Optimal Capacity Research Articles

Capacity Optimization for RSMA-Based Multi-User System over Underwater Turbulence Channel

The underwater environment used for communication is harsh and complex, necessitating heightened standards for spectral efficiency and reliability in underwater wireless optical communication (UWOC) systems. The focus of this work is on the performance of multi-user UWOC systems operating in oblique channels of ocean turbulence downlink, where users are randomly distributed at a certain depth. A joint optimization scheme is proposed, which joints rate-splitting multiple access (RSMA) and power allocation so that the system’s ergodic sum capacity is optimized to improve the transmission bandwidth. Furthermore, the probability density function (PDF) and cumulative distribution function (CDF) models for the received signal-to-noise ratio (SNR) of a multi-user multiple-input multiple-output (MIMO) system operating in the turbulent underwater oblique channels are established, accounting for the avalanche photodiode (APD) shot noise and solar radiation noise. Theoretical derivations are presented to quantify the ergodic capacity and outage probability of the multi-user system utilizing the RSMA technology. Subsequently, a numerical analysis is conducted to investigate the influence of the power allocation coefficient, RSMA, and the joint optimization algorithm on the performance of a two-user MIMO system leveraging RSMA. The simulation results show that our optimization scheme effectively reduces the outage probability, thereby achieving the maximum system sum rate and validating the practical feasibility and efficacy of the proposed scheme.

Performance Investigations on All-Solid-State Polymer-Ceramic Sodium-Ion Batteries through a Spatially Resolved Electrochemical Model

Rechargeable batteries are crucial in modern energy storage, with lithium-ion batteries dominating the market. However, the scarcity and environmental concerns associated with lithium have spurred interest in alternative battery chemistries, particularly sodium-ion batteries (SIBs), which utilize abundant sodium resources. Despite extensive experimental research on all-solid-state SIBs (ASSSIBs), theoretical investigations have primarily focused on molecular-level analyses, overlooking the impact of cell composition on overall performance. This paper aims to address this gap by developing a physical model for simulating ASSSIBs at the particle scale. Our methodology involves integrating experimental data with simulation results to identify key factors influencing battery performance. The study reveals slow sodium ion transport as a significant bottleneck, attributed to factors such as low porosity of the half-cell and limited electrolyte ionic conductivity. Simulation outcomes emphasize the importance of advancing fast-ion-conducting solid electrolytes to enhance ASSSIB performance. Moreover, the results suggest that electrodes with high electrolyte active filler content and reduced thickness are necessary for achieving optimal battery capacity utilization. Overall, this research underscores the intricate relationship between electrode microstructure and battery performance, offering valuable insights for the design and optimization of sustainable sodium-ion battery systems suitable for stationary and mobile applications.

An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network

In this study, a stochastic multi-objective structure for optimization of the intelligent electric parking lots (EPLs) is implemented in the distribution network for minimizing the power losses annual costs, power purchased from the main grid, unsupplied energy of subscribers, cost of vehicles to the grid as well as minimizing the network voltage deviations considering battery degradation cost (BDC) and network load uncertainty (NLUn). In this research, the unscented transformation method (UTM) is used for NLUn modeling and this method is easily applicable and has a low computational cost. An improved meta-heuristic algorithm named improved fire hawks optimization (IFHO) is utilized for decision variables finding defined as the site and size of the EPLs in the distribution network. The conventional fire hawks optimization (FHO) algorithm is inspired by the fire hawks foraging behavior and in this research, the Taylor-based neighborhood technique (TBNT) is used to reduce the dependency and the possibility of becoming trapped in local optimal. To evaluate the proposed methodology, the simulations are implemented in three scenarios (1) EPLs optimization without BDC and NLUn based-UTM, (2) EPLs optimization with BDC and without NLUn, and (3) EPLs optimization with BDC and NLUn. The results of the third scenario considering BDC and NLUn showed that the EPLs optimization integrated with a multi-objective framework by finding the EPL's optimal size and capacity in the network via the IFHO has reduced the annual losses, voltage deviations, ENS cost, and substation cost by 21.06%, 12.15%, 70.82%, and 39.10%, respectively compared to the base distribution network. Additionally, the results demonstrated that incorporating the BDC and NLUn, the annual losses, voltage oscillations, ENS cost, grid cost, and EPLs have increased in comparison with the EPLs optimization without BDC and NLUn based-UTM. In addition, the TBNT based-IFHO superiority has been confirmed in different scenarios by achieving better values of the objectives and also obtaining the convergence process with lower convergence tolerance and higher convergence accuracy.

An intelligent decision support framework for nursing home resource planning with enhanced heterogeneous service demand modeling

Demand-based nursing home resource planning is of great importance to ensure adequate resources (e.g., beds and staffs) available to provide care services with desired quality, yet challenging. The challenge mainly lies in modeling heterogeneous demand of nursing home residents, reflected by various individual characteristics, diverse dwelling duration with multiple competing discharge dispositions, and diverse daily service need. Existing studies often assumed a homogeneous population of patients and neglected the complexity of demand heterogeneity and uncertainty, leading to biased demand estimation and misguided decisions. The objective of this work is to improve nursing home resource planning decisions in response to the complex demand heterogeneity and uncertainty. To address the challenges, we propose a novel knowledge-guided and data-driven decision support framework. This is the first work of integrating domain knowledge with predictive and decision analytics to enhance modeling fidelity and decision performance for nursing home resource planning. Specifically, to effectively capture different aspects of heterogeneous demand, we develop a novel knowledge-guided demand modeling module with predictive models, including a length-of-stay model with competing risk for duration analysis, a tree-based system for learning daily service need variations, and a demand simulator for capturing uncertainty of fluctuating demand. Moreover, to determine optimal capacity and staffing decisions under demand heterogeneity and uncertainty, we develop a demand-based decision-making module with effective optimization models and solution algorithms, ensuring satisfactory quality of care at reduced costs. Furthermore, to demonstrate the improved prediction and decision performances of the proposed framework, we provide a proof-of-the-concept case study using real data from our industrial collaborator and investigate how demand heterogeneity and uncertainty will impact resource planning decisions. The proposed framework also demonstrates its appealing adaptability under changing resident census compositions.

Take-away and sit-down service operations under inequity aversion

Profit contributed by take-away service has become an increasingly essential element of the restaurant operating revenue. Since take-away service usually relies on the third-party platform and there are many differences between the cost of take-away service and that of sit-down service. We focus on the restaurant which provides both sit-down service and take-away service, the service system is modeled as a two-stage tandem queueing system. We study the restaurant’s optimal capacity level for each stage. Besides, as there exist price/waiting time difference between the two services, inequity aversion is also investigated in our model. We study symmetrical and asymmetrical inequity aversion. We find that the optimal service capacity consists of two parts, base capacity and safety capacity. And the loss resulted from waiting time lag is equal to the waste of resources caused by fluctuations in arrival rate. Further, when customers really long for the restaurant, even if high price will lead to severe inequity aversion, restaurant can always earn more by raising the price in service channel with high revenue. While when customers are indifferent of the restaurant and the others, the price gap is meant to result in revenue decrease. In addition, reduction in customers’ susceptibility can help to enhance operation profit in general. Market environment plays a decisive role in choosing the optimal service level.

Optimized control of hybrid energy storage systems for microgrids

Abstract The development of new energy sources is considered to be one of the important ways to solve the current energy shortage problem. Clean energy sources such as wind and light have attracted much attention. Still, the volatility and uncertainty of their output power create difficulties for the large-scale connection of the system to the power grid. Based on the microgrid system, this paper focuses on the energy optimization control problem of storage batteries and supercapacitors. A low-pass filter is used based on the charge state SOC expressed as a formula. The working state of the supercapacitor charge is divided into five regions, with each region having a corresponding reference value for output power correction. The capacity optimization model of the hybrid energy storage device is constructed, and the Gaussian variational quantum particle swarm algorithm (GM-QPSO) is used to optimize the capacity of the hybrid energy storage device. It is found that the capacity optimization method proposed in this paper not only improves the stability of the operation of the system but also ensures the overall economy of the system.

Enhancing yield of modern maize (ZeamaysL.) hybrids through the optimization of population photosynthetic capacity and light-nitrogen efficiency under high density

Due to the breeding of dense-resistant and lodging-resistant varieties in maize production, dense planting has become an effective means for achieving high and stable yields, while excellent hybrids are a prerequisite for reasonable dense planting in maize production. Nonetheless, the photosynthetic mechanism of improving plant density tolerance of maize hybrids released at different era in China remains unclear. This study aims to investigate the 40-year breeding effort for enhanced photosynthetic trait at different densities, and elucidate the physiological and ecological mechanisms of improving the density tolerance of maize hybrids. We conducted a 3-year study in 2019, 2020, and 2021. From 1970 to 2009, a comparison was made between the eight major hybrids promoted in China, divided into four decades, under three planting densities (45,000 (D1), 67,500 (D2), and 90,000 (D3) plants ha−1). At high density, modern hybrids had more rational canopy structure and leaf photosynthetic performance compared with old hybrids and specific leaf nitrogen has decreased slightly. Among all treatments, the modern hybrids (2000s) were able to maintain higher net photosynthetic rate and photosynthetic nitrogen utilization efficiency (PNUE) at D3 density, and therefore possessed the highest grain yield (GY), which was 118.47% higher than that of the old hybrids (1970s). Leaf area duration after anthesis, total chlorophyll content, photosynthesis key enzyme activities, and maximum efficiency of PSII photochemistry were all positively correlated with GY, with PNUE was more significantly correlated with GY indeed and is a key indicator for maize hybrids optimization. Based on these results, breeders should continue to conduct hybrid selections under adverse and high-density conditions, focusing on the optimization of population structure and the continuous improvement of photosynthetic capacity, searching for the optimal leaf nitrogen-content status, so as to select and breed high-yielding and density-tolerance hybrids, which resulted in a sustained increase in maize GY.

Traffic analysis of different geometries of a junction for optimization of vehicle capacity and traffic safety increase

Abstract Identifying various vehicle movement characteristics inside a network requires a careful assessment and different measurements technics, in regards to road traffic studies. These measures are influenced by a variety of variables, including the direction of traffic, changes in daily transportation demand, junction geometry and location in areal of study, style of road and also the size of the study area. Examining various geometries and arrangements of lanes with different distributions of vehicle platoons of the junction based on traffic-related elements is the goal of this essay. The analysis of the junction in regards to the speed, volume, capacity, pollution and traffic safety can give alternative solution and provide a foundation for the implementation of various political strategies in the area.

Electrochemical Synthesis of Metal‐Organic Frameworks Based on Zinc(II) and Mixed Ligands Benzene‐1,4‐Dicarboxylate (BDC) and 4,4′‐Bipyridine (Bpy) and its Preliminary Study on CO2 Adsorption Capacities

AbstractEfficient and environmentally friendly synthesis methods for metal‐organic frameworks (MOFs) have emerged as a compelling topic in the organometallic field. In this study, we successfully electro‐synthesized a MOF based on mixed ligands, benzene‐1,4‐dicarboxylate (BDC) and 4,4′‐bipyridine (Bpy), denoted as MOF‐508b, and investigated its CO2 adsorption capacity, comparing it with that of Zn‐BDC and Zn‐Bpy coordination polymers. Unlike the circular shapes of Zn‐BDC and Zn‐Bpy, MOF‐508 adopts a three‐dimensional structure upon binding with the pillar ligand, featuring mesoporous pore sizes. MOF‐508b demonstrates superior stability at room temperature, exhibiting a thermal stability of 400 °C. The CO2 capture potential of the materials was assessed under low‐pressure conditions at room temperature. The pillared layer of MOF‐508b results in the reduction of open metal sites, facilitating the binding of active CO2 through coordination, thereby influencing its optimum CO2 adsorption capacity (2.51 mmol g−1), which was found to be lower than that of Zn‐Bpy (3.83 mmol g−1) and Zn‐BDC (4.46 mmol g−1). The adsorption mechanism on MOF‐508b follows the Weber‐Morris model, emphasizing diffusion within particles over direct attachment to the material surface.

Damage mitigation strategies for progressive collapse of modular steel buildings following sudden corner module removal

AbstractThis study evaluates gravity‐induced progressive collapse performance and damage mitigation strategies in modular steel buildings (MSBs) A typical three‐dimensional six‐story structure was modeled and analyzed using the finite element method, focusing on the instantaneous removal of a corner module. The study includes a parametric evaluation of the influence of beam moment capacity on progressive collapse and investigates the effect of using concrete as an infill for square hollow section columns. Additionally, the effectiveness of short K‐shaped braces in limiting gravity‐induced collapse was assessed. A response/damage index (DI) was proposed to quantify the overall structural response. The findings indicate that semirigid beam‐column connections outperform fully rigid connections. Both concrete‐filled columns and K‐shaped braces mitigate structural damage, with K‐shaped braces proving more effective in preventing collapse. Although increasing the beam moment‐resisting capacity reduced the DI, improvements were marginal beyond 50% of the fully rigid connection capacity. Higher beam moment capacities reduced local vertical displacement at the corner joint but led to undesirable lateral deformations. Reducing beam rigidity/capacity by 25% mitigated global structural deformations. The optimal beam moment capacity was identified as 75% of the total section capacity when using concrete‐filled columns. K‐shaped braces applied at the column midpoint entirely prevented progressive gravity‐induced collapse and no lateral deformations were observed in braced structures with fully rigid beam‐column connections, unlike unbraced structures. Concrete‐filled columns offer a cost‐effective solution for preliminary production of MSBs, while K‐shaped braces are suitable for retrofitting existing buildings. Further research is necessary to address other parameters influencing the mitigation of progressive collapse in MSBs.

Revenue Maximization for a Battery Storage With Optimal Capacity Revamping Due to Cyclic Fade

Revenue Maximization for a Battery Storage With Optimal Capacity Revamping Due to Cyclic Fade

Optimization of capacity configuration and comprehensive evaluation of a renewable energy electrolysis of water for hydrogen production system

The global green hydrogen industry is experiencing rapid growth, but the high production costs are hindering its widespread adoption. To address this challenge, it is particularly important to rationally configure a renewable energy hydrogen production system. For this purpose, the study proposes a model for capacity optimization configuration of a renewable energy hydrogen production system, which integrates wind power, photovoltaic (PV) power, and concentrating solar power (CSP) with alkaline electrolyzer. It conducts capacity optimization configuration and comprehensive evaluations of the hydrogen production system across various scenarios. To minimize the total daily consumption cost, the CPLEX solver is utilized to solve the objective function and determine the capacity configuration of the renewable energy electrolysis of water hydrogen production system generator set under various scenarios. This approach achieves a utilization rate of over 99% for renewable energy. Through comprehensive evaluation, research has found that renewable energy-coupled hydrogen production significantly reduces generator capacity and electricity generation costs compared to separate hydrogen production, enhancing the economic efficiency of the system. The Wind-PV-CSP coupling hydrogen production system has the smallest generator assembly capacity and the lowest hydrogen production cost, which is 18.84 CNY·kg−1, significantly lower than the cost of PV-CSP coupling hydrogen production (25.78 CNY·kg−1) and wind-PV coupling hydrogen production (25.86 CNY·kg−1). It has good development prospects and plays an important role in exploring the development path of large-scale on-site consumption of new energy.

A novel multi-objective decision-making model to determine optimum resource and capacity configuration for hybrid electricity generation systems: A comparative case study in Türkiye

The rapid depletion of fossil fuels, their negative environmental impacts due to harmful carbon emissions, and the drawbacks of the sole use of intermittent renewable energy resources on system reliability necessitate the use of more advanced energy system technologies. As a result, hybrid electricity generation systems offer an innovative alternative to conventional energy systems. However, the technical complexity and variability of these systems require the application of multi-objective optimization techniques to determine the optimal sizing and system design. Therefore, in this study, we suggest a multi-objective decision-making (MODM) model that involves five conflicting objectives to find the optimal resource configuration and auxiliary source capacity allocation within the framework of the multi-source electricity production method in Türkiye. The MODM model aims to maximize the utilization of resource potentials and the jobs created, while simultaneously minimizing the levelized cost of energy (LCOE), construction duration, and carbon emissions. The MODM model was solved using fmincon and fgoalattain multi-objective optimization algorithms using the Goal Attainment Method in MATLAB and the model results were subsequently compared to the real-world data. According to the model results, the optimal capacity to be allocated for the auxiliary source units is 13,749.58 MWm, while the capacity allocated according to the real-world data as of March 2024 is 2495.49 MWm. In contrast to the model's prediction of 90.8 % solar-based auxiliary unit installations, 99.8 % of auxiliary units are based on solar PV in the actual case. Furthermore, hydro or geothermal-based auxiliary sources are not allocated any capacity, which is consistent with the actual situation. The capacity allocated for the wind (primary source) + solar (auxiliary source) configuration is 76.2 %, whereas in reality, it accounts for 65.6 % of the total installations. Consequently, the model findings suggest that the maximum capacity allocation is for the wind (primary source) + solar (auxiliary source) type hybrid electricity generation system.

Exceptional indoor carbon capture using epoxide-modified polyamine functionalized materials

In addressing concerns related to symptoms like dizziness, nausea, and reduced productivity stemming from elevated indoor CO2 levels, the primary objective of the current CO2 adsorption technology is to enhance the adsorption capacity. However, the stability and applicability of the adsorbent are of greater importance in indoor carbon capture than in other settings. This study examines the performance and stability of indoor CO2 capture using propylene oxide-modified tetraethylenepentamine supported on novel super carbon, gas-phase hydrophilic silica dioxide, and graphitic phase carbon nitride carriers. The corresponding painted films were developed to facilitate the application in indoor CO2 capture. Thermogravimetric analysis demonstrates that the propylene oxide-modified materials are more stable than the unmodified compositions. The findings indicate that an optimal adsorption capacity is achieved when water is used as the solvent and carboxymethyl cellulose sodium as a dispersant, while maintaining a carrier-to-tetraethylenepentamine mass ratio of 1:0.75. Furthermore, the painted films are stably formed by using isopropanol instead of water, and the tetraethylenepentamine-to-propylene oxide molar ratio of 1:2 for super carbon and graphitic phase carbon nitride as carriers, which exhibit notable enhancements in adsorption performance by 160.0 % and 41.8 %, respectively. The film using super carbon as supporter exhibited the highest adsorption capacity of 122.83 mg/g. This suggests that the compositions are more readily available for indoor CO2 capture by coating in everyday scenarios. Notably, even after undergoing five adsorption–desorption cycles, the desorption efficiency of the solid amine adsorbent remains consistently above 95 %.

Developing an optimization framework for capacity planning of hydrogen-based residential energy hub

— This paper introduces a comprehensive modeling approach for optimizing the capacities of various energy converters and storage systems within a residential Energy Hub (EH). The model focuses on minimizing energy supply costs to customers while maximizing the utilization of renewable and hydrogen-based technologies. Through the optimization process, we determined that implementing CHP units in the EH could reduce total costs by approximately 78% with green tax incentives and by 46.7% without them. Additionally, the inclusion of an incentive to the EH resulted in a 27.2% reduction in total costs imposed on subscribers.The study also evaluates the impact of uncertainties in photovoltaic (PV) electricity generation, demonstrating the benefits of using stochastic optimization over deterministic methods. The results indicate that the economic advantage of this approach over deterministic methods is 86.75%–95.74% greater, depending on the scenario. Sensitivity analysis further reveals that the investment costs of fuel cells significantly influence the optimal capacity design and economic viability of the EH model.

Capacity configuration of cascaded hydro-wind-photovoltaic complementary systems considering comprehensive benefits based on a synergetic-orderly framework

The rational configuration of hydro-wind-photovoltaic complementary system is fundamental to fully leveraging the regulation capacity of hydropower and the complementarity between different natural resources. However, complementary systems involving cascaded reservoirs often have comprehensive utilization requirements, which current techno-economic frameworks struggle to address. To solve the above issues, this study proposes a novel framework based on the theory of synergetics to determine the optimal capacities for wind and photovoltaic power considering the comprehensive benefits. Firstly, an inner operation model aimed at maximizing overall order degree is established to optimize the system’s operational performance. Secondly, Kolmogorov entropy is utilized in the outer layer to characterize the synergy of different wind and photovoltaic capacity schemes, thereby selecting the one with the best synergistic effect. Additionally, current techno-economic evaluation indicators are introduced to validate the framework’s effectiveness. A case study of the clean energy base on the upper Yellow River was conducted, and the results indicate that: (1) The proposed framework effectively addresses the comprehensive benefits of multiple subsystems and the optimal capacity scheme performs well in economic and technical aspects. (2) Compared to variations in wind and solar resources, inflow conditions and agricultural water demand have a greater impact on the capacity configuration and operational performance. (3) The optimal capacity ratio of hydro to renewable energy in the upper Yellow River is around 1:0.59. Therefore, this study provides important theoretical support for expanding capacity configuration methods in multi-energy complementary systems.

Analysis of 5G Capacity in Line-Of-Sight Condition of Urban Macro (UMa) Using 3.5 GHz Frequency

This study is projected to be the initial design for the implementation of 5G technology, particularly in the Urban Macro (UMa) scenario, with a frequency of 3.5GHz and a bandwidth of 100 MHz. 3GPP (3rd Generation Partnership Project) TR 38.901 recommends performing performance analysis with line-of-sight (LOS) situations using the propagation model urban macro (uMa). In this study, 100 users are distributed at random distances to examine system capacity using the frequency reuse factors K = 1/3 and K = 1/7. According to the calculation results, this study demonstrates that system capacity is dependent on user distribution arrangement and the frequency reuse factor approach used. A high frequency reuse factor increases bandwidth efficiency and yields more optimum capacity. Furthermore, distributing users near the base station will boost capacity.

Distributed photovoltaic supportability consumption method considering energy storage configuration mode and random events

In order to improve the control capability of distributed photovoltaic support, a distributed photovoltaic support consumption method based on energy storage configuration mode and random events is proposed. A networked and constrained parameter analysis model for distributed photovoltaic power supply control was constructed. Based on the direct flexible mode of optical storage, an AC/DC voltage level control model for distributed solar power supply control was constructed. In the operation mode of DC hybrid distribution network, the demand response tracking identification method was used to analyze the uncertain characteristic parameters of distributed solar power supply load, and combined with the planned energy storage capacity parameters, the distributed solar power supply load and photovoltaic output were estimated. By configuring the optimal energy storage capacity, adjusting the power distribution of the microgrid, and integrating the analysis of uncertain factors and random events in the energy storage configuration mode, the design of distributed photovoltaic support consumption has been achieved. The experimental results show that the distributed photovoltaic absorption control using this method has lower load requirements, can effectively reduce the exchange power of the interconnection line, and improve the configuration scale, system reliability, and economy of the photovoltaic energy storage system.

Climate Change Impacts for Multipurpose Reservoir Planning: Basnagoda Reservoir, Sri Lanka

Climate change via rainfall and temperature changes poses threats to water resources. Traditionally, these variations are rarely considered in irrigation infrastructure planning. Integrating climate impacts into early irrigation planning ensures resilience and sustainability.This study aims to assess the climate change impacts on Basnagoda reservoir using a reservoir operation study, system designing and sizing, water use strategies and making recommendations as mitigation and adaptation strategies.This study employed reservoir operation model recommended in the Irrigation guideline, Sri Lanka to assess four climatic scenarios derived from predicted temperature and rainfall changes. Sensitivity analysis included reservoir sizing for the most critical scenario. Mitigation and adaptation methodologies such as 1) Reservoir capacity enhancement 2) Seasonal shifting 3) Conveyance enhancement were analyzed. Reservoir operations currently meet 100% of domestic demand, irrigating total 950 ha annually (380 ha Maha, 570 ha Yala) with environmental flow. Design capacity fulfils 78% of domestic demand, with environmental flow for projected period. The maximum cultivable area at the design stage while providing 25% of design domestic demand is 544 ha in Maha and 680 ha in Yala season. With a 20% rainfall reduction posing the worst scenario, domestic water fulfilment reduced to 20% (Maha) and 19% (Yala). Optimal reservoir capacity enhancement is 15%, expanding cultivable area to 390 ha, while providing 25% of design domestic demand. Conveyance enhancement increases cultivable area from reservoir water by 15%. This study is unique for integrating climate change impacts into reservoir infrastructure planning and design stages.

Flexible resource allocation optimization model considering global K-means load clustering and renewable-energy consumption

Abstract Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy transformation. The investment construction cost and operation cost of various flexible resources are different, and the adjustment ability is different in different timescales. Therefore, the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources. In this paper, the global K-means load clustering model is proposed and the 365-day net load is reduced to eight typical daily net loads by clustering. Secondly, a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed. The flexible resources involved include the flexible transformation of thermal power, hydropower, pumped storage, energy storage, and demand response. The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal. The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy. As the abandonment rate of renewable energy decreases, the optimal capacity of pumped storage, electrochemical energy storage, and hydropower units increases. When the power-abandonment rate of renewable energy is 5%, the optimal allocation capacity of thermal power flexibility transformation, pumped storage, electrochemical energy storage, hydropower unit, and adjustable load in Province A is 5313, 17 090, 5830, 72 113, and 4250 MW, respectively. Under the condition that the renewable-energy abandonment rate is 0, 5%, and 10% respectively, the configured capacity of pumped storage is 20 000, 17 090, and 14 847 MW, respectively.