Multiple Battery Energy Storage Systems Research Articles

Deep Reinforcement Learning-Based Security-Constrained Battery Scheduling in Home Energy System

The home energy system today involves multiple renewable energy sources and battery energy storage systems, which can be considered as a microgrid. The battery energy storage system is a key component in the home energy system for the sake of filling the gap between the user demand and volatile energy supplies to maximize the techno and economic performances. However, the battery scheduling must suffer the stochastic nature of renewable energy resources and loads, which results in an intractable multi-period stochastic optimization problem with security constraints. An improved actor-critic-based reinforcement learning is proposed for this issue, where a distributional critic net is applied for faster and more accurate reward estimation under uncertainties, and a policy net incorporating protective secondary control is adopted to satisfy security constraints, preventing the unsafe state of batteries during the trial-and-error process. Numerical tests show that the proposed approach outperforms conventional reinforcement learning algorithms, as well as the rule-based battery scheduling approach while guaranteeing safe operation. The robustness and adaptability of the proposed method are also verified in case studies with different optimization tasks.

Differential Power Processing Based Control Framework for Multiple Battery Energy Storage Systems in DC Microgrids

Differential Power Processing Based Control Framework for Multiple Battery Energy Storage Systems in DC Microgrids

Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

In recent years, there has been a rapid increase in distributed generation (DG) technologies incorporated into distribution networks (DNs) to meet the challenge of load growth. However, the stochastic nature of renewable energy, such as photovoltaic (PV), makes the amount of energy produced uncertain. This uncertainty, along with changes in generation and load demand, can increase energy losses and voltage instability. To address this issue, energy storage systems can be integrated to decrease the effects of the intermittency associated with renewable technologies. This paper proposes a new variant of an equilibrium optimizer (EO) based on reinforced learning, named RLEO, for optimal incorporation of multiple battery energy storage (BES) units integrated synchronously with solar PVs into distribution systems while minimizing energy loss. The RLEO algorithm employs reinforced learning mechanisms to prevent premature convergence of the EO and improve its exploration and exploitation capabilities. The performance of the RLEO algorithm is assessed using standard CEC 2017 benchmark functions and compared with the original EO and other popular algorithms using various statistical criteria. The RLEO algorithm is also applied to determine the optimal size and position of multiple PV units in IEEE 69-bus and 118-bus DNs with single and multi-objective optimization problems. Using the developed algorithm, the optimal arrangement of three non-dispatchable PVs results in a slight increase in the percentage reduction of energy loss across various load profiles: 53.0035 % for commercial, 19.6372 % for industrial, and 28.1783 % for residential. In contrast, by employing the optimal configuration of three PV + BES units, the reduction in energy loss percentage experiences a remarkable surge to 68.3466 % for commercial, 68.0917 % for industrial, and 68.1779 % for residential load scenarios using the proposed algorithm. This clearly indicates that the proposed RLEO algorithm significantly surpasses recent optimization methods documented in the literature when it comes to addressing the challenge of optimal allocation for multiple DGs. Furthermore, its applicability extends to more intricate optimization problems.

The DC Microgrid-Based SoC Adaptive Droop Control Algorithm

In the direct current (DC) microgrid, a communication-less control algorithm to keep a stable state of the isolated DC microgrid is presented in this paper. In order to get balance of the state of charge (SoC) among multiple battery energy storage systems (BESs), a control algorithm with adaptive droop coefficient is proposed. When the SoC of BESs has a deviation, the droop coefficient will change with the SoC. The DC bus voltage is kept at a stable value within a small range of deviation using the proposed adaptive control algorithm, thus improving the voltage quality. The coordinated control strategy is significant to keep the system stable and to ensure the power balance of the DC microgrid. Finally, several representative cases are discussed and verified. The simulation results show that the DC bus voltage regulation has better stability and robustness without communication and the proposed control algorithm has a desirable performance on the balance of the SoC.

Optimal Allocation of BESS to Maximize Efficiency Under Constrained SOC in Parallel Inverter-Based Scaled-Up Slack Bus

The parallel inverter-based scaled-up slack bus includes multiple battery energy storage systems (BESSs) to support the slack operation without short of real power. The multiple BESSs require a well-designed state of the charge (SOC) balancing algorithm for maximally effective usage. Until now, however, the SOC balancing algorithms only focus on strong SOC balancing without careful consideration of BESS efficiency during its charging and discharging. This paper proposes a soft SOC balancing algorithm based on the Levenberg-Marquardt (L-M) algorithm considering BESS’s charging and discharging efficiency. The charges or discharges of the BESSs are primarily determined by their efficiencies that indirectly reflect the SOCs. As a result, the proposed method provides some margins to improve efficiency rather than tightly focusing on only the SOCs balancing between multiple BESSs (e.g., two BESSs with 0.7 and 0.4 pu of SOCs discharge rather than only one BESS with large SOC discharges when total output is 0.86 pus). The optimizations and balancing are conducted by MATLAB software. Their related converter interactions are simulated using a power system computer-aided design/electromagnetic transient including DC (PSCAD/EMTDC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {TM}}$ </tex-math></inline-formula> ), to measure data for optimization.

Hierarchical cooperative control strategy of distributed hybrid energy storage system in an island direct current microgrid

This paper presents a distributed hybrid energy storage system (HESS) for an island DC microgrid (MG) with a central superconducting magnetic energy storage (SMES) system and multiple distributed battery energy storage systems (BESS). A hierarchical cooperative control scheme is proposed to realize coordinate power sharing among distributed HESS and uninterrupted operation of SMES. The control scheme adopts filtration control to decouple imbalance power into high frequency component and low frequency component, which will be compensated by SMES and distributed BESSs. The proposed mode switch control (MSC) enables SMES to have higher transient large power regulation capability than filtration control. When the DC microgrid suffers from 400 kW power drops and power surges, the maximum DC bus voltage overshoot is 5.6 % and 5.3 % under MSC control, while the overshoot increases to 26.9 % and 8.2 % under the filtration control. Meanwhile, an autonomous state-of-charge (SOC) recovery control is designed for SMES to regulate its SOC within a set range (0.64, 0.725), which enables uninterrupted operation of SMES without performance trade off. A design scheme is proposed to make sure the desired transient power sharing dynamics and SOC recovery with negligible interactions. Additionally, the proposed distributed HESS structure and cooperative control method allows acceptable communication failure and physical faults. Cases verification conducted in PSCAD/EMTDC demonstrates the feasibility and superiority of the proposed system and control scheme against various kinds of power fluctuations.

Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems

The regulation of the grid voltage within operational limits becomes increasingly challenging as residential photovoltaic (PV) adoption rises. Therefore, this study proposes a method for the efficient planning of multiple community battery energy storage systems (BESS) in low voltage distribution systems embedded with high residential rooftop PV units. A bi-level optimization method based on a Neural Network Optimization Algorithm is developed to regulate the voltage in grid-connected solar PV. Since BESS characteristics are crucial for the reliable operation of the distribution networks, the objective of the bi-level optimization problem is to optimally place and operate BESS collectively at a distributed system level. The charging/discharging protocol of the batteries management system is obtained utilizing linear programming that minimizes the daily voltage signal. Simulations were carried out on a modified IEEE low voltage test feeder to examine the impact of PV integration and BESS installation on the voltage profile. Experimental results show the efficacy of the proposed method in enabling the utility to determine the optimal location, capacity, and number of BESS in the distribution system to keep the network voltage magnitude within acceptable bounds. In addition, results demonstrate that the network topology, load profiles, and amount of PV power highly influence the BESS characteristics.

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.

Grid-Following Mode Operation of Small-Scale Distributed Battery Energy Storages for Fast Frequency Regulation in a Mixed-Source Microgrid

As the share of power converter-based renewable energy sources (RESs) is high, a microgrid, in islanded mode, is more vulnerable to frequency instability due to (1) sudden power imbalance and (2) low inertia. One of the most common approaches to address this issue is to provide virtual inertia to the system by appropriately controlling the grid-side converter of the RESs. However, the primary frequency controller (PFC) presented in this paper focuses on the fast compensation of power imbalance without adding inertia to the system. The proposed method is based on estimating the real-time power imbalance caused by a disturbance and compensating it using multiple small-scale distributed battery energy storage systems (BESSs). The power imbalance is estimated by observing the initial rate of change of frequency (RoCoF) following a disturbance. Based on the estimated power imbalance and the rating of the BESSs, the reference power for the BESSs is determined. The BESSs are controlled in grid-following mode to compensate for the power imbalance. The performance of the proposed PFC is verified using a Typhoon real-time simulator for various scenarios and is compared with the conventional virtual synchronous generator (VSG) controller.

Branching Dueling Q-Network-Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems

This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. The developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.

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.

Reducing grid peak load through the coordinated control of battery energy storage systems located at electric vehicle charging parks

Both global climate change and the decreasing cost of lithium-ion batteries are enablers of electric vehicles as an alternative form of transportation in the private sector. However, a high electric vehicle penetration in urban distribution grids leads to challenges, such as line over loading for the grid operator. In such a case installation of grid integrated storage systems represent an alternative to conventional grid reinforcement. This paper proposes a method of coordinated control for multiple battery energy storage systems located at electrical vehicle charging parks in a distribution grid using linear optimization in conjunction with time series modeling. The objective is to reduce the peak power at the point of common coupling in existing distribution grids with a high share of electric vehicles. An open source simulation tool has been developed that aims to couple a stand alone power flow model with a model of a stand alone battery energy storage system. This combination of previously disjointed tools enables more realistic simulation of the effects of storage systems in different operating modes on the distribution grid. Further information is derived from a detailed analysis of the storage system based on six key characteristics. The case study involves three charging parks with various sizes of coupled storage systems in a test grid in order to apply the developed method. By operating these storage systems using the coordinated control strategy, the maximum peak load can be reduced by 44.9%. The rise in peak load reduction increases linearly with small storage capacities, whereas saturation behavior can be observed above 800kWh.

A Low-Voltage DC Backbone with Aggregated RES and BESS: Benefits Compared to a Traditional Low-Voltage AC System

The increasing penetration of PV into the distribution grid leads to congestion, causing detrimental power quality issues. Moreover, the multiple small photovoltaic (PV) systems and battery energy storage systems (BESSs) result in increasing conversion losses. A low-voltage DC (LVDC) backbone to interconnect these assets would decrease the conversion losses and is a promising solution for a more optimal integration of PV systems. The multiple small PV systems can be replaced by shared assets with large common PV installations and a large BESS. Sharing renewable energy and aggregation are activities that are stimulated by the European Commission and lead to a substantial benefit in terms of self-consumption index (SCI) and self-sufficiency index (SSI). In this study, the benefit of an LVDC backbone is investigated compared to using a low-voltage AC (LVAC) system. It is found that the cable losses increase by 0.9 percent points and the conversion losses decrease by 12 percent points compared to the traditional low-voltage AC (LVAC) system. The SCI increases by 2 percent points and the SSI increases by 6 percent points compared to using an LVAC system with shared meter. It is shown that an LVDC backbone is only beneficial with a PV penetration level of 65% and that the BESS can be reduced by 22% for the same SSI.

Multiobjective nested optimization framework for simultaneous integration of multiple photovoltaic and battery energy storage systems in distribution networks

The rapid growth of renewables in modern distribution networks results in the spilling of energy due to the limited hosting capacity of these networks, violation of system constraints, reduced network efficiency, and improper utilization of resources. Battery energy storage system (BESS), in spite of its high cost, a shorter life and complex control, offers a flexible solution for the problem. In this paper, a multiobjective nested optimization framework is developed for the simultaneous optimal allocation of multiple solar photovoltaics (SPVs) and BESSs in the distribution networks. The framework involves a two-layered structure; the outer layer provides tentative planning solutions to the inner layer that optimizes the desired objectives of network operations and then returns the functional values back to the outer layer. The purpose of the inner-layer is to satisfy the operational constraints of the networks and ensure the optimal utilization of BESS capacities, suggested by the outer layer, at the time of planning itself. A new BESS operating strategy is proposed for optimum utilization of BESS. The nested multiobjective optimization problem is handled by suggesting a new weighted sum approach in conjunction with a recently developed swarm intelligence-based algorithm, i.e. moth search optimization. Overall, the proposed deterministic model essentially ensures the high penetration of SPVs and the optimal utilization of BESSs to justify their installation. The optimization model is investigated on a benchmark 33-bus test distribution network. The application results highlight enhanced energy efficiency, peak load shaving, high renewable penetration, voltage profile improvement, and mitigation of reverse power flow while effectively absorbing the excess renewable power generation during light load hours.

Aggregated BESS Dynamic Models for Active Distribution Network Studies

This article proposes a transmission-system-level aggregated model of Battery Energy Storage Systems (BESSs) distributed through Active Distribution Networks (ADNs), to study the dynamic performance and services provided by these systems to power grids. ADNs comprise intelligent loads, local generation, particularly solar PV, and BESSs, which can provide different services to transmission grids, including voltage control, oscillation damping, frequency regulation, and active and reactive power injections. Proper equivalent models of the ADN components allow to evaluate the impact and integration of these networks on power grids. In this article, ADN’s measurements of the aggregated response of the BESSs at the boundary bus with the transmission system are used to develop an aggregated black-box model based on two Neural Networks (NNs), one for active power and another for reactive power, with their optimal topology obtained using a Genetic Algorithm (GA). Detailed simulations are performed, using a commercial-grade software for power system analysis, of multiple BESSs connected to a CIGRE benchmark ADN connected to a bus of the 9-bus WSCC benchmark transmission network; the test ADN is then replaced by the proposed black-box model, with aggregated models of the loads and PV generation, demonstrating that the proposed model can accurately reproduce the results obtained.

Robust Operation of Shipboard Microgrids With Multiple-Battery Energy Storage System Under Navigation Uncertainties

Energy storage system (ESS) management benefits the operation of all-electric ships (AESs) since its ability to balance the generation and demand. However, current works rarely consider the influence of navigation uncertainties, i.e., uncertain water waves and winds, and fail to address two characteristics of shipboard ESS, i.e., redundant capacity and distributed locations. In this paper, a two-stage robust optimal shipboard microgrid operation method is proposed to mitigate the pre-voyage and intra-voyage navigation uncertainties. The first stage is to regulate the onboard generation and voyage variables for addressing the worst pre-voyage navigation uncertainties and the second stage is an on-line recourse action which acts on intra-voyage uncertainty realization. To address the characteristics of shipboard ESS, the onboard batteries are separated into two groups and a multi-battery management strategy is proposed for the battery lifetime extension. An AES with 4 DGs and 4 batteries is used in the case studies. The simulation results show that the proposed method can well adapt to the navigation uncertainties and the multi-battery management can extend the battery lifetime by iteratively using different battery groups.

Location-dependent distributed control of battery energy storage systems for fast frequency response

This paper proposes a distributed strategy to control multiple battery energy storage systems (BESS) delivering fast frequency response in low-inertial power systems with high penetration of renewable energy sources. A novel distributed control algorithm is designed to optimise the operation of an arbitrary number of BESS while incorporating frequency-related constraints to meet the service requirements of fast frequency response. The proposed algorithm uses a consensus-based alternating direction method of multipliers (ADMM) method to solve the formulated optimisation problem. Since the BESS contribution to the system frequency response depends on the BESS locations in the network, a location-dependent term is incorporated into the control strategy to account for the effect of BESS locations on the frequency response. Using a multi-agent system framework the proposed control strategy can be implemented in a distributed fashion to efficiently utilise and coordinate the flexibility of multiple storage systems. Simulation studies demonstrate the effectiveness of incorporating both a location term and frequency-related constraints into the control strategy to provide a cost-effective improved frequency response as well as the suitability of the proposed solution to coordinate a large and arbitrary number of BESS.

Distributed Control of Battery Energy Storage Systems for Improved Frequency Regulation

In this paper a distributed control strategy for coordinating multiple battery energy storage systems to support frequency regulation in power systems with high penetration of renewable generation is proposed. The approach is based on an online convex optimisation framework that considers both the operating costs of storage systems and the frequency regulation requirements. The proposed framework uses a dynamic regret based on the last available information up to the current point in time of renewable generation and demand, which are uncertainty sources. The approach provides more accurate control than the currently employed prediction-based algorithms and it can be implemented in a distributed manner using a multi-agent system framework that facilitates plug-and-play functionality. The effectiveness and scalability of the proposed strategy is assessed through several case studies.

Analysis of power flow control strategies in heterogeneous battery energy storage systems

• A method for the systematic analysis of several power flow control strategies in a heterogeneous battery energy storage system is provided. • A comparison of five power flow control strategies in heterogeneous battery energy storage systems based on two distinct application-oriented scenarios is carried out. • Power flow control strategies are assessed by two target indicators, namely performance and efficiency. • A simulation model is set up and components are validated with measurements. • Performance and efficiency can be enhanced by using the appropriate power flow control strategy with respect to the individual scenario. This paper provides a method for the systematic analysis of several power flow control strategies (PFCS) in a heterogeneous multiple battery energy storage system (BESS). Due to the difficulty of comparing different PFCS in different scenarios and system configurations, in total five static and dynamic PFCS are investigated in two distinct application-oriented scenarios and are assessed by two target indicators, namely performance and efficiency. A simulation model based on a heterogeneous multiple BESS with a hierarchical control scheme is set up, the components of the model are validated and a real test bench verifies the functionality of the PFCS. Simulations in MATLAB/Simulink are carried out to analyze the performance and the efficiency of the applied PFCS in an energy- and a load-related application scenario. This approach allows a systematic comparison of PFCS in consideration of varying system parameters and different scenarios. The results suggest that depending on the objective to be achieved and the applied application scenario, the choice of the right PFCS is crucial. Thus, this paper provides a workflow to analyze the selection of proper PFCS. Furthermore, heterogeneity plays a decisive role in analyzing different PFCS, since operating limits of the BESS become more significant.

Consensus based SoC trajectory tracking control design for economic-dispatched distributed battery energy storage system.

The state-of-charge (SoC) of an energy storage system (ESS) should be kept in a certain safe range for ensuring its state-of-health (SoH) as well as higher efficiency. This procedure maximizes the power capacity of the ESSs all the times. Furthermore, economic load dispatch (ELD) is implemented to allocate power among various ESSs, with the aim of fully meeting the load demand and reducing the total operating cost. In this research article, a distributed multi-agent consensus based control algorithm is proposed for multiple battery energy storage systems (BESSs), operating in a microgrid (MG), for fulfilling several objectives, including: SoC trajectories tracking control, economic load dispatch, active and reactive power sharing control, and voltage and frequency regulation (using the leader-follower consensus approach). The proposed algorithm considers the hierarchical control structure of the BESSs and the frequency/voltage droop controllers with limited information exchange among the BESSs. It embodies both self and communication time-delays, and achieves its objectives along with offering plug-and-play capability and robustness against communication link failure. Matlab/Simulink platform is used to test and validate the performance of the proposed algorithm under load disturbances through extensive simulations carried out on a modified IEEE 57-bus system. A detailed comparative analysis of the proposed distributed control strategy is carried out with the distributed PI-based conventional control strategy for demonstrating its superior performance.