Energy Storage Capacity Configuration Research Articles

Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information

Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs’ alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO’s net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods.

Research on energy storage capacity planning based on AGC strategy of new energy power station

Abstract With the rapid development of emerging energy technologies, the percentage of clean energy in the power system has increased. However, the uncertainty surrounding the output of new energy sources contributes to the need for further analysis and evaluation, and its grid supply point will bring challenges to the network frequency. This paper studies this issue. First, different frequency response models are established. Secondly, it proposes a frequency response fuzzy control strategy to the frequency response capability of new energy power stations for the generation control of the collaborative wind energy and energy storage system. Meanwhile, the particle swarm optimization algorithm is used to solve the optimal configuration of energy storage capacity. The actual case is analyzed to verify that the AGC strategy is effective.

Study on the optimization of power planning and design based on the integration of source-grid-load-storage

Abstract In order to improve the level of clean energy utilization and the operation efficiency of the power system, the power supply scale and energy storage capacity configuration of the integrated park project are solved. Based on the historical data on the electricity load in the park, this paper analyzes the characteristics of the monthly and daily electricity load. An integrated mathematical model with operating cost and new energy consumption as the optimization goal and multiple energy operation characteristics as the constraints is proposed to realize the optimization of the comprehensive planning of source-grid-load-storage in the region. Taking an electrolytic aluminium project as a case study, the overall curtailment rate of new energy in the optimized scheme is 3.56%, and the proportion of electricity consumed by new energy is 51.19% of the overall electricity consumption of the project.

Operation strategy and capacity configuration of digital renewable energy power station with energy storage

As the utilization of renewable energy sources continues to expand, energy storage systems assume a crucial role in enabling the effective integration and utilization of renewable energy. This underscores their fundamental significance in mitigating the inherent intermittency and variability associated with renewable energy sources. This study focuses on the involvement of photovoltaic (PV) plants in medium and long-term transactions. It also explores the participation of battery energy storage system (BESS) in electricity trading and frequency regulation ancillary services. The objective is to establish a strategic research model for maximizing the benefits of PV plant and the BESS in the energy arbitrage and frequency regulation markets. Sensitivity analysis was conducted to assess the impact of variations in both the rated power and maximum continuous energy storage duration of the BESS. Base on the NSGA-II algorithm and TOPSIS algorithm, an optimization model for energy storage capacity configuration is developed. The optimal capacity configuration and maximum continuous energy storage duration are determined through computational analysis, yielding values of 30.8 MW and 4.521 h, respectively. At this configuration, the daily average revenue is 2.362 × 105 yuan, the initial investment cost is 1.45 × 109 yuan, and the payback period is 4.562 years.

The short-term intermittency evaluation of distributed photovoltaic power

Uncertainty of distributed photovoltaic(PV) power brings great challenges to the safe and stable operation of power system, in which the intermittency problem is more challenging than the fluctuation. This paper focuses on the intermittency problem of distributed PV power, firstly, an intermittency evaluation method based on generalized extreme value(GEV) theory is proposed to solve the problem of the lack of "small-sample big-gap" events. Secondly, focusing on high risk events such as high-frequency and warning mutation events, probabilistic evaluation indexes were established, including T-hour-period mutation intensity and T-hour-period mutation frequency. Finally, a two-dimensional joint probability distribution of intermittency elements is established, and the concepts of return period and return level are introduced into the photovoltaic field to provide data support for the configuration of energy storage capacity and duration, and to provide auxiliary decision-making for the execution time and development cycle of interactive projects. The proposed method provides a solution for intermittency evaluation in small sample status quo with strong data compatibility and engineering replicability.

Shared Energy Storage Capacity Configuration of a Distribution Network System with Multiple Microgrids Based on a Stackelberg Game

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy.

Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park

The increasing uncertainty and volatility of net load caused by the high penetration of renewable energy leads to higher demand tariffs for industrial park and potentially impacts their economic benefits. To tackle these issues, this paper develops a novel business mode to enable rental energy storage sharing among multiple users within an industrial park, and propose a robust optimization and demand defense-based iterative bi-layer planning framework. The upper layer focuses on the maximization of the investment profitability of shared rental energy storage by developing a robust information gap decision theory optimization. Meanwhile, the lower layer is dedicated to enhancing the demand defense ability of shared rental energy storage in real-time operation through the formulation of a distributed model predictive control. After that, the synchronous alternating direction multiplier method with consistency theory is derived for solving the distributed optimization. Numerical results demonstrate that the proposed shared rental energy storage is 6.391% and 7.714% more economical than shared and self-built energy storage, respectively. Moreover, the iterative bi-layer planning enables flexible energy storage capacity configuration, reduces the impact of net load uncertainty, improves the ability of demand defense, and enhances the system’s overall economy.

Energy Storage Capacity Configuration Planning Considering Dual Scenarios of Peak Shaving and Emergency Frequency Regulation

New energy storage methods based on electrochemistry can not only participate in peak shaving of the power grid but also provide inertia and emergency power support. It is necessary to analyze the planning problem of energy storage from multiple application scenarios, such as peak shaving and emergency frequency regulation. This article proposes an energy storage capacity configuration planning method that considers both peak shaving and emergency frequency regulation scenarios. A frequency response model based on emergency frequency regulation combined with low-frequency load shedding is established, taking into account the frequency safety constraints of the system and the principle of idle time reuse, to establish a bi-level programming model. In the upper-level model, the optimization objective is to minimize the annual operating cost of the system during the planning period, combined with the constraints of power grid operation to plan the energy storage capacity. The lower-level model embeds frequency safety constraints with the optimization objective of minimizing the cost of fault losses. To solve the bi-level optimization problem, the Karush–Kuhn–Tucher (KKT) condition and Big-M method were used to transform the bi-level model into a single-layer linear model. Finally, an improved IEEE RTS-24 system was used for numerical verification. The results show that the method proposed in this article can reasonably plan the capacity of energy storage, improve frequency safety during system operation, and reduce the operating cost of the power grid.

Optimal configuration of energy storage system capacity in traction power supply system considering photovoltaic consumption

In order to achieve energy savings and promote on-site integration of photovoltaic energy in electrified railways, a topology structure is proposed for the integration of photovoltaic (PV) and the energy storage system (ESS) into the traction power supply system (TPSS) based on a railway power conditioner (RPC). This paper analyzes the composition and operation principles of this structure. To assess the economic benefits brought by the integration of photovoltaic and energy storage systems, a bilevel optimization model is established, with the objectives of optimizing energy storage capacity configuration and photovoltaic energy integration. The KKT (Karush–Kuhn–Tucker) method is employed to transform the model into a single-layer mixed-integer linear programming model, which is then solved using the CPLEX solver in MATLAB. The research findings indicate that, with the configuration of the ESS, the optimal PV consumption rate achieved is 96.8749%. Compared to a 100% PV consumption rate, the ESS capacity configuration is reduced by 13.14%, and the overall operational cost of the TPSS is at its lowest. The study suggests that the proposed bilevel optimization algorithm can more effectively consider PV consumption, leading to enhanced economic performance of the TPSS operation.

Thermal energy storage capacity configuration and energy distribution scheme for a 1000MWe S–CO2 coal-fired power plant to realize high-efficiency full-load adjustability

The flexibility transformation of coal-fired power plants (CFPP) is of significant importance for the new power system primarily based on new energy sources. Coupling thermal energy storage (TES) technology is one effective approach to enhance the load-following capability of CFPPs. In this study, the S–CO2 CFPP coupled with TES technology is taken as the research object. An integrated dynamic energy analysis model, from components to the entire system, is established for variable working conditions. A comprehensive comparison is made among different TES methods, including flue gas TES, CO2 TES and electrical heating TES, in terms of system's minimum output power, thermal efficiency, energy round-trip efficiency and other off-design performance indicators. Furthermore, through hierarchical integrated configuration of the three thermal energy storage methods, efficient load regulation from 0% to 100% is achieved for the S–CO2 CFPP. The results indicate that, to achieve efficient load regulation from 0% to 100% for a 1000 MWe S–CO2 CFPP, the priority configuration for thermal energy storage is CO2 TES, followed by flue gas TES and electrical heating TES, with powers of 285.17 MWth, 342.80 MWth, and 329.95 MWth, respectively. The overall heat storage/release ratio is 3.43:1 and the energy storage round-trip efficiency is 73.58%. Compared to using only electrical heating TES, the addition of 142.34 MWth of TES improves the energy round-trip efficiency by 11 percentage points.

An Energy Storage Capacity Configuration Method for a Provincial Power System Considering Flexible Adjustment of the Tie-Line

A high proportion of renewable generators are widely integrated into the power system. Due to the output uncertainty of renewable energy, the demand for flexible resources is greatly increased in order to meet the real-time balance of the system. But the investment cost of flexible resources, such as energy storage equipment, is still high. It is necessary to propose a method for determining the capacity of energy storage scientifically. An optimization and planning method of energy storage capacity is proposed. It is characterized by determining the optimal capacity of energy storage by carrying out 8760 hours of time series simulation for a provincial power grid with energy storage. Firstly, the current situation of power supply and demand for provincial power grids is analyzed. The difficulty of realizing a power balance at different time scales is analyzed. Then, the source load balancing solutions at different time scales are proposed. The difficulty of a long-term power balance can be alleviated by flexibly adjusting the power on the tie-line of the provincial power grid. And the difficulty of a short-term power balance can be met through energy storage. In addition, an optimal planning model of an energy storage system is established with the power supply cost as the objective function. The optimal capacity of the energy storage is determined by comparing the objective function of different planning schemes. Finally, a case study is carried out. It is found that flexible adjustment of interprovincial interconnection lines can reduce the maximum demand for electricity from 8.439 billion kWh to 2.299 billion kWh. At the same time, the curtailment ratio of renewable electricity can be decreased from 12.6% to 5.0% by using energy storage. However, the average power supply cost of the system gradually increases from 0.307 CNY/kWh to 0.485 CNY/kWh. It is necessary to fully tap into the various values of energy storage equipment.

Optimization design of hybrid energy storage capacity configuration for electric ship

Electric propulsion has gained significant popularity in the marine vessel industry due to its numerous advantages. One of the key challenges faced in this domain is effectively managing the fluctuation of power grid caused by sudden load changes. While energy storage systems offer a viable solution, striking the right balance between cost and benefit remains a complex task. To address this issue, establish an optimization model and constraint conditions for capacity configuration of hybrid energy storage systems, and propose a decision-making method based on NSGA-II algorithm and cost-effectiveness method. To address the issue of NSGA-II algorithm being prone to local optima and premature convergence, chaotic thinking and adaptive crossover operators are introduced to improve the NSGA-II algorithm; In order to obtain the optimal configuration scheme from the obtained Pareto optimal solution set, a decision method based on cost-effectiveness was adopted to further analyze the bias between the Pareto optimal solution set and each objective. By comparing the decision results with the ideal solution (TOPSIS), the optimal capacity configuration scheme was obtained.

Optimal configuration of multi microgrid electric hydrogen hybrid energy storage capacity based on distributed robustness

The combination of energy storage and microgrids is an important technical path to address the uncertainty of distributed wind and solar resources and reduce their impact on the safety and stability of large power grids. With the increasing penetration rate of distributed wind and solar power generation, how to optimize capacity configuration of hybrid energy storage capacity to improve system economy and reliability has become a research hotspot. This article establishes a multi microgrid interaction system with electric‑hydrogen hybrid energy storage. The microgrid system uses distributed wind and solar power as the power source. Then, considering the uncertainty of wind and solar power, a distributed robust model with the goal of system operation economy and reliability was established. This model is used to optimize the configuration of energy storage capacity for electric‑hydrogen hybrid energy storage multi microgrid system and compare the economic costs of the system under different energy storage plans. Finally, the article analyzes the impact of key factors such as hydrogen energy storage investment cost, hydrogen price, and system loss rate on energy storage capacity. The results indicate that reducing the investment cost of hydrogen energy storage is the key to reduce operating cost of multi microgrid hybrid energy storage system.

Configuration and Robust Optimization Method of Energy Storage Capacity on the User Side of Power Grid Based on Improved Grey Wolf Algorithm

The configuration and optimization of energy storage capacity on the user side of the power grid are currently active research areas in the power system. This article presents a method that utilizes an enhanced Grey Wolf algorithm to address the issues of configuring and optimizing energy storage capacity on the user side. The Grey Wolf algorithm is improved by incorporating differential evolution and calculating the fitness value. Based on this, the objective function for the configuration model of user-side energy storage capacity is established, considering the uncertainty parameters as uncertainty set to achieve robust optimization. The design of the method for user-side energy storage capacity configuration and robust optimization, utilizing the improved Grey Wolf algorithm, is accomplished. The experimental results confirm the practicality of the proposed method.

Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition

The capacity configuration of the energy storage system plays a crucial role in enhancing the reliability of the power supply, power quality, and renewable energy utilization in microgrids. Based on variational mode decomposition (VMD), a capacity optimization configuration model for a hybrid energy storage system (HESS) consisting of batteries and supercapacitors is established to achieve the optimal configuration of energy storage capacity in wind–solar complementary islanded microgrids. Firstly, based on the energy mapping relationship between the frequency domain and time domain, the decomposition mode number K of VMD is determined based on the principle of minimum total mode aliasing energy. Then, considering the smoothing fluctuation characteristics of different energy storage components, the dividing point N of high frequency and low frequency in the unbalanced power between the source and load in the microgrid is selected to allocate charging and discharging power instructions for the battery and supercapacitor. Finally, taking the annual comprehensive cost of the HESS as the objective function, a hybrid energy storage capacity optimization configuration model is established, and the dividing point N is used as the optimization variable to solve the model in order to obtain the optimal configuration results. The case study results show that the proposed method is more economical and feasible than the existing energy storage configuration methods.

Research on Calculation Method of Energy Storage Capacity Configuration for Primary Frequency Control of Photovoltaic Power Station

An energy storage capacity allocation method is proposed to support primary frequency control of photovoltaic power station, which is difficult to achieve safe and stable operation after a high proportion of photovoltaic connected to public power grid. In this paper, by taking the photovoltaic power plant containing energy storage as an example, and based on the fluctuation characteristics of photovoltaic power output and the performance requirements of primary frequency control response, the required battery storage capacity of photovoltaic power station for primary frequency control is calculated without considering the power abandonment and power limitation conditions, and the typical case is established based on power system simulation software to verify the reasonableness and effectiveness.

Optimal Allocation of Energy Storage Capacity in Distributed Micro-grid Based on Improved Whale Algorithm

At present, the setting of monitoring nodes for optimal allocation of energy storage capacity in micro-grid is generally target-based, with slow allocation speed and weak pertinence, which can easily lead to the increase of the final difference adjustment coefficient and affect the final allocation effect. Therefore, the research and analysis on the optimal allocation method of distributed micro-grid energy storage capacity based on the improved whale algorithm are proposed. According to the actual disposal requirements and standards, the basic operation frequency and reserve capacity of the micro-grid are determined, the multi-level monitoring node deployment form is set, the monitoring range is expanded, and the speed and pertinence of configuration are improved to complete the layout of energy storage capacity monitoring nodes. The optimal allocation model of the improved whale calculation of grid energy storage capacity is constructed. The optimal allocation is realized by constraint programming. The test results verify that the energy storage power of the micro-grid will be enhanced with the increase of the frequency modulation times of energy storage calculated by the whale calculation optimization configuration test group. In this context, the difference adjustment coefficient of the micro-grid configuration becomes lower, only 1.03, indicating that in the process of practical application, the energy storage capacity configuration of micro-grid is optimized. The frequency modulation fluctuation is slowed down, the running frequency is faster, and the method has practical application value.

A Hybrid Energy Storage System Strategy for Smoothing Photovoltaic Power Fluctuation Based on Improved HHO-VMD

To solve the problems of large fluctuation of photovoltaic output power affecting the safe operation of the power grid, a hybrid energy storage capacity configuration strategy based on the improved Harris hawks optimization algorithm optimizing variational mode decomposition (IHHO-VMD) is proposed. In this strategy, the improved Harris hawk optimization algorithm is used to adaptively select k and α in VMD parameters and decompose the photovoltaic output power and distinguish between correlated and uncorrelated modes. Similarly, the moving average method (MA) is used to extract the continuous component signal in the uncorrelated mode, and it is reconstructed with the related mode as the grid-connected power that meets the national standard. The hybrid energy storage system (HESS) is used to stabilize the fluctuation component signal. The minimum annual configuration cost of the energy storage system is established as the objective function. The simulation results show that the improved algorithm reduces the cost of the hybrid energy storage system by 6.15% compared with the original algorithm, suppresses the power fluctuation, and improves the economy and stability of the system.

Research on energy storage allocation strategy considering smoothing the fluctuation of renewable energy

Energy storage technology can effectively solve the problems caused by large-scale grid connection of renewable energy with volatility and uncertainty. Due to the high cost of the energy storage system, the research on capacity allocation of energy storage system has important theoretical and application value. In this paper, an optimization method for determining the capacity of energy storage system for smoothing the power output of renewable energy is proposed. First, based on the actual data of Ulanqab, the output characteristics of wind power and photovoltaic power generation are studied, and the K-means algorithm is used to select typical days. Then, the energy storage configuration model is built according to the objective function and constraints. Finally, genetic algorithm is used to solve the optimization model, obtain the corresponding parameters, and complete the configuration of energy storage capacity. Based on the results of renewable energy spectrum analysis, the minimum capacity of the energy storage system that meets the constraint of target power output volatility after compensation by the energy storage system can be optimized. The simulation results show that at 1 and 10 min, the flattened volatility is about 2% and 5%, while the actual penetration volatility is about 20% and 30%. The volatility of the optimized model is greatly reduced, which proves the effectiveness of the proposed strategy.

Optimal capacity configuration of the wind-storage combined frequency regulation system considering secondary frequency drop

With wind power integrated into the power system on a large scale, the system has become vulnerable to the frequency stability issue. The battery energy storage system (BESS) is considered the key solution to improving the system frequency regulation performance due to its fast response ability. Furthermore, the construction of wind-storage combined frequency regulation systems has been developed for many years, in which the optimal capacity configuration of the wind-storage system is getting more attention. However, the secondary frequency drop (SFD) caused by wind turbines (WTs) participating in primary frequency regulation (PFR) is neglected in most existing capacity configurations, which is worthy of further study. In this paper, the optimal capacity of the wind-storage combined frequency regulation system is studied from the perspective of SFD. The time-domain expressions of two-stage system frequency response considering SFD are derived based on the wind-storage combined frequency regulation model. Next, considering the technical and economic characteristics of wind-storage combined frequency regulation, an optimization model of the energy storage capacity configuration is established with the objective of minimizing the sum of the maximum frequency deviations in two stages and the energy storage cost. The optimization model is solved by the multi-objective salp swarm algorithm (MSSA) to obtain the setting value of wind-storage combined frequency regulation parameters and the optimal energy storage capacity. The effectiveness of the proposed method is verified in MATLAB. The simulation results show that the proposed model can effectively improve the frequency regulation effect of the system and ensure the optimal capacity configuration with better economy.