Distributed Energy Storage Units Research Articles

A Fast State-of-Charge (SOC) Balancing and Current Sharing Control Strategy for Distributed Energy Storage Units in a DC Microgrid

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios.

Energy balancing strategy for the multi-storage islanded DC microgrid based on hierarchical cooperative control

To simultaneously solve the problems of the state-of-charge (SOC) equalization and accurate current distribution among distributed energy storage units (DESUs) with different capacities in isolated DC microgrids, a multi-storage DC microgrid energy equalization strategy based on the hierarchical cooperative control is proposed. In the primary control layer, the link between the droop coefficient and SOC is established through a logarithmic function, and the droop coefficient is adaptively adjusted according to the SOC value in order to achieve fast SOC equalization. In the secondary control layer, by designing a coordinated state factor, only one PI controller is required to eliminate the influence of the line impedance on the accurate distribution of the output current and the DC bus voltage drop. In the communication layer, local nodes only need to communicate with neighboring nodes without the need for a central controller, and the dynamic consistency algorithm is used to obtain the average value information about the energy storage system (ESS). Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental analysis on the DC microgrid hardware-in-the-loop experimental platform.

Flexible allocation and optimal configuration of multi-level energy exploitation units for heterogeneous energy systems considering resource distribution

The energy systems are evolving into heterogeneous energy systems (HESs) with complicated integration of sources, networks, loads, and storage towards a decarbonized future of human beings. This paper develops the flexible allocation and optimal configuration of multi-level energy exploitation units (MEEUs) for the HES considering resource distribution including local energy conditions and actual coupled relationship of various energy networks. A bi-level optimal planning model of MEEUs for the HES considering initial investment cost, carbon emission cost, operation cost and line loss is developed to figure out the optimal location, structure, configuration and corresponding operation strategy. To address the non-convex and non-linear model, a novel hybrid solution method considering the mixing characteristics of mixed-integer second-order cone programming (MISOCP) model and quadratic non-convex model is proposed. The solution method adopts MISOCP model for figuring out the optimal location, structure and configuration of MEEUs and quadratic non-convex model for optimal operation strategy of MEEUs, which not only improves the computational efficiency but also ensures the solution accuracy. Finally, the related case studies are conducted to compare the proposed MEEU and distributed energy storage unit (DESU) in terms of energy consumption, renewable energy absorbency, carbon emission and line loss. The results verify the significant improvement of the comprehensive energy efficiency, the investment potentiality and the environmental benefits obtained by the proposed planning model.

Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

A Resilient Control for Distributed Energy Storage Units in an Islanded AC Microgrid

A high number of charge/discharge cycles results in premature aging of Distributed Energy Storage Units (DESUs). Since classical droop control does not consider this issue and leads to MG voltage and frequency fluctuations in steady state, controls such as State of Charge (SoC) synchronization control are used to minimize DESUs number of charge/discharge cycles. However, few methods in the literature investigate frequency and voltage (f & V) restoration while ensuring a functional SoC equalization control in both charge and discharge modes. In this paper, an Adaptive Frequency Droop based on Virtual Power (AFDVP) method is proposed to achieve DESUs SoC equalization to prolong their lifespan while ensuring MG voltage and frequency regulation. In addition, AFDVP strategy ensures optimal use of second life batteries, since they are naturally less solicited due to the decrease in their capacity. DESUs SoC equalization is achieved thanks to virtual active powers using a PI controller. A complete stability analysis is provided to choose optimal parameters for the proposed distributed control. Experimental tests are performed to check the performance of the AFDVP technique in several study cases. A comparison is also performed with a SoC equalization strategy from the literature.

Fast State-of-Charge-Balancing Strategy for Distributed Energy Storage Units Interfacing with DC–DC Boost Converters

State-of-charge balance is vital for allowing multiple energy storage units (ESUs) to make the most of stored energy and ensure safe operation. Concerning scenarios wherein boost converters are used as the interfaces between ESUs and loads, this paper proposes a balancing strategy for realizing consistent state-of-charge (SoC) levels and equal currents among different ESUs. This strategy is valid for both parallel and series applications. Its advantages also include its high precision of SoC equalization without extra sensors and fast convergence. A common outer voltage loop is used to accomplish tight voltage regulation, while multiple inner current loops are utilized to achieve current control and SoC balance simultaneously. Firstly, by introducing SoC-based current distribution ratios (CDRs) to modify current references online, the currents are gradually adjusted to eliminate SoC deviations. Secondly, to expedite the balancing process, current saturations are further adopted. Thirdly, the influences of accelerating factor and current limits in CDR expressions are analyzed, and their selection guidelines are subsequently provided. Fourthly, the controller design, consisting of a dual loop, is illustrated to guarantee sufficient stability margins. Fifthly, an experimental platform consisting of three battery ESUs is developed to verify the proposed strategy.

SOC Balance Control Strategy for Distributed Energy Storage System in DC Microgrid

By integrating a distributed energy storage system (ESS), a standalone DC microgrid can maintain power balance and voltage stability between distributed energy sources and loads. However, without establishing the relationship between the output power of the distributed energy storage units and their state of charge (SOC), inconsistent SOC among the storage units can occur. This inconsistency can lead to issues such as overcharging and over-discharging, significantly impacting the ESS lifespan. To address this, the paper proposes a SOC droop control scheme utilizing the arctangent function to rapidly achieve SOC balancing of the energy storage units. During the SOC balancing process, as the SOC difference between units decreases, the balancing speed also decreases. To overcome this, a variable acceleration factor is introduced in the arctangent function SOC droop control strategy. This can ensure a progressive increment in the acceleration factor as the SOC gap between energy storage units diminishes. In addition, the integration of DC bus voltage compensation control effectively mitigates voltage deviations within the bus. Finally, the paper presents and simulates a variable acceleration factor-based arctangent function SOC droop control. It demonstrates its effectiveness in achieving SOC balance and maintaining stable bus voltage during charging and discharging scenarios in distributed energy storage systems.

Research on Optimal Energy Storage Strategy Based on Consistency Algorithm

With the acceleration of globalization and economic development, the microgrid combining renewable energy generation, storage and load has become the unit controlling the entire system, through the autonomous microgrid cluster to more efficient, reliable and economical use of renewable energy to meet the multiple needs of the power system. This paper proposes a scheme to optimize energy storage strategy by using consistency algorithm. By improving sag control, optimizing energy storage unit design and adjusting SOC balance between the same/different capacities, the efficiency and stability of DC microgrid can be improved. The scheme can achieve SOC balance and voltage regulation, avoid voltage quality degradation, and improve the reliability and practicability of distributed energy storage system. In the simulation experiment, no matter the capacity of Distributed Energy Storage Unit(DESU) is the same or different, the energy storage state can be balanced and the output active power ratio can be maintained, which further verifies the feasibility and advantages of the scheme.

Research on the operation state prediction of energy storage unit based on neural network

As a device that can conduct energy storage and release, the energy storage system can accelerate the utilization of new energy and enhance the operation stability of the grid. The main significance of the time series prediction is to analyze the future change trend of the data based on historical data. To more exactly predict the future change trend of the operating state parameters of the energy storage unit, the charge and discharge current data and operating voltage data of the energy storage unit are analyzed and studied. The long short-term memory is used to predict the operating voltage and charge and discharge current of the distributed energy storage unit.

MILP Model for Optimal Day-Ahead PDS Scheduling Considering TSO-DSO Interconnection Power Flow Commitment Under Uncertainty

We propose a comprehensive framework for the optimal day-ahead scheduling of power distribution systems (PDS), based on a mixed-integer linear programming (MILP) model. The interaction between transmission system operator (TSO) and distribution system operator (DSO) is considered, where TSO informs DSO of the day-ahead forecast concerning the expected power flow (PF) commitment at the TSO-DSO interconnection points and the violation costs. We propose a MILP model to determine the optimal voltage and power settings of distributed energy resources (DERs), such as dispatchable distributed generators and energy storage units and optimal adjustments of capacitor banks controlled by current. The objective function includes the minimization of energy losses, voltage violations, power curtailment of DERs using volt-watt strategy, and violation of TSO-DSO interconnection PF commitment. Furthermore, we propose a method to estimate the degree of uncertainty in the PF commitment. Therefore, the proposed method can help achieve an optimal operation of the distribution system; in addition, the TSO can best model uncertainty at TSO-DSO interface points and thereby procure reduced amounts of resources to address these uncertainties. Numerical results obtained for a system based on real data highlight the several potential applications of the proposed framework.

Exploring the profitability of using electric bus fleets for transport and power grid services

Electric buses (E-buses) are increasingly replacing internal combustion powered bus fleets. They can further function as distributed energy storage units. We investigate the feasibility of the novel “Bus-to-Grid” (B2G) or “E-Buses as Power Storage” (EBaPS) concept, which allows battery E-buses to provide transportation as well as power-grid services. By aggregating the electrified bus fleets into Virtual Power Plants and discharging the energy to the grid, a bus operator can provide grid services and open up a secondary revenue source in addition to fareboxes. We discuss two contracts the bus operator can arrange with the grid. The first one is “Sale by Market Price (SbMP)”, presuming that the buses can discharge and sell the energy at real-time market prices. The second one is “Frequency Control Reserve (FCR)”, in which the buses discharge on immediate requests of the grid. We formulate the two contracts and their associated fleet allocation problems as two non-linear optimization problems maximizing the operator’s profit. Optimal bus charging, discharging, and dispatching plans are solved on an aggregate level for strategic planning, allowing scalability to large fleet sizes. The profitability of the contracts is illustrated based on the schedule and demand of a real bus line and observed time-of-day dependent price variations of the energy market.

Accurate Power Allocation of Multienergy Storage Island DC Microgrid Based on Virtual Power Rating

For isolated island dc microgrid connected with multidistributed energy storage, the initial state of charge (SOC) of energy storage is inconsistent and the power distribution of distributed energy storage unit (DESU) may be affected by the mismatched line impedance. Therefore, an adaptive droop coefficient control method based on virtual power rating is designed, which can quickly equalize SOC and eliminate the influence of mismatched line impedance. In the proposed control strategy, the arctangent function is introduced to combine the droop coefficient with the SOC of the energy storage unit, and the characteristic of the arctangent function is used to ensure the fast SOC equilibrium and limit the adaptive adjustment range of the droop coefficient. Considering the actual operating point together with the virtual operating point of the energy storage unit, the virtual power rating is used to construct the compensation term, which can automatically eliminate the output power deviation according to the line impedance difference of the DESU. In addition, the stability analysis is given theoretical support for the proposed control strategy. Finally, the reliability and effectiveness of the proposed strategy are verified by the RTLAB experiment.

A Stochastic Approach to Integrating Electrical Thermal Storage in Distributed Demand Response for Nordic Communities With Wind Power Generation

Demand response and distributed energy storage play a crucial role in improving the efficiency and reliability of electric grids. This paper describes a strategy for optimally integrating distributed energy storage units within a forward market to address space heating demand under a Stackelberg game in isolated microgrids. The proposed strategy performs distributed management in an offline fashion through proximal decomposition methods. It leverages stochastic programming to consider user flexibility degree and wind power generation uncertainties. Also, flexibility for demand response is realized through electric thermal storage (ETS). The performance of the proposed strategy is evaluated via simulation studies carried out through a case study in Kuujjuaq. Ten residential agents compose the demand side, each with flexibility levels and economic preferences. Economic benefits are evaluated on both sides across different ETS acceptance levels. The simulation results demonstrate significant benefits for the coordinator and customers. The proposed strategy reveals that ETS, in the presence of dynamic tariffs, reduces diesel consumption, maximizes renewable production and reduces grid stress.

Simulation of Power Router-Based DC Distribution Systems with Distributed Generation and Energy Storage Units

The development of the electric power industry needs to be understood against the current backdrop of the transition to technological platforms facilitating the adoption of smart grids. Smart grids can be made up of separate clusters (microgrids) consisting of power consumers, power grids, and distributed generation (DG) units. To improve energy efficiency, DC microgrids can be integrated into smart grids to deliver power to consumers within a building (or several buildings) and at the sites of C&I facilities. It is advisable to carry out integrations of DC and AC microgrids with DG and energy storage units on the basis of power routers used to couple grids of different voltage classes. This study outlines a computer model of power router-based integration of DC and AC microgrids with distributed generation and energy storage units. The model was developed in the MATLAB environment. The paper also features the results of a study of the proposed methods as applied to voltage control under normal and emergency operating conditions of a DC and AC distribution grid.

Decentralized optimal management of a large-scale EV fleet: Optimality and computational complexity comparison between an adaptive MAS and MILP

Increasing the penetration of variable and uncertain renewables and electric vehicles in power systems may give rise to problems (such as network congestion and commitment mismatches) if not controlled strategically. This demands control solutions in the form of energy management strategies for active distribution networks which would control the connected distributed energy resources and storage units in real-time to address the mentioned challenges. Centralized strategies may fail to serve this purpose for large-scale distribution networks due to their inherent shortcomings like vulnerability to single point of failures and large computing times. Unlike centralized approaches, decentralized control strategies show more potential. This paper presents one such solution, based on an adaptive multi-agent system, to control a large-scale distribution network in real-time. Its performance is compared with the results obtained with the corresponding centralized optimization problem, modeled as a mixed integer linear programming problem. Both the centralized version and the decentralized multi-agent version of the problem under consideration are presented and a case study is designed for the comparison. The comparison shows that the designed multi-agent system produces a near-optimal solution in real-time while the centralized optimization strategy struggles in terms of computational complexities for larger distribution networks.© 2017 Elsevier Inc. All rights reserved.

Autonomous Power Management of Distributed Energy Storage Systems in Islanded Microgrids

In this paper, an autonomous power management strategy is proposed for distributed energy storage units deployed in islanded microgrids with photovoltaic (PV) and droop controlled units. The proposed strategy offers controlled and selective prioritization of the charging/discharging actions while coordinating with PV and droop units to maintain power balance in the microgrid. The selective priority allows battery units with low state-of-charge (SOC) to keep charging, at a reduced or a full rate, while other battery units meet the demand. This is in contrast to the autonomous techniques in the literature where all units can only charge/discharge simultaneously. This is achieved by employing the proposed adaptive piecewise <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P/f</i> characteristics, with breakpoints that enable seamless transition between power control and frequency regulation. The charging/discharging decisions and the amount of power exchanged with the microgrid, is determined autonomously at each unit using local measurements only. Also, the strategy offers a unidirectional shutdown mechanism where, below a certain SOC limit, it modifies the structure of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P/f</i> characteristics so the unit cannot discharge, yet it can still charge anytime surplus generation becomes available. Simulation results from a detailed switching model, and also an averaged model, for prolonged simulation times, are used to validate the proposed strategy.

State of charge balancing strategy for energy storage system in islanded DC microgrid based on micro-tuning virtual resistance

For the islanded DC microgrid, the energy storage system (ESS) can be employed to maintain the balance between the power generation and load consumption. In order to avoid the over-charging or over-discharging situation of the certain distributed energy storage unit (DESU), the accurate current sharing strategy considering unmatched line resistances is proposed to balance the state of charge (SoC) among DESUs. The micro-tuning resistance is designed to eliminate the impact of line resistances on SoC balancing and achieves the accurate current sharing without affecting the bus voltage deviation. Then, the DESUs adaptively adjust the virtual resistance according to the SoC or depth of discharge (DoD) during charging and discharging to quickly balance the SoC. Additionally, the stability analysis based on the system characteristic equation is provided to theoretically guarantee the stability operation of the DC microgrid. Finally, the effectiveness of the proposed control strategy is proved by several comprehensive test cases based on real-time digital simulator (RTDS) platform.

Clustering distributed Energy Storage units for the aggregation of optimized local solar energy

Active communities are emerging thanks to the necessity of creating a cleaner and safer energy system. The growing concern regarding climate change urges a solution to remove fossil fuels from the production equation. The Distributed Generation (DG) technologies are presented as a substitute, but the main resources’ behavior is highly uncertain. Flexibility from the demand side is needed. In this way, the authors resort to mixed-integer linear programming optimization to schedule the active resources introduced by the Smart Grid concept: DG, Demand Response programs, and Energy Storage Systems. In this study, the last one is the focus where the impact of these technologies in an active community is analyzed and discussed. The authors performed a clustering method to identify patterns on Energy Storage System (ESS) profiles, finding the optimal number of clusters first. The results show the importance of ESS from both Aggregator and active consumer perspectives.

Distributed cooperative control for economic dispatch and SOC balance in DC microgrids with vanadium redox batteries

Abstract With the high penetration of renewable-type resources, the distributed energy storage unit (ESU) will be a key component against the stochastic and intermittent characteristics of the renewable energy resource in the DC microgrid. The vanadium redox battery (VRB) is a promising ESU for the islanded microgrid applications. Therefore, this paper presents a consensus-based distributed cooperative control to alleviate voltage fluctuations, realize economic dispatch, and maintain the state of charge (SOC) balance simultaneously in the DC islanded microgrid with VRBs. Specifically, the proposed solution includes a consensus term that achieves convergence to a common incremental cost value and an additional term which used to eliminate power imbalance and ensure that the total generation is equal to the total consumption. Furthermore, the proposed method is subject to the sum injected/output power real-time equilibrium and the constraints of the SOC for simulating a more practical situation. Finally, the charging process and discharging process numerical simulations are employed in this paper to test the effectiveness of the presented algorithm in an islanded DC microgrid composed of six DERs.

State-of-charge dynamic balancing strategy for distributed energy storage system in DC shipboard microgrid

In this paper, a State-of-Charge (SoC) dynamic balancing control strategy considering system communication failure and energy storage capacity difference is proposed to reach the SoC balancing and proper current sharing for distributed energy storage units (DESUs) in DC shipboard microgrid. Firstly, the distributed equalization algorithm is applied in the DESU’s controller when the system communication is normal, each DESU exchanges their SoC information through low bandwidth communication (LBC) and dynamically adjusts the droop coefficient according to the change of their SoC, so as to realize the SoC dynamic balance of the energy storage system. Secondly, the DESU’s controller is switched to the decentralized equalization algorithm with communication failure, each DESU can reach SoC balancing and load current reasonable distribution only according to the local information. After that, a DC bus voltage compensation method is introduced to solve the problem of bus voltage deviation caused by the proposed SoC equalization. Finally, the simulation model comprised of three DESUs is implemented and the results show that the proposed control strategy can make each DESU reach SoC equilibrium state quickly and effectively reduce the bus voltage deviation under the normal and failure communication conditions.