Operation Of Battery Energy Storage System Research Articles

Assessing the economic viability of BESS in distributed PV generation on public buildings in Brazil: A 2030 outlook

This paper proposes a method to assess the financial attractiveness provided by adding a Battery Energy Storage System (BESS) in distributed photovoltaic (PV) generation on public buildings in Brazil. The method is applicable to prosumer units (PU) connected on the medium voltage grid operating under time-based electricity tariffs. The BESS primary objective was to achieve optimal reductions in electric energy expenses through effective energy arbitrage mechanisms. The procedural steps of the methodology begin with an analysis encompassing assessment of solar resource, PU consumption profile, and BESS sizing and operation. Subsequently, steps entail contracted power optimization, PU net-metering analysis, and evaluation of BESS impacts on electric energy expenses. Lastly, a regulatory and economic analysis is conducted incorporation considerations on BESS taxation, behind-the-meter regulations, and a sensitivity assessment extending to the 2030 outlook. By 2050, it is anticipated that PV generation will surpass hydropower, becoming the predominant component of Brazil's energy mix. This shift is expected to create an increased need for solutions, notably storage systems, capable of meeting flexibility requirements and maintaining grid resilience. The results suggest that the financial viability of incorporating BESS becomes favourable when the battery cost is below 365 $/kWh. In approximately 50% of the Brazilian territory, prevailing economic conditions support the adoption of BESS. Nationwide feasibility of integration is anticipated for the year 2027. It was observed that government policies to exempt BESS taxation, even if temporary, would be extremely interesting to promote the widespread adoption of this technology. The 2030 outlook of the transition to these benign renewable energy technologies is already in place, and will dominate the energy mix.

Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

This paper investigates an energy management problem in the microgrid by scheduling heating ventilation air conditioning (HVAC) and battery energy storage system (BESS) with a distributed algorithm. A multi-layer energy management architecture is presented at a system-level to co-optimize the HVAC-BESS by taking into account solar energy forecasts. A surplus-based consensus algorithm is proposed to solve the optimization problem, where the local power mismatch is introduced as a surplus term, and the HVAC-BESS can thus be co-scheduled to maximize renewable energy efficiency at the peak generation time. A set of the convex cost functions are formulated to minimize the HVAC's user dissatisfaction degree and alleviate power loss during the BESS operation. The goal is to collectively minimize the total energy cost in a distributed manner, subject to individual load constraints and power balance constraints. It is theoretically proved that a global convergence of the proposed algorithm is achieved provided that the directed network is strongly connected. The results from a number of case studies are promising, demonstrating the effectiveness and robustness of the algorithm under practical scenarios.

A real-time thermal behaviour monitoring and analysis of a 500 kWh grid-connected battery energy storage system

Statistical analysis yields critical data for risk evaluation and management of a stationary battery energy storage system (BESS). Lithium-ion (Li-ion) batteries have attained huge attention for both stationary and non-stationary applications due to their lucrative features such as lightweight, high energy density efficiency, and long lifespan. However, detailed analysis and trends subjected to thermal behaviour of the device especially in grid-connected BESS application is still neglected. Therefore, this paper presents a statistical analysis for thermal behaviour of a grid connected BESS. The significance of Li-ion battery employing battery thermal management is presented, which can guarantee a reliable and safe operation as well as examining the effect of voltage, current and state of charge (SOC) on BESS operation. The large group of datasets recorded daily with five-minute intervals are difficult to be analysed numerically in a timely manner. Thus, the analysis can be made by visualizing the numerical data that was retrieved through representational state transfer (REST API) for easier interpretation and trend analysis. The visualization is made using Microsoft Power BI and presented in this paper.

Operation scheduling strategy of battery energy storage system with the integration of differenced power constraint factor

The battery energy storage system (BESS) as a flexible resource can effectively achieve peak shaving and valley filling for the daily load power curve. However, the different load power levels have a differenced demand on the charging and discharging power of BESS and its operation mode. For further improving the efficiency of BESS in a demand response process, the differenced power demand should be incorporated into the BESS operation schedule. Therefore, this paper proposes a BESS operation scheduling strategy to satisfy the differenced demand based on the control of the power constraint factor. Firstly, a boundary moving method is used to solve the charging and discharging thresholds of BESS to determine the optimal charging and discharging periods. Secondly, a charging and discharging demand degree model is constructed based on the hourly charging and discharging demand. Thirdly, a BESS scheduling model with a minimum standard deviation of daily load curve is established for enhancing the peak shaving and valley filling. Finally, the case study shows that the proposed strategy can further improve the efficiency of BESS participating in demand response, improve the smoothness of the load power curve, and decrease the peak-valley difference.

Management of electric vehicle charging stations in low-voltage distribution networks integrated with wind turbine–battery energy storage systems using metaheuristic optimization

This study investigates the IEEE 69-bus distribution network with three wind turbines (WTs) connected at the same buses of three battery energy storage systems (BESSs), with three 20- or 30-outlet electric vehicle charging stations (EVCSs) for charging electric vehicles (EVs). The honey badger algorithm (HBA) is adopted to minimize daily energy loss. The HBA determines the best size and position for three WT-BESS buses and three EVCS buses. The HBA calculates BESS size and operation mode to minimize daily energy loss. The demand of EVCSs varies throughout the day depending on the random choice of the number and state of charge of EVs entering the station. This results in the active and reactive energy losses and utility input energy decreasing by 63.5%, 60.6% and 59.6%, respectively, and the minimum voltage increasing from 0.9256 to 0.9839 pu. The network voltage profile and stability are also improved.

Service stacking on residential BESS: RES integration by flexibility provision on ancillary services markets

Opening the Balancing Markets (BMs) to Renewable Energy Sources (RES) and Battery Energy Storage Systems (BESS) could support the integration of RES in decarbonizing power systems; nevertheless, the limited energy content of BESS can reduce their reliability on the BMs. A novel control strategy for revenue stacking of behind-the-meter and front-of-the-meter services on a domestic prosumer equipped with photovoltaic production and BESS is presented in this study. The aim is to maximize self-consumption and the economics while guaranteeing BESS state-of-charge management. A new control (and bidding) strategy is developed to offer the available energy (and power) margins on the Italian BMs, without saturating or depleting the energy content. The results show synergies between self-consumption maximization and flexibility provision, i.e., the proposed approach demonstrates how a multiservice-oriented operation of BESS improves the economic sustainability of the solution, with a payback time between 5 to 9 years, decreases the energy exchange with the public grid and the RES imbalance (imbalance reduced by 90%), and provides flexibility with high reliability (ranging 89%–97%). Due to this, the strategy of reducing the imbalance of variable RES and then providing flexibility with the left margins candidates itself as a standard routine for massively integrating RES via BESS.

Development, validation, and testing of advanced mathematical models for the optimization of BESS operation

Current decarbonization strategies are driven by the fast-paced diffusion of non-programmable renewable energy sources (NP-RESs), mainly through solar and wind power generation. Energy storage technologies are emerging as key solutions for coping with the variability and low-inertia characteristics of NP-RESs. Particularly, battery energy storage systems (BESS) are diffusing more widely for both behind-the-meter (BTM) and utility-scale applications. In this context, we still lack a shared solution on how to proceed from the on-field data collected about the performance of BESS to reliable and fast mathematical formulations for operational optimization. This study provides a validated modeling framework that can be exploited during or after BESS commissioning to (i) identify and derive the useful parameters to characterize BESS performances, (ii) formalize them in a mathematical formulation while being aware of its specific trade-off between accuracy and computational effort, and (iii) exploit the selected BESS model within a multi-energy system optimization problem. We discuss three different modeling approaches that we developed for optimizing BESS operation, with each providing a different balance between modeling accuracy and computational effort. These three mathematical models were validated against a numerical simulation model based on on-field performance data, and they were eventually tested on a reference case study. The results indicate that it is possible to restrict the average error in estimating BESS efficiency while simultaneously limiting the computational effort of the model. Regarding the operation of BESS, the conducted simulations demonstrate that an approximate BESS model may result in an overestimation of the expected revenues.

Power management of grid-connected PV wind hybrid system incorporated with energy storage system

Renewable Energy Sources (RES) such as solar photovoltaic (PV) and wind-based generation cannot meet dynamic load demand all the time because of their intermittent nature. Battery Energy Storage System (BESS) is integrated with RES to meet dynamic load demands. Proper power management is needed for the efficient and reliable performance of the system. This paper presents power management of PV-Wind- Battery hybrid system for both grid-connected and islanded modes of operation as well. The power balance is maintained by a power management system (PMS) under different environmental conditions and load conditions, and modes of operation. The control method for state-of-charge (SOC) and battery charge/discharge is applied to ensure efficient performance and safe operation of BESS. Different cases, such as surplus/deficit power available from RES with respect to load demand and battery capacity, have been considered and carried out to verify the performance of PMS using the MATLAB/Simulink platform.

Multi-objective architecture for strategic integration of distributed energy resources and battery storage system in microgrids

Microgrids with integrated renewable energy-based distributed generation (RDG) and battery energy storage systems (BESS) should be effectively designed and controlled to reap the potential benefits. In this context, this study recommends a novel Multi-objective Artificial Hummingbird Algorithm (MOAHA) based framework for optimal RDG allocation and sizing, along with BESS operation strategy, to enhance the voltage stability margin (VSM) and reduce the overall yearly expenses. In this work, the developed formulation has been evaluated on the IEEE 33-bus system, IEEE 69-bus system, and the Masirah Island distribution grid of Oman. Furthermore, the proposed method’s Pareto fronts are concluded to be superior to four of the recent metaheuristics employed in this research domain: Multi-objective Multiverse Optimization method (MOMVO), Multi-objective Equilibrium Optimization Technique (MOEOT), Multi-objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). According to the findings, the MOAHA optimal Pareto solution candidates (PSC) for the three test systems satisfied the VSM constraints and cost objectives while providing substantial energy transfer during off-peak and peak demand hours. Additionally, all PSCs effectively avoided voltage violations, and the active power losses during each optimization period and the total energy losses were significantly reduced.

Evaluating Machine-Learning-Based Methods for Modeling a Digital Twin of Battery Systems Providing Frequency Regulation

Battery energy storage systems (BESSs) have the potential to become providers of frequency regulation services in future energy systems. In this case, the reliable operation of BESS has a significant impact on system stability. One of the tools that ensures the secure BESS operation is applying a digital twin to forecast the BESS state of charge (SOC). It allows to predict BESS behavior and identify potential failures or cyberattacks in BESS. In this article, we compare multiple data-driven approaches to forecast the SOC of a BESS based on realistic dataset and real battery operation. Recurrent neural networks, e.g., feedforward neural network, gated recurrent unit, long short-term memory, support vector regression, random forest, and AdaBoost methods are applied to evaluate each method's performance. We compare the methods based on the maximum and average forecast errors, the required computational capacity, as well as expected training speed. To validate the results, in addition to the data gathered from simulations, we used experimental setup with a BESS of 79 kWh providing containment reserve for normal operation, which is a BESS service with a high economical potential in Nordic Region. Furthermore, we provide recommendations for the methods that are suitable to be applied for modeling the digital twin of a utility-scale battery digital twin.

A bi-level optimization method for voltage control in distribution networks using batteries and smart inverters with high wind and photovoltaic penetrations

In electrical distribution systems, the voltage quality problem considers the most restrictive issue that hinders high photovoltaic (PV) and wind integration. Therefore, this study aims at providing a reliable control method for the voltage problem in distribution networks embedded with high PV and wind sources. The proposed voltage management strategy consists of two controlling stages. The first stage uses battery energy storage systems (BESS), while the second stage employs the smart PV inverters' reactive power injection capability. For BESS operation, a bi-level optimization method based on metaheuristic optimization algorithms (MOA) is developed to regulate the voltage levels by governing the batteries charging/discharging rates. The bi-level optimization problem aims to maximize PV and wind energy exploitation while improving the voltage profile. In this study, a detailed comparison is conducted between three MOA: Social Spider Optimization, Particle Swarm Optimization, and Cuckoo Search Optimization. In addition, considering the uncertainty associated with PV system generation, a PV generation forecast is embedded in the voltage control strategy to reinforce the decision-making process of BESS operation. The efficacy of the proposed method is deployed on a modified IEEE-34 bus test feeder with real-world load and PV and wind power generation data. Experimental results show the effectiveness of the proposed method and indicate that proper coordination between the BESS and smart PV inverters is beneficial for distribution system operations that seamlessly integrate PV and wind energy. Overall, the proposed voltage management strategy can be used to control the voltage to any desired range at different locations.

System and Market-Wide Impact Analysis of Coordinated Demand Response and Battery Storage Operation by a Load-Serving Entity

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions.

Modeling and Control-Tuning of a Single-Stage MMC-Based BESS

In recent years, the integration of battery energy storage systems (BESSs) with multilevel modular converters (MMCs) has received interest in power system applications. In this work, this configuration is called a MMC-based BESS. The batteries are connected directly to the MMCs on submodules (SMs), called the single-stage approach. Several control strategies have been proposed to guarantee the proper operation of a MMC-based BESS. This system is complex due to the control strategy. Another challenge is in obtaining the controller gains for a MMC-based BESS converter. In this sense, there is a gap in the methodology used to calculate the controller gains. Thus, this work aimed to tune the analytical expressions of a MMC-based BESS by considering the single-stage approach. The methodology is validated through detailed simulation models of 10.9 MVA/5.76 MWh connected to a 13.8 kV power system. Finally, to validate the dynamics of the controllers, the simulation results in the PLECS software for the charging and discharging processes are presented.

Prescriptive analytics for optimal multi-use battery energy storage systems operation: State-of-the-art and research directions

This paper presents the state-of-the-art and latest advances in implementing multi-use practices on BESS applications to the power system grid. Representative papers on modeling and optimization methods were selected, most of them working with realistic use cases, but none reporting on real-world implementations. Some major findings from reviewing key representative papers are that current optimization methods were able to handle uncertainty related to prices and other parameters either in the look-ahead planning stage or real-time control for obtaining an optimal dispatch schedule, thus addressing previously pointed-out gaps. Further, a recommendation for future work is to implement current multi-use methods, found to be mature enough, to real-world BESS use cases for validation and gaining experimental experience. Furthermore, the multi-use performance can further be improved by joining the strong contributions from each paper's method. Current methods have shown great potential for bringing simultaneous benefits to the grid, as well as being economical for the owner. A continued exploration and inclusion of new future BESS applications in prescriptive analytics for BESS multi-use suggest robustness for future market changes. However, further assessments are needed to evaluate whether the technology is economical and green for each particular use case and also for a potential systematic upscaling of the technology as a means to facilitate the green shift.

Optimal Scheduling of Battery Energy Storage Systems Using a Reinforcement Learning-based Approach

This article proposes a novel energy management algorithm that controls the battery energy storage system (BESS) and on-grid supply. It employs the deep-Q-network agent with prioritized experience replay, and its efficacy is validated and verified by comparison to a benchmark method for mixed integer linear programming. The grid and energy storage systems are governed by switching operations initiated by BESS controllers via the automatic transfer switch. The primary objective is to accomplish optimal scheduling of batteries one day in advance to reduce electricity costs while maintaining battery health and primary power supply reliability. The methods proposed in this work provide practicable grid and battery operation patterns that test all conceivable planning scenarios for energy storage operation. Finally, a comparative analysis is performed to evaluate the efficacy of the proposed BESS operation scheduling methods.

An Energy Management System for the Optimal Operation of BESS in DC Microgrids: A Robust Convex Programming Approach

This paper uses a convex reformulation to deal with the robust, optimal energy management of battery energy storage systems (BESS) and renewable energies in DC microgrids. This relaxed mathematical model guarantees the global optimum regarding energy management problems, even when including uncertainties in demand and renewable energy. The proposed robust model can reach the best scheduling for energy management in worst-case cost scenarios while satisfying all requirements. Furthermore, a model for power transfer losses in converter devices is added to the proposed model by using a binary polynomial representation, which is convexified. This convexification is performed by transforming the nonlinear non-convex equations of the optimal energy management model into second-order cone constraints. Four scenarios implemented in the modified IEEE 123-bus radial distribution feeder are proposed in order to analyze the robust optimal energy management strategy: the deterministic model, demand uncertainty, uncertainty in solar and wind generators, and uncertainty in demand and solar and wind generators. In all scenarios, the energy management model reduces the total energy costs. Simulation scenario 1 showed daily operating costs of about US$ 4376.82, while simulation scenarios 2, 3, and 4, including demand and generation uncertainties, increased the daily operating costs by about US$ 5243.23 (19.79%), US$ 5072.23 (15.88%), and US$ 5738.75 (31.12%), respectively. Scenario 4 showed the highest costs, as it involves more uncertainty. Hence, the robust optimal energy management strategy dispatches more energy from conventional generators, increasing the operating costs to satisfy those of the worst case.

Effect Quantification of BESS Providing Frequency Response on Penetration Limit of VER in Power Systems

Increasing the penetration of variable energy resources (VER) can reduce the inertia and frequency response performance of power systems supported by replacement synchronous power generation. Therefore, it is necessary to manage the VER penetration limit in power systems for stable operation and to increase the operability to the desired level. This study proposes a method to evaluate and quantify the effect of increasing the penetration limit of VER by controlling a battery energy storage system (BESS). The BESS can provide a fast response, but frequency response performance varies depending on the operating conditions. In the proposed quantification method, various control methods of a BESS, operating conditions of the power system, and penetration conditions of additional VER were analyzed, and the effect of the BESS on increasing the penetration limit of VER was evaluated. This evaluation and analysis enabled the selection of the BESS operating conditions to achieve the target VER capacity in the power system. The proposed quantification method was analyzed through simulations based on the Korean power system model. Therefore, it can contribute to estimating the required performance of the BESS for each system operating condition required to achieve the VER target.

Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks

Due to fast response time and the ability to charge and discharge efficiently, the battery energy storage system (BESS) has become a promising option for ancillary services in low voltage networks. The BESS planning is more critical where only selected cost parameters were considered, and the sustainability aspects were ignored in most cases. In this paper, BESS planning is re-imagined from a circular economy perspective that primarily allows the optimal use of battery capacities without compromising the grid support. A novel method for optimal siting and sizing of commercially available residential BESS for ancillary support in low voltage residential feeders is proposed. This method incorporates the cost of BESS operation in the planning stages to provide an accurate estimate of BESS for grid support. The implications of introducing BESS operation cost at the planning stages are demonstrated by solving four different optimization problems with the costs defined as over-voltage, voltage fluctuations, BESS costs (investment, O&M costs), and multiple objective operations. To validate, the proposed method is applied to an IEEE European low voltage feeder and a modified version of this test feeder to an Australian scenario using particle swarm optimization (PSO) algorithm. It is found that the proposed method successfully reduces the BESS costs and provides an appropriate estimation of how the BESS installations can provide grid voltage support. Compared to the existing methodologies for mitigating over-voltage, the net required BESS capacity for the European and Australian feeders was reduced by 7.21 percent and 9.58 percent, respectively. The total BESS capacity in the European and Australian grids was decreased by 41.48 percent and 48.06 percent, simultaneously compared with the methodologies for reducing the voltage fluctuations. Overall, the optimal sizing and siting approach we proposed not only reduces the voltage fluctuations and required battery capacities but also promotes circular economy by lowering the resources in the BESS planing for ancillary services.

Hierarchical Distributed Coordinated Control for Battery Energy Storage Systems Participating in Frequency Regulation

At present, battery energy storage systems (BESS) have become an important resource for improving the frequency control performance of power grids under the situation of high penetration rates of new energy. Aiming at the problem that the existing control strategy is not sufficient for allocating the frequency regulation power instructions, a hierarchical distributed coordinated control strategy for BESS to participate in the automatic generation control (AGC) of a regional power grid is proposed. At the upper layer, the state of charge (SOC) of BESS and the technical characteristics of different frequency regulation power sources are comprehensively considered to complete the coordinated distribution of frequency regulation commands between BESS and traditional generators; at the lower layer, for the purpose of optimizing the economic operation of the regional power grid, the distributed consistency algorithm is used to control the distributed BESS in order to realize the fine management of power output of BESS. The simulation results indicate that this control strategy can give full play to the technical characteristics of different frequency power sources and improve the frequency regulation of the power grid. The excessive power consumption of BESS is successfully avoided, and the continuous operation of BESS is realized.

Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems

In this paper, distribution systems are optimized to accommodate different renewable energy sources, including PhotoVoltaic (PV) and Wind Turbine (WT) units with existing Electric Vehicles Charging stations (EVCS) connected to specific locations of distribution systems. Battery Energy Storage systems (BES) are provided at the exact locations of the PV and WT units. The BES units are optimized to control the power flow inside the distribution systems and enhance their performance by minimizing the proposed fitness functions. Each BES's generated or consumed power depends mainly on its State of Charge (SoC) and system constraints. The Gorilla Troop Optimizer (GTO) algorithm is adopted to solve the optimization problem either as a single-objective or multi-objective optimization. The adopted fitness functions minimize power loss and total voltage deviation (TVD) of the 108-bus distribution system. Two stages are involved in the GTO solution of the optimization problem. The GTO algorithm finds the ideal positions and sizes of the PV and WT units in the first stage. Then the GTO optimizes the BES operation in the second stage after integrating the EVCS into the system, using distinct profiles with two charging levels on weekdays and weekends. The obtained results show that the percentage reductions in the active energy loss with optimal allocations of the PV/WT are 45.29 % and 45.56 % for Level 1 and Level 2 of EVCS charging. The counterpart reductions increase to 48.22 % and 48.33 % when integrating the BES units. Furthermore, with PV/WT integration, utility power consumptions are reduced by 65.74 % and 65.65 % for the two charging levels, while the reductions increase to 70.63 % and 70.54 % when including BES units. These findings emphasize the importance of optimal BES operation in distribution networks equipped with renewable energy sources. The findings demonstrate the superior capabilities of the GTO algorithm during the optimization phase on an equal footing.