Distributed Battery Energy Storage Research Articles

Distributed control of battery energy storage systems in distribution networks for voltage regulation at transmission–distribution network interconnection points

This paper describes a control framework that enables distributed battery energy storage systems (BESS) connected to distribution networks (DNs) to track voltage setpoints requested by the transmission system operator (TSO) at specific interconnection points in an optimal and coordinated manner. The control design is based on an optimisation problem whose objective is to minimise the real-time voltage-tracking mismatch while satisfying local physical network constraints. A novel agent-based control scheme adopting an online convex optimisation (OCO) framework is developed and solved in a distributed fashion to guarantee the solution’s scalability and the service provision within the required time. The BESS agents under the proposed control framework automatically adapt to the time-varying network conditions so as to track the required voltage setpoints whilst fulfilling the technical operating requirements of the local network. The designed OCO-based framework addresses the existing conflict between the accuracy and optimality of the solution and the communication and computational efficiency. The convergence to the optimal solution is demonstrated. Several case studies are performed to corroborate the analytical results and assess the performance of the proposed approach.

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.

A Novel Multi-Agent Model-Free Control for State-of-Charge Balancing Between Distributed Battery Energy Storage Systems

This article proposes a novel state of charge (SoC) balancing control strategy based on multi-agent control between distributed the battery energy storage systems (BESSs) in super-UPS. The proposed control strategy has plug and play capability. Batteries with different capacities are considered in the control system. The battery capacity degradation under long term operation is further taken into account. In addition, a model-free iPI control strategy is designed for primary current control to achieve the tracking response of BESS to the reference current. Firstly, the distributed voltage and per-unit current dynamic consensus algorithm is introduced to maintain the DC bus voltage and achieve the battery current sharing. Then, a SoC balancing algorithm based on multi-agent control strategy is proposed. Furthermore, an adaptive capacity estimation algorithm is proposed considering the battery capacity degradation. The efficiency of the proposed control strategy is demonstrated in MATLAB/Simulink.

A Coordinated Control of Offshore Wind Power and BESS to Provide Power System Flexibility

The massive integration of variable renewable energy (VRE) in modern power systems is imposing several challenges; one of them is the increased need for balancing services. Coping with the high variability of the future generation mix with incredible high shares of VER, the power system requires developing and enabling sources of flexibility. This paper proposes and demonstrates a single layer control system for coordinating the steady-state operation of battery energy storage system (BESS) and wind power plants via multi-terminal high voltage direct current (HVDC). The proposed coordinated controller is a single layer controller on the top of the power converter-based technologies. Specifically, the coordinated controller uses the capabilities of the distributed battery energy storage systems (BESS) to store electricity when a logic function is fulfilled. The proposed approach has been implemented considering a control logic based on the power flow in the DC undersea cables and coordinated to charging distributed-BESS assets. The implemented coordinated controller has been tested using numerical simulations in a modified version of the classical IEEE 14-bus test system, including tree-HVDC converter stations. A 24-h (1-min resolution) quasi-dynamic simulation was used to demonstrate the suitability of the proposed coordinated control. The controller demonstrated the capacity of fulfilling the defined control logic. Finally, the instantaneous flexibility power was calculated, demonstrating the suitability of the proposed coordinated controller to provide flexibility and decreased requirements for balancing power.

Centralized nonlinear switching control strategy for distributed energy storage systems communicating via a network with large time delays

This paper proposes a novel centralized switching controller for the state of charge balancing of battery energy storage systems distributed in a DC microgrid. The main advantage of the proposed controller is that it can tolerate non-uniform and time-varying delays in the communication links between a central processor and each battery energy storage system. The central processor generates predictive estimates of the renewable energy generation/load and state of charge of each battery energy storage. The switching controller uses the predictive estimates to generate participation rates of battery energy storage systems. The proposed control strategy is integrated with the primary droop control and the secondary voltage control for the power sharing and microgrid voltage restoration. In addition, the proposed control method guarantees that the active battery energy storage systems are either charging or discharging thus eliminating circulating currents. RTDS real-time simulator is used to verify the proposed large time delay tolerant control scheme on a DC microgrid with four distributed battery energy storage systems, loads and a PV generation. Moreover, mathematically rigorous analysis of the proposed control scheme is given.

An adaptive virtual inertia control strategy for distributed battery energy storage system in microgrids

Recently with the large-scale access of renewable energy into power system through power electronics, distributed energy systems attract more attention. However, low inertia decreases the voltage or frequency stability and anti-disturbance capability of systems, causing power quality is vulnerable to the intermittency and uncertainty in photovoltaics or wind plants. Therefore, the virtual inertia control (VIC) is proposed to maintain system stability. This paper proposes a virtual adaptive inertia control (VAIC) strategy. The states of energy storage battery packs (ESBPs) are estimated online by the dual extended Kalman filter. Then the virtual inertia and droop parameters are designed through the fuzzy logic and virtual battery algorithms based on battery states and bus voltage fluctuations, aiming at distributing inertia and power in the dynamic and steady periods respectively. The stability is analyzed using the small signal model, and its feasibility is verified on the Matlab/Simulink platform. In microgrids with multiple ESBPs, the VAIC distributes power and inertia among ESBPs considering their different capabilities of power supplying and the system requirement for inertia. It can suppress the voltage fluctuations, improve the system stability, and achieve the decentralized and coordinated control during the whole running process of microgrid.

Sizing domestic batteries for load smoothing and peak shaving based on real-world demand data

Distributed battery energy storage provides a potential system-wide solution to issues of increasing variability in electricity supply and demand. In this research, we take a demand-driven approach to determining residential battery capacity based on a detailed analysis of measured time series (with per-minute resolution) of individual household demand. We consider two modes of battery operation: (i) load smoothing around the average and (ii) peak shaving, where the battery ensures grid power demand does not exceed a set threshold. We determine the battery capacity (in kWh) required for each mode of operation based on an individual household’s demand patterns—independent of specific battery characteristics. In addition, we also compare the battery capacity required for the individual houses with that required for the aggregated demand of a collection of households. Our results show that the battery capacity requirements for these modes of operation can vary by more than 10 kWh per house and have a large seasonal variation. They also show that aggregation reduces the per-house battery requirements by 50% for load smoothing and 90% for peak shaving. These results have important implications for battery deployment strategies. In particular, they show that coordinated battery deployment at the street or building complex level is likely to have significant economic benefits.

Distributed battery energy storage systems operation framework for grid power levelling in the distribution networks

Distributed battery energy storage systems operation framework for grid power levelling in the distribution networks

Aggregated impact of coordinated commercial-scale battery energy storage systems on network peak demand, and financial outcomes

The penetration of distributed battery energy storage systems in electricity grids globally is forecast to grow rapidly over the next 10 years. These systems can provide benefits for the customers that install them, but also introduce new opportunities and challenges for distribution network operators. This paper presents an algorithm that optimises the sizing and operation of a cluster of battery energy storage systems in commercial buildings. It aims to either optimise the battery energy storage system independently to maximise the financial benefits for each building or coordinate them to maximise the value of reducing demand peaks for both the customer and the local zone substation. To achieve the latter, the algorithm assumes a certain percentage of the network deferment value associated with reducing the peak demand for the zone substation is passed through to customers. Scenario analysis show that the peak demand reduction and financial outcomes for both the building owners and the network result from a complex interaction between the customer and network load profiles, the tariff structure, the size and cost of the BESS, and the percentage of the network deferment value that is passed through.

An adaptive droop control for distributed battery energy storage systems in microgrids with DAB converters

With the progress of renewable energy technologies, distributed energy system (DES) has become attractive due to its flexibility and interaction with power systems. Battery energy storage system (BESS) is an indispensable part of DESs, the control strategies of which have a great influence on system performance. In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries. The state and model parameters of energy storage batteries are estimated simultaneously online by dual extended Kalman filter (DEKF) algorithm. Then the droop controller is designed based on the battery parameters by the “virtual battery” algorithm, benefit from which power can be distributed among battery packs adaptively for their power characteristics, meanwhile SOC balancing can be achieved automatically. The proposed control strategy is validated in MATLAB/Simulink environment with a direct-current (DC) microgrid model where battery packs are connected to the DC bus through dual active bridge (DAB) converters. Compared with conventional droop controls, the proposed ADC method considers the battery characteristics and battery aging, and it is more suitable for DES with multiple sets of batteries.

Distributed Voltage Regulation for Low-Voltage and High-PV-Penetration Networks With Battery Energy Storage Systems Subject to Communication Delay

The increasing penetration level of photovoltaic (PV) systems in low-voltage networks causes voltage regulation issues. This brief proposes a new voltage regulation strategy utilizing distributed battery energy storage systems (BESSs) while incorporating the inevitable communication delays. The proposed strategy ensures that the voltage regulation burden is appropriately shared among all involved BESSs in the network. The effects of communication delays on the performance of the proposed strategy are analyzed and an admissible delay bound is established. Case studies are conducted to verify the effectiveness of the proposed strategy.

A Fully Filter-Based Decentralized Control With State of Charge Balancing Strategy for Battery Energy Storage Systems in Autonomous DC Microgrid Applications

State of charge (SoC) imbalance and dc bus voltage deviations are significant issues for distributed battery energy storage systems in autonomous dc microgrid applications. Accordingly, a high-pass filter (HPF) based SoC balancing method is proposed to achieve SoC balance by considering different SoCs and capacities; A band-pass filter (BPF) based power droop control is used to accomplish power-sharing and voltage compensation. Through this approach, battery storage units (BSUs) with higher SoC and capacity deliver more power during discharging mode than those with lower SoC and capacity. During charging mode, those BSUs with higher SoC and lower capacity are controlled to absorb less power than those with higher capacity and lower SoC. Therefore, SoC balance can be achieved between distributed BSUs. Further, the dc bus voltage is maintained within the desired range by adopting the proposed method. In addition, the control method employed also considers the impact of line resistance. The proposed control strategy is implemented in a fully decentralized way which does not require any communication link, while maintaining system stability. MATLAB/Simulink and processor-in-the loop (PIL) simulation results verify that the proposed control strategy is effective and feasible.

Battery Energy Storage System Integration and Monitoring Method Based on 5G and Cloud Technology

The large-scale battery energy storage scatted accessing to distribution power grid is difficult to manage, which is difficult to make full use of its fast response ability in peak shaving and frequency modulation. With the rapid development of 5G and cloud technology, it is possible to realize interconnection of distributed battery energy storage system (BESS), cloud integration of energy storage system (ESS) and data edge computing. In this paper, a BESS integration and monitoring method based on 5G and cloud technology is proposed, containing the system overall architecture, 5G key technology points, system margin calculation. Therefore, rapid, accurate and flexible control of BESS can be realized, which make more use of BESS in peak shaving and shifting, new energy consumption, electric power bidding platform and other fields.

Distributed Battery Energy Storage Co-Operation for Renewable Energy Sources Integration

This paper presents a multiagent system (MAS) day-ahead co-operation framework between renewable energy resources (RESs) and Battery Energy Storage Systems (BESSs) owned by different stakeholders. BESSs offer their storage services to RESs by shifting RES power to sell it during profitable peak-hours (aka; time-shifting). The MAS framework consists of three phases. Phase-one is a pre-auction phase that defines the maximum charging and discharging BESS power limits. These limits guarantee a reliable distribution system operation without violating the buses’ voltage limits or the ampacity of the branches. Phase-two is an auctioning phase between the BESS-agents and the RES-agents. Each agent has a different owner with a specific profit agenda and risk levels. The agent tries to maximize the profit potential of the owner. The agents use historical trade data and expected weather conditions to maximize profitability. Phase-three is called the post-auctioning phase, in which the agreement between the BESS- and RES-agents is finalized, and the agents are ready for another 3-phases trade. Case studies compare different auctioning strategies and prove the effectiveness of the proposed MAS system.

A Case for Using Distributed Energy Storage for Load Balancing and Power Loss Minimization in Distribution Networks

—We introduce an algorithm to solve the problem of load balancing and loss minimization in distribution networks impacted by temporary service restoration activities. The novelty of the proposed algorithm lies in employing utility directed usage of customer distributed battery energy storage systems, which are assumed to be present and available in the network. With increasing penetration of distributed renewable energy sources, such as photovoltaics and wind turbines, it is projected that batteries will also increasingly be adopted to address some of the new challenges with renewables, such as the so-called duck curve challenge. The deployment of the proposed solution is achieved through demand response signals. To verify its benefits, we develop a co-simulation framework which can be used to develop and study distribution level optimization techniques that exploit the interaction between a smart electric grid, smart buildings and distributed energy storage to achieve energy and cost savings and better energy management practices beyond what one can achieve through techniques applied at the building or network levels only. The proposed algorithm is implemented and verified within the co-simulation framework tool, SmartBuilds. Simulations show that energy storage systems can be used for temporary relief of distribution networks impacted by line failures.

Siting and Sizing of Energy Storage Systems: Towards a Unified Approach for Transmission and Distribution System Operators for Reserve Provision and Grid Support

This paper presents a method to determine the optimal location, energy capacity, and power rating of distributed battery energy storage systems at multiple voltage levels to accomplish grid control and reserve provision. We model operational scenarios at a one-hour resolution, where deviations of stochastic loads and renewable generation (modeled through scenarios) from a day-ahead unit commitment and violations of grid constraints are compensated by either dispatchable power plants (conventional reserves) or injections from battery energy storage systems. By plugging-in costs of conventional reserves and capital costs of converter power ratings and energy storage capacity, the model is able to derive requirements for storage deployment that achieve the technical-economical optimum of the problem. The method leverages an efficient linearized formulation of the grid constraints of both the HV (High Voltage) and MV (Medium Voltage) grids while still retaining fundamental modeling aspects of the power system (such as transmission losses, effect of reactive power, OLTC at the MV/HV interface, unideal efficiency of battery energy storage systems) and models of conventional generator. A proof-of-concept by simulations is provided with the IEEE 9-bus system coupled with the CIGRE’ benchmark system for MV grids, realistic costs of power reserves, active power rating and energy capacity of batteries, and load and renewable generation profile from real measurements.

Manage Distributed Energy Storage Charging and Discharging Strategy: Models and Algorithms

The stable, efficient and low-cost operation of the grid is the basis for the economic development. The amount of power generation and power consumption must be balanced in real time. Traditionally the grid needs to quickly detect the electrical load of users in real time and adjust the power generation to maintain the balance between electrical supply and demand, which brings great cost from the output modulation of the generator set. This article focuses on the distributed battery energy storage systems (BESSs) and the power dispatch between the generators and distributed BESSs to supply electricity and reduce electrical supply costs. The cost analysis of electrical supply from the generators and BESSs is proposed. Then, this article introduces a consensus control algorithm (CCA) to dispatch the power output and track the load in a decentralized manner. A nonuniform CCA (NCCA) is proposed to improve the convergence speed especially when the power of BESSs reach upper/lower bounds. Finally, the case study implemented in a 5-bus system with the real load data of a district in Beijing verifies the effectiveness and efficiency of the proposed method.

Planning of distribution networks islanded operation: from simulation to live demonstration

The integration of distributed Battery Energy Storage Systems (BESS) at the Medium Voltage (MV) and Low Voltage (LV) networks increases the distribution grid flexibility to deal with high penetration of Renewable Energy Sources (RES). In addition, it also enables the deployment of key self-healing functionalities, which allow the islanded operation of small sections of the distribution network. However, new planning and real-time operation strategies are required to allow the BESS coordinated control, as well as a cost-effective and stable operation. This paper presents new tools developed for the planning and real-time operation of distribution networks integrating BESS, particularly when operating islanding. For real-time operation, a short-term emergency operation-planning tool assesses the feasibility of islanded operation of a small section of the distribution network. The long-term impact of a BESS control strategy for islanded operation is assessed through a Life Cycle Analysis (LCA) tool. The results and implementation experience in real distribution network are also discussed.

Robust Control Scheme for Distributed Battery Energy Storage Systems in Load Frequency Control

This paper proposes a robust control scheme to involve the distributed Battery Energy Storage Systems (BESSs) in Load Frequency Control (LFC) through BESS aggregators with sparse communication networks. In order to cope with the uncertainties associated with system operation, a two-layer Model Predictive Control (MPC) is developed so that more efficient control signals are provided to improve the response of BESSs to make larger contribution to the LFC. The outer layer in the proposed structure produces the command signal for the aggregator based on signals which are produced by the inner layer as well as the signal provided from the actual system. These command signals are provided so as to achieve the least value of error in Area Control Error (ACE) with a minimum control effort taking a variety of operational and physical constraints into consideration. Optimization procedures are also carried out to compute the optimal value of weighting coefficients contained in the objective functions. The capability of controller to cope with uncertainties is compared with a conventional single-layer MPC. In addition, the delay caused by propagation channels in delivering control signals to BESSs is modeled, and its impact on the performance of frequency regulation is evaluated. An intelligent fuzzy coordination control is then developed to coordinate the BESS aggregator and conventional power plants to avoid extra power injection/withdrawal by the conventional power plants in case of long delays. Case studies are conducted to illustrate the effectiveness of the proposed structure in controlling distributed BESSs with diverse energy capacities, rated powers, charging/discharging coefficients and time constants; and State of Charges (SoCs).