Configuration Of Energy Storage System Research Articles

An energy storage system configuration strategy of public buildings for PV local consumption and three-phase unbalance management

An energy storage system configuration strategy of public buildings for PV local consumption and three-phase unbalance management

Optimal configuration of energy storage considering flexibility requirements and operational risks in a power system

The integration of renewable energy units into power systems brings a huge challenge to the flexible regulation ability. As an efficient and convenient flexible resource, energy storage systems (ESSs) have the advantages of fast-response characteristics and bi-directional power conversion, which can provide flexible support for the power system. This paper establishes an optimization model for the ESS based on a bi-level programming model. The upper-level model optimizes the decision strategy of ESS configuration planning. The lower-level model is based on scenario analysis theory to simulate the operation of typical daily scenarios. Flexibility requirement constraints are added to characterize the required flexibility resources of the power system. In addition, the conditional value-at-risk (CVaR) is applied to characterize the risk of wind curtailment and load shedding during operation. To simplify the model, a set of association constraints is introduced to convert the original bi-level programming model into a direct-solvable single-level mixed-integer linear programming (MILP) model. Finally, the effectiveness of the proposed model is verified through case studies.

Performance analysis of the comprehensive energy system based on active energy storage-discharge technology under time-sharing electricity price operation strategy

Multi-energy complementary comprehensive energy systems based on renewable energy can reliably meet the energy needs of communities and significantly mitigate carbon emissions. This is of paramount significance in the establishment of low-carbon sustainable communities. The effective operation of an integrated energy system necessitates a thorough examination of demand-side load, the dynamic performance of the energy supply system, the configuration of energy storage systems, local electricity pricing policies, and operational or control strategies for the system. It confronts substantial challenges in practical application. Considering the noteworthy performance variations of comprehensive energy systems under diverse demand-side load, fluctuating electricity prices, and various operating modes of energy storage equipment, this paper initially conducts a practical analysis of demand-side load. A comprehensive energy system with multi-energy complementary based on source-load-storage coordination (SLS-CES) model was constructed. From the perspective of system operation strategy, we identify the current electricity consumption pattern in the region, characterized by peak and valley periods. We integrate this information with the time-of-use electricity pricing policy and the equipment within the target area. An operation strategy based on local time-of-use electricity price policy was proposed. An in-depth analysis of the thermal performance, environmental performance, and economic performance of the system in the full-year dimension and typical daily dimension under this operating strategy was conducted. The results show that under this operation strategy, the system's annual net income is 415.90 thousand yuan, of which the system's energy storage battery power supply revenue during peak period reaches 254.40 thousand yuan. The initial investment is 5.03 million yuan, and the static investment payback period is 12.09 years. The annual primary energy saving rate of the system is 36.00%, and the maximum CO2 emission reduction rate is 72.23%. The maximum primary energy saving rates on typical days in winter and typical days in summer are 74.74% and 97.14% respectively. The total annual CO2 emissions were reduced by 5.47 tons and 4.54 tons respectively. Through this study, we found that a system incorporating energy storage equipment, combined with an operation strategy based on electricity price policy, can yield additional economic benefits. This approach helps alleviate peak power supply pressure on the grid and mitigates the challenges associated with the high initial investment in renewable energy systems, all while maintaining good thermal performance and environmental benefits.

Energy Storage Dynamic Configuration of Active Distribution Networks—Joint Planning of Grid Structures

The integration of distributed power generation mainly consisting of photovoltaic and wind power into active distribution networks can lead to safety accidents in grid operation. At the same time, climate change can also cause voltage fluctuations, direct current injection, harmonic pollution, frequency fluctuations, and other issues. To achieve economic and safe operation of the distribution network, an active distribution network-network planning model considering the dynamic configuration of energy storage system energy storage is constructed. This model focuses on energy storage batteries with high ease of use, high modularity, and strong mobility. The route location planning involving different load operating scenarios in spring, summer, autumn, and winter is constructed. The objective function includes the revenue from selling electricity in the distribution network, the expenditure on purchasing electricity in the distribution network, and the cost during the planned construction period. The constraints include three major constraints: distribution network operation, network topology, and energy storage system operation. Three numerical examples are set up to analyze the impact of energy storage system dynamic configuration on grid planning. The results confirmed the active distribution network-grid planning model for dynamic configuration of energy storage systems. Both Example 2 and Example 3 had 3 ESS configurations. Case 3 showed different access methods for ESS in different seasons. The access nodes for ESS in spring and winter were 4, 5, and 6, while the access nodes for summer and autumn were 3, 5, and 6. After applying the proposed method, the daily energy storage investment of commercial, residential, and industrial users gradually stabilized. The reliability of electricity consumption was improved, with an improvement rate of about 40%. The research has brought considerable economic benefits to distribution network operators. It has forward-looking academic value in the joint planning of grid structures and energy storage.

Optimal Configuration Analysis Method of Energy Storage System Based on “Equal Area Criterion”

In order to solve the problem of randomness and volatility caused by the rapid growth of renewable energy (RE), energy storage systems (ESSs)—as an important means of regulation—can effectively improve the flexible regulation capacity of power systems utilizing a high proportion of RE. Most of the current ESS capacity configuration procedures are carried out based on the typical scenario method or time series production simulation. This method tends to determine the size of the ESS configuration through multiple trial simulations. Uncertainty of simulation prediction data can result in the existence of an excess capacity or lack of configured capacity. In addition, this method reflects the ESS demand under specific targets, but it fails to fully utilize RE generation characteristics. The configuration process lacks the mathematical mechanism of RE consumption, and the calculation process is too complicated. In view of the shortcomings of traditional ESS optimal configuration methods, this paper examines the mathematical mechanism of RE consumption and proposes the ESS optimal configuration analysis method based on “equal area criterion”. First, the principle of RE consumption is analyzed and the “RE consumption characteristic curve” is proposed according to RE characteristics. In addition, a working principle diagram of RE consumption, including ESS, is constructed to visually show the consumption capacity of RE and the working position of ESS. Then, the ESS optimal configuration process, based on the “equal area criterion”, is proposed to achieve an accurate match between ESS capacity demand and RE consumption targets. Finally, the power grid of a region in China is taken as an example. We prove that the proposed method can save 1.41 × 103 MWh of ESS capacity and provide a more “mathematical” and “convenient” systematic solution for RE consumption and ESS optimization compared to the production simulation method.

Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

To enhance the charging and discharging strategy of the energy storage system (ESS) and optimize its economic efficiency, this paper proposes a novel approach based on the enhanced whale algorithm. Recognizing that the standard whale algorithm can sometimes suffer from local optima in high-dimensional multiobjective optimization, this study introduces chaotic mapping and individual information exchange mechanisms to address this challenge. The proposed algorithm explores optimal configurations for different energy device placements and capacities through encircling and bubble searches, evaluating various multiobjective functions for optimization. In addition, the algorithm refines both the system operation model and the ESS configuration model, with the objective function being the analysis of the average annual revenue of the ESS. Model testing results demonstrate that this algorithm yields more moderate energy storage (ES) capacity decay, extending operational time to 3,124 days and achieving a full-life cycle benefit of the ESS as high as 1,821,623.68 yuan. Also, our algorithm demonstrates high efficiency, with minimal test time (68.36 seconds) and quick optimization (0.031 seconds per cycle), regardless of problem complexity.

The Capacity Configuration of Energy Storage System in DC Microgrid with Source-storage Integration

The occurrence of a fault in a DC microgrid can significantly jeopardize the system’s safe operation, making it a critical area of research for scholars. One key focus of this research is on developing methods to suppress the fault current, which has become a research hotspot for many scholars. Each distributed power source is connected to DC microgrid through a battery storage system to form a source-storage integration DC microgrid structure, which can have good fault current control ability and obvious improvement effect on reducing the protection technology difficulty of DC microgrid and enhancing the flexibility of distribution network. Compared with the traditional DC microgrid structure, the source-storage integration DC microgrid, whose battery storage system is changed from centralized to distributed, will smooth fluctuations in distributed power output by modifying energy storage planning principles, and also maintain bus voltage and realize fault current control with distributed power sources. Capacity planning principles for energy storage systems proposed in this paper were studied for DC microgrids with source-storage integration, and the total energy storage capacity and power under two DC microgrid structures were compared and analyzed with a scenic complementary power station as an example. Although a DC microgrid with a changed grid structure would require less than a 20% increase in the overall capacity and power of the energy storage system, according to the calculation results, the operation of the microgrid can be significantly improved, the flexibility of power dispatching of the distribution network was enhanced, and the investment cost of fault protection was reduced. Research into the capacity configuration of energy storage systems has demonstrated that DC microgrids with integrated source and storage capabilities hold significant engineering application value and practical significance.

Optimal energy storage configuration for joint energy‐regulation market participating renewable energy plants with excess revenue recovery mechanism

AbstractThe fluctuation and stochastic characteristics of renewable energy resources challenge the secure system operation and also impose significant financial risks for the market participating renewable energy plants (REPs). Energy storage systems (ESSs) can serve as effective tools in enhancing the operating flexibility of REPs, thus improving their profitability while making them grid‐friendly. However, current studies focussing on the energy market participation of ESS‐equipped REPs neglect ESSs' frequency regulation performance. Moreover, power output deviation and power curtailment of REPs bring difficulties to the integration of renewable energy. To address these challenges, an optimal ESS configuration method for REPs participating in the joint energy‐regulation market is proposed first. A method considering constraints on frequency regulation performance is applied. Then, to reduce power output deviation and power curtailment of REPs, an incentive mechanism based on the excess revenue recovery is implemented to induce the grid‐friendly power outputs of REPs. Numerical analysis on XJTU‐ROTS2017 systems demonstrates that the optimal ESS configuration results obtained from the proposed method could promote the profitability of ESS‐equipped REPs. The results also verify the effectiveness of the excess revenue recovery mechanism in facilitating the grid‐friendly operation behaviours of the ESS‐equipped REPs.

A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks

Introducing energy storage systems (ESSs) into active distribution networks (ADNs) has attracted increasing attention due to the ability to smooth power fluctuations and improve resilience against fault disturbances. This paper proposes a methodology for simultaneously optimizing the configuration of battery ESSs and the operation of ADNs, and the goal is to increase the resilience of the ADNs withstanding multi-faults. Firstly, based on random sampling and K-means clustering, a generation strategy of typical N-1 and N-2 fault scenarios is designed for the ADNs. Then, a two-layer optimization model is established, where the inner model is to optimize the fault recovery performance from the operational perspective, and the outer model is to obtain the optimal site and size of ESSs from the economic perspective. Further, the second-order cone relaxing (SOCR) method and the hybrid gray wolf optimal and particle swarm optimal (GWO-PSO) algorithm are applied to solve the optimization model. Using MATLAB, the modified IEEE 33-node and 118-node systems are built to check the proposed approach's performance. Different periods are considered to show the multi-faults' development, and by introducing a resilience assessment system with node voltage deviation, fault recovery rate, and network loss rate, the resilience of the ADNs is analyzed. From the comparative results, the proposed approach can optimally configure the battery ESSs, and adjust the network structure as well as the distributed generation outputs. Following the ESS configuration cost reduction of 53.19% and 9.8%, the resilience of the ADNs against the multi-faults will increase by 13.36% and 8.25% for the 33-node and 118-node systems.

Optimized Power and Capacity Configuration Strategy of a Grid-Side Energy Storage System for Peak Regulation

The optimal configuration of the rated capacity, rated power and daily output power is an important prerequisite for energy storage systems to participate in peak regulation on the grid side. Economic benefits are the main reason driving investment in energy storage systems. In this paper, the relationship between the economic indicators of an energy storage system and its configuration is first analyzed, and the optimization objective function is formulated. Then, according to the objective limitations of the energy storage system configuration and operation, the constraints are formulated. A set of typical parameters is selected, and the CPLEX (IBM ILOG CPLEX Optimization Studio) solver is used in MATLAB to solve the optimal configuration results. Several sets of optimization results are obtained by taking different subjective coefficient β values, and the economic and social benefits of the optimization results are analyzed. When the economic benefits of the energy storage system are more important, the value of β needs to be smaller, such as a value of 1000. Conversely, when the peak-regulation effect is more important, the value of β should be larger. Finally, configuration results of the control groups are given, and the effect of optimizing the calculation for improving economic benefits is verified.

Conceptual review and optimization of liquid air energy storage system configurations for large scale energy storage

Among energy storage systems, Liquid Air Energy Storage (LAES) is attractive because of high energy density, ease of being scaled up, absence of geographical constraints, mature technology and use of safe materials/working fluids. This work presents a critical review of LAES system configurations in the literature to identify the criteria behind their conceptual development. The goal is achieved following the HEATSEP methodology, which allowed identifying a common thread in the evolution of all LAES configurations in the literature based on very few layouts, named “basic configurations”. The optimization of these few layouts, taking into account all possible internal heat transfers, provides a unique and comprehensive overview of existing configurations in spite of their seemingly great complexity. This picture clearly indicates the link between topology and performance improvement and provides insight into the limits of the maximum possible performance gain over the existing literature. The optimal results show that the margin for improvement is quite narrow. In the case of a complete thermal integration between charge and discharge phases with the addition of Organic Rankine Cycle system a gain from 61.9 % to 64.3 % is obtained over the best performing configuration in the literature.

An energy storage allocation method for renewable energy stations based on standardized supply curve

The goal of carbon emission peak and carbon neutrality requires China to vigorously develop renewable energy. However, renewable energy has obvious randomness and volatility. Therefore, it is necessary to configure energy storage systems for renewable energy stations to ensure the safe and stable operation of power systems. Given the problem of energy storage system configuration in renewable energy stations, it is necessary to consider the system load characteristics and design appropriate principles to formulate planned output curves for renewable energy stations, so that the joint output curve of renewable energy and energy storage system can match the system load curve as much as possible, which will reduce the system peak shaving pressure and promote the utilization of renewable energy. This paper first considers the impact of renewable energy stations with the different installed scales on the power system and designs the standardized supply curves differentially, and defines the supply curve deviation index to characterize the difference between the renewable energy–energy storage system joint output curve and the standardized supply curve. Then, to minimize energy storage system investment costs and supply deviation costs, an optimization model for energy storage system configuration in renewable energy stations is established, and output deviation control constraints are set to ensure that the operation of energy storage systems conforms to actual conditions. Finally, case studies analyze the energy storage system configuration results and the typical scenario operation results of a single renewable energy station and a renewable energy power generation base, which verify the rationality and effectiveness of the method proposed in this paper.

Benefits analysis of energy storage system configured on the renewable energy gathering stations

Due to the rapid development of renewable energy (RE), the power transmission and transformation equipment of some renewable energy gathering stations are congested especially at noon. Therefore, an operation simulation method considering energy storage system (ESS) is proposed, and some evaluation indices of source-network-storage are given. This paper proposes two solutions to alleviate the contradiction between renewable energy utilization and equipment congestion: expanding equipment and configuring ESS. To compare the utilization rate of renewable energy and the investment cost of the power grid, the cost of two schemes of expanding key gathering station equipment and configuring ESS under the condition of increasing the same utilization rate of renewable energy are compared, to give suggestions for the coordinated development of renewable energy and power transmission and transformation investment. ROTS system is used to compare two schemes of equipment expansion and ESS configuration in some renewable energy gathering stations with transmission and transformation congestion. The appropriate scheme is selected according to the indices proposed.

Fast Sizing Methodology and Assessment of Energy Storage Configuration on the Flight Time of a Multirotor Aerial Vehicle

Urban air mobility (UAM), defined as safe and efficient air traffic operations in a metropolitan area for manned aircraft and unmanned aircraft systems, is being researched and developed by industry, academia, and government. This kind of mobility offers an opportunity to construct a green and sustainable sub-sector, building upon the lessons learned over decades by aviation. Thanks to their non-polluting operation and simple air traffic management, electric vertical take-off and landing (eVTOL) aircraft technologies are currently being developed and experimented with for this purpose. However, to successfully complete the certification and commercialization stage, several challenges need to be overcome, particularly in terms of performance, such as flight time and endurance, and reliability. In this paper, a fast methodology for sizing and selecting the propulsion chain components of an eVTOL multirotor aerial vehicle was developed and validated on a reduced-scale prototype of an electric multirotor vehicle with a GTOW of 15 kg. This methodology is associated with a comparative study of energy storage system configurations, in order to assess their effect on the flight time of the aerial vehicle. First, the optimal pair motor/propeller was selected using a global nonlinear optimization in order to maximize the specific efficiency of these components. Second, five energy storage technologies were sized in order to evaluate their influence on the aerial vehicle flight time. Finally, based on this sizing process, the optimized propulsion chain gross take-off weight (GTOW) was evaluated for each energy storage configuration using regression-based methods based on propulsion chain supplier data.

A Rolling-Adaptive Peak Clipping Control Strategy Coordinating RBE Recycling and PV Consumption

Electrified railway is one of the major users of electricity in the power system. The access of energy storage system (ESS) effectively solves the problems of peak traction load (TL) shock and regenerative braking energy (RBE) recycling, and the access of photovoltaic (PV) system improves environmental and economic benefits. However, in the current control strategy based on the electrified railway with ESS and PV, there are still problems of less optimal overall peak clipping effect of TL and insufficient penetration of renewable energy sources (RES) due to low utilization and lack of pre-processing capacity of ESS. Therefore, this paper proposes a rolling-adaptive peak clipping (RAPC) control strategy coordinating RBE recycling and PV consumption. Firstly, perform rolling peak clipping through dynamically divided implementation windows to make full use of the available energy in each time period. Secondly, according to the state of charge (SOC) of ESS and the specific working condition of TL, the ESS output is modified accordingly to perform adaptive deep peak clipping maximizing the consumption of PV energy and RBE. The simulation verified that the proposed strategy can improve the overall peak-clipping effect of TL, and achieve full recovery of RBE and complete consumption of PV energy under the same ESS configuration compared to the global optimization method. The results show that the RAPC control strategy can realize optimal peak-clipping and RBE utilization, and increase the PV penetration with limited ESS capacity, thus improving the economic benefits of the traction power supply system (TPSS) and promoting the energy transition of TPSS.

Power distribution optimization of a fully active hybrid energy storage system configuration for vehicular applications

As an effective solution to limitations of vehicle-mounted single-battery energy storage system, the super-capacitor (SC)/battery hybrid energy storage system (HESS) is a research topic that is receiving more and more attention and emphasis from scientific research institutions in various countries. Despite its current widespread recognition in vehicle field, low energy utilization and low work efficiency under the variable working conditions of vehicle particularly limit its practical application and popularity. The power distribution optimization is critical to obtain a HESS with superior performance such as good reliability and high efficiency. This work presents a variable voltage SC/battery HESS configuration based on series-parallel switching technology of SCs, which can realize multiple working modes of the HESS to meet the needs of complex and variable working conditions of vehicle, and increase the power output range of the HESS. Taking the proposed HESS as application object, an energy efficiency model of the HESS is established, as well as an optimization model with energy efficiency maximization as the optimization goal. Power distribution optimization methods based on Genetic Algorithm (GA) and Gray Wolf Algorithm (GWA) respectively are proposed, and compared to fixed rules-based and fuzzy-based control strategies. The optimization algorithms-based methods can almost obtain the highest energy efficiency (from 91% to 87%) within the entire power demand range (from 100 W to 500 W), have obvious advantages in comparison of several other methods. The efficiency in the whole New European Driving Cycle (NEDC) driving condition exceeds 85%, and in the most periods of this driving condition exceeds 90%. The results clearly demonstrate validity and reliability of the power distribution optimization methods proposed in this paper. This work aims to solve the problem concerning the optimal power distribution based on energy efficiency maximization of HESS, and provide solution for improving the power output capacity and energy utilization of HESS. We firmly believe that this work has considerable theoretical significance and engineering application value for the promotion of HESS.

Jilin Province Energy Storage in the Source-grid-load Multi-scene Construction

With large-scale access to renewable energy, the configuration of energy storage systems has become an absolutely necessary way to improve the flexibility and reliability of power grid. To solve the problem of low utilization of traditional energy storage systems in a single scenario, this paper discusses the construction of a multi-scene energy storage scenario on the source-grid-load side. Taking the full-scene applications of energy storage in Jilin province for example, which accomplishes the goal of building a new power system with the interaction of all parties in the scenario, the optimal allocation of energy storage is realized, the utilization rate of energy is elevated, and the voltage stability of the grid is improved in the end.

A Two-layer Optimization Model for Energy Storage System Configuration Considering Life Cycle Cost

For solving grid voltage fluctuation as a result of the increase of renewable energy penetration, a two-layer optimization strategy considering the life-cycle cost and benefit is proposed. First, a model of life cycle cost-benefit is established to calculate various costs and benefits of energy storage. Then, the combination of genetic algorithms is determined, which is applied to the inner and outer layers. Finally, the proposed optimal configuration strategy is simulated with a Benchmark low-voltage microgrid. The example is given to verify the effectiveness of the proposed method.

Optimal planning of hybrid electric-hydrogen energy storage systems via multi-objective particle swarm optimization

Abstract: In recent years, hydrogen is rapidly developing because it is environmentally friendly and sustainable. In this case, hydrogen energy storage systems (HESSs) can be widely used in the distribution network. The application of hybrid electric-hydrogen energy storage systems can solve the adverse effects caused by renewable energy access to the distribution network. In order to ensure the rationality and effectiveness of energy storage systems (ESSs) configuration, economic indicators of battery energy storage systems (BESSs) and hydrogen energy storage systems, power loss, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, multi-objective particle swarm optimization (MOPSO) is used to solve Pareto non-dominated set of energy storage systems’ optimal configuration scheme, in which the technique for order preference by similarity to ideal solution (TOPSIS) based on information entropy weight (IEW) is used select the optimal solution in Pareto non-dominated solution set. Based on the extended IEEE-33 system and IEEE-69 system, the rationality of energy storage systems configuration scheme under 20% and 35% renewable energy penetration rate is analyzed. The simulation results show that the power loss can be reduced by 7.9%–22.8% and the voltage fluctuation can be reduced by 40.0%–71% when the renewable energy penetration rate is 20% and 35% respectively in IEEE-33 and 69 nodes systems. Therefore, it can be concluded that the locations and capacities of energy storage systems obtained by multi-objective particle swarm optimization can improve the distribution network stability and economy after accessing renewable generation.

Improvement and Optimization Configuration of Inland Ship Power and Propulsion System

Advances in power and propulsion and energy management improvements can significantly contribute to reducing emissions. The International Maritime Organization (IMO) Marpol regulations impose increasingly stringent restrictions on ship’s emission. According to the measured data of the target ship in typical working profiles, the power fluctuation, fuel consumption and emission data are analyzed, and the result represented that there are serious fuel consumption and pollution problems in the diesel engine power system. Based on the ship-engine propeller matching design theory, the ship-engine propeller model was built, and the new propulsion system power of the target ship was obtained by simulation. From the perspectives of power, economy and green, the performance and emission indexes of diesel engine and LNG engine are compared and analyzed, and the fuel cost advantage, green advantage and power performance disadvantage of LNG engine compared with diesel engine are determined. By comparing the topological structures of different hybrid propulsion forms, the new propulsion form of the ship is improved to be the gas-electric hybrid propulsion system based on the ESS (Energy Storage System), and the selection of the supercapacitors and lithium batteries is compared. Based on the low-pass filter strategy, the power distribution of the ultracapacitor and lithium battery is distributed. In order to determine the optimal ESS configuration, a capacity configuration model with investment cost, fuel cost and energy storage life as objective functions was established. NGSA-II algorithm was used to calculate the model and scheme selection was completed based on the scheme decision model. In this case, the optimal scheme significantly reduces pollutant emissions, it also reduces daily fuel costs by 38% and the result shows that we can complete the cost recovery in 1.28 years.