Optimal Operation Strategy Research Articles

Enhancing periodic mobility planning: ARIMA-driven prognostications for subway passenger volume dynamics

Abstract. This study aims to accurately predict urban subway passenger flow using the Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, with the intention of providing a scientific basis for subway operation management. By meticulously sorting and analyzing the existing traffic data of a large city's subway system, this research constructs a model that comprehensively considering the seasonal, trend, and stochastic characteristics of time series data. During the model construction process, data preprocessing is carried out, including stationarity tests, differencing, model order determination, and parameter estimation, ultimately identifying an ARIMA and SARIMA model with good fitting results, effectively identifying and forecasting characteristics such as numerical fluctuations. Additionally, this study conducts a special analysis of the prediction effects at diverse time scales, verifying the applicability and superiority of the ARIMA and SARIMA model in subway passenger flow prediction. This research not only provides a powerful tool for passenger flow prediction for subway operation management departments, aiding in the reasonable allocation of capacity and optimization of operation strategies, but also holds significant reference and application value for the passenger flow prediction and management of other urban public transportation systems.

Optimal operating strategy of hybrid heat pump − boiler systems with photovoltaics and battery storage

The growing need to reduce energy consumption and greenhouse gas emissions is driving the search for more efficient heating solutions in buildings. Hybrid heating systems, which combine air-to-water heat pumps (AWHP) with traditional gas boilers, are a common solution after refurbishment investments. However, managing these systems effectively, particularly when integrated with photovoltaic (PV) panels and battery energy storage systems (BESS), remains a complex task. For instance, heat pumps perform poorly in very cold conditions, making boilers a more efficient option; however, it might be advantageous to use it to increase electricity self-consumption. Optimal management depends on multiple factors, including future forecast data. In this paper, a daily optimization program is developed by means of a brute-force approach using forecast data. The core innovation of this paper is the use of an artificial neural network (ANN) that, trained on predictive optimization results, can determine the optimal solution in real-time without the need for future forecasts. The ANN achieved a 99.16% accuracy in new scenarios, successfully optimizing costs, CO2 emissions, and primary energy use. Results indicate up to 19% cost savings in colder cities, a 12% reduction in CO2 emissions, and a 3% decrease in primary energy consumption. This approach holds significant potential for enhancing the integration of renewable energy sources, contributing to long-term sustainability goals.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Research on energy interactive operation optimization strategy of multi-energy network with integrated energy based on cooperative game

Abstract Integrated energy includes multi-energy networks such as electricity, gas, cold, and heat. Horizontal multi-energy coordination and vertical source network storage load interaction can effectively support large-scale new energy consumption. In this paper, a multi-energy flow model of the integrated energy multi-energy network is constructed, an optimization model of energy sharing and mutual benefit of the multi-energy network is established based on Nash negotiation, and then the model is solved to obtain the optimal distribution scheme of cooperation benefits. Finally, a numerical example is given to verify the proposed energy sharing and mutual benefit method. The results show that the total power generation cost of each park is reduced by 27%, and the profit distribution of each park is more reasonable.

Optimization of operational strategies for industrial applications of solar-based green hydrogen

Renewable-based green hydrogen is emerging as a viable solution for decarbonizing energy-intensive industries. However, the fluctuating and intermittent nature of renewable energy sources requires careful consideration of operational strategies to ensure the safe and economical use of green hydrogen in industrial applications. This study examines the influence of operational strategies for electrolyzers and downstream processes on the cost of solar-based green hydrogen. The results demonstrate that optimal operational strategies for electrolyzers significantly impact the sizes of batteries and electrolyzers, reducing the Levelized Cost of Hydrogen (LCOH) by an average of 5.3%. Case studies further demonstrate that decentralized end-user supply strategies and cooperation with downstream processes can reduce LCOH by up to 39.8% for fully distributed end-users and by 24.2% with the introduction of supply tolerance. Additionally, the study evaluates the impact of grid backup, revealing that grid utilization can reduce LCOH by 12.9% in Houston, underscoring the potential for further cost reductions through grid decarbonization. These findings highlight the importance of optimizing operational strategies to improve the economic performance and market viability of green hydrogen systems.

The efficient strategy of operating the active cooling systems incorporated with photovoltaic panels for the three climatic regions: A case study

ABSTRACT Photovoltaic stations in Algeria are considered an effective solution to overcome the problem of its lack of electrical power and achieve the goals of sustainable development. The weather in Algeria is continental, it has three main climate zones from north to south, and the climate in Algeria is desert, which causes an increase in the temperature of photovoltaic modules and thus a decrease in their production of electrical power. Then the active cooling systems that use water as a cooling medium represent a good option to reduce the PV module temperature and thus increase electrical power generation rates. Due to the change in weather in Algeria from north to south and during the four seasons, in some periods, the operation of cooling systems is ineffective and leads to a decrease in the net output electrical power of photovoltaic stations. Therefore, this manuscript aims to develop an advanced strategy for operating active cooling systems incorporated with PV stations to achieve the highest net electrical power output from PV stations in Algeria. To achieve this goal, the theoretical study was conducted on three different regions in Algeria that express its continental climate, namely, Algiers (36°46′ N, 3°03′ E), northern Algeria, and Djelfa (34°40′ N 3°15′ E) in central Algeria and Ghardaïa (32°29′ N 3°40′ E) in southern Algeria, to get the optimal cooling strategies that achieve the highest net electrical power produced by PV stations. The simulation results presented that the average improvement in the electrical power generated from the PV panels incorporated with active cooling systems reached 15.1%, 17.8%, and 19.7% under the climate conditions of Algiers, Djelfa, and Ghardaïa, respectively. The optimal operating strategies of active cooling systems integrated with PV stations in Algeria increase the average net output electrical power of the PV stations by a rate reached 7.65%, 7.13%, and 4.91% for Algiers, Djelfa, and Ghardaïa, respectively.

Bi-Level Operation Optimization and Performance Analysis for a Distributed Energy System with Energy Network

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

Study on the operation strategy of the water cooling system in the single-phase immersion cooling data center considering cooling tower performance

Energy analysis from an entire systems perspective can further improve the energy efficiency of single-phase immersion cooling technology. In this study, a water cooling system for the single-phase immersion data center was developed using a combination of FLOWNEX SE and FLUENT software. Firstly, the energy consumption analysis was carried out from the perspective of cooling system, and an optimal operation strategy was identified to minimize energy consumption. Furthermore, the influence of ambient temperature and chip power on the operation strategy and energy consumption were analyzed, and the optimal operation strategy under different conditions was given. Finally, a comparison was made between the two operating modes of the water cooling system. The analysis results showed that there was an optimal operation strategy which should be adapted based on ambient temperature and chip power to reduce the power consumption. Moreover, it was noted that when the environmental temperature rises above 22 °C, the PUE of the water cooling system surpasses 1.2, indicating that the water cooling system becomes unsuitable for single-phase immersion data centers in such conditions. In addition, the excessive cooling of the chip can be avoided through the optimal operation strategy. And when the chip power was 600 W, the optimal operation strategy can reduce the PUE by 16 % and save 5.71 kW energy.

Experimental evaluations of Berkeley thermal sensation and comfort models in electric vehicle cabin under cold outdoor conditions

As the automobile industry is transitioning toward electric vehicles, manufacturers have started implementing local warmers alongside cabin heating, ventilation, and air conditioning (HVAC) systems for effective thermal comfort management. However, optimal operating strategies need to be developed for integrating local warmers with HVAC systems. Although the Berkeley models comprising local/overall thermal sensation and comfort models offer insights in this regard, they lack follow-up assessments for occupants transitioning from very cold states. In this study, Berkeley models were evaluated using two sets of experimental data collected in a transient vehicle cabin under cold outdoor conditions: test (A) with cabin HVAC alone and test (B) with both HVAC and local warmers. The findings confirm the satisfactory performances of the Berkeley models for predicting overall sensation and comfort, with a maximum root mean-squared error (RMSE) of 0.15. The local comfort model performed poorly with the original coefficients across both datasets (maximum RMSE of 1.96). Therefore, the model coefficients were regressed for the dataset from test A and validated against the dataset from test B to achieve a maximum RMSE of 0.49. With these regressed coefficients, it was observed that moving toward a neutral overall state diminished the potential to maximize local comfort. Conversely, the local sensation model showed poor agreement (maximum RMSE of 1.9); we confirmed that accurate adaptive setpoint temperatures are a prerequisite for ensuring good predictions from the model. These findings are expected to contribute toward future efforts in using Berkeley models to formulate effective local warmer–HVAC operational strategies in electric vehicles.

Differential Pressure Power Generation in UGS: Operational Optimization Model and Its Implications for Carbon Emission Reduction

Underground Gas Storage (UGS) is an important facility for natural gas peak adjustment and ensuring the balance between energy supply and demand. The daily gas injection and production capacity of large UGS can reach several hundred million or even billions of cubic meters, and the energy consumed by the injection and production equipment is not to be underestimated, which also contains a large amount of pressure energy that can be utilized for power generation. In this context, the optimization of low-carbon operation of UGS is of great practical significance in engineering. In this study, an innovative UGS system operation optimization method is proposed to integrate a natural gas differential pressure generator set in the UGS system. Considering the complex interconnections among multiple storage areas, multiple wells and wellsites, and the hydro-thermodynamic constraints during the operation of injection and production pipeline network, and with the objective of minimizing the carbon emission, the UGS integrated with Differential Pressure Power Generation System (UGSIDPPGS) is established. In order to solve this complex model, this paper designs an optimization solution framework based on variational assignment, which effectively avoids the problem of falling into inferior solutions during the solution process. The optimization model is applied to the case study of a large-scale depleted gas reservoir in China, and the optimal operation schemes under three scenarios are successfully obtained. The results show that the UGSIDPPGS optimization model, not only effectively utilizes the pressure energy, generates up to 56.908×106kW·h per month, and reduces the CO2 emission by 56,738 tons, but also achieves the low-carbon and economic operation of the UGS. In conclusion, the operation strategy proposed in this study is of great value for optimizing the operation of UGS, and provides practical guidance and suggestions for the low carbon and environmental protection of UGS and the utilization of new energy sources.

Optimal operation of multi-energy microgrids with shared energy storage clusters by master-slave games

Abstract As shared energy storage power plants have become a research hotspot, the cooperation between them and microgrids has attracted attention. In this paper, we propose a master-slave game optimization operation strategy for multi-energy microgrids, taking into account shared energy storage (ESS) clusters. Firstly, the inclusion of ESS clusters is considered in a combined heat and power (CHP) type multi-energy microgrid. Then, the optimal operation model is established and solved in two layers. The master-slave game model in the context of ESS clusters is proposed by analyzing the game relationship between the microgrid side and the user side, the arithmetic simulation with MATLAB, and the modeling and solving are carried out by the Yalmip tool and the CPLEX solver, and the combination of heuristic algorithms and the solver is used to optimize the strategies of the microgrid side and the user side. The optimization of the system after the optimization of this paper’s methodology can enhance the system economy.

A low-impact nature-based solution for reducing aquatic microplastics from freshwater ecosystems

Effective nature-based solutions (NBS) and strategies for freshwater microplastic (MP) pollution are beneficial for reducing ecological and human health risks. This study proposed an innovative NBS for the in-situ retention of aquatic MPs. By evaluating the tolerance and MP retention efficiency of different submerged macrophytes, Myriophyllum aquaticum was identified as a well-suited system for optimization as NBS for operational MP retainment practice. The response surface method and artificial neural network modeling were applied to determine the optimal operational strategy of this solution, which was determined to be at a flow rate of 60 L/h, aeration intensity of 5 m3/(m2·h), and plant density of 190 plants/m2. Under this strategy, an average MP retention of 93.38% was achieved for the actual tested lake. The retention of MPs was particularly effective for particle sizes larger than 100 μm (especially films and fragments) and for the 4 polymer types. At the same time, also total nitrogen and phosphorus levels in the treated waters were reduced by 80.0% and 78.4% respectively, reflecting the added environmental value for water purification. This NBS provides a feasible strategy for mitigating MP pollution, but further research is needed on its long-term applicability and potential ecological effects in a wider range of specific environments, and effective development of plant harvesting cycle strategies is also essential to achieve long-lasting MP pollution removal.

Optimal operation strategy for renewable power plants based on 5G base stations response

Abstract The integration of large-scale new energy sources has led to a significant challenge in electricity supply and demand balancing within the power system. To address this issue, it is crucial to establish an optimization strategy of renewable power plant based on 5G base stations response. Firstly, according to the operating state of the base station, the energy storage schedulable strategy of the base station was designed. Then, through the regulation function of the base station’s reserve energy storage and the operation principle of the renewable energy power plant, the operation model of the renewable power plant considering the base station’s participation and response was established. Finally, evaluation indexes such as the absolute peak-to-valley difference, the standard deviation of daily load and the peak-valley difference rate can be used to measure system optimization results. The results show that the proposed strategy improves the source load imbalance in the power system while enhancing reliability and economy of new energy.

Including dynamic capacity impact in an improved model for optimal flood control operation in river-type reservoirs

Flood control operations in river-type reservoirs are significantly affected by both the dynamic reservoir capacity and flood propagation within the reservoir area. However, the traditional reservoir flood control optimal operation (RFCOO) model is usually based on the static capacity method, which may bring large errors in scheduling calculations for river-type reservoirs. To address this issue, an improved model, the reservoir dynamic capacity flood control optimal operation (RDCFCOO) model, that includes the influence of the dynamic reservoir capacity and flood propagation, was developed to improve the accuracy of flood regulation calculations in river-type reservoirs. This improved model includes a 1D unsteady flow simulation and a rederived objective function based on the maximum peak clipping criterion, and expresses the state transformation equations using the de Saint-Venant equations. Furthermore, a multi-core parallel DP (PDP) algorithm that incorporates reduction of state dimensionality (RSD) was developed to effectively derive optimal operation schemes. The improved model and algorithm developed herein were validated through an application to the Xiangjiaba Reservoir, China. The results show that: (1) By adding the item (qiw) to the objective function and substituting the de Saint-Venant equations for the water balance equation, the RDCFCOO model developed in this paper can account for the impact of dynamic capacity and flood propagation. As a result, it exhibits a more accurate and detailed flood control process that was closer to the actual conditions of river-type reservoirs compared to the RFCOO model; (2) By removing the invalid discrete state points from the search space and fully utilizing multi-core processors, the proposed PDP with RSD can significantly improve the computational efficiency of the DP in solving RDCFCOO problems. The improved model and algorithm can effectively improve the calculation accuracy of flood control optimal scheduling in river-type reservoirs and provide more information for decision-makers.

Dynamic Simulation and Performance Analysis of Alkaline Water Electrolyzers for Renewable Energy-Powered Hydrogen Production

This paper presents a comprehensive study on the dynamic simulation modeling of alkaline water electrolyzers. Detailed experimental testing and characteristic analysis reveal that alkaline water electrolyzers have long startup times, rapid dynamic responses, and poor dynamic stability. These characteristics are critical for the development of accurate models and effective strategies. A dynamic simulation model was established in MATLAB R2022b and Simulink, enabling standalone simulation operation and module encapsulation. This model facilitates the construction of hydrogen production clusters and serves as a foundational tool for system strategy research. Simulations of rated current loading and unloading for four electrolyzers over 6 h showed significant differences in startup and operation. Key parameters such as cell voltage, maximum loadable power, hydrogen production efficiency, and energy consumption were analyzed. Temperature simulations indicated significant differences in thermal equilibrium points and cooling modes among the electrolyzers, as determined by structural design and cooling system efficiency. These findings highlight the need for efficiency improvements in high-current density electrolyzers. Overall, the model effectively represents commercial electrolyzer characteristics and offers a reliable tool for future research on control strategies for adapting hydrogen production systems to renewable energy power fluctuations, laying a solid foundation for the optimization of electrolyzer design and operation strategies.

Operation optimization of large-scale natural gas pipeline networks based on intelligent algorithm

With the expansion of natural gas pipelines into larger networks, there is a growing concern regarding energy consumption in the operations of pipeline systems. However, the intricate network structure and hydraulic properties pose challenges for operation optimization. In this paper, a Mixed Integer Nonlinear Programming model is proposed to optimize the steady-state operation of natural gas pipeline network, aiming to minimize the energy consumption of compressor units. The model incorporates constraints related to flow direction, flow balance, pressure drop in pipeline sections, and pressure increase at compressor stations. To solve the complex nonlinear model efficiently, a stochastic optimization algorithm that integrates particle swarm optimization and high-fidelity simulation is proposed. The case study is conducted on a large-scale natural gas pipeline network consisting of forty-three pipeline sections and four compressor stations. The optimal operation scheme is calculated, and the outlet pressures of each compressor are determined. The results demonstrate that the stochastic optimization algorithm proposed in this paper can reduce energy consumption by 21.23 % at most and 19.77 % on average during pipeline operation, which can provide guidance for the operation management of large-scale natural gas pipeline network.

A Wide-Range TCSC Based ADN in Mountainous Areas Considering Hydropower-Photovoltaic-ESS Complementarity.

Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

Optimal control strategies for energy storage systems for HUB substation considering multiple distribution networks

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.