Optimal Capacity Research Articles

Physicochemical Properties and Metal Ion-Binding Capacity of Thermal-Induced Antarctic Krill Protein Aggregates under Different pH Conditions.

Protein properties can be modified by thermal treatment at different pH values, resulting in the formation of protein aggregates with diverse morphologies and functionalities. This study investigated the morphology of aggregates of Antarctic krill protein (AKPS) formed by thermal treatment (90 °C, 15 min) at pH 2, 3, 4, and 6-12, characterized the different morphologies of AKPS, and determined the metal ion (Ca2+, Fe2+, and Zn2+)-binding capacities. Results showed that heat treatment with different pH values generated various AKPS with distinct morphology and metal ion-binding abilities. AKPS have formed fibrils at pH ≤ 3, particles at pH 4-7, and amorphous aggregates at pH ≥ 8. Fibrous AKPS (pH 2) exhibited a high solubility (80.36%) and strong reducing effect on iron. The binding capacities of Ca2+ and Fe2+ reached 36.08% and 66.75%. Particulate AKPS (pH 6 and 7) primarily only showed Zn2+-binding capacity similar to that of casein phosphopeptide (17.33%). Amorphous AKPS (pH 9-11) displayed the optimal capacity to bind Zn2+ and Fe2+ (26.90% and 74.94%). The alterations in morphology and functional characteristics of AKPS permit the design of various nanostructures for food-derived mineral supplements, thus developing their potential for application in functional foods.

Multi‐operating characteristic analysis and loss optimization of novel large capacity high‐speed permanent magnet generator

AbstractRecently, the development trend of large capacity generator has gradually shifted from electrical excitation generator to permanent magnet generator (PMG). As a result, the large capacity high‐speed permanent magnet generator (HSPMG) with high power density and high efficiency have received widespread attention. This paper aims to analyse the loss optimization of novel large capacity HSPMG under multi‐operating conditions. In terms of the parameter requirements, a 2.5 MW and 7000 rpm HSPMG is designed. To verify the accuracy of simulation model and the rationality of generator design, the output characteristics of large capacity HSPMG which has been combined with prototype experimental data are calculated under both no‐load and load conditions. Based on the load power characteristics of large capacity HSPMG, the optimization range of the magnetization angle under multi‐operating conditions is explored. By analysing the no‐load back electromotive force (EMF) and loss characteristics of large capacity HSPMG, the optimal magnetization angle that corresponds to the minimum loss under three conditions is obtained. The correctness of the analysis and optimization method is verified by comparing the loss distribution and electromagnetic results before and after the optimization. The research results provide a reference for the multi‐operating characteristics analysis and loss optimization of large capacity HSPMG.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

Capacity Optimization of Wind–Solar–Storage Multi-Power Microgrid Based on Two-Layer Model and an Improved Snake Optimization Algorithm

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar power supply and storage capacity as decision variables and the construction cost of the whole life cycle as the objective function. At the lower level, the optimal scheduling model is established, considering the output characteristics of various types of power supplies and energy storage, microgrid sales, and purchases of power as constraints. At the same time, the model considers constraints, such as the power balance, the operating state of the energy storage system, the power sales and purchases, and the network fluctuations, to ensure the system operates efficiently. Taking a microgrid in South China as an application scenario, the model is solved and the optimal capacity allocation scheme of the microgrid is obtained. Meanwhile, the demand response mechanism and the influence of planning years are introduced to further optimize the configuration scheme, and the impact of different rigid–flexible load ratios and various planning horizons on microgrid capacity optimization is analyzed, respectively, by a numerical example. The comparison shows that the ISOA has better optimization performance in solving the proposed two-layer model.

Ergotropy and capacity optimization in Heisenberg spin-chain quantum batteries

Ergotropy and capacity optimization in Heisenberg spin-chain quantum batteries

Removal of Methylene Blue dye from aqueous solution using Fenton-modified submerged aquatic plant Schoenoplectus species (Bulrush)

When dumping coloured effluent into the ecosystem, methylene blue dye contributes significantly to environmental health problems. In this investigation, Schoenoplectus species, commonly known as Bulrush, underwent modification using a Fenton reagent to enhance its effectiveness in adsorbing methylene blue (MB) from effluent. The Fenton-treated Bulrush was analysed using SEM, FTIR, BET, and TGA techniques, while various operational parameters were systematically explored to optimise the adsorbent. The FTIR results revealed an increase in the intensity of chemical compounds (–OH, C=C, and C–O, etc.) The FTIR data showed an increase in the intensity of chemical compounds (-OH, C=C, and C-O, among others) and aromatic structures on the exterior of the adsorbent, which aided in MB removal. The results exhibited conformity with the Langmuir model, with an R2 value of 0.9904 with an optimal adsorption capacity of 41.23 mg/g achieved for the Fenton-treated Bulrush. Kinetic analysis indicated adherence to the pseudo-second-order model (R2 = 1), with chemical bonding adsorption primarily governing the adsorption rate. Optimal adsorption conditions were determined to be 26°C, pH 8, a reaction time of 60 minutes, a dosing amount of 2 g, and a preliminary concentration of 100 mg/L. Enhanced pH and increased dosing of Fenton-treated Bulrush led to improved adsorption capacity. Crystal structural characteristics and surface functional groupings of Fenton-treated Bulrush emerged as crucial factors influencing adsorption, describing the three main processes that control MB absorption by the adsorbent: physical adsorption, electrostatic attraction, and π-π interaction. These processes demonstrated the economical, efficient, and environmentally friendly attributes of Fenton-treated Bulrush, which shows promise for eliminating dye contaminants from aquatic environments.

Zn2Mo3O8/ZnMo8O10/Mo8O23 nanocomposites; structural properties, synthesis and its emerging application in electrochemical hydrogen storage

Electrode materials based on zinc molybdenum oxides were designed by the saccharide-assisted sol-gel procedure for electrochemical hydrogen storage (EHS). By using different characterization methods to observe the formation of nanostructures, it was possible to determine that carbon texture was present on the nanostructured surfaces of zinc molybdenum oxides. The effects of employing various saccharides (mono, di, and poly saccharides) as carbon sources and fuel on the microstructure, electrochemical behaviors, and purity of synthesized samples were compared. The growth of nanocomposites including Zn2Mo3O8, ZnMo8O10, and Mo8O23 phases was facilitated by the saccharide. A charge-discharge method was used to build three-electrode cells and expose them to galvanostatic cycling. To ascertain the stability of electrode architectures, cycling performance at various rates was carried out. The samples' hydrogen storage capabilities were examined, and the sample generated with glucose showed an optimum capacity about 1017 mAhg−1 after 15th cycle.

Implementasi Metode Artificial Bee Colony Untuk Penempatan Distributed Generator Pada Penyulang Rayon Tanjung

Distributed Generation (DG) is a power plant that is spread over the power distribution network. In principle, electrical power output is generated by power plants located far from the load center and electrical power is transmitted to the load center through transmission and distribution networks. The longer the cable from the power plant or substation, the lower the voltage at the end load. Sub-optimal DG placement and size can result in increased power losses and affect the system voltage profile. In addition, suboptimal generation capacity can lead to voltage instability, reduced system security, and potential impacts on system frequency and emissions. Therefore, this study will use the Artificial Bee Colony (ABC) method to determine the optimal DG capacity and location. In the base case condition, the power losses in the system are 118 KW and 99 Kvar. Power losses after the installation of 1 DG active power loss becomes 37.4 KW and reactive power loss becomes 31.65 Kvar. Placement of 2 DGs reduces active power loss to 17 KW and reactive power loss to 15 Kvar. While the placement of 3 DG reduces active power loss to 33 KW and reactive power loss to 28 KVar.

Renewable Energy Investments, Support Schemes and the Dirty Option

ABSTRACTIn a real options framework, we analyze the behavior of a large energy producer who can invest in a portfolio of Renewable Energy Source (RES) and dirty energy source. Competitive fuel prices challenge the investments in RES. Given a budget constraint, the agent allocates the optimal capacities of both energy instalments and selects the optimal investment time. We use the model to compare the effectiveness of classical support schemes such as Feed‐in Tariffs or Green Certificate with respect to forms of taxation of dirty technology such as Carbon Taxes or Carbon Permits. This paper proposes a conceptual framework and qualitative analysis to understand which support system enhances the attractiveness of renewable energy investments.

Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

Efficient defluoridation of drinking water using mesoporous magnetic malachite nanocomposites

The study evaluated the efficacy of magnetic mesoporous Malachite nanoparticles (NPs) in eliminating fluoride (F−) from drinking water. Screening experiments were conducted to gauge the F− adsorption capabilities of the synthesized material under different Fe3O4 loading conditions. Among the various nanomaterials examined, 0.25-Fe-M demonstrated optimal performance, exhibiting consistent Fe3O4 distribution with a crystal size of 16.66 nm with revealed irregular morphology exhibiting magnetic properties, a surface area of 13.595 m2/g and a pore size of 1.6574 nm. The optimized reaction conditions determined were: 10 min of contact time, a NC dose of 0.5 mg/mL, and an F− concentration of 10 mg/L. The maximum adsorption capacities recorded were 6.57 mg/g for Fe3O4 NPs and 7.87 mg/g for malachite NPs. Notably, the optimal adsorption capacity for F− removal was achieved with 0.25 Fe-M-NCs, reaching 8.44 mg/g, demonstrating superior performance compared to other NCs. The interplay between surface area, pore volume, and adsorption is intricate and contingent upon the unique properties of the adsorbent and adsorbate, with specific interactions governing the adsorption process. Furthermore, this study unveiled accelerated adsorption with shorter contact time and high adsorption capacity at the working pH.

Experimental Evaluation of an Innovative Connection for the Reinforcement of Existing Infilled RC Buildings

The retrofitting of existing reinforced concrete (RC) buildings with cross-laminated timber (CLT) panels presents a promising approach for enhancing seismic performance and overall structural resilience. However, effective integration of CLT with existing RC structures poses significant challenges, particularly concerning the design of connections between CLT panels and the RC structure. This paper introduces a novel connection that addresses these challenges by focusing on both structural and architectural considerations. Structurally, the connection is engineered to provide optimal stiffness, strength, and deformation capacity, ensuring robust performance under seismic and dynamic loads. Architecturally, the design incorporates a predefined weak component that facilitates easy access and rapid replacement of damaged parts, thereby reducing downtime and maintenance efforts. The proposed connection was evaluated through a series of monotonic and cyclic loading tests, demonstrating its structural efficiency and reliability. The results indicate that the new connection system not only meets the necessary structural requirements but also offers practical benefits for maintenance and repair, contributing to the overall sustainability and resilience of retrofitted RC buildings. This innovative approach represents a significant advancement in the field of structural retrofitting, providing a viable solution for integrating CLT panels into existing RC frameworks.

Designing an Economical Water Harvesting System Using a Tank with Numerical Simulation Model WASH_2D

Newly incorporated module into the WASH_2D model has enabled simulating a rainwater harvesting system (RWHS) using a tank. The incorporated module in WASH_2D was tested for two field experiments to determine the optimal tank capacity and cultivated area that give the highest net income for farmers. The first experiment was composed of treatments A, B, and C having the same cultivated and harvested areas (plastic sheets) of 24 m2 and 12.5 m2, respectively. The capacity of the tanks for treatments A, B, and C was set at 500, 300, and 200 L, corresponding to storability of 21, 13, and 8 mm, respectively, while in the second experiment we carried out three treatments: F, G, and H having the same tank capacity of 300 L and harvested area of 12.5 m2 with variable cultivated areas as G and H were larger by two and three times than F (10.5 m2), respectively. Water was applied automatically through a drip irrigation system by monitoring soil water suction. Results of the first experiment showed that the optimal storability and seasonal net income simulated by WASH_2D were 17 mm and 5.82 USD yr−1, which were fairly close to 18 mm and 5.75 USD yr−1 observed from field data, respectively. Similarly, the results of the second experiment revealed that simulated net incomes for different cultivated areas agreed well with the observed data. We concluded that the use of the simulation model WASH_2D can be economically useful to promote small-scale irrigation in semi-arid regions and guide planning irrigation or rainwater harvesting investments.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Application of pole allocation to optimize passive viscous dampers represented by the Maxwell model

The Maxwell model is used to understand the realistic effectiveness of vibration reduction in the building structures. The model consists of a dashpot and spring in series. The dashpot and spring represent a viscous damper and joint between the damper and the structural frame, respectively. However, few studies have investigated the optimal damper capacity and the corresponding spring stiffness in the practical parameter ranges. Additionally, the optimal damper derived using the fixed-point theory has no relationships with that derived via pole allocation. Therefore, in this study, to examine the effects of the joint spring, the pole allocation method was used to design a structural system wherein the Maxwell model was incorporated into a single-degree-of-freedom damped model. We introduced a closed-form expression, which explicitly described the relationships between the target structural damping ratio, damper capacity, and joint spring. The Maxwell model constrained the control effectiveness using damper parameters and simultaneously suggested an optimal and realistic joint spring for the damper. Pole allocation was also applied to a multi-degree-of-freedom (M-DOF) damped structural system employing multiple Maxwell models. The newly proposed optimized joint spring could easily control the additional damping effect on the M-DOF system. The Maxwell model can be optimized almost manually. The scheme is more useful in the preliminary design stage than the previous numerical optimizations. This study extended the mathematical equation governing the building vibrations to the Maxwell model. The extended equation served as a unified description for considering structural passive control.

Effect of SiO2 on the Performance of Cellulose/Poly(Vinylidene Fluoride) Films as Polymer Electrolytes for Lithium Ion Battery

AbstractPolymer electrolytes serve as highly efficient alternatives to liquid electrolytes, especially in terms of safety in lithium ion batteries. Among various polymers, Cellulose contains electron‐donating atoms which can coordinate with lithium salts and be used as polymer electrolyte. Also, cellulose can be blended with other polymers to improve their properties. Polymer electrolytes suffer from poor mechanical properties. Adding nanoparticles not only surmounts the drawback of poor mechanical properties but also improves physical and electrochemical properties. In this study, different weight ratios of PVDF/cellulose blend films containing silicon dioxide (SiO2) are prepared via solution casting method. The results explored ionic conductivity in order of 10−4 S cm−1. In addition, high transfer numbers (0.74<t+<0.98), wide electrochemical window (up to 6 V), acceptable charge capacity, and long cycle stability are attained. In details, for 90/10 (wt./wt.) cellulose/PVDF, the ionic conductivity reached 4.4×10−⁴ S cm−1, and the ion transfer number was obtained 0.98. Additionally, the electrochemical stability window for these gel polymer electrolytes exceeded 5 V. The sample containing 90 % cellulose achieved an optimal charge capacity of 225.3, with 93.4 % capacity retention after 200 cycles at 0.2 C.

Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Multi-objective simulation–optimization for water resources management and uses in multi-dam systems in low-water regions

One of the challenging issues in surface water resources management is the optimal operation of multi-dam systems. In addition to the applicability in determining the optimal pattern of operation of existing dams, this approach can be implemented in specifying the optimal capacity of under design dams. In this research, the simulation–optimization technique is used along with the reservoir zoning method in order to specify the optimal role curve of each of the dams in a multi-dam system (i.e., the Marun and Jarreh dams) in monthly intervals. The objective of this study is to develop a multi-objective algorithm on the basis of the zoning of reservoirs for the optimal operation of multi-dam systems. In this technique, instead of increasing the reliability of supplying demands in the whole period regardless the dry months, some of the inflow of rainy periods is stored in dam reservoirs to be consumed in dry months in order to mitigate the severity of deficiency. To examine the efficiency of the proposed model, the results obtained from the system operation in the current conditions are compared under two scenarios including optimal and reference. In the reference scenario, during 30-years, in some dry and low-water years, especially in the last six years of planning, the percentage of the demand supply is zero or less than five percent in several consecutive months. After optimizing the system, the minimum supply percentage in critical and dry months reaches 30 to 60 percent. Also, in the optimization scenario, the percentage of downstream ecological demands is improved. This research indicates that using the solution of this research leads to the better management of reservoirs in multi-dam systems and reduces the severity of deficiency in supplying various uses in low-water months.

Optimal Capacity Allocation for Life Cycle Multiobjective Integrated Energy Systems Considering Capacity Tariffs and Eco-Indicator 99

Traditional energy systems pose a significant threat to human social development due to fossil fuel depletion and environmental pollution. Integrated energy systems (IESs) are widely studied and applied due to their clean and low-carbon characteristics to achieve sustainable development. However, as integrated energy systems expand, their impact on ecosystems becomes more pronounced. This paper introduces the concept of the ecological damage index (EDI) to promote the sustainable development of integrated energy systems. Moreover, the introduction of a capacity tariff mechanism will impact the energy structure, making it essential to consider its effects on capacity allocation within integrated energy systems. This paper proposes a multiobjective optimization framework for constructing a capacity planning model for integrated energy systems, focusing on achieving a multidimensional balance between the economy, environment, and ecosystem using the life cycle assessment (LCA) method. Finally, the nondominated sorting genetic algorithm-II (NSGA-II) is employed to optimize the three objectives and obtain the Pareto frontier solution set. The optimal solution is selected from the solution set by combining the technique for order preference by similarity to ideal solution (TOPSIS) and Shannon entropy method. In comparison to scenarios with incomplete considerations, the multiobjective capacity optimization model proposed in this study exhibits significant improvements across the three metrics of cost, carbon emissions, and the ecological damage index, with a 19.05% reduction in costs, a 26.24% decrease in carbon emissions, and an 8.85% decrease in the ecological damage index. The study demonstrates that the model abandons traditional single-objective research methods by incorporating a multidimensional balance of the economy, environment, and ecosystems. This approach forms a foundational basis for selecting the optimal energy mix and achieving sustainable development in integrated energy systems. The life cycle assessment methodology evaluates impacts across all stages of integrated energy systems, providing a comprehensive basis for assessing and planning the sustainable development of the systems. The study offers guidance for the rational allocation of the integrated energy system capacity and advances the sustainable development of such systems.