Multiple Energy Storage Systems Research Articles

From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company

Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while balancing the electricity grid. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company’s MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of electrical industrial forklifts, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal, electrical, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19%–80% depending on electricity and carbon removal prices), total annual costs, operational carbon footprint, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.

An Economic Dispatch for a Shared Energy Storage System Using MILP Optimization: A Case Study of a Moroccan Microgrid

Energy storage systems are an effective solution to manage the intermittency of renewable energies, balance supply, and demand. Numerous studies recommend adopting a shared energy storage system (ESS) as opposed to multiple single ESSs because of their high prices and inefficiency. Thus, this study examines a shared storage system in a grid-connected microgrid. By modifying the power outputs of the energy resources, this work intends to implement an economic dispatch of a shared ESS in order to satisfy the power balance and lower the overall cost of electricity. In this context, an optimization problem was formulated and developed using a mixed-integer linear programming (MILP) model. Furthermore, a pilot project (the Solar Decathlon Africa Village) in the Green & Smart Building Park (GSBP), Benguerir, Morocco, was employed to evaluate and verify the proposed approach. Some comparable scenarios were therefore run in a MATLAB environment. The collected findings demonstrate the efficacy of the developed algorithm in terms of optimizing energy cost reductions and enhancing the integration of renewable resources into the Moroccan energy mix.

Collaborative optimization strategy of source‐grid‐load‐storage considering dynamic time series complementarity of multiple storages

AbstractThe multi‐scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system. This paper first considers the interaction mechanism of multi‐type storage peak regulation time sequences based on the Euclidian distance, dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source‐grid‐load‐storage (SGLS) was constructed. To attain a low‐carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time‐series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K‐means clustering and dual‐structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

Battery state of charge based improved adaptive droop control for power management of a microgrid having large scale renewable generation

Power management in a standalone microgrid having large scale renewable generation is generally based upon droop control strategies. However, the renewable sources such as photovoltaics (PV), which are interfaced with microgrid through power electronic converters have low inertia. Because of low inertia, the transient power variation during switching events like change in load may be very large and may result into variation in voltage and frequency beyond permissible limits. In this paper an adaptive scheme for power management of a standalone microgrid is proposed, which is based on available state of charge (SoC) of the batteries to mitigate these issues. The proposed battery SoC based adaptive droop control for power management allows smooth transition of distributed energy resources (DERs) from maximum power point tracking (MPPT) mode to adaptive droop mode and vice versa. The transient behaviour is handled with the help of batteries and supercapacitor (SC), which is further improved with the help of fuzzy logic based proportional integral controller (FLC-PI). A significant drop in SC power during switching events is obtained with FLC-PI. The test microgrid is composed of multiple PV generation at different locations as primary source of generation, multiple batteries energy storage system (ESS) at different locations, fuel cell (FC), supercapacitor (SC) and a dynamic load.

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant

In this paper, the electrical parameters of a hybrid power system made of hybrid renewable energy sources (HRES) generation are primarily discussed. The main components of HRES with energy storage (ES) systems are the resources coordinated with multiple photovoltaic (PV) cell units, a biogas generator, and multiple ES systems, including superconducting magnetic energy storage (SMES) and pumped hydro energy storage (PHES). The performance characteristics of the HRES are determined by the constant power generation from various sources, as well as the shifting load perturbations. Constant power generation from a variety of sources, as well as shifting load perturbations, dictate the HRES’s performance characteristics. As a result of the fluctuating load demand, there will be steady generation but also fluctuating frequency and power. A suitable control strategy is therefore needed to overcome the frequency and power deviations under the aforementioned load demand and generation conditions. An integration in the environment of fractional order (FO) calculus for proportion-al-integral-derivative (PID) controllers and fuzzy controllers, referred to as FO-Fuzzy-PID controllers, tuned with the opposition-based whale optimization algorithm (OWOA), and compared with QOHSA, TBLOA, and PSO has been proposed to control the frequency deviation and power deviations in each power generation unites. The results of the frequency deviation obtained by using FO-fuzzy-PID controllers with OWOA tuned are 1.05%, 2.01%, and 2.73% lower than when QOHSA, TBLOA, and PSO have been used to tune, respectively. Through this analysis, the algorithm’s efficiency is determined. Sensitivity studies are also carried out to demonstrate the robustness of the technique under consideration in relation to changes in the sizes of the HRES and ES system parameters.

An Overview on Potential Problems and Solution of Hybrid Microgrids

The world's energy demand has increased significantly in the past two decades, and there is a growing concern over energy security and adequacy. The use of renewable energy sources and microgrids can play a significant role in ensuring a sustainable energy future. Effective energy resource planning is critical to achieving global commitments towards sustainable development. It involves managing and optimizing the supply and storage of energy resources, including renewable energy sources. Microgrids can contribute to this effort by improving the efficiency of energy systems and reducing energy losses. Hybrid microgrids, which combine multiple energy resources and storage systems, offer several benefits for energy management and optimization. However, integrating renewable energy sources into the existing energy system can pose several challenges, such as voltage and frequency regulation, low-inertia issues, and system stability. Researchers have been working on developing advanced control strategies and optimization techniques, including metaheuristic optimization techniques, to address these challenges and improve the performance of hybrid microgrids. Overall, energy resource management and the use of microgrids and renewable energy sources are essential for achieving a sustainable energy future and mitigating the impacts of climate change.

The DC Microgrid-Based SoC Adaptive Droop Control Algorithm

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

Full-deployed energy management system tested in a microgrid cluster

Under a hierarchical structure perspective, the integration of tools like the internet of things, cloud computing, and machine learning into a microgrid real-time energy management system involves superior capabilities like an autonomous and scalable design, massive storage capabilities, real-time information analysis and processing, and security issues control, to mention a few. This paper evaluates most of them using a cloud-based real-time energy management system integrated into a real-life hardware-in-the-loop testbed. The proposed cloud-based system is tested by solving an economic power dispatch problem including the equalization of the multiple battery-based energy storage systems interacting within a multi-microgrid environment. The test assessment combined reviewing and running microgrid models, incorporating these models into a real-life power-hardware-in-the-loop unit, linking the testbed to a cloud server, and merging the energy management system with on-demand computing tools, primarily machine learning and the internet of things. As established by the experimental evidence, this paper cites the benefits of combining machine learning techniques and internet of things tools with a scalable and autonomous real-life cloud-based energy management system architecture to improve the framework's functionality, enhance the energy forecasting for generation and usage, and cut down the price paid to the service provider.

A Model Predictive Control Based Optimal Task Allocation among Multiple Energy Storage Systems for Secondary Frequency Regulation Service Provision

Power system stability has been suffering increasing threats with the ever-growing penetration of intermittent renewable generation such as wind power and solar power. Battery energy storage systems (BESSs) could mitigate frequency fluctuation of the power system because of their accurate regulation capability and rapid response. By dividing the Area Control Error (ACE) signal and the State of Charge (SOC) of battery energy storage systems into different intervals, the frequency control task of BESSs could be determined by considering the frequency control demand of the power grid in each interval and SOC self-recovery. The well-developed model predictive control can be employed to attain the optimal control variable sequence of BESSs in the following time, which can determine the output depths of BESSs and action timing at different intervals. The simulation platform MATLAB/Simulink is used to build two secondary frequency control scenarios of BESSs for providing frequency regulation service. The feasibility of the presented strategy is demonstrated by simulation results of a sample system.

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

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

Metal Phosphates: Emerging Materials for Energy Storage

In recent years metal phosphates have gained the limelight due to their unique properties and versatile electrochemical behaviour. These materials offer a wide range of compositions, crystal structures, and redox properties, enabling their application in various energy storage systems. This review provides an overview of the recent advancements in metal phosphates for energy storage, focusing on their synthesis, electrochemical performance, and potential applications. Metal phosphates hold great promise as emerging materials for energy storage applications. Their tunable properties, versatile electrochemical behaviour, and potential for multiple energy storage systems make them potential candidates for nextgeneration energy storage technologies. Ongoing research and development efforts are crucial to revealing the full potential of metal phosphates and accelerating their integration into practical energy storage devices

Self-Adaptive Dragonfly Algorithm for Optimal Allocation of Multiple Energy Storage Systems with VAr Support for Islanded Microgrid Operation

Self-Adaptive Dragonfly Algorithm for Optimal Allocation of Multiple Energy Storage Systems with VAr Support for Islanded Microgrid Operation

Recent Advances in Hybrid Energy Storage System Integrated Renewable Power Generation: Configuration, Control, Applications, and Future Directions

The increased usage of renewable energy sources (RESs) and the intermittent nature of the power they provide lead to several issues related to stability, reliability, and power quality. In such instances, energy storage systems (ESSs) offer a promising solution to such related RES issues. Hence, several ESS techniques were proposed in the literature to solve these issues; however, a single ESS does not fulfill all the requirements for certain operations and has different tradeoffs for overall system performance. This is mainly due to the limited capability of a single ESS and the potency concerning cost, lifespan, power and energy density, and dynamic response. In order to overcome the tradeoff issue resulting from using a single ESS system, a hybrid energy storage system (HESS) consisting of two or more ESSs appears as an effective solution. Many studies have been considered lately to develop and propose different HESSs for different applications showing the great advantages of using multiple ESSs in one combined system. Although these individual methods have been well documented, a comprehensive review of HESS-integrated RE has not been fully investigated in the literature before. Thus, as a novel contribution to the literature, this study aims to review and analyze the importance and impact of HESSs in the presence of renewable energy towards sustainable development that will facilitate this newly emerging topic to researchers in this field. In this regard, the present scenario and recent trend of HESSs in RESs at the global level, including a comparison with main ESS features, are discussed and analyzed along with the concept, design, classifications, and a detailed comparison of HESSs. The emerging role of HESSs in terms of their benefits and applications has been analyzed. Recent control and optimization methods of HESSs associated with RESs and their advantages and disadvantages have been reviewed. Finally, open issues and new challenges toward more efficient, sustainable, and green energy have also been highlighted herein. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of an advanced HESS for future renewable energy optimal operation.

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

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

Variable State of Charge-based Power Leveling Method using Multiple Energy Storage Systems

This paper proposes a power-leveling method in which a lithium-ion battery and a flywheel energy storage are installed in a photovoltaic power generation plant. The proposed method is a division method that focuses on the amplitude component of the compensating power required to level the generated power of photovoltaic power generation. Furthermore, by adding the flywheel state-of-charge automatic adjustment method to the proposed method, the coverage period of the flywheel is improved. The effectiveness of the proposed method is verified on an actual 40kW plant.

Study of Energy Loss for Distributed Power-Flow Assignment in a Smart Home Environment

Today, renewable energy resources are a critical component of distributed energy systems. However, their intermittent nature makes them unstable energy sources, making them very difficult to use optimally in any energy system. Battery storage is a viable solution for this issue. In this paper, we consider distributed power-flow assignment consisting of unstable power generators, unpredictable power loads, and multiple energy storage systems (ESSs), with different combinations of logical power connections between them. We propose power-flow assignment (PFA) algorithms to deal with single and multiple loads to address the possibility of reducing energy loss and improving distributed power-flow assignment with the presence of ESSs in a smart home environment. Simulation results reveal that the increment of logical power connections between generators, loads, and storage systems can significantly reduce energy loss. The proposed PFA algorithms can reduce energy loss by about 67% compared to a power-flow assignment for which all the generated power is stored in an ESS directly during winter. The results further show that spring has the highest energy loss and stored energy in ESS compared to other seasons.

Chance-constrained model predictive control-based operation management of more-electric aircraft using energy storage systems under uncertainty

On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). Considering the growing number of onboard electric loads and the increasing complexity of EPS architecture due to the integration of multiple power converters and energy storage systems (ESSs), system-level operation control is required to manage power distribution, load scheduling, and ESSs. In this paper, a chance-constrained stochastic model predictive control (CC-SMPC) method is proposed to improve both the system operation in terms of the system's cost and reconfiguration activities as well as the ability to cope with uncertainties due to fluctuating load demands. Both normal and faulty operating conditions are investigated with multi-failure cases, resulting in different uncertainty propagation paths. The system's operational and technical requirements are formulated as a set of deterministic and probabilistic constraints in the CC-SMPC model. To verify the effectiveness of the proposed strategy, a comprehensive comparison study is conducted. Two uncertainty/failure cases are taken into account and simulations are performed for both offline and online control strategies while the Monte-Carlo algorithm is used for scenario generation. The results are evaluated using the proposed evaluation framework, showing that the CC-SMPC achieves better performance compared to deterministic MPC (DMPC) in both cases. In an offline testing framework, comparing the performance of DMPC and CC-SMPC strategies shows that CC-SMPC reduces the power constraint violations for batteries and generators in all cases following the selected confidence level. In addition, in an online testing framework with 1 % violation probability, the following results are observed in the two cases: In the EPS normal condition, CC-SMPC reduces the total cost by 31.4 % and the overall constraint violation cost by 93 %; while in the EPS faulty condition, CC-SMPC reduces the total cost by 4.37 %, and the overall constraint violation cost by 96 %.

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

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

Comparative performance of multiple energy storage systems in unified voltage and frequency regulation of power system including electric vehicles

AbstractModern power systems are very complicated and have more obligation to uphold the supply of power be reliable, stable, and of decent quality at both the transmission and distribution phases. Preserving grid balance is a significant concern if there is an unforeseen generation deficit or grid disturbance, or intermittent renewable energy sources, such as bio‐diesel, electric vehicles (EVs), and solar power are incorporated into the energy mix. To compensate for this imbalance and enhance the power system's reliability and stability, the energy storage systems (ESS) can be regarded as an appropriate measure. Thus, ESS contributes to greenhouse gas (GHG) emission reductions by successfully integrating more renewable energy sources into the grid. This article presents multiple ESSs, such as pumped hydroelectric storage (PHS), accurate flywheel energy storage (AFES), battery energy storage (BES), capacitive energy storage (CE), and superconducting magnetic energy storage (SMEs) and their comparative performance analysis in unified voltage and frequency control of power system. The proposed interconnected power system includes thermal, solar‐thermal, bio‐diesel‐engine generator, and EVs. In both control systems, a maiden attempt was made to utilize Harris' Hawks optimization‐based fuzzy‐fractional‐order‐tilt‐integral‐derivative (FFOTID) controller. The repercussion of PHS proclaims a substantial amelioration in system performance than other ESSs. Likewise, the system exhibits better dynamics with the coalition of PHS and power system stabilizer (PSS) of AVR compared to their separate intervention. Lastly, sensitivity analysis of proposed controller is analyzed by alteration of load perturbation and nonlinearity of thermal plants, which ensures optimal controller parameters accomplished at nominal conditions are resilient enough.

A Novel Model Predictive Control for Stability Improvement of Small Scaled Zero-inertia Multiple DGs Micro-grid

A zero-inertia micro-grid is a power system consisting of multiple renewable energy power sources and energy storage systems without the presence of conventional synchronous generators. In such a system, a large variation of the load or source sides during the islanded mode of operation extremely degrades the micro-grid's voltage and frequency stability. This study presents a virtual inertia-based predictive control strategy for a small-scale zero-inertia multiple distributed generators (DGs) micro-grid. In islanded mode, Voltage Model Predictive Control (VMPC) was implemented to control and maintain the voltage and frequency of the micro-grid. However, instabilities in frequency and voltage may rise at the Point of Common Coupling (PCC) due to large variations at both source and load sides. Therefore, the proposed virtual inertia loop calculates the amount of active power to be delivered or absorbed by each DG, and its effect is reflected in the estimated d current component of the VMPC, thus providing better frequency regulation. In grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. The control approach also enables the DG plug and play characteristics. The performance of the control strategy was investigated and verified using the PSCAD/EMTDC software platform.