Capacity Of Storage System Research Articles

Decarbonising energy supply: the potential impact on district heating networks of the integration of thermal energy storage and substitution of peak load with base load

In 2021, district heating networks provided 6.4 TWh of thermal demand in Switzerland, representing a market share of about 6%. This share is forecast to reach 38% by 2050. More than 50% of those networks rely on base load provided by biomass, complemented by fossil fuel peak load. This paper describes a strategy that would see storage being integrated to substitute peak load with base load. A university campus in Scotland acts as the reference use case to test the suggested fixed-order control strategy. Given these study conditions, the capacities of different storage systems are varied, and their potential for substitution and decarbonisation is accounted for. The study indicates that the integration of small storage volumes seems very promising albeit leading to limited results pertaining to decarbonisation. Depending on fossil fuel prices, a full decarbonisation potential depending on large seasonal storage volumes could be found and a value of 0.14 €/kWh could be calculated as economic threshold to fully decarbonise the reference case.

Estimation of the Optimal Storage Capacity of Rainwater Harvesting System Considering Regional Climate Characteristics

Due to the influence of climate change, the frequency and intensity of droughts in the Korean Peninsula regions are increasing. Moreover, regional rainfall deviations are increasing, increasing the severity of localized droughts. Accordingly, it is necessary to enhance water resource utilization through recycling and establishing a robust safety net for water supply. This necessitates the implementation of rainwater harvesting systems to augment water reuse capabilities. When planning the storage capacity of a rainwater harvesting system, it is important to calculate the appropriate capacity. Therefore, we created an Excel tool for water balance analysis in this study. It incorporates diverse rainwater uses, their respective usage conditions, and daily usage quantities. A water balance analysis was performed, applying the climate characteristics of different regions. A cost-benefit analysis was applied to the economic feasibility analysis, and a B/C regression curve by region and catchment area was derived based on the results of the water balance analysis. The tool is believed to be an effective support measurement to help decision-makers to calculate the appropriate storage capacity of rainwater harvesting systems in the relevant regions.

Hybrid emperor penguin glowworm swarm optimiser for techno-economical optimisation with demand side management in microgrid using multi-objective function

Recently, microgrids have been engaged with Demand Side Management (DSM), which helps to save more energy. Hence, the existing model suffers based on the slow response, and also it cannot restore the system voltage and frequency. Consequently, the voltage and frequency fluctuations in the microgrid become a challenging task. Thus, this research work aims to develop a new model for the microgrid. Here, a new Hybrid Emperor Penguin Glowworm Swarm Optimisation algorithm is developed with the integration of Emperor Penguin Optimiser (EPO) and Glowworm Swarm Optimisation (GSO). Moreover, the techno-economical model minimises the energy supply cost, Diesel Generator (DG), power loss and voltage variations. It implements the Wind Turbines, DG, optimal capacity of PV and Energy Storage Systems, through techno-economical optimisation for offering renewable energy sources. Moreover, the designed model helps to solve the multi-objective function regarding metrics like initial cost, operation cost, DSM, voltage fluctuations, fuel cost, Electricity Supply Costs and network power losses. The performance of the developed model shows 17.6%, 36.9%, 2.4% and 0.4% better performance than HHO, EPO, GSO and IMP-SPA in terms of voltage fluctuations. Throughout the validation, the offered model shows superior performance when compared with state-of-art models.

Enhanced Operation of Ice Storage System for Peak Load Management in Shopping Malls across Diverse Climate Zones

There exists a notable research gap concerning the application of ice storage systems in shopping mall settings at the urban scale. The characteristics of large pedestrian flow, high energy consumption, and high peak loads in shopping malls make their advantages in energy conservation. This study researches sustainable cooling solutions by undertaking an economic analysis of the ice storage systems within shopping malls across 11 distinct cities, each system operating under varied electricity pricing frameworks. The methodology begins with creating baseline mall models using AutoBPS and refining them with OpenStudio. Before starting to adjust the model, measured data were used to verify the accuracy of the baseline model, the coefficient of variation of the root mean square error (CVRMSE) and normalized mean bias error (NMBE) metrics were calculated for the model energy consumption, with CVRMSE values of 8.6% and NMBE values of 1.57% for the electricity consumption, while the metrics for the gas consumption were 12.9% and 1.24%, respectively. The study extends its inquiry to encompass comprehensive economic evaluations based on the unique electricity pricing of each city. This rigorous assessment discerns the relationship between capacity, operational strategies, and economic performance. Particularly striking are the so-called peak-shaving and valley-filling effects verified in regions characterized by lower latitudes and substantial cooling loads. The interaction between ice storage capacity and operational schedules significantly influences both economic viability and cooling efficiency. Based on the temporal dynamics of time-of-use (TOU) power pricing, a finely calibrated operational schedule for the ice storage system is proposed. This operational strategy entails charging during periods of reduced electricity pricing to undertake cooling loads during peak electricity pricing intervals, culminating in substantial reductions in electricity charges of buildings. Moreover, the strategic reallocation of energy, characterized by a reduced chiller capacity and a corresponding elevation in ice storage system capacity, augments cooling efficiency and diminishes cooling-related electricity expenses. This study offers valuable insights for optimizing and deploying ice storage systems in diverse climatic regions, particularly for shopping malls. As a guiding reference, this paper provides stakeholders with a framework to reasonably apply and adjust ice storage systems, ushering in an era of energy-efficient and environmentally conscious cooling solutions tailored to shopping mall environments.

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.

THE TECHNO-ECONOMIC EVALUATION OF THE BETWEEN A GRID EXTENSION AND HYBRID RENEWABLE SYSTEMS CONSIDERING DEFERRABLE LOADS

The rapid depletion of fossil energy resources significantly increases the need for renewable energy resources (RES) in electricity production. Hybrid power systems (HPS) are a promising solution for rural electrification where grid extensions are uneconomical. This study investigated the technical, economic, and environmental aspects of on-grid or off-grid HPS performance for optimal rural electrification. In addition, the effects of different deferrable load values on grid extension distance (GE) and optimal off-grid system sizing were investigated. Sensitivity analyses were conducted to evaluate the effects of variations in solar irradiation potential, diesel fuel costs, and discount rates on optimal HPS sizing. In scenarios where the deferrable load is above 9%, the GEs were zero, while below 5%, they increased to 24.2 km. In contrast, when the diesel generator (DG) was integrated into HPS, the photovoltaic (PV) and energy storage system (ESS) capacities were reduced by half in the optimal scenarios, and it was found that the GE was zeros regardless of the deferrable load. In the case of the highest deferrable load, the NPC is 22.6% lower than when there is no deferrable load. NPC surpasses the energy cost in the grid-only condition when solar irradiation is less than 4 kWh/m2/day, and ESS cost multipliers are greater than 2. This study will help researchers find optimal electrification solutions that support hybrid renewable energy and environmentally friendly options.

Research on the Stability of Grid Connected Wind Turbine Combined with Energy Storage Power System Based on a New Wind Power Fluctuation Smoothing Strategy

Wind power equipped with an energy storage system (ESS) has been demonstrated as the best potential configuration for a rapid global energy transition in the future. In the traditional anti-pulse average filtering control strategy for controlling wind power fluctuations, the smoothing window interval is a constant value, when the forecast output power error is considerable, the fixed window interval width may not be able to meet the smoothing demand, so the traditional strategy has the problems of bad smoothing effect and high probability for power fluctuations exceeding the limit. We propose an optimization strategy for energy storage operation in this paper. Firstly, this strategy can dynamically adjust the window interval width according to the power prediction error, and find the optimal window interval width that makes the output power volatility satisfy requirements for grid connection, so as to achieve the effect of improving the smoothing effect. Secondly, by optimizing hydrogen storage systems operation to reduce the demand for storage system capacity, the odds of output power volatility exceeding the limits are reduced. The outcomes indicate that this new smoothing strategy is effective in improving the stability of the wind power grid connection.

DFT study of Hydrogen storage capacity on Hf doped graphene

We explored the molecular hydrogen storage capacity of complex system with Hafnium doped graphene (Gr-Hf) using the Density Functional Theory (DFT) method. We efficiently adsorbed molecular six hydrogens on the Hf doped graphene surface. The quantum chemically calculated adsorption energy is found negatively in the range of - 344.433 Ry to -333.836 Ry this implies as increasing the adsorbed hydrogen molecule on hafnium doped graphene (Gr-Hf) sheet the adsorption energy decreases continuously. The binding energy of after adsorbing second hydrogen molecule too much larger than the next adsorbed H2 molecules i.e., the binding energy per hydrogen molecule highly decreases when we increase adsorbed atom (2.197 Ry in 2H2 to 1.048 Ry in 3H2) then small decreases for next adsorbed H2 molecules. The extracted binding energy found in the range 2.197 Ry to 2.120 Ry, fermi energy found minimum for 1H2 shows the minimum electron occupancy at the different energy levels. The fermi energy increases accordingly, the electron occupancy also increases and evaluates higher electron occupancy with fermi energy 2.850 eV for 6H2 and density of states (DOS) confirm the weak interaction between σ bonding electrons of H2 molecule with Hf doped graphene complex system. The proposed system opens a new insight for hydrogen storage-based devices.

Research on key technologies of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization

Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.

Multi‐time scale adequacy evaluation of the power system with high penetration of renewable energy sources based on empirical mode decomposition

AbstractAdequacy, especially in the power system with high penetration of renewable energy sources, is a crucial consideration in power system planning and operational dispatching. A multi‐time scale adequacy evaluation method based on empirical mode decomposition (EMD) is proposed, taking into account the multi‐time scale characteristics of renewable energy sources and the response characteristics of flexible regulation resources. In this method, a net load curve is decomposed into several component curves by the EMD algorithm. Then, the adequacy demand on each time scale is obtained by waveform recognition. Furthermore, the available adequacy resources on different time scales are quantified according to the regulation models of flexible resources. By analysing the adequacy demand and available adequacy resources of the power system on the same time scale, the adequacy evaluation indices on each time scale can be calculated and weighted to form the comprehensive indices. Finally, the adequacy evaluation indices on each time scale with different capacities of renewable energy sources and energy storage systems are compared and analysed using a practical power system as a case. Simulation results show the validity of the multi‐time scale adequacy evaluation method.

Modelling of the Standalone Onshore Charging Station: The Nexus between Offshore Renewables and All-Electric Ships

The environmental sustainability of the battery-powered electric ferry (BEF) depends on the share of renewable energy sources (RESs) in the power system. The BEFs impose significant load on the power grid and it is challenging to achieve their sustainability in isolated energy systems (IES), such as islands that have limited land available for integrating RESs. The proposed solution for achieving the sustainability of BEF in such challenging environments is the integration of a standalone onshore charging station (OCS) powered with a hybrid offshore RES. The nexus between the electricity supply of the offshore photovoltaic (PV) and wind power systems and the electricity demand of BEF can reduce the required capacity of the energy storage systems (ESSs) and improve the environmental and economic performance of the standalone OCS. This study considered 45 standalone OCS scenarios to select the share of the offshore RES that matches the BEF electricity demand and improves the economic and environmental indicators of the standalone OCS. The model included characterisation of the offshore RES electricity supply, BEF electricity demand, critical excess of electricity production (CEEP), critical deficit of electricity production (CDEP) and costs. Simulations were conducted in EnergyPLAN, which used a high-resolution spatiotemporal dataset (8784 h) for the Island of Cres for 2030. The results confirmed that standalone OCS with the hybrid offshore RES provides a better match with BEF electricity demand compared to the scenarios based on an individual offshore RES.

Optimal Capacity of a Battery Energy Storage System Based on Solar Variability Index to Smooth out Power Fluctuations in PV-Diesel Microgrids

Battery energy storage systems can be integrated with photovoltaic (PV)-diesel microgrids as an enabling technology to increase the penetration of PV systems and aid microgrid stability by smoothing out the power fluctuations of the PV systems. This paper focuses on this topic and aims to derive correlations between the optimal capacity of the smoothing batteries and variabilities in the daily solar irradiance. To this end, the two most commonly used moving average and ramp rate control techniques are employed on a real solar irradiance dataset with a 1-min resolution for a full calendar year across 11 sites in Australia. The paper then presents the developed empirical model, based on linear regressions, to estimate the batteries’ optimal capacity without requiring detailed simulation studies, which are useful for practitioners at the early stages of a project’s feasibility evaluation. The performance of the developed technique is validated through numerical simulation studies in MATLAB®. The study demonstrates that the empirical model provided reasonably accurate estimates when using the moving average smoothing technique but had limited accuracy under the ramp rate control technique.

Economic evaluation of energy storage integrated with wind power

Energy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A Particle Swarm Optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm.

Comprehensive performance analysis of a rural building integrated PV/T wall in hot summer and cold winter region

The building-integrated PV/T technologies have garnered significant attention due to their considerable potential in low-carbon and zero-energy buildings. To gain deeper insights into the practical application of this technology, a comprehensive study was conducted on a rural building integrated with PV/T walls for demonstration purposes. A novel simulation approach, coupling the numerical models of the PV/T walls with the building energy software, was proposed and experimentally validated. Based on the validated simulation approach, this study investigated the comprehensive performance of the demo building and explored the optimal capacity of the energy storage system (ESS). The main findings are as follows: (1) The proposed simulation approach could accurately predict the energy behaviors of the multi-room building integrated PV/T wall. (2) The demo building with the optimized EES yielded 1355.32 kWh of clean electricity and 455.3 kWh of heat gain for water heating, while reducing cooling and heating loads by 5% and 12%, respectively. (3) The optimal azimuth for the building in the studied region was 45°, and the total energy benefits of the building decreased by 28.5% with an increase in dust density from 0 g/m2 to 10 g/m2.

Energy Storage Capacity Optimization for Improving the Autonomy of Grid-Connected Microgrid

To support the autonomy and economy of grid-connected microgrid (MG), we propose an energy storage system (ESS) capacity optimization model considering the internal energy autonomy indicator and grid supply point (GSP) resilience management method to quantitatively characterize the energy balance and power stability characteristics. Based on these, we establish a three-stage coupled model including investment decision, day-ahead operation strategy, and real-time power fluctuation smoothing control. Investment processes solve the balance of ESS investment and internal energy autonomy. Day-ahead operational scheduling mainly solves unit commitment problems in MG, which are constrained by the ESS capacities. For real-time power fluctuation smoothing, we propose a power fluctuation smoothing control strategy, coordinated by ESS and direct load control (DLC), to achieve GSP resilience management. Furthermore, we use Monte Carlo Simulations, stochastic scenario combinations, and uncertainty set to characterize the fluctuations of both supply and demand at the above different stages, respectively. To solve the established model with multi-objective, multi-uncertainties, and multi-stage coupling, a robust counterpart method has been used to convert the uncertainty problem to the deterministic one, and then decomposition-based multi-objective evolutionary algorithm (MOEA/D) to efficiently used to solve the problem. Finally, simulations are conducted to verify the effectiveness of the proposed model.

Hierarchical optimal allocation of BESS using APT-FPSO based on stochastic programming model considering voltage sensitivity and eigenvalues analyses

Integration of energy storage systems (ESSs) to microgrids (MGs) due to the growing penetration of intermittent energy resources is a great motivation to develop studies in the context of optimal sizing and placement of these systems. In this paper, a multi-objective bi-level optimization problem is addressed based on the stochastic nature and dynamic model of power system elements.As a preliminary step, the optimal size and capacity of the battery energy storage system (BESS) are determined to minimize frequency fluctuations. This level has been performed based on three approaches considering the daily intra-hour stochastic data. In the secondary level, the dynamic behaviour of all MG elements has been considered and correspondingly a dynamic objective function based on system eigenvalues and voltage sensitivity index (VSI) has been developed. According to the proposed objective function, the secondary step for sizing and placement of the battery units has been performed by adaptive particularly tuneable fuzzy particle swarm optimization (APT-FPSO) algorithm to reduce power losses, improve the voltage profile/ stability, and mitigate low-frequency fluctuations. Finally, the effectiveness of the proposed approach is evaluated using time-domain simulations, voltage sensitivity studies, and eigenvalue analysis.

Linking geological and infrastructural requirements for large-scale underground hydrogen storage in Germany

Hydrogen storage might be key to the success of the hydrogen economy, and hence the energy transition in Germany. One option for cost-effective storage of large quantities of hydrogen is the geological subsurface. However, previous experience with underground hydrogen storage is restricted to salt caverns, which are limited in size and space. In contrast, pore storage facilities in aquifers -and/or depleted hydrocarbon reservoirs- could play a vital role in meeting base load needs due to their wide availability and large storage capacity, but experiences are limited to past operations with hydrogen-bearing town gas. To overcome this barrier, here we investigate hydrogen storage in porous storage systems in a two-step process: 1) First, we investigate positive and cautionary indicators for safe operations of hydrogen storage in pore storage systems. 2) Second, we estimate hydrogen storage capacities of pore storage systems in (current and decommissioned) underground natural gas storage systems and saline aquifers. Our systematic review highlights that optimal storage conditions in terms of energy content and hydrogen quality are found in sandstone reservoirs in absence of carbonate and iron bearing accessory minerals at a depth of approx. 1,100 m and a temperature of at least 40°C. Porosity and permeability of the reservoir formation should be at least 20% and 5 × 10−13 m2 (∼500 mD), respectively. In addition, the pH of the brine should fall below 6 and the salinity should exceed 100 mg/L. Based on these estimates, the total hydrogen storage capacity in underground natural gas storages is estimated to be up to 8 billion cubic meters or (0.72 Mt at STP) corresponding to 29 TWh of energy equivalent of hydrogen. Saline aquifers may offer additional storage capacities of 81.6–691.8 Mt of hydrogen, which amounts to 3.2 to 27.3 PWh of energy equivalent of hydrogen, the majority of which is located in the North German basin. Pore storage systems could therefore become a crucial element of the future German hydrogen infrastructure, especially in regions with large industrial hydrogen (storage) demand and likely hydrogen imports via pipelines and ships.

Determination of a New Performance Indicator for the Assessment of Stand-Alone PV System

The use of stand-alone PV systems (SAPV) must be efficient and profitable for a better integration of solar energy in the global energy mix. However, the performance indicators that allow the evaluation of SAPV systems do not clearly inform us about the actual level of use of their sized and installed capacity. This article aims to determine a new performance indicator, called the theoretical power factor (TPF) by an original method based on the modelling of the SAPV system in the form of a matrix equation. The resolution of this matrix equation, makes it possible to bring out the reactive energy of the system during operation. A case study is presented and scenario I represents the case where the main elements are all assumed to operate at their rated capacity. scenario II represents the case were the rated capacity of storage system is reduced of 40%, scenario III represents the case were the rated current capacity of charge controller is reduced of 40%, and finally scenario IV represents the case were the rated power capacity of inverter is also reduced of 40%. The results obtained after implementation in the Spyder environment (python 5.1) show the effectiveness of TPF in the performance evaluation of SAPV systems. And also show how the TPF is substantially related to the capacity of each main element of the system. This being proved by the results obtained after the simulation of the four scenarios mentioned above. One can observe an increase in TPF of 0.1% in Scenario II during the period of low irradiance, and no change in TPF for the other scenarios in the same period. During the period of high irradiance, an increase in TPF of 17.9% is observed in scenario II and a decrease in TPF of 15.4% and 1.2% respectively in scenarios III and IV.

Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage

Integrated energy system (IES) is characterized as a heterogeneous system, providing energetic, economic and environmental benefits by cooperating with multiple energy carriers. However, current studies rarely focus on utilizing hybrid electric/thermal storage in coordination with harvesting heat flexibly in IES, which should be further considered in the planning period. To this end, a novel IES combined with hybrid electric/thermal storage and multi-output organic Rankine cycle (ORC) is proposed. Considering operating modes of gas turbine, ORC’s working manners, and converting patterns of harvesting heat in IES, comparisons of case study and operating characteristics on typical season days are analyzed. A bi-level optimal planning model of hybrid electric/thermal storage with ORC is proposed, consisting of a lower-level operating model and an upper-level planning model. Results show that there is the least operating cost of IES on typical season days under the off-design mode of gas turbine, ORC’s combined heat and power (CHP) mode and harvesting heat flexibly, when enabling multi-flows to adjust thermoelectric ratio effectively. ORC’s pure power generating mode in IES with low electrical load is not economically attractive, while ORC’s CHP mode is beneficial to enhance ORC’s consistent dispatching time and electricity producing amount. State of charge of battery energy storage system has similar varying patterns, while heat storage degree and cold storage degree of thermal energy storage system change differently owing to varying thermal loads. The IES is thermodynamically feasible and economically attractive thanks to the bi-level optimization, achieving optimal battery energy storage system capacity of 1773 kWh, thermal energy storage system capacity of 4823 kWh and ORC capacity of 91.25 kW.

Switching control strategy for an energy storage system based on multi-level logic judgment

Energy storage is a new, flexibly adjusting resource with prospects for broad application in power systems with high proportions of renewable energy integration. However, energy storage systems have spare capacity under stable working conditions and may be idle for some periods. These actions are primarily selected for peak shaving and valley filling, frequency regulation, and voltage regulation as the only control target; thus, energy storage cannot be used effectively, which weakens the effect of energy storage on grid support. To improve the utilization rate and economic benefits of the energy storage system and enhance the support performance of energy storage for the safe operation of the power grid, this article proposes a switching control strategy for an energy storage system based on multi-layer logic judgment to maximize energy storage benefits and ensure safe and stable power grid operation. First, this study analyzed the potential multi-ancillary service operation requirements of the energy storage system, combined with the auxiliary compensation benefits of the energy storage power station. Using this information, the study proposed a comprehensive index that considers the economy of the energy storage system and the stable operation of the power grid to support the evaluation needs of energy storage control. Based on this, the study then pre-set multi-layer judgment logic for the operation control of the energy storage system. A multi-objective judgment and smooth switching strategy for the coordinated operation of the energy storage system was proposed based on the typical operating conditions of the energy storage system participating in the grid peak shaving and valley filling, frequency regulation, and voltage regulation. This switching control method effectively utilized the idle capacity of the energy storage system and improved the energy storage system’s support effect on the power grid. Through the improved energy storage control model based on MATLAB/Simulink, this study also verified the effectiveness of the proposed smooth switching strategy of the energy storage system.