Battery Energy Storage System Units Research Articles

Voltage Optimization on Low Voltage Distribution Transformer Zones Using Batteries in Uganda

In the context of Uganda's rapidly growing energy demands and the need for sustainable solutions, this study explores the implementation of voltage optimization techniques in Low Voltage (LV) distribution transformer zones. The research focuses on the innovative integration of batteries to optimize voltage levels, thereby enhancing the efficiency and reliability of the electrical distribution system. By analyzing real-time data from various LV transformer zones in Uganda, this study investigates the impact of voltage fluctuations on the overall power distribution network. The research methodology involves the design and deployment of battery energy storage systems (BESS) strategically placed within LV distribution transformer zones. These BESS units are utilized to store excess energy during periods of low demand and release it during peak hours, ensuring consistent voltage levels and minimizing losses in the distribution network. The study evaluates the effectiveness of this approach through extensive simulations and on-site experiments, considering factors such as battery capacity, charging/discharging rates, and load variations. A comprehensive cost-benefit analysis is conducted to evaluate the potential financial savings and environmental impact associated with this sustainable energy solution. The findings of this research indicate significant improvements in voltage regulation, reduced system losses, and enhanced reliability in LV distribution transformer zones. Additionally, the study demonstrates the feasibility of integrating batteries into the existing infrastructure, thereby contributing to the optimization of the energy distribution system in Uganda. The outcomes of this research provide valuable insights for policymakers, utility companies, and researchers, emphasizing the importance of embracing innovative technologies to address the energy challenges faced by developing nations like Uganda.

Simulation-Based Evaluation of Sliding Mode Control with Washout Filter for Power Balancing in Battery Energy Storage Systems

This research paper presents a simulation-based evaluation of a novel sliding mode control (SMC) approach with a washout filter for power balancing in battery energy storage systems (BESS). Unlike traditional proportional-plus integral control methods, the proposed SMC technique is specifically tailored for charging and discharging BESS units. The primary objective of this study is to investigate the efficacy of the proposed SMC-based scheme in achieving optimal power balancing at the DC bus of the BESS. A comparative analysis is conducted to assess the performance of the developed SMC strategy against the conventional proportional-plus integral controller. MATLAB simulations utilizing Simulink and the Sim Power System toolbox are employed to obtain comprehensive results. The findings of this study provide valuable insights into the effectiveness of the SMC with washout filter for enhancing power balancing in BESS applications, thereby contributing to advancements in energy storage system control methodologies. Keywords: Sliding Mode Control (SMC), Washout Filter, Power Balancing, Battery Energy Storage Systems (BESS), Simulation-based Evaluation, MATLAB Simulation

BESS Reserve-Based Frequency Support During Emergency in Islanded Residential Microgrids

Due to limited reserves and 100% inverter-based resources in an islanded residential microgrid (IRM), largefrequency oscillations may arise during load/generation fluctuations. As an independent grid-forming unit, a battery energy storage system (BESS) can participate in load-frequency control (LFC) to achieve environment-friendly and reliable supply in IRMs. The formation of an interconnected islanded residential microgrid (IIRM) system and coordinated active power interchange among IRMs can help overcome BESS units limited reserve constraints. In this work, a reserve control (RC) level is proposed to be added in the conventional LFC architecture to provide frequency support to disturbance-affected areas with the help of a set of BESS units available reserve capacity. Following a significant disturbance detected, the RC level is activated based on their pre-defined contracted demands and net generation change information. The RC level also considers real-time monitoring of each microgrid's active power reserve to avoid frequency instability during emergency conditions. System performance with the proposed LFC model is analyzed with extensive tests conducted on a CIGRE TF C6:04:02 benchmark IIRM system. The proposed algorithms are also validated using a real-time digital simulator with hardware-in-the-loop setup.

MPC Based Dynamic Voltage Regulation Using Grid-Side BESPS With the Consideration of Communication Delay

Battery energy storage power stations (BESPS) play an important role in the power flow regulation in large-scaled micro-grids. It is proposed that the static Var compensator (SVC) be replaced by BESPS through activation of its dynamic voltage regulation (DVR) function so that the cost of SVC can be saved. An additional controller named energy storage coordination controller (ESCC) is needed to support the control algorithm of DVR and coordinate the individual battery energy storage system units. However, communication delay between the ESCC and the commercial BESS units is compounded by the difficulty of measuring the micro-grid (MG) bus voltage by ESCC. These issues should be considered in the design of the BESPS controller to avoid compromising the micro-grid operation. In this paper, we will be investigating model predictive control (MPC), Smith Predictor and PI based control schemes to deal with the communication delay issue. Kalman Filter based observer is designed to monitor the MG bus voltage as a feedback signal of the ESCC. The stability of the proposed method is further discussed and the results using the control hardware-in-the-loop (CHIL) testing are presented.

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.

Distributed secure balancing control for battery energy storage systems subject to random denial‐of‐service attacks

SummaryThis paper is concerned with the distributed secure balancing control problem for battery energy storage systems (BESSs) in a Direct Current (DC) microgrid. The Denial‐of‐service (DoS) attacks, which appear in a random way, are taken into account during the data exchange of individual BESS units. By introducing a well‐designed compensation term, an improved distributed consensus algorithm is developed to deal with the state‐of‐charge (SOC) balance issue in the presence of randomly occurring cyber attacks. With the aid of the Lyapunov stability theory, sufficient conditions are established for the networked control system such that the balance of the SOC for each BESS unit is achieved under certain constraints. The effectiveness of the proposed balance scheme is verified by a series of illustrative simulation experiments.

Investigating the participation of battery energy storage systems in the Nordic ancillary services markets from a business perspective

Battery energy storage systems (BESSs) are gaining potential recognition in renewable-based power systems. To maintain the stability of such systems, BESSs units are being deployed for the provision of ancillary services (ASs). For BESS owners, it is vital to assess the business value of providing ASs to engage in a profitable trade. However, studies so far have mainly focused on the operational control of BESS units in ancillary services markets (ASMs) while ignoring investigations from a business viewpoint. Therefore, in this paper, we analyse the BESS-based provision of Nordic ASs from a BESS-business perspective. To this aim, we identify the profitable bidding hours for the BESS units by investigating hourly patterns in the market prices of the past six years. We also analyse the monthly and yearly trends in historic price datasets of ASs to help BESS owners develop long-term business plans. Moreover, for the BESS owners to choose between different markets, we investigate the revenue streams associated with each ASs and determine the yearly income from their availability and capacity payments. Our findings imply that BESS business viability in the Nordic ASMs can be increased by stacking ASs based on their availability payments, energy content, and activation requirements.

Decentralized Energy Management and Voltage Regulation in Islanded DC Microgrids

Battery energy storage systems (BESSs) are essential units in islanded dc microgrids (MGs) with tangible penetration of distributed renewable energy sources. These units play a crucial role in energy management and preferably should be controlled in a decentralized manner. In this regard, the conventional droop control is a well-known technique to implement a decentralized control for load sharing in the presence of various-sized distributed generator (DG) units and variable state-of-charge (SoC) level of BESS units. This method provides plug-and-play (PnP) feature for DG units; however, load sharing accuracy is poor, and voltage is unregulated. This article proposes a novel control system for energy management in islanded dc MGs while providing regulated voltage and highly accurate load sharing simultaneously in a decentralized way. The method uses the SoC level of BESS units in a modified droop control technique. A superimposed ac voltage on the nominal dc voltage of the network is used by the control system, bearing a frequency representing the network's load state. This frequency defines the operation mode of BESS units. Conducting simulation studies using piecewise linear electrical circuit simulation (PLECS) validates the effectiveness of the proposed control system. The experimental results confirm the viability and feasibility of the proposed control system.

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.

Mitigating low fault current in microgrids through renewables-battery hybrid units

One of the major problems with inverter-based distributed generation (IBDG) compared to synchronous generators, is the little fault current contribution during fault condition in islanded mode of operation. Particularly, the increased penetration of IBDGs such as renewable energy sources (RES) into the microgrid (MG) has a substantial impact, as it would lower the fault current level; such diminished fault current induces mal-trip and fail to trip of protective relays causing problems in fault detection and isolation and consequently degrades the MG protection. To tackle this issue, this paper proposes an adaptive protection scheme that considers RES, Battery Energy Storage System (BESS), Hybrid units in the MG. The proposed scheme applies an internal cooperation strategy between RES and BESS to ensure reliable output power. Then, a power flow-based fault analysis is performed to determine the fault current paths. A fault current zone (FCZ) is formed out of the IBDGs that are found to be on the observed fault current paths and an optimal residual capacity is determined for the BESS units within FCZ; the optimization is formulated as a nonlinear program for maximizing the residual capacity of the BESS unit. Validation of the proposed scheme is carried out through simulation of the IEEE 33 bus systems. The simulation results confirm the effectiveness of the proposed protection scheme and its superiority to competing approaches in the literature.

Design of Virtual Inertia Controller for DC Microgrid Using Zero Placement Technique

With DC microgrids becoming popular, the concept of virtual inertia control (VIC) is gaining attention to minimize the DC bus voltage fluctuations caused by intermittent sources and loads. VIC scheme controls the power electronic converter (PEC) to achieve a smooth voltage profile. However, the intrinsic output impedance of the converter acts as an output disturbance and distorts the inertial response. An additional feed-forward controller has been used in previous works to mitigate this. However, the design proposed in this paper eliminates the additional controller while achieving a smooth voltage profile. This paper proposes a zero placement technique-based design of virtual inertia controller for a DC microgrid. Small-signal models for the system with the proposed controller designed using zero placement were developed to analyze the stability and the effect of parameter variation for optimal performance. Next, the application of the proposed design methodology is extended to a system employing two battery energy storage system units for analyzing parallel operation. Finally, the effectiveness of the controller designed using the zero placement technique was verified under different loading conditions through MATLAB/Simulink simulations and experiments carried out on the developed laboratory prototype.

Allocation of battery energy storage in power distribution network: Placement, sizing, and scheduling

ABSTRACT The present power distribution infrastructure faces significantly high demand in most of the time hours of the day. Thus, it needs to manage power intelligently in those high-demand hours. Optimal allocation of battery energy storage system (BESS) can foster power management in the distribution system by exchanging energy with it. However, BESS should be strategically allocated to achieve maximum power delivery efficiency with lower investment. BESS units also need to be strategically scheduled to charge in light load hours and discharged in peak demand hours. The present study proposes a strategic planning model that can minimize the cost of BESS allocation and improve the distribution network’s technical performance. The cost function is framed considering battery life, investment in BESS, and investment in the inverter. The distribution system’s performance is realized with a typical network performance index (NPI). The NPI is synthesized from voltage deviation of buses and change in total active power losses in the network. A multiobjective optimization algorithm-based solution technique is presented to comprehend the planning model’s outcome in the most suitable location, size, and battery scheduling scenario. The proposed planning approach has been tested on a 28-bus Indian power distribution network. The result of techno-economic optimization confirms a good trade-off solution with 37.5% of network performance improvement with respect to the base configuration and INR 39.06LAC of total investment. The proposed approach has been verified with the existing BESS allocation model, demonstrating its efficacy.

An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks

In developing a sustainable and efficient power systems network while reducing carbon footprint, renewable energy (RE)-based Distribution Generation (DG) units are highly recommended. Furthermore, Battery Energy Storage Systems (BESS) and other passive electronic units are adopted to improve grid performance and mitigate the effects of high variability from RE power. Hence, planning frameworks are developed to optimally allocate these units to distribution networks. However, current planning mechanisms do not consider the relative effect of different allocated units in planning frameworks. To bridge this gap, this paper presents a novel comprehensive planning framework for allocating DG units, BESS units, and Electric Vehicle Charging Station (EVCS) facilities in a distribution network while optimizing its technical, economic, and environmental benefits. The proposed framework uses a recombination technique to generate more solutions by dynamically updating the DG and BESS units’ locations in one iteration. A Reinforcement Learning (RL)-based algorithm is introduced to coordinate EV charging that suggests the optimal EVCS location in relation to other units’ locations. To cope with the complexity ensuing from searching a larger solution space, a multi-stage, hybrid optimization scheme is developed to produce optimal allocation variables. A category-based multiobjective framework is further developed to simultaneously optimize many objective functions — power loss, voltage stability, voltage deviation, installation and operation cost, and emission cost. Through numerical simulations on the IEEE 33- and 118-bus distribution network, it is shown that the proposed optimization scheme achieves higher metric values than the adopted benchmark optimization schemes. A validation process was also carried out on the proposed multiobjective optimization approach, comparing it with other approaches. Using the Spacing metric, the proposed approach proves to be efficient, depicting a good spread of Pareto optimal solutions.

Smart deployment of energy storage and renewable energy sources for improving distribution system efficacy

<abstract><p>Climate change, global warming, the depletion of fossil fuels, and rising energy demand are the main forces behind the increase in renewable energy sources. However, the unpredictability of power output from these renewable energy sources presents distribution system integration issues such as limited feeder capacity, unstable voltage, and network power loss. This study analyses the African vulture optimisation algorithm to determine the best allocation of distribution generators, with an emphasis on reducing the ageing of distribution transformers and delaying investment in feeders. The optimization technique provides faster global convergence and outperforms existing bio-inspired algorithms verified with benchmark uni-modal functions as a result of a larger crossover between the exploration and exploitation phases. The key aim is to decrease active power loss while simultaneously enhancing security margin and voltage stability. The IEEE 69-bus RDS system is utilised to validate the case studies for appropriate allocation of photovoltaic, wind turbine generation, and battery energy storage systems units, as well as offering the ideal energy management approach. During simulation, uncertainty on the characteristics of renewable energy source is accounted for. The results demonstrate the efficacy of the proposed algorithm with a substantial improvement in voltage profile, the benefit of lower CO2 emissions, an increase in security margin of up to 143%, and the advantage of extending the feeder investment deferral period by more than 50 years. In addition, the distribution transformer ageing acceleration factor improves significantly in the case of an increase in load demand.</p></abstract>

A Hybrid Cloud and Edge Control Strategy for Demand Responses Using Deep Reinforcement Learning and Transfer Learning

A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient on-demand computing resources. However, the current buildings suffer from the high cost of computing resources and lack a cost-effective automation system, which becomes the main obstacle to the popularization and implementation of the DR program. Therefore, in this paper, we present a hybrid cloud and edge control strategy for BESS and HVAC based on deep reinforcement learning (DRL). On the cloud infrastructure, the agent learns the control strategy online based on the proposed continuous dueling deep Q-learning (C-DDQN) algorithm, and the learned strategy is distributed to the edge devices for execution. Under this framework, the data-intensive application of cloud computing in real-time DR shows advantages in high processing speed, unlimited data aggregation, fault-tolerant, cost-saving, security, and confidentiality. However, if every controller is trained from the beginning, the cloud resources are wasted to a large extent. Therefore, we propose a transfer deep reinforcement learning methodology to transfer the control strategies between BESS and HVAC units. The transfer learning is realized based on fine-tuning and the proposed Evolving Domain Adaptation Network (EDAN). In case studies, it is verified that the proposed transfer deep reinforcement learning algorithm shows better convergence and learning capability compared with not applying transfer learning technologies. Compared with the conventional model-based method, the proposed methodology speeds up the decision-making time by 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> times.

Distributed energy resource allocation using multi-objective grasshopper optimization algorithm

The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.

Dynamic state estimation method for multiple battery energy storage systems with droop-based consensus control

Consensus control is a decentralized method to balance reserved energy of multiple battery energy storage systems (BESSs) in a power network. It only requires each BESS unit to communicate with its neighboring units to share a few measurements. However, disturbance in data transmission may degrade the performance of consensus control and even undermine the system stability. The objective of this paper is to propose a droop-based consensus control method for a microgrid system with multiple BESS units and an extended Kalman filter (EKF) based dynamic state estimation (DSE) framework to withstand disturbances and measurement noise. Note that a distributed dynamic state estimator is employed for each individual BESS. To validate the performance of the control mechanism and DSE framework, MATLAB/SimPowerSystems is used to develop electromagnetic transient simulation testbeds for two microgrids with multiple BESS units in both grid-connected and islanded modes. The results verify that communication disturbances, including data loss, false data injection, communication interruption, along with measurement noise can be properly handled by the proposed approach.

BESS Assisted Frequency Management for Black Start Process of Microgrid

This paper proposes a method for restoring the nominal frequency and improving the system recovery time using battery energy storage system (BESS) for an islanded microgrid (MG) which is operated by a black start unit (BSU). The frequency stability is controlled by varying the apparent power (MVA) rating of the BESS after simultaneously connecting with the BSU during the post fault scenarios. Simulations are performed on the IEEE Std. 399-1997 test MG using DIgSILENT PowerFactory. Results show that the nominal frequency of the system can be retained by connecting the BESS during the transient period and increasing the MVA rating up to a maximum value. Simulation results also show that with a higher distributed connection of the BESS units over the system, the frequency recovery time can be reduced.

Decision Support Framework for Resilience-Oriented Cost-Effective Distributed Generation Expansion in Power Systems

Resilience of electricity grids to rare but severely disruptive events, such as natural disasters, has emerged in recent years as an important aspect in power system planning. Development of resilience-oriented techniques are needed due to the limitations of reliability-oriented methods in addressing large unexpected outages. This article presents a novel decision analysis approach to enhance the resilience of a power distribution system by leveraging the distributed nature of solar photovoltaic (PV) and battery energy storage system (BESS) resources. The proposed decision-making framework uses an analytic hierarchy process to evaluate different possible allocations of PV and BESS resources to load buses for multiple contingencies based on resilience enhancement and cost. Each allocation plan provides the size of PV arrays and BESS units to be installed at each bus within the distribution system. A deterministic formulation ranks line outage scenarios based on unserved load. The tradeoff between cost and resilience is modeled via cost-effectiveness analysis (CEA) that indicates the preferability of each allocation plan. The main contribution of this article is the development of an adaptable and computationally feasible decision support framework to determine cost-effective configurations of PV arrays and BESSs for mitigating the effects of power line outage contingencies. To evaluate the proposed framework, a case study is carried out on the IEEE 33-bus radial distribution system. The results show that the proposed method can offer effective guidance to the planner for making informed investment decisions and achieving cost-effective resilience enhancement.

Loss-Voltage Sensitivity Analysis Based Battery Energy Storage Systems Allocation and Distributed Generation Capacity Upgrade

Distributed Generation (DG) has become a key component in modern power industry due its significant advantages over the traditional power generation methods. Nevertheless, best expected outcomes can only be achieved with optimal allocation of DG resources where inappropriate allocation may impose problems in power system stability, protection and quality. This paper presents analytical approaches for optimizing the DG size, BESS (Battery Energy Storage Systems) capacities and power dispatch in Medium Voltage (MV) networks. Since most of the existing analytical approaches related to optimizing DG sizes for minimizing network losses and voltage deviations have considered individual objective function separately, both parameters may not be minimized simultaneously. Thus, in this paper, an analytical methodology was formulated based on an objective function built on new parameter Loss-Voltage Sensitivity Index (LVSI) that evaluates both minimum impact of network loss and voltage variations for optimizing the DG size. The results obtained from this approach were compared with a conventional Genetic Algorithm (GA) formulated by the authors. The BESS capacities are determined considering the effects of Load Proportionality Factor (LPF), State of Charge limits (SOC) of battery storages, number of load areas and the portion of daily off-peak solar generation period energy consumption expected to be served by each BESS unit. The significance of this BESS capacity determination methodology is highlighted as the BESS capacities can be numerically calculated whereas in existing work, they are heavily relying on conventional optimization techniques which do not give an idea about the internal behavior of parameters which determine the capacities of BESS units. Moreover, an optimal BESS dispatch algorithm is also presented in this paper for minimizing the energy losses and voltage deviations. The applicability of the proposed methodologies are verified using the standard IEEE-33 and IEEE-69 test bus systems. Simulations carried out in MATLAB is used to illustrate the accuracy and the appropriateness of the proposed approaches.