Battery Energy Storage System System Research Articles

Innovative energy solutions: Evaluating reinforcement learning algorithms for battery storage optimization in residential settings

The implementation of BESS (battery energy storage systems) and the efficient optimization of their scheduling are crucial research challenges in effectively managing the intermittency and volatility of solar-PV (photovoltaic) systems. Nevertheless, an examination of the existing body of knowledge uncovers notable deficiencies in the ideal arrangement of energy systems' timetables. Most models primarily concentrate on a single aim, whereas only a few tackle the intricacies of multi-objective scenarios. This study examines homes connected to the power grid equipped with a BESS and a solar PV system. It leverages four distinct reinforcement learning (RL) algorithms, selected for their unique training methodologies, to develop effective scheduling models. The findings demonstrate that the RL model using Trust Region Policy Optimization (TRPO) effectively manages the BESS and PV system despite real-world uncertainties. This case study confirms the suitability and effectiveness of this approach. The TRPO-based RL framework surpasses previous models in decision-making by choosing the most optimal BESS scheduling strategies. The TRPO model exhibited the highest mean self-sufficiency rates compared to the A3C (Asynchronous Advantage Actor-Critic), DDPG (Deep Deterministic Policy Gradient), and TAC (Twin Actor Cretic) models, surpassing them by ∼ 3 %, 0.72 %, and 3.5 %, correspondingly. This results in enhanced autonomy and economic benefits by adapting to dynamic real-world conditions. Consequently, our approach was strategically designed to deliver an optimized outcome. This framework is primarily intended for seamless integration into an automated energy plant environment, facilitating regular electricity trading among multiple buildings. Backed by initiatives like the Renewable Energy Certificate weight, this technology is expected to play a crucial role in maintaining a balance between power generation and consumption. The MILP (Mixed Integer Linear Programming) architecture achieved a self-sufficiency rate of 29.12 %, surpassing the rates of A3C, TRPO, DDPG, and TAC by 2.48 %, 0.64 %, 2 %, and 3.04 %, correspondingly.

Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.

Adaptation of Microinverter Reference Design for Integration with Battery Energy Storage Systems in Microgrids

The paper presents an adaptation of the microinverter platform from Texas Instruments to incorporate a battery energy storage system (BESS) alongside the development of the BESS system itself. Initially designed for unidirectional power flow between PV panels and an electric grid, the platform required modifications to accommodate bidirectional energy transfer for BESS integration. These modifications encompass software adjustments and hardware enhancements, which are all detailed within the paper. The electrical configuration includes selecting and deploying components such as DCDC power converters, microcontrollers, measured signals, and actuating signals to facilitate battery connection to the platform’s DC bus. Furthermore, a supervisory control and data acquisition (SCADA) system is devised for supervisory control and monitoring, with its implementation outlined. Control software tailored for the chosen microcontroller of the DCDC converters is described in terms of structure and functionality. A hardware-in-the-loop (HIL) methodology is employed to validate the proposed modifications and microgrid configuration. Utilizing the real-time simulator OPAL-RT, the paper presents experimental results and their analysis within the considered microgrid environment.

Improved rule-based power distribution algorithm for hybrid battery storage systems and real-world validation

Large-scale battery energy storage systems (BESS) can serve many applications and are already widely used for grid services. The rapidly growing BESS market and the recent interest in their deployment accentuate the need for safe, reliable, and highly available energy management systems (EMS) for automated control. However, the EMS and their integrated power distribution algorithms (PDA) can still be optimized to adapt various characteristics of the BESS. This study investigates a new version of a PDA with a particular focus on battery aging and system efficiency. The rule-based PDA has been validated on a 6 MW/7.5 MWh BESS system with five battery technologies providing frequency containment reserve to the German power grid. The results underline the PDA's capability to exploit the individual strengths of each battery technology. The PDA sets objectives for the state of charge, energy throughput, and power of the batteries to extend battery life. The distribution of energy throughputs among batteries can be selected in advance through the new implementation of the PDA. At the same time, the inverters are significantly less often activated and used in the optimal efficiency range, increasing the overall system efficiency to approximately 82 %. The optimized switching behavior leads to less frequent power switching between individual battery units and longer phases with more constant power. In addition, the operational efficiency of BESS can be improved by the choice of battery technology and the overall system layout on the hardware side. The improvements on the software side are only possible by increasing the overall power requests through multi-use operation by about 6 % compared to our benchmark test. The results can be used by BESS operators to increase operational profits due to longer battery life and fewer efficiency losses.

Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level

Installing the battery energy storage system (BESS) and optimizing its schedule to effectively address the intermittency and volatility of photovoltaic (PV) systems has emerged as a critical research challenge. Nonetheless, some existing studies still have limitations in terms of the efficiency of the BESS scheduling due to the lack of comprehensive consideration of diverse user objectives. As a response to this gap, this study aimed to develop a reinforcement learning (RL)-based optimal scheduling model to better reflect the continuous behaviors in the complex real world. To this end, focused on residential buildings connected to the grid and equipped with a BESS and PV system, its optimal scheduling models were developed using four algorithms from among the various RL techniques according to training methods. The results of the case study showed that the developed RL-based optimal scheduling model using Proximal Policy Optimization (PPO) can be applied to effectively operate the BESS with a PV system, considering possible uncertainties in the real world. The case study demonstrated the effectiveness and feasibility of the developed RL-based optimal scheduling model. Compared to other algorithms, the PPO-based RL model has better decision-making for optimal BESS scheduling strategies to maximize their self-sufficiency rate and economic profits by coping with changing variables in the real world. Therefore, the RL-based BESS scheduling model will offer an optimal solution, specifically tailored for use within a virtual power plant, where numerous buildings continuously share electricity.

Reliant Monotonic Charging Controllers for Parallel-Connected Battery Storage Units to Reduce PV Power Ramp Rate and Battery Aging

The inherited intermittency of solar photovoltaic (PV) systems impacts the power grid by creating power fluctuations, which are mitigated by the integration of battery energy storage systems (BESS) augmented with a smoothing controller. However, the conventional charging and discharging schemes result in a repetitive and chaotic state of charge (SOC) which might degrade the BESS performance. This paper proposes a reliant monotonic charging/discharging controller (RMCC) to reduce battery degradation while maintaining smoothing efficiency. In this configuration, two battery units operate concurrently, one charging, and the other discharging, which helps to reduce battery cycles and extend BESS lifetime. In addition, the paper presents a smart RMCC (S-RMCC) controller to control the operation of a BESS made up of an even number of battery units greater than two. The controller forecasts the short-term irradiance and identifies the existence of power ramps using a ramp event detector. Hence, the forecasted irradiance is then utilized to obtain the PV output power and to compute the forecasted needed charging and discharging energy to compensate for PV power intermittency. These estimates are then compared to the remaining energy of battery units to determine whether to work with the entire BESS system or a subset of units. The proposed controllers were tested for battery degradation, ramp rate reduction, and power smoothing efficiency. The battery degradation is significantly minimal when using RMCC-operated units, accounting for 0.1877 % and 0.1918 %, as compared to 9.1514 % when using a single BESS for smoothing.

Optimal Sizing of a Photovoltaic/Battery Energy Storage System to Supply Electric Substation Auxiliary Systems under Contingency

Electric substations (ESS) are important facilities that must operate even under contingency to guarantee the electrical system’s performance. To achieve this goal, the Brazilian national electricity system operator establishes that alternating current (AC) auxiliary systems of ESS must have, at least, two power supplies, and in the case of failure of these sources, an emergency generator (EG) must at least supply energy to the essential loads. In order to improve the availability of auxiliary systems, a microgrid with other sources, such as photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), can be an alternative. In this case, an economical optimization of the PV/BESS system must be addressed considering the costs associated with the installation and maintenance of equipment, and the gains from the credits generated by the photovoltaic system in the net metering scheme. In this paper, the size of the BESS system was determined to supply energy to the load of auxiliary systems of an ESS, as well as a PV system to achieve a null total cost. Furthermore, multi-objective optimization using the genetic algorithm technique was employed to optimize the size of the hybrid PV/BESS to minimize the investment cost and time when the demand was not met. Simulations under different scenarios of contingency were allowed to obtain the Pareto frontier for the optimal sizing of a PV/BESS system to supply energy to AC auxiliary systems in an ESS under contingency.

Residential photovoltaic and energy storage systems for sustainable development: An economic analysis applied to incentive mechanisms

AbstractThe photovoltaic (PV) system has a very significant growing global trend and its role is essential in combating climate change. However, its intermittent nature requires integration with a battery energy storage system (BES). This work proposes an economic analysis based on net present value (NPV) for an integrated PV + BES system in a mature market (Italy). The analyses are applied to different policy (used for both PV and BES) and market (purchase price, selling price) contexts. Results show that the NPV(PV) ranges from 1061 to 7426 €/kW. The work identifies the conditions under which BES is affordable. The required increase in self‐consumption varies in the 14%–35% range. The purchase price and the percentage of energy‐self‐consumption play a crucial role in the profitability assessment of a PV + BES system. Incentive policies based on subsidized tax deductions and subsidies for energy produced and self‐consumed can enable a more sustainable energy future in the residential sector. This work suggests a mix of policy choices: (i) a subsidized tax deduction larger than 50% and a bonus for energy produced and self‐consumed for PV plants; (ii) subsidized tax deduction also for BESs but at a lower value than for PV plants; and (iii) encouraging a recycling industry.

Coordination of BESS and PV system with bidirectional power control strategy in AC microgrid

An AC microgrid in collaboration with Battery Energy Storage Systems (BESSs) and PV systems suffers uncertainties in power flow. The State of Charge (SoC) of an operating BESS reduces with time, degrading the DC link voltage and inverter’s output frequency. The conventional control on P-f droop characteristics has limitations in control flexibility and for that, the proposed droop control is designed on P-δ droop characteristics to maintain frequency and power factor even in discharging mode of the battery. The BESS follows a secondary distributed coordination scheme where SoC balancing using an average consensus technique is used to equalise the battery unit power contribution. The control scheme of BESS introduces an advanced polynomial regression-based changeover technique that adaptively changes battery mode ( discharging to charging or vice versa) to maintain DC link voltage, avoiding voltage cutoff of the battery. For PV systems, the decentralised control scheme is designed to contribute power using its local controller and is effective, especially in communication failure conditions. The control architecture of BESS and PV units has been configured differently and tested on a CIGRE LV feeder using MATLAB simulation. To validate the control strategy in real-time, hardware is implemented on the single-bus distribution network. The selection of control coefficients is made by small-signal analysis.

Influence of the control strategy on the performance of hybrid polygeneration energy system using a prescient model predictive control

Hybrid Polygeneration Energy Systems (HPES) can be effective solutions to reach COP26 goals. In particular, Combined Heat and Power (CHP) systems can increase the total system efficiency when both electrical and thermal power are required. The integration of a Battery Energy Storage System (BESS) can further improve the plant efficiency and economy, assuring higher operational flexibility. Configuration, sizing and control strategy definition are of primary concern for these systems when the best possible performances are sought. This work aims to quantitatively assess the importance of the adopted control strategy in the operation and performance of a possible sub-system of a grid-connected Hybrid Polygeneration Energy Systems (HPES), consisting in this study of a CHP plant assisted by a BESS. A simulation code of the plant from an energetic point of view was used, and the main economic indicator was also calculated according to the legislative reference scenario. Then a Prescient Model Predictive Control (MPC) was coded to achieve near-optimal plant operation, using a novel system state description to reduce the computational burden linked to the Optimal Control Problem (OCP) solution. The BESS system was modelled, including cycle battery ageing. Subsequently, the performances of the adopted prescient MPC have been compared to the previous results given by a multi-objective plant sizing with a rule-based control. The results show that the control strategy can enhance the performance of the CHP system, achieving remarkable overall better performances, with up to 12% higher Primary Energy Savings. Moreover, the research findings highlight how the proposed control variable ensure a reduction of the computational time by more than 70%, also improving the quality of the found solutions to the OCP. The results also suggest that proper control strategies should be adopted even in a preliminary optimal sizing phase of the CHP plant, since there is a large room of improvement in predicting the achievable plant performance when more basic rule-based control strategies are overcome and replaced by MPC.

The Impact of the Electric Double-Layer Capacitor (EDLC) in Reducing Stress and Improving Battery Lifespan in a Hybrid Energy Storage System (HESS) System

This paper investigates the effect of the electric double layer capacitor (EDLC) in reducing stress and prolonging the battery lifespan in a hybrid energy storage system (HESS). A 65 F, 16.2 V EDLC supercapacitor was connected in a laboratory experiment to produce its charge/discharge profile at a constant current of 5 and 10 A. The EDLC’s Faranda or “two branch model” mathematical parameters were extracted from the experimental charge/discharge profile. The extracted parameters were used as inputs to design the Python/MATLAB/Simulink (PMS)-hybrid model of the EDLC. The charge/discharge profiles of the simulated PMS model of the EDLC were then compared to the charge/discharge profiles derived from the experimental setup of the EDLC and were found to match. The PMS model of the EDLC was then used as a subcomponent in an HESS system modelled in MATLAB/Simulink. Using constant load conditions, the battery’s voltage, current, power and state of charge (SOC) were analyzed for a battery energy storage system (BESS) without a supercapacitor and then compared to an HESS system with a supercapacitor in an experimental setup. This process was repeated with the simulated PMS model of the EDLC in MATLAB/Simulink for HESS and without the EDLC for BESS. Finally using a variable load in an experimental setup, the battery’s voltage and current were analyzed for a BESS system and compared to an HESS system. All these data show that, in an HESS system with a supercapacitor, there is less stress on the battery with a load applied. This is indicated by the voltage and current values in an HESS system being consistently more stable with respect to time as compared to the BESS system. As a result, in an HESS system, the battery will have a longer lifespan.

Multisource Grid-Connected DC/AC Hybrid Microgrid Decentralized Control Under Input Sources Variation

This article proposes a multisource distributed generation-based dc/ac grid-interactive microgrid providing quality power to a distribution network featured with a constant power load (CPL). The hybrid dc/ac system is a consolidation of a Permanent Magnet Synchronous Generator (PMSG) based wind turbine, Photovoltaic array, and Battery Energy Storage System (BESS) at a common dc-bus. The BESS system ensures continuous power support and takes care of the infrequent nature of photovoltaic and wind. The decentralized control is applied in the proposed grid-interactive configuration during variable wind speed and solar irradiance source disturbance to achieve three goals, namely, regulating the dc-bus voltage, extracting maximum power from distributed generation, and supplying energy-efficient power to the grid. The control strategy combines Maximum Power Point Tracking (MPPT), phase-locked loop (PLL) for the grid-linked synchronization, reduction of harmonic output current control, active/reactive power (P/Q) control, and virtual droop control used simultaneously. Applying virtual dc bus voltage droop control and MPPT can regulate the proper power-sharing among sources in a dc sub-grid. During the switching of the interlinking converter, high-frequency harmonics are created, which are eventually mitigated by LCL filters employed in the ac bus. Simulations are developed in Matlab Simulink, and it is verified using real-time digital simulator OPAL-RT to ratify the effectiveness of the suggested control strategy.

A case study in the identification of best combination of energy resources for hybrid power generation in rural communities through techno-economic assessment

This paper illustrates techno-economic analysis on hybrid energy systems for a remote community near Gulbarga in Karnataka state, India, which is blessed with the best of solar radiation and wind velocity. In this paper, the possible combinations with solar PV, wind, and Battery Energy Storage System (BESS) are simulated for an annual period in HOMER (Hybrid Combination Model for Electric Renewable) software. The objective of the work is to identify the best combination among these hybrid energy systems based on the Net Present Cost (NPC), Cost of Energy (COE) and Levelised Cost of Energy (LCOE) and economic calculations with the input of community load data, seasonal data, and plant equipment data. The comparison is performed among the proposed systems based on economic energy parameters. The proposed configurations are simulated for residential and commercial loads and concluded that the NPC, LCOE, capital cost and operating cost of wind and BESS system is lower than the solar BESS and solar and wind BESS systems. Finally, the hybrid energy combination of wind and BESS system is more economically feasible compared to other designed models for the selected area and the NPC of the system is 0.696$.

Development and Performance Verification of Frequency Control Algorithm and Hardware Controller Using Real-Time Cyber Physical System Simulator

Frequency stability is a critical factor in maintaining the quality of the power grid system. A battery energy storage system (BESS) with quick response and flexibility has recently been used as a primary frequency control (PFC) resource, and many studies on its control algorithms have been conducted. The cyber physic system (CPS) simulator, which can perform virtual physical modelling and verification of many hardware systems connected to the network, is an optimal solution for the performance verification of control algorithms and hardware systems. This study introduces a large-scale real-time dynamic simulator that includes the national power system. This simulator comprises a power grid model, an energy management system (EMS) model, a BESS system model, and a communication model. It performs the control algorithm performance evaluation and the hardware controller’s response performance evaluation. The performance of the control algorithm was evaluated by tracking the power system’s characteristic trajectory in the transient state based on the physical response delay time between the output instruction of the frequency regulation controller (FRC), a hardware controller, and the output response of the BESS. Based on this, we examined the response performance evaluation results by linking them to the optimally designed actual FRC. As a result, we present an analysis of the BESS’s characteristic trajectories in the transient state, such as frequency, power system inertia, and power grid constant, and provide FRC response performance evaluation results at a level of 163 ms, by connecting the BESS installed at the actual site with the CPS simulator.

Multi-Agent Reinforcement Learning for Decentralized Resilient Secondary Control of Energy Storage Systems Against DoS Attacks

While distributed secondary controllers have been studied for multiple energy storage systems in islanded microgrids, information infrastructure has to be added for the extensive information transmission among these secondary controllers and the additional communication among distributed controllers is costly and increases the vulnerability surface to cyberattacks. In this work, a data-driven decentralized secondary control scheme is proposed for multiple heterogeneous battery energy storage systems (BESSs). The proposed secondary control scheme can achieve frequency regulation and the state-of-charge (SoC) balancing simultaneously for BESSs without requiring accurate BESS models. This scheme leverages an asynchronous advantage actor-critic (A3C) based multi-agent deep reinforcement learning (MA-DRL) algorithm where the centralized off-line learning with shared convolutional neural networks (CNN) is designed to maximize global rewards and ensure the performance of the entire system and a decentralized online execution mechanism is applied to each BESS. Furthermore, in view of possible denial-of-service (DoS) attack on local communication networks used for signal transfer between secondary controllers and remote sensors, a signal-to-interference-plus-noise ratio (SINR)-based dynamic and proactive event-triggered communication mechanism is proposed to alleviate the impact of DoS attacks and reduce the occupation of communication resources. Simulation results on a four-bus multiple BESS system show that the proposed decentralized secondary controller can achieve simultaneous frequency regulation and SoC balancing. Comparison results with other event-triggered mechanisms and MA-DRL algorithms show the A3C based MA-DRL algorithm with CNN can obtain a comparatively optimal policy through training and the designed event-triggered strategy can dynamically adapt the release frequency based on real-time SINR and significantly reduce the occupied network bandwidth and packet loss rate (PER) induced by DoS attacks.

Optimal sizing and comparative analysis of rooftop PV and battery for grid-connected households with all-electric and gas-electricity utility

This study evaluates the optimal sizing and economic analysis of the rooftop solar photovoltaic (PV) and lithium-ion battery energy storage system (BESS) for grid-connected households. Two types of households are investigated, i.e., all-electric homes and those supplied with both gas and electricity. Each type of household is investigated under three system configurations: (i) without PV and BESS, (ii) with PV only, and (iii) with PV and BESS (PV-BESS) system. The objective is to minimize the net present value (NPV) of operating each type of house and examine the impact of the generation and storage technologies on these. For the all-electric household, gas demand is converted to electricity demand based on the gas and electric cooking and water heating appliance efficiencies used in this study. The houses NPVs are calculated using real monitored gas and electricity usage data, along with measured, solar irradiation and ambient air temperatures. It is found that the NPV varies for the all-electric and the gas and electric house, and is more prominent for the customers with gas and electric when no PV and BESS are installed. However, the key finding of this study highlights that the PV-BESS system is more economic for the all-electric houses.

Simulation and modeling for active distribution network BESS system in DIgSILENT

By analyzing traditional distributed energy resource (DER) modeling methods, this paper proposes a feasible method of DER modeling based on static generator in the simulation software — DIgSILENT. Behavioral model is proposed in this paper to simplify the DER model, in which simple electronic circuits or mathematical functions are used to reflect the physical characteristics of a certain model. With the application of the modeling method, the battery energy storage system (BESS) are implemented in DIgSILENT simulation environment. In each simulation case, the behavioral model as well as the control strategy is illustrated and the simulation result is analyzed in the end.

Wind generator transient response analysis and improvement using BESS

Transient stability is very important for safety in the power system. In this paper, transient stability analysis of a wind power generator connected to distribution network is presented. The analysis was performed on a model of a realistic 12-bus system. In order to improve transient response capabilities of the system, battery energy storage system (BESS) is included in the simulations. It was demonstrated that BESS has significant influence on the stability of the network during the fault conditions. All simulations were performed in the DigSILENT Power Factory software. The analysis and assessment of the network was performed for the case of three-phase symmetrical fault with all lines in service and following a single line outage (failure and N-1 criterion). The results show that the connection of the wind generators to distribution network depends on the distance from the primary source of the grid (slack bus). However, BESS system can be used to improve transient stability response in which case distance from the primary source station is not critical. Also, this analysis adds additional evidence on the importance of transient stability calculations during the process of new generator unit connection to the power system.

Stacking Battery Energy Storage Revenues in Future Distribution Networks

Distribution system operators are attracted to battery energy storage systems (BESS) as a smart option to support the distribution network. However, due to its high capital cost, BESS profitability is dependent on the participation in multiple services to stack revenues and rationalize their existence. Yet, revenue stacking is location-dependent based on the available services and regulations. In this paper, specific revenue stacking frameworks are proposed for BESS installed in modern distribution networks that consider the conflicts and synergies that may occur from the involvement in multiple services in practice. A simple yet effective sizing formulation is introduced to find the BESS system size based on the primary service which is to solve the distribution network violations. BESS scheduling is simulated in accordance with the proposed frameworks to maximize the stackable profits for a case study of Northern Ireland. The BESS profitability is investigated through cost-benefit analyses of different technologies for the sole and stacked services. The results show that revenue stacking can boost the annual revenues by 129% with a payback period of 8 years on average. The presented insights are useful for network operators and energy investors in understanding and assessing the profitability of different BESS technologies for various applications.

Autonomous Frequency Regulation of a Grid-Following Converter Powered by Battery Energy Storage System

This paper deals with an Autonomous Frequency Regulation (AFR) of a grid-following (GFL) converter powered by a Battery Energy Storage System(BESS). It deals with the frequency stabilization function of a small-sized BESS system that will be widely used along with the spread of electric vehicles and eco-friendly housing. As requirements of AFR, it should be able to actively respond to the frequency deviation using the measured voltage at a PCC (Point of Common Coupling). Due to battery capacity limitation, the variation of energy in a BESS for AFR should be limited to a level that does not affect the original installation purpose (e.g., load transfer, uninterruptible power supply, etc.). So in this paper, a new AFR method has been proposed, which uses a PI-based selective frequency damping and achieves cooperative frequency support while obeying the Electric Power System (EPS)’ frequency control scheme. As a verification, the effect of the proposed AFR algorithm mounted on the GFL converter is simulated in a modified IEEE-14 bus system having multiple random loads and multiple distributed ESSs.