Multiple Energy Storage Units Research Articles

Prediction Analysis of Fault Mode Sets for High Permeability Distributed Energy Distribution Networks Based on Multivariate Data

This technical abstract describes a prediction analysis methodology for fault mode sets in high-permeability distributed energy distribution networks (DEDNs). DEDNs are complex systems composed of multiple interconnected energy sources, storage units, and loads, which are managed and controlled by intelligent devices. These systems have become increasingly important in modern electricity networks due to their ability to integrate a high share of renewable and distributed energy resources. The proposed prediction analysis aims to identify and classify the various fault modes that can occur in high-permeability DEDNs based on multivariate data. It includes information about the system's operational parameters, such as voltage, current, and frequency, as well as data on weather conditions, load profiles, and the state of the network. The methodology involves collecting and pre-processing the data using suitable techniques, such as data filtering and noise removal.

Research on Adaptive Droop Control Strategy for a Solar-Storage DC Microgrid

When the solar-storage DC microgrid operates in islanded mode, the battery needs to stabilize the bus voltage and keep the state of charge (SOC) balanced in order to extend the service life of the battery and the islanded operation time. When there are multiple energy storage units in the DC microgrid, it is necessary to solve the problem of unbalanced circulation and the state of charge between batteries using a reasonable droop control method. In this paper, firstly, the DC–DC charging and discharging circuit of the battery is designed, and the unbalanced SOC of the battery caused by the different impedances of the line is analyzed. Secondly, an adaptive droop control method is proposed to solve the problems of SOC imbalance and current circulation between the batteries. Thirdly, based on MATLAB/SIMULINK R2021b simulation software, the proposed control method is modeled and simulated. Compared with the traditional droop control, the effectiveness of the proposed method is validated.

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.

Coordinated control strategy for microgrid based on improved virtual DC motor

The coordinated control strategy based on the improved virtual DC motor (VDCM) is proposed to address the bus voltage fluctuation of an isolated DC microgrid and the power distribution problem of multiple energy storage units. Firstly, the memory converter control process unit is enhanced by adding the VDCM link and low-pass filtering link. Secondly, considering factors such as photovoltaic power, constant-power loads, and battery charging state, six working modes are defined to ensure stable DC bus voltage in each mode. Finally, a simulation model of the isolated DC microgrid is found by using simulation software to verify the validity of this method.

Incorporating a Load-Shifting Algorithm for Optimal Energy Storage Capacity Design in Smart Homes

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.

A distributionally robust optimization approach for optimal load dispatch of energy hub considering multiple energy storage units and demand response programs

The coupling relationship among various energy sources has been consistently reinforced by the advancement of the energy Internet. In the context of modeling multi-energy systems, the energy hub (EH) represents a significant and valuable approach. To attain optimal operation of the EH, this study proposes an optimal load dispatch model for a system that incorporates a combined heat and power (CHP) unit, gas boiler (GB), electric chiller (EC), absorption chiller (AC), heat energy storage (HES) unit, and electrical energy storage (EES) unit. A two-stage distributionally robust optimization (DRO) method that is driven by data is employed to address the uncertainties of electricity prices. Additionally, the proposed two-stage DRO model is effectively solved through the utilization of the column and constraint generation (C&CG) algorithm. Three cases are discussed in the paper with various energy storage units and demand response (DR) settings. The simulation results show that by deploying energy storage units and participating in DR projects, the EH system can reduce the total costs by 2.56 % and 10 %, respectively. Meanwhile, simulation results reveal that the proposed data-driven DRO model can achieve a compromise between the economy and robustness of the scheduling model compared with stochastic optimization (SO) and robust optimization (RO) methods. The simulation results illustrate the effectiveness of the proposed model for ensuring the economical and efficient operation of the system in the face of electricity price uncertainties.

State-of-charge balancing strategy of battery energy storage units with a voltage balance function for a Bipolar DC mircrogrid

For an islanded bipolar DC microgrid, a special problem of making the better compromise between a state-of-charge (SOC) balance among multiple battery energy storage units (MBESUs) in positive and negative polar, and bus voltage balance, should be considered. In order to solve this problem, three kinds of the simplified load equivalent circuits on the different operation status of voltage balancer (VB) are established; based on these equivalent circuits, the SOC unbalance mechanism is analyzed and the rules of SOC deviation and voltage unbalance factor are summarized, which are influenced by micro-source and load changing; then a SOC automatic balancing strategy among MBESUs with a voltage balance function is proposed in this paper. The proposed controlling strategy takes into consideration the energy flow relationship under various working conditions; with the aim of controlling the equilibrium time and energy flow direction of SOC, creates a SOC balancing power bridge with the SOC deviation as the main control variable; adopts the adaptive virtual resistance algorithm for both positive and negative BESUs according to the variation rule of the SOC deviation. When the SOC deviation is significant, according to the net power difference of the system, the values of the virtual resistances can be adaptively adjusted to alter the operation mode of BESUs and the VB control strategy is also adjusted so as to ensure the stable running of the system and accelerate the SOC balancing speed via maximizing the balancing power by VB. In order to decrease the voltage unbalance on SOC balancing, apart from adding bus voltage recovery control to the positive and negative batteries, the virtual resistances are related to the SOC deviation when the SOC deviation is slight. The simulation results demonstrate that the control strategy proposed in this paper can quickly realize SOC balancing when the SOC deviation is significant, has superior equilibrium performance as load and micro-source power varying when the SOC deviation is slight, and is also feasible in decreasing the bus voltage unbalance.

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.

Power Performance Evaluation of Standalone Renewable Energy Source Energy Management Using Pass Filter

Hybrid energy storage systems have shown promise in enhancing solar panel systems reliability and efficiency. However, managing the power distribution balance with multiple energy storage units remains a challenge. This research addresses power distribution balancing during solar irradiation intermittency by employing a first-order filter with lowpass and highpass characteristics. The study aims to investigate energy and power management issues for off-grid electrical systems using this filter. Results from numerical simulations and experiments with a hybrid energy storage setup comprising a battery and supercapacitor show that the first-order filter effectively allocates the first-order signal to the main energy storage and subsequent orders to the supporting energy storage. During increased intermittency, the battery contributes 62 W and stores 387 W, while the supercapacitor contributes 167 W and stores 297 W. Conversely, during reduced intermittency, the battery stores 390 W and contributes 62 W, and the supercapacitor stores 295 W and contributes 164 W. The findings demonstrate the filter's efficacy in optimizing power distribution balance within hybrid energy storage systems. However, using the supercapacitor as the main energy storage does not result in higher power efficiency compared to the battery. In conclusion, the First Order Filter presents a viable solution for addressing power distribution challenges in hybrid energy storage systems, contributing to improved energy and power management for off-grid electrical systems. Further research on cost-effectiveness, maintenance, and environmental impact is warranted for practical implementation

The coordinated control strategy of DC microgrid based on multiple energy storage units

Recently, the DC microgrid (MG) has caught people’s attention because of its simpler control system than the AC microgrid. In this paper, the bus voltage layering control method based on droop control is used for DC microgrid coordination control. According to the working characteristics of the DC bus, the voltage of the DC bus is divided into five sections, corresponding to 5 operating modes respectively. Inside the DC microgrid, each unit converts the operation mode by local control according to voltage amplitude. The photovoltaic power generation unit adopts MPPT control and constant voltage control to adjust the running point tracking, so as to realize changing-over smoothly between voltage regulator mode and MPPT. The battery system adopts a hierarchical operation control method of several batteries on the basis of the state of charge (SOC). To realize the target, which means getting rational active power allocation and SOC equilibrium of each battery unit, as well as different batteries changing-over the charge and discharge modes on the basis of SOC size, so as to adjust the droop curve according to the power size. Finally, the balance between the three objectives of the highest photovoltaic energy utilization rate, energy storage SOC balance, and stable bus voltage is achieved.

Distributed dual objective control of an energy storage system under jointly connected switching networks

This paper studies a dual objective control problem for an energy storage system (ESS) consisting of multiple independently-controlled energy storage units (ESUs). The power output of the entire ESS is designed to meet its reference, and meanwhile the state-of-energy (SOE) of all the ESUs maintains to be balanced. In practice, the communication among ESUs might be unreliable and thus is modeled by jointly connected switching networks. By making use of the so-called Generalized Krasovskii–LaSalle Theorem together with a novel two-step recursive analysis method, it is rigorously proven that the dual objective control problem subject to jointly connected switching networks can be solved by a consensus-based distributed control algorithm.

Grouping Control Strategy for Battery Energy Storage Power Stations Considering the Wind and Solar Power Generation Trend

For the optimal power distribution problem of battery energy storage power stations containing multiple energy storage units, a grouping control strategy considering the wind and solar power generation trend is proposed. Firstly, a state of charge (SOC) consistency algorithm based on multi-agent is proposed. The adaptive power distribution among the units started can be realized using this algorithm. Then, considering the trend of wind and solar power generation, a reasonable grouping control strategy is formulated. The grouping situation of the units is determined by using the probability distribution characteristics of energy storage charging and discharging, which reduces the number of charging and discharging conversions and extends the power station life. Finally, the actual data of a wind–solar energy storage microgrid is used to verify the method. The simulation results demonstrate that the proposed method has certain advantages in terms of control effect, SOC consistency, and extending the power station life.

Techno-economic analysis of combined cooling, heating, and power (CCHP) system integrated with multiple renewable energy sources and energy storage units

This study presents the optimal design of a multi-source renewable energy combined cooling, heating, and power (M−RCCHP) system integrated with energy storage units for an apartment community from techno-economic perspectives using response surface methodology (RSM) and dynamic evaluation. The proposed M−RCCHP system is consisted of photovoltaic/thermal (PV/T) collectors, wind turbines (WTs), fuel cell subsystem, heat pump (HP), while the battery banks and hydrogen storage tank are the energy storage subsystems. The behavior of the system is examined by a dynamic approach using the TRNSYS simulation platform. Besides, the main design variables of the M−RCCHP system are selected to find the foremost combination of them using the design of experiments (DOE) technique. The number of PV/T panels, number of WT, fuel cell power, HP cooling capacity and HP heating capacity were considered as decision variables. Overall electricity consumption (OEC) and boiler fuel consumption (BFC) as indicators of primary energy consumption of the system and predicted average vote (PMV) as an index of thermal comfort of the system and life cycle cost (LCC) as economic criteria as 4 objective functions were selected for optimization. The optimization results showed that the M−RCCHP system with 225 PV/T collectors, 15 wt, fuel cells with a power of 74.96 kW, HP cooling capacity of 50.25 kW and HP heating capacity of 20 kW achieved the best techno-economic performance during the period. The results of the system modeling showed that the fuel cell subsystem, PV/T collectors and WTs with the maximum power of 75 kW, 52 kW and 24 kW have the largest contribution in electricity production. The optimization results showed that the system cycle cost (LCC) is 323,004.96 $ per year, the system comfort coefficient (PMV) is 0.115 per year, the boiler fuel consumption is 41,067.88 cubic meters per year, and the total electricity consumption is −317,194.94 kWh per year, as well as the amount The optimal cooling and heating capacities of HP were calculated as 50.25 and 20 kW, respectively. And finally, the error percentage of the optimization method was reported as 6.53 %.

Entropy weight and grey relational analysis based virtual capacitance collaborative control for multiple energy storage units

Flexible virtual capacitance control (FVCC) for converter can improve the stability and dynamics of DC bus voltage, but among multiple converters, essential inertia distribution is capable of optimizing the performance. To improve the rationality of inertia distribution, a virtual capacitance collaborative control (VCCC) strategy for multiple energy storage units (ESUs) is proposed. Firstly, the system required total inertia is adjusted according to each individual battery's rated capacity and state-of-charge (SOC). Then comes the establishment of an evaluation mechanism, which takes the accounts of battery SOC margin, converter power margin and battery power regulation speed. The entropy weight and grey relational analysis are used to calculate each ESU's distribution coefficient and then adjust their virtual capacitance in real-time. The results show that the adjustment and distribution of the total inertia are beneficial to controlling the SOC of the batteries within the safe range. The ESU with the smallest converter power margin or the slowest battery power regulation speed supports least virtual inertia, which can reduce the problems such as overload protection action or auxiliary power limitation, so as to improve the cooperative control effects. Finally, the effectiveness of the proposed control strategy is verified by hardware-in-the-loop (HIL) simulation experiments.

Cooperative adaptive inertial control for PV and energy storage units with multiple constraints

Virtual synchronous generator (VSG) control for increasing inverter-based sources is characterized by attractive features such as supporting virtual inertia, flexible design, and enhancing frequency stability. However, the performance and status of each VSG unit lead to differences in the inertial support capabilities. This paper investigates a cooperative adaptive inertial control method for multiple photovoltaic and energy storage units (PV-ESUs) to improve system inertia distribution capability during transient events. The frequency discrimination of power disturbances is realized through filters, and the operating ranges of different types of energy storage devices are determined. For the coordination problem with each VSG unit under low-frequency disturbance. The coordination controller, which regulates virtual inertia values by using technique for order preference by similarity to ideal solution (TOPSIS) evaluation algorithm, is proposed to adaptively adjust the inertial output capability of each VSG unit and provide optimized dynamic frequency support to the grid. The state of charge (SOC) of energy storage, converter adjustable capacity, the maximum adjustable output power of the system per unit time, and other influence factors are considered as actual multiple operation constraints. Finally, the effectiveness and engineering application value of the proposed control strategy is verified by the hardware-in-the-loop (HIL) experimental test platform.

SOC Balancing and Coordinated Control Based on Adaptive Droop Coefficient Algorithm for Energy Storage Units in DC Microgrid

In order to achieve a state-of-charge (SOC) balance among multiple energy storage units (MESUs) in an islanded DC microgrid, a SOC balancing and coordinated control strategy based on the adaptive droop coefficient algorithm for MESUs is proposed. When the SOC deviation is significant, the droop coefficient for an energy storage unit (ESU) with a higher (or lower) SOC is set to a minimum value when discharging (or charging). The ESU with the higher (or lower) SOC is controlled to discharge (or charge) with the rated power, while the other ESU compensates for the remaining power when the demanded discharging (or charging) power is greater than the rated power of the individual ESU. Otherwise, when the demanded discharging (or charging) power is lower than the rated power of either ESU, the ESU with the higher (or lower) SOC releases (or absorbs) almost all the required power while the other ESU barely absorbs or releases power, thus quickly realizing SOC balancing. When the SOC deviation is slight, the fuzzy logic algorithm dynamically adjusts the droop coefficient and changes the power distribution relationship to balance the SOC accurately. Furthermore, a bus voltage recovery control scheme is employed to regulate the bus voltage, thus improving the voltage quality. The energy coordinated management strategy is adopted to ensure the power balance and stabilize the bus voltage in the DC microgrid. A simulation model is built in MATLAB/Simulink, and the simulation results demonstrate the effectiveness of the proposed control strategy in achieving fast and accurate SOC balance and regulating the bus voltage.

Rule-based energy buffer strategy of energy router considering efficiency optimization

Energy router with energy buffer will significantly facilitate performance improvement such as loss compensation, and instruction implementation of dispatching center in Energy Internet. To achieve high power density, high energy density, and high reliability, the energy buffer is implemented via a hybrid energy storage system and paralleled multiple modules. A minimum loss model of energy buffer devices is constructed to optimize the operating efficiency and energy management of multiple energy storage units. Based on the minimum loss model, a rule-based energy buffer strategy for the energy router is proposed to achieve minimum loss and energy management in real-time control. A novel power allocation method for different types of storage units is designed to better eliminate over-limit power in energy-type storage units in peak periods, thus extending their lifetime. Simulation results demonstrate the effectiveness of the proposed rule-based energy buffer strategy for energy routers in achieving highly efficient and seamless operation and energy management of energy storage units.

Communication-Free Power Management Strategy for the Multiple DAB-Based Energy Storage System in Islanded DC Microgrid

Along with the development of the renewable energy, such as the photovoltaics and the wind turbine, the energy storage system (ESS) is becoming as a critical part for the renewable-based microgrids. In this article, dual-active-bridge (DAB) dc-dc converter with bidirectional power flowing ability, wide soft-switching range, and ultrafast dynamic characteristic is adopted for integrating multiple energy storage units (ESUs) for balancing the power flowing between the renewable energy and the loads in a islanded dc microgrids. For the multiple DAB-based ESS, a communication-free power management strategy is proposed in this article to maintain the dc-link voltage for the islanded dc microgrid, and high robustness of the dc-link voltage can be ensured when the output voltage of energy storage equipment, the load condition, and the power sharing performance of the ESS are changed. The proposed strategy also ensures seamless ESU plug-in or plug-out operations. Finally, the small-scale simulation model and experimental platform are both employed to verify the effectiveness of the proposed communication-free power management scheme.

Development of an off-grid electrical vehicle charging station hybridized with renewables including battery cooling system and multiple energy storage units

Electric vehicles expansion is accelerating rapidly due to e-mobility’s massive contribution in reducing fossil fuel consumption and CO2 emissions. Fulfilling the charging requirements of millions of electrical vehicles from the grid would overload the network and introduce substantial burden on the power sector. This study proposes, and thermodynamically assesses, a grid-independent and renewable energy-based, stand-alone electrical vehicle charging station consisting of CPV/T, wind turbine and biomass combustion-based steam Rankine cycle plant. Hydrogen and ammonia-based fuel cells are integrated in the design along with electrochemical, chemical and thermal storage units to ensure uninterrupted charging services during night times and unfavorable weather conditions. Since the proposed design is suggested for use in the State of Qatar, which is located in a hot region, an absorption cooling system is incorporated to cool the produced NH3 gas and convert it into liquid phase for optimal storage purposes and to maintain the operating temperature of the battery system within the allowable limits. The thermodynamic analysis followed in this study is based on writing the balance equations for mass, energy, entropy and exergy for the system’s components along with their energy and exergy efficiency equations. The results show that the energy generated from renewable energy sources and fuel cells are sufficient to fast-charge 80 electrical vehicles daily. The energy efficiencies of H2 fuel cell, NH3 fuel cell, CPV/T, wind turbine and energetic COP of the absorption cooling system are found to be 77%, 72%, 45%, 43% and 0.72, respectively. The exergy efficiency of CPV/T and the exergetic COP of the absorption cooling system are found to be 37% and 0.19, respectively. The overall energy and exergy efficiencies of the proposed integrated system are found to be 45% and 19%, respectively

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.