Battery Energy Storage System Units Research Articles

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.

Application of battery energy storage systems in industrial facilities

For any facility, reliability and availability of power are key. Traditional gas- or diesel-driven power generation designs for facilities rely on generated spinning reserve to achieve power system stability and availability for defined operational scenarios and expected transients. Spinning reserve is extra generating capacity that is usually introduced by running additional power generator(s). Battery energy storage systems (BESSs) as energy storage units provide for a virtual spinning reserve in a hot standby arrangement to achieve the same effect for a set period during operating scenarios and transient events. Use of BESS technology is becoming more frequent within electrical network systems, remote sites and industrial facilities on the back of improved battery technology. This lends itself to better BESS reliability, effectiveness and lower associated cost to procure and install. Many of Clough’s projects are remote and islanded where they need to be self-sufficient, generating and distributing their own power needs. BESS units are scalable energy storage systems that can be used as a part of power generation solutions for facilities installed onshore or offshore. In addition to supplementing the primary generation on a facility as static storage units, BESS units offer benefits such as reduced emissions on facilities by not burning fossil fuels to achieve spinning reserve; they also allow for power management of generation systems, store any excess power from primary generators, allow for integration of renewables, offer constructability benefits and reduced operational/maintenance costs. The commercial and environmental benefits of BESS units are key drivers in Clough’s decision to embrace their use on future projects.

Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation

In this paper, a stochastic multi-objective economic dispatch model is presented under renewable obligation policy framework. This proposed model minimises the total operating costs of generators and spinning reserves under renewable obligation while maximising renewable penetration. The intermittent nature of the wind and photovoltaic power plants is incorporated into the renewable obligation model. In order to minimise the cycling costs associated with ramping the thermal generators, the battery energy storage system units are included in the model to assist the system spinning reserves. Dynamic scenarios are created to deal with the intermittency of renewable energy sources. Due to the computational complexity of all possible scenarios, a scenario reduction method is applied to reduce the number of scenarios and solve the proposed stochastic renewable obligation model. A Pareto optimal solution is presented for the renewable obligation, and further decision making is conducted to assess the trade-offs associated with the Pareto front. To show the effectiveness of the proposed stochastic renewable obligation model, two IEEE test systems are used, i.e., the modified IEEE 30-bus and IEEE 118-bus system. In both test systems, the proposed model can attain high renewable penetration while minimising the expected operating cost. In the large IEEE 118-bus test system, the computational efficiency of the renewable obligation model is demonstrated by reducing the line constraints by 87% which minimises the computing time. A comparative study evaluates the impact of the stochastic model to the deterministic one, and it shows that the stochastic model can achieve high renewable penetration.

Analysis of power allocation strategies in the smoothing of wind farm power fluctuations considering lifetime extension of BESS units

The wind farm output power fluctuates because of the random intermittent speed of the wind. When the capacity of wind farms increases, the effect of wind power fluctuations on the power system stability becomes more considerable. Therefore, the fluctuations are mitigated by use of Battery Energy Storage System (BESS) units. The high cost of the BESS has always been one of the major challenges in the investment for the wind farms. Therefore, it is necessary to protect the lifetime of BESS units used in wind farms by using a proper power allocation for them. In this paper, a three-layer control method is proposed to reduce the required BESS in the wind farm, while satisfying the utility constraint and increasing the lifetime of the BESS units. Moreover, 9 power allocation strategies for charging/discharging process in the BESS units are investigated and the most appropriate strategy is introduced. The simulation results based on the output power data of a real 99 MW wind farm indicate that the proposed heuristic-based method is very successful in controlling the switching actions, because the maximum and the average achieved value of switching actions are equal to 2 and 1.14, respectively. In addition, the variance of the Number Of charging/discharging Cycles (NOC) of BESS units is 0.33 which reveals that the number of BESS units with switching actions higher than the average is very small. Moreover, the low average value of life-expended of all the BESS units represents the efficient performance of the proposed control method.

Optimal Asset Planning for Prosumers Considering Energy Storage and Photovoltaic (PV) Units: A Stochastic Approach

In the distribution system, customers have increasingly use renewable energy sources and battery energy storage systems (BESS), transforming traditional loads into active prosumers. Therefore, methodologies are needed to provide prosumers with tools to optimize their investments and increase business opportunities. In this paper, a stochastic mixed integer linear programming (MILP) formulation is proposed to solve for optimal sizes of prosumer assets, considering the use of a BESS and photovoltaic (PV) units. The objective is to minimize the total cost of the system, which is defined as the combination of a solar PV system investment, BESS investment, maintenance costs of assets, and the cost of electricity supplied by the grid. The developed method defines the optimal size of PV units, the power/energy capacities of the BESS, and the optimal value for initial energy stored in the BESS. Both deterministic and stochastic approaches were explored. For each approach, the proposed model was tested for three cases, providing a varying combination of the use of grid power, PV units, and BESS. The optimal values from each case were compared, showing that there is potential to achieve more economic plans for prosumers when PV and BESS technologies are taken into account.

A Multi-Timescale Two-Stage Robust Grid-Friendly Dispatch Model for Microgrid Operation

Uncertainty in renewable energy generation and load consumption is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. In this paper, a multi-timescale, two-stage robust unit commitment and economic dispatch model is proposed to optimize the microgrid operation. The first stage is a combination of day-ahead hourly and real-time sub-hourly model, which means the day-ahead dispatch result must also satisfy the real-time condition at the same time. The second stage is to verify the feasibility of the day-ahead dispatch result in worst-case condition considering high-level uncertainty in renewable energy dispatch and load consumptions. In the proposed model, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system. The BESS plays an essential role to balance the variable output of solar PV units, which keeps the combined solar-storage system output unchanged on an hourly basis. In this way, it largely neutralizes the impact of solar uncertainty and makes the microgrid operation grid friendly. Furthermore, in order to enhance the flexibility and resilience of the microgrid, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controlled hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes.

An Improved Droop Control for Balancing State of Charge of Battery Energy Storage Systems in AC Microgrid

In order to avoid overuse of a certain battery energy storage system (BESS) and prolong the cycle life of battery in AC microgrid, an improved SoC-based droop control based on multi-agent system (MAS) is proposed for achieving State of Charge (SoC) balance of multiple BESS units. A proportional-integral (PI) adjustment item using the average SoC is added to the P -f droop to regulate the charging and discharging power of BESS, then SoC changes towards in the direction of consistency and finally reaches equalization. The dynamic average consensus algorithm is utilized to obtain the average SoC. The proposed SoC-based droop method needs not to be changed in different operating mode of BESS and can achieve SoC balance regardless of whether the capacities of different batteries are the same, which improve the applicability of this method. A complete small-signal state space model including all BESS units is built and analyzed to select the appropriate control parameters of the improved droop. The impact of communication time delay on the improved droop method for balancing SoC is investigated and the effectiveness of the proposed strategy is verified through the simulation results of different case studies.

Flexible AC Power Flow Control in Distribution Systems by Coordinated Control of Distributed Solar-PV and Battery Energy Storage Units

The flexible AC transmission systems (FACTS) have been used for controlling power flows in the transmission corridors to improve both the steady and dynamic states of operation. This paper proposes to extend the FACTS concept to power distribution systems, where the focus is in controlling renewable energy resources (RERs) and battery energy storage systems (BESSs) to provide a flexible AC power flow control in the distribution corridors of a medium voltage (MV) distribution system. The outputs of the proposed flexible AC power flow control system (FACPFCS) can be used to help the distribution network operator (DNO) to optimize the use of RERs and BESSs in the existing distribution grid to obtain the following benefits: congestion relief, sag reduction in long lines, mitigation of reverse power flow in the grid, deferral of distribution upgrade, Volt/VAr support, peak shaving, and a controllable storage bus that can act as a generator or load. To validate the proposed FACPFCS, simulation studies have been carried out using a practical radial distribution system topology derived from the electricity networks of New South Wales, Australia. The simulation results demonstrate that FACPFCS can provide an effective coordinated control of BESS and solar-PV units in the distribution systems to provide the mentioned benefits by controlling the power flows in the distribution lines.

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities.

Distributed Robust Control Strategy of Grid-Connected Inverters for Energy Storage Systems’ State-of-Charge Balancing

Battery energy storage system (BESS) plays an important role in enhancing system flexibility, stability, and reliability of the power grid. This paper proposes a fully distributed two-level control strategy of the grid-connected inverters for BESSs. The upper-level control determines the charging/discharging power references for the BESS units according to the state of charge levels and the supply demand balance among intermittent distributed generations and time-varying load demands. The lower-level control adopts the proposed discrete robust control algorithm which enables the BESS to track the power references accurately. The uncertainty and disturbance are estimated by the disturbance estimation technique and the chattering problem is alleviated by introducing a saturation function. The convergence and stability of the proposed distributed control strategy are guaranteed through rigorous analysis and the effectiveness is validated by simulation results.

Power Hardware-in-the-Loop Simulation Study on Frequency Regulation Through Direct Load Control of Thermal and Electrical Energy Storage Resources

Variable-speed heat pumps (VSHPs) and battery energy storage systems (BESSs) have been considered as promising energy storage resources (ESRs) that can be used for grid frequency regulation (GFR) through direct load control (DLC). In this paper, a power hardware-in-the-loop (PHIL) simulation setup has been developed to analyze the effects of real DLC-enabled VSHP and BESS units (not simulation models) on real-time GFR. Using the PHIL application, the dynamic response characteristics of the complicated hardware units have been comprehensively incorporated into the GFR analysis. In addition, a new real-time GFR scheme has been proposed to reduce the frequency deviation and the required reserve capacity of generators in a laboratory-scale islanded microgrid. In the proposed GFR method, the fast-response BESS compensates for the high-frequency components of load demand variations, while the rotary VSHP and the conventional generators are controlled to mitigate the low-frequency variations. PHIL simulation studies have been performed to compare the conventional and proposed GFR schemes under various conditions. The simulation results show that the VSHP and BESS can be effectively exploited as DLC-enabled ESRs to improve frequency stability.

A Feeder Investment Model for Distribution System Planning Including Battery Energy Storage

The distribution system asset (DSA) planning process for a new or existing subdivision, which entails new loads and/or distributed generation, involves a comparison of a variety of plans followed by the selection of the least costly one. The costs comprise the cost of new assets (feeders and transformers), their replacements, operation and maintenance costs, as well as customer interruption costs. For a fair comparison, all costs reflect the current year and are annualized over a specific study period. The optimal plan must be capable of addressing the connection of new loads and/or generation while satisfying performance standards at the lowest annualized cost. Current DSA planning algorithms, however, fail to take battery energy storage systems (BESSs) into account, hence impeding their inclusion as a solution in DSA plans. This paper proposes a new DSA planning algorithm that includes the installation and optimal sizing of BESS units in addition to traditional assets, such as feeders and transformers. It incorporates plan and asset lifetimes as a means of establishing the minimum total annualized costs of new and replacement assets, operation and maintenance, and customer interruptions. The algorithm is successfully validated on a 3-bus system and a 33-bus distribution system.

A Proactive and Survivability-Constrained Operation Strategy for Enhancing Resilience of Microgrids Using Energy Storage System

A resilience-constrained operation strategy is proposed in this paper using a battery energy storage system (BESS) as a resilience resource. A proactive operation scheme is proposed for the pre-disturbance phase by formulating resiliency cuts for the state of charge (SoC) of BESS units. These resiliency cuts can assure the survivability of critical loads for n intervals after the occurrence of an event. A resilience constraint index is formulated to access the feasibility of the operation model for each selected value of n. In the emergency mode, different possible scenarios are analyzed and a unified survivability enhancement strategy is proposed, which comprises of dynamic penalty costs and target SoC. Dynamic penalty costs can potentially avoid the unnecessary load shedding by shifting it toward the possible end of the scheduling horizon. Similarly, the inclusion of target SoC for the last interval of the scheduling horizon can mitigate the load shedding of critical loads on the following day. A two-step adaptive robust optimization scheme is adopted to incorporate the prevailing uncertainties in loads and renewables in the operation model. Finally, the impact of various decision parameters, involved in the problem formulation, is analyzed with reference to the proposed resilience-constrained operation strategy for hybrid microgrids.

Optimal Asset Expansion in Distribution Networks Considering Battery Nonlinear Characteristics Expansion optimale des actifs dans les réseaux de distribution en tenant compte des caractéristiques non linéaires des batteries

Asset expansion planning in distribution systems is vital and should be extended to consider utility-scale energy storage systems such as batteries. Unlike other assets, usage parameters such as number of storage cycles and depth of discharge (DOD) have a dramatic nonlinear effect on the lifetime of battery energy storage systems (BESSs). Hence, it is imperative to include the relationship between lifetime, number of storage cycles, and DOD of BESS in the optimal asset planning formulation. This paper presents a new formulation and solution for the optimization problem of asset expansion planning in power distribution systems. The research considers adding new BESS units to existing distribution grids. The nonlinear life-cycling-usage relationship of BESS has been modeled for the first time in the context of asset expansion planning in power systems. The formulation aims at minimizing the annualized cost of the expansion plan while satisfying forecasted demand and other distribution system requirements. The methodology is used to optimally plan for the expansion of 6-bus and 33-bus distribution networks. The results show the effect of considering the life-cycling-usage relationship of BESS on optimal asset expansion plans including the optimal size and capacity of the assets. In addition, the impact of the ratio of off-peak load to peak load on total asset cost is analyzed and reported. It is shown that an annual cost saving of 51.79% is possible via the proposed approach. Findings of this paper will capture the attention of planning and asset management departments of electric distribution utilities.

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

Fuzzy Logic-Based Operation of Battery Energy Storage Systems (BESSs) for Enhancing the Resiliency of Hybrid Microgrids

The resiliency of power systems can be enhanced during emergency situations by using microgrids, due to their capability to supply local loads. However, precise prediction of disturbance events is very difficult rather the occurrence probability can be expressed as, high, medium, or low, etc. Therefore, a fuzzy logic-based battery energy storage system (BESS) operation controller is proposed in this study. In addition to BESS state-of-charge and market price signals, event occurrence probability is taken as crisp input for the BESS operation controller. After assessing the membership levels of all the three inputs, BESS operation controller decides the operation mode (subservient or resilient) of BESS units. In subservient mode, BESS is fully controlled by an energy management system (EMS) while in the case of resilient mode, the EMS follows the commands of the BESS operation controller for scheduling BESS units. Therefore, the proposed hybrid microgrid model can operate in normal, resilient, and emergency modes with the respective objective functions and scheduling horizons. Due to the consideration of resilient mode, load curtailment can be reduced during emergency operation periods. Numerical simulations have demonstrated the effectiveness of the proposed strategy for enhancing the resiliency of hybrid microgrids.

Mitigation of windfarm power fluctuation by adaptive linear neuron‐based power tracking method with flexible learning rate

Most wind turbine generators installed in large windfarms are of variable speed types operating at the maximum power point tracking mode to generate the maximum amount of power. Owing to this fact and regarding the random nature of the windspeed, the output power of the windfarm fluctuates. Fluctuating power is a serious problem for high capacity power plants and should be smoothed. As an effective factor on the required battery energy storage system (BESS) capacity value, tracking is the most important part performed by a coordinated control system in the power smoothing process. An ADAptive LInear NEuron (ADALINE)-based power tracking method with a flexible learning rate is proposed in this study. Furthermore, a particle swarm optimisation-based calculation of the learning rate is presented for optimising the proposed tracking method which reduces the required BESS capacity and the investment cost. Moreover, a charging/discharging algorithm for the BESS units is proposed which decreases the number of required BESS units and increases their useful life by reducing the switching activity as well. To evaluate the performance of the proposed coordinated control approach, the real output data of a 99 MW windfarm are tested. The simulation results verify the effectiveness of the proposed approach.