage in conjunction with renewable sources (e.g., photovoltaic power plants) represents a flexible solution for grid stabilization, but it is also associated with additional costs. Nowadays, grid authorities penalize a destabilization of the grid resulting from an increased imbalance between electricity generation and consumption and reward contributions to the system balance. Hence, there is a motivation for larger prosumers to make use of this mechanism to reduce their operational costs by better aligning their energy needs with the grid. This study explores the possibility of utilizing battery storage when it is not needed to fulfil its primary function of supporting charging electric vehicles, to generate some additional profit from providing a counter-imbalance. To test this idea, we develop an optimization model that maximizes the economic profit, considering system imbalance penalties/rewards, photovoltaic production, electric vehicle charging demand, and battery storage utilization. By means of computer simulation, we assess the overall operational costs while varying key installation parameters such as battery capacity and power, the installed power of photovoltaic panels and the prediction model’s accuracy. We identify conditions when counter-imbalance has proven to be a viable way to reduce installation costs. These conditions include temporal distribution of charging demand, electricity prices and photovoltaic production. For the morning time window, with a suitable setting of the installation parameters, the cost reduction reaches up to 14% compared to the situation without counter-imbalance.

An adaptive power management approach for hybrid PV-wind desalination plant using recurrent neural networks

In hybrid Photovoltaic (PV)-wind desalination plants, power management is necessary because renewable energy sources such as PV and wind are highly variable. An optimal power management system also helps extend the equipment's life by preventing overloading or underloading of the system. This paper proposes a recurrent neural network (RNN) based power management for a desalination plant. In the proposed work, renewable energy sources like solar and wind are utilized to power the reverse osmosis (RO) desalination unit. The developed RNN model optimizes renewable energy sources while maintaining a stable function of the desalination process. The RNN power management module utilizes historical data to predict the power generation of the PV and wind sources and then adjusts the system's output to meet the power demand of the desalination plant while considering the limitations of the RO unit and the water profile requirements. The developed model was implemented in MATLAB tool, and the results are estimated. Simulation outcomes demonstrate that the developed model improves the hybrid system's performance in terms of resource and battery storage utilization and minimizes energy loss.

Agent based online learning approach for power flow control of electric vehicle fast charging station integrated with smart microgrid

AbstractIn stochastic power systems, electric vehicle (EV) fast charging stations (FCS) are rapidly being installed, while adversely impacts the distribution network. Due to this, the improper offline charging control policies for EVs may increase the voltage fluctuation and instability. To analyse these aspects, this paper investigates the problems associated with offline (dis)charging control for effective utilization of battery storage and grid power through different modes of operations. Further, the need to develop real‐time charging control is identified to mitigate the adverse impacts of FCS on the distribution network. Hence, an online controller using reinforcement learning (RL) is designed to distinguish the uncertainties in real‐time and to schedule the (dis)charging of an EV against the uncertainties based on travelling pattern. The RL based online controller uses deep neural network (DNN), where the agents are programmed to control the bi‐directional power flow (V2G/G2V). The effectiveness of the RL rewards controller is fulfilled by the different charging states of the battery. The performance of online (dis)charging controllers that utilize DNN to act at its optimal power flow set of points for all sessions are examined in the details. Finally, the effectiveness of online RL controller and hardware results have been realized using real time hardware‐in‐the loop simulator.

Assessment of battery storage utilization in distribution feeders

The paper is concerned with the assessment of energy storage systems at the distribution level. Several projects related to energy storage are reviewed and analyzed for a better understanding of the motivation and benefits gained from such technology. Different applications and technologies of energy storage (ES) are identified, as well as the distinguishing characteristics of these ES technologies. The role of ES in the transition to a sustainable energy system is also identified and discussed. A sizing and location optimization study is conducted to realize the operational impact of installing battery energy storage systems (BESSs) in an existed distribution network in Riyadh, Saudi Arabia. The results will quantify the economic return that BESSs can offer for the electrical network, which would improve the decision making of the electric utility towards handling increasing load demand and power quality issues.

Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the trade-offs between economic performance and wind generation facilitation

The aim of this paper was to investigate the trade-offs that can be achieved between optimizing the electricity costs of a building integrated microgrid, while simultaneously facilitating high levels of wind penetration in a smart grid. This study applied multi-objective optimization to obtain a daily charge and discharge schedule of a battery bank, which was used to store electricity from the microgrid and smart grid and could also provide electricity to the building and smart grid. Multi-objective optimization was employed due to the independent objectives of minimizing building operating cost and maximizing the facilitation of wind energy from the smart grid. The trade-offs between the two objectives were simulated, evaluated and analyzed. A priority weighting factor (α) was applied to each objective. The purpose of α was to vary the importance of each objective relative to the other in an inversely proportional manner. This enabled the algorithm to optimize the battery operating schedule for the economic performance of the microgrid, the facilitation of wind generation from the smart grid or for trade-offs in between. The results present a comprehensive evaluation of 96 scenarios with varying daily weather conditions, building electricity demand, electricity pricing, microgrid output and wind penetration from the smart grid. A multi-objective optimization approach was then applied for each of the 96 scenarios with 11 α values to determine optimal trade-offs in these scenarios. Generally for the 96 scenarios analyzed, when the α value was 20% or higher, the amount of extra wind generation facilitation obtained was negligible while microgrid operating costs continued to increase. The results showed that when changing from an α value of 0% to an α value of 20%, there was a large increase in wind generation facilitation compared to the corresponding increase in cost, with wind generation facilitation increasing from its minimum value to within 89% of its maximum value (10.7% to 14.3% of facilitated wind generation). The corresponding building cost increased from its minimum value to within 13% of its maximum value (€1.14/day to €1.37/day). This produced a cost of approximately €0.06 for every 1% increase in wind generation facilitation. In comparison to this, changing from an α value of 20% to an α value of 100% implied a cost of approximately €3.64 for every 1% increase in wind generation facilitation. These results indicated that smart grids with large percentages of wind penetration may be substantially aided by utilizing the storage capacity of building integrated microgrids for a relatively low monetary cost.

An Emergency Energy Management for AC/DC Micro-grids in Industrial Park

Abstract Industrial parks have the trend to be designed to operate as microgrids with renewable distributed generations and battery storage. In this paper, an energy management system is proposed for AC/DC microgrids. This paper introduces a mixed-integer nonlinear programming method to realize the multiple objectives of emergency energy management. Combined with the comprehensive consideration of the amount of load supply, the cost of power generation and the utilization of battery storage, as well as the number of the switch operation, this paper establishes a composite objective function by synthetically evaluating several criteria. Then, examples are given to illustrate the effectiveness of the proposed method by considering the islanded mode with a generator fault.