Minimum Total Cost Research Articles

Generalized multi-manufacturer multi-retailer production-delivery supply chain model and its minimum cost solution policy

Production of a product at multiple sources and its deliveries to multiple destinations are a common practice in business. Minimization of the integrated total cost of performing operations and transportation considering the relevant factors such as the minimum order quantity contract, capacities of transport vehicles, and times of transportation in this supply chain is essential, in supplying products to customers at reasonable lesser prices. However, such a supply chain has received little attention in terms of minimizing the integrated total cost taking into account these related factors explicitly. So, there lies a research scope on this topic to fulfill the growing need of minimizing the cost of production-deliveries of products to meet customers’ demands fruitfully. Here we develop such a generalized mathematical model to minimize the integrated total cost of carrying out operations at manufacturers and retailers, and transporting batches (sub-lots) of lots from sources to destinations considering the mentioned realistic constraints. The integrated production-delivery flow is synchronized by delivering lots with batches of equal and/or unequal sizes. First without considering transportation costs, we obtain optimal batches to minimize the total cost of the model. Each of these optimal batches is proportionally distributed at manufacturers as supplies and at retailers as demands. Then the minimum transportation cost solution from manufacturers to retailers is incorporated to the earlier solution to obtain the final result. We illustrate this solution policy with numerical example problems. Sensitivity analyses are performed to see the effect of increasing values of parameters on the minimum total cost.

An innovative quick-decision strategy for evaluating the applicability of extractive distillation process with a preconcentration column in separating high-concentration organic waste liquid

In the face of the high energy consumption challenge in separating high-concentration organic waste liquid through extractive distillation (ED), the concept of preconcentration provides a new strategic direction. Therefore, it has become crucial to determine the suitability of the ED process with a preconcentration column for processing such complex mixture. Taking this into consideration, the study proposes an innovative quick-decision method that utilizes thermodynamic analysis of ternary phase diagram to determine the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate, which serves as the basis for deciding whether to choose the ED process with a preconcentration column for handling such separation tasks. Three azeotropic systems with different thermodynamic characteristics are chosen as case studies: cyclohexane/ethyl acetate/ethanol (Case 1), ethyl acetate/ethanol/water (Case 2), and n-hexane/ethyl acetate/acetonitrile (Case 3). The task of separating the above three systems is accomplished through the application of both three-column extractive distillation configuration (TCED), and four-column extractive distillation configuration (FCED). The objective functions for multi-objective optimization of all separation configurations include the minimum total annual cost (TAC), the minimum entropy generation and the minimum CO2 emissions. The research findings indicate that when the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate is not less than 0.56, or when there is a significant concentration difference among the components in the feed stream, the FCED configuration exhibits clear advantages over the TCED configuration in terms of economic, environmental, and thermodynamic aspects.

A novel configuration optimization approach for IES considering exergy-degradation and non-energy costs of equipment

Improving the exergy utilization and economic performance is the core goal for the configuration optimization of the integrated energy system (IES). Achieving such a goal requires accurate evaluation of the exergy efficiency and economy. However, IES is a multisource heterogeneous and strongly coupled system. The development of reasonable exergy efficiency-economy characteristic evaluation indicators is a complex issue that limits its application in IES configuration. This article proposes a novel IES configuration optimization approach based on the concept of the equipment exergyeconomic cost coefficient. The exergyeconomic cost coefficient consists of the exergy-degradation and non-energy costs, which can quantitatively characterize the exergy efficiency and economy of the IES under a unified benchmark. A mechanism model for the equipment exergyeconomic cost coefficient, output of various equipment and total exergyeconomic cost is established. Furthermore, exergy flow attribute classification method is proposed to obtain the equipment exergyeconomic cost coefficient in the IES. The proposed method is verified in a real-world study case located in Yancheng, China. Compared to the conventional configuration method, the capacity of the renewable energy and combined heat and power generation (CHP) are enhanced, while the capacity of purchased electricity and electric boiler (EB) are reduced. It can obtain the minimum total exergyeconomic cost (TEC) for the IES. This study offers a new direction for the optimal configuration of IES.

Reliable Cost-Efficient Integration of Pumped Hydro Storage in Islanded Hybrid Microgrid for Optimum Decarbonization

The employment of renewable energy-based resources is growing rapidly, necessitating the use of energy storage technologies to effectively manage their intermittent power generation. This study proposes an optimal planning for various distributed generators in a microgrid system. The objective is to size and operate a reliable hybrid islanded microgrid with minimum total system operational cost. To determine the optimal energy management and size of each unit, the problem is formulated applying Particle Swarm Optimization methodology utilizing sets of historical data such as wind speed, solar irradiation, and load demand on an hourly basis. With the proposed methodology, the capacities of photovoltaic, wind turbine, pumped hydro storage, and fuel cell are optimized as 1300 kW, 1125 kW, 400 kW, and 1590 kW, respectively. The obtained results concluded that optimum size reduces 14.85% and 22.6% in the fuel cell consumption in summer and fall respectively. Furthermore, the amount of CO2 emission is reduced by 10.6 ton in those mentioned seasons. The results obtained indicate that the proposed system having cost of energy (COE) of 0.2961 ($/kWh), and an excess energy of 196.208 MWh. An effectiveness analysis highlights the significant impact of incorporating pumped hydro storage, increasing the reliability index by 50.9%.

Economic evaluation of an outsourced fourth-party logistics under a flexible production system

Competition in the production business is getting harder day by day for the manufacturer and the corresponding retailer of the supply chain. When a supply chain deals with deteriorating products, an efficient production system, and a logistic provider are two important pillars to keep the business profitable. Logistics facility for deteriorating products requires additional attention, especially when deteriorating products require a refrigerate-storage facility. This study investigates the economic evaluation of flexible production and logistics outsourcing for deteriorating products with the help of logistics outsourcing. The retailer outsources logistics facilities through a fourth-party logistics provider. Fourth-party logistics uses reefer shipping to transport deteriorating products to prevent deterioration during transportation. The refrigerant of the shipping container has a random leakage during shipping. In the meantime, the manufacturer uses preservation facility to reduce the random deterioration rate of products. A classical optimization finds the global minimum total cost for a unique solution of decision variables. Results prove that flexible production is very sensitive to machinery systems and fresh products are more cost-effective for transportation than frozen products. The proposed system saves 38.38% of the total cost in comparison with a constant production rate. Sensitivity analysis finds transportation costs of 4PL are the most sensitive parameters of the system.

Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products.

The Optimal Experimental Design for Exponentiated Frech’et Lifetime Products

In many manufacturing industries, the lifetime performance index CL is utilized to assess the manufacturing process performance for products following some lifetime distributions and subjecting them to progressive type I interval censoring. This paper aims to explore the sampling design required to achieve a specified level of significance and test power for products with lifetimes following the Exponentiated Frech’et distribution. Since lifetime distribution is an asymmetrical probability distribution, this investigation is related to the topic of asymmetrical probability distributions and applications in various fields. When the termination time is fixed but the number of intervals is variable, the optimal number of inspection intervals and sample sizes yielding the minimized total experimental costs are determined and tabulated. When the termination time is varying, the optimal number of inspection intervals, sample sizes, and equal interval lengths achieving the minimum total experimental costs are determined and tabulated. Optimal parameter values are displayed in tabular form for feasible applications for users. Additionally, a practical example is provided to illustrate how this sampling design can be used to collect data by using the optimal setup of parameters, followed by a testing procedure to assess the capability of the production process.

A Novel Approach to Energy Management with Power Quality Enhancement in Hydrogen Based Microgrids through Numerical Simulation

A hydrogen-based microgrid (MG) is an energy system that uses hydrogen as a primary energy carrier within a localized grid. Numerous alternative approaches and concepts are found concerning the management of renewable energy systems. This study proposes a novel approach to assess the energy management system (EMS) and optimal hydrogen-based Energy Storage Systems (HBESS) at minimal total cost, employing particle swarm optimization (PSO) and fuzzy control in stand-alone microgrids. Together, these methods effectively address control and management challenges within hybrid microgrids (HMGs). This has been proposed to enhance energy management and to improve power quality. The findings reveal that PSO is the most advantageous and efficient approach. Its utilization proves instrumental in reducing costs, boosting reliability, and optimizing operational schedules within HMGs. Furthermore, the power profile holds considerable importance in this study, significantly enhancing system reliability and stability. This study has achieved an impressive 6.147% improvement in cost-effectiveness compared to traditional methods. This has been put into practice and validated through implementation within a MATLAB (9.13.0 (R2022b))/Simulink framework.

Research on Optimizing Human Resource Expenditure in the Allocation of Materials in Universities

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies.

Operational optimization of demand management strategy for hybrid power system based on battery capacity fading characteristics

In the hybrid power system (HPS), taking battery capacity fading characteristics and demand management strategy (DMS) into consideration, an operational optimization method is developed in this work. From the energy utilization and economic perspectives, the minimum total renewable energy curtailment rate (TCR), minimum total outsourced electricity (TOE) from the grid and minimum total operating cost (TOC) are respectively taken as the optimization objectives. The corresponding mathematical models of the proposed method are established, which feature mixed integer nonlinear programming (MINLP) ones. A case study of a solar-wind-biomass-battery HPS is adopted to verify and illustrate the application and effectiveness of this proposed method. Results show that the optimal operating schemes and the DMSs of the HPS based on battery capacity fading characteristics can be determined by minimizing the TCR, TOE and TOC. The target values of TOE and TOC of the HPS with adopting DMS are 15,995.3 kWh and 18,112.1 $, which are about 70 % these of the HPS without DMS. Furthermore, the relationships between battery capacity fading characteristics and DMS are comprehensively investigated in this operational optimization of the HPS. Finally, the effects of different battery types, outsourced electricity price changes on the operation of the HPS are analyzed and discussed. This work provides a valuable method for the operational optimization of the HPS.

Hierarchical optimization for the energy management of a greenhouse integrated with grid-tied photovoltaic–battery systems

This study addresses the challenges of high energy consumption and environmental concerns in traditional greenhouse operations by exploring an integrated greenhouse with grid-tied photovoltaic (PV)-battery systems. A two-layer hierarchical optimization framework is proposed for effective energy management. In the upper layer, greenhouse operations are optimized with the Energy Consumption Minimization (ECM) strategy and the Energy Cost Minimization (ECoM) strategy, ensuring suitable climate conditions while reducing energy use and costs. In the lower layer, the scheduling of the hybrid energy system is optimized with the Self-consumption Maximization (SCM) strategy and the Total Cost Minimization (TCM) strategy, aiming to maximize the self-consumption of PV power and minimize total costs, including energy costs, battery aging costs, and carbon emission costs, while meeting the greenhouse’s electricity demand. A sensitivity analysis is conducted to investigate the impact of electricity prices, feed-in tariffs, battery capacity, and PV array area on hybrid energy system scheduling. Results reveal that the ECoM strategy reduces costs by 6.98% compared to the ECM strategy, and the TCM strategy reduces total costs by 43.50% compared to the SCM strategy. Battery capacity and PV array area are found to have a more significant influence on total costs than electricity prices and feed-in tariffs. This study can offer crucial insights for realizing cost-effective and environmentally friendly greenhouse operations, contributing to the advancement of sustainable agriculture.

Optimizing Raw Material Inventory Control with Continuous and Periodic Review

PT XYZ is a company that operates in the field of plywood processing with various variations that produce products every day. Plywood or plywood is the product with the most demand every month. This company experiences problems, namely ordering raw materials that are uncertain, ordering raw materials because of probabilistic demand with an uncertain period and the quantity of raw materials that does not match the needs used in the production process, so that it often experiences overstock and stockouts. This research aims to control the optimal supply of sengon wood at PT. XYZ by comparing the company's method with the continuous review (Q) back order method and the periodic review (P) back order method to obtain the minimum total inventory costs. Based on calculations using the company method, the total inventory cost is IDR 32,502,564,700 and the minimum total inventory cost is obtained using the continuous review (Q) back order method of IDR IDR 28,829,527,844. The forecasted demand for sengon wood from May 2024 to April 2025 is 42,109 m3. The company made a total order of 944 m3 and a reorder point of 146 m3, so the total inventory cost generated was IDR 36,151,230,302.

Capacity optimization configuration of multi-source independent microgrids based on TLC-GOLD-GWO algorithm

Abstract To guarantee the economic efficiency and reliability of standalone microgrids, this paper introduces an enhanced Grey Wolf optimization algorithm (GWO) [1] that leverages chaos mapping techniques, nonlinear convergence factor, and golden operator for solving multi-source capacity allocation problems. Taking the reliability of the power supply as a significant constraint, an optimization model is constructed with the minimization of the annual total economic cost as the objective function. Given the issues of local convergence and premature convergence encountered during the solution process of the fundamental Grey Wolf optimization algorithm, we employ chaotic mapping technology to address the challenge of unequal initial population distribution. Subsequently, a nonlinear convergence factor is introduced to expedite the convergence rate, enhancing the algorithm’s performance. Lastly, to update the grey wolf’s position, the golden sine operator is used, thereby refining the solution space and enhancing the effectiveness of the optimization process. The simulation outcomes reveal that the enhanced Grey Wolf optimization algorithm notably enhances the precision of the objective function, delivering superior performance. Compared to the Grey Wolf optimization algorithm and Whale optimization algorithm (WOA) [2], the enhanced algorithm demonstrates superiority in terms of reducing economic costs, enhancing the capability for global optimal search, and accelerating the optimization process.

Bi-Objective Mixed Integer Nonlinear Programming Model for Low Carbon Location-Inventory-Routing Problem with Time Windows and Customer Satisfaction

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction.

Coordinated optimization of configuration and operation of a photovoltaic integrated building cooling system with electricity and ice storages under source-load uncertainties

Solar photovoltaic integration into buildings provides an effective and attractive method to decrease fossil energy consumption and reduce greenhouse gases emission. This study proposes a flexible photovoltaic integrated building cooling system with electricity and ice storages to address the challenges posed by the instability of solar energy and the integration and coordination of multiple components. Uncertainties in solar irradiance, ambient temperature, and cooling load are characterized using a scenario analysis method that combines Latin Hypercube Sampling and the K-Means++ algorithm. Then, aiming to achieve the minimal total cost, including investment, operational, and CO2 emission costs, a two-layer stochastic optimization model is developed to simultaneously determine the optimal capacities and operational strategies for each device. Genetic algorithm and mixed integer linear programming are, respectively, used to solve the two-layer optimization problem. The comparisons between optimum schemes with and without CO2 emission cost demonstrates that when the base chiller’s capacities are 1179 kW and 1081 kW in cases 1 and 2 respectively, it can independently meet 72.37 % and 61.27 % of the cooling load demand throughout the cooling season. In addition, the optimal capacity of photovoltaics, considering CO2 emission, increases by 109.7 %, and its penetration rate reaches 18.3 %, which is 9.7 % higher than the optimal scheme not considering CO2 emission. However, the annualized investment cost increases by 52.3 %. The developed methods provide effective tools for the system design and operation management of solar energy integrated building cooling system.

Straw Logistics Network Optimization Considering Cost Importance and Carbon Emission under the Concept of Sustainable Development

As biomass power generation projects progress, the supply chain of biomass resources has become a key link for the green development of biomass power facilities. The role of biomass recycling logistics in the sustainable development of biomass resources and power generation endeavors has grown increasingly significant. To realize environmentally friendly and economical straw logistics, it is imperative to establish an efficient, economic and sustainable straw recycling logistics network. Therefore, based on the weighting theory and top-down method, this study proposes a planning model aiming at minimum total cost and minimum carbon emission to locate the logistics network. The immune algorithm is applied to solve and analyze the proposed examples. It is concluded that under the condition that the total cost is reasonable, reducing transportation cost. The verification model can enhance the environmental and economic performance of straw logistics networks. From the perspective of theory and practice, we provide ideas for the optimization of relevant biomass logistics networks and promote the realization of low-carbon sustainable development of logistics networks.

Solution approaches to the three-index assignment problem

This paper delves into the axial Three-index Assignment Problem (3IAP), alternatively known as the Multi-dimensional Assignment Problem, defined as an extension of the classical two-dimensional assignment problem. The 3IAP entails allocating $n$ tasks to $n$ machines in $n$ factories, ensuring one task is completed by one machine in one factory at a minimum total cost. This combinatorial optimization problem is classified as \emph{NP}-hard due to its inherent complexity and being the subject of much scholarly research and investigation. The study employs an algorithmic approach to devise rapid and effective solutions for the 3IAP. A new heuristic Greedy-style Procedure (GSP) is introduced for solving the 3IAP, achieving feasible solutions within polynomial time. Particular configurations of cost matrices enable us to reach quality solutions. Examining tie-cases and matrix ordering unveiled innovative variants. Further investigation of cost matrix attributes facilitates the development of two new heuristic categories, offering optimal or nearly optimal solutions for the 3IAP. Extensive numerical experiments validate the effectiveness of the heuristics, generating quality solutions in a short computational time. Furthermore, we implement two potent methods using optimization solvers, achieving optimal solutions for the 3IAP within competitive CPU times.

Energy, exergy, exergoeconomic, economic, and environmental analyses and multi-objective optimization of a novel combined cooling and power system with dual-pressure Kalina cycle-absorption refrigeration

A novel combined cooling and power system, the dual-pressure Kalina cycle-absorption refrigeration (DPKC-AR-CCP), is proposed for the cascade utilization of waste heat from high-temperature flue gas and jacket water from internal combustion engines. A pneumatic booster pump unit is employed to increase the inlet pressure of the low-pressure expander, with heat generated during the compression process utilized for preheating the ammonia/water mixture, and exhaust gas from the low-pressure expander is combined with driving gas for absorption refrigeration. By integrating the first and second laws of thermodynamics with the SPECO Methodology, models for energy, exergy, exergoeconomy, economy, and environment of the DPKC-AR-CCP system are developed. A multi-objective optimization model is formulated with maximum exergy efficiency, minimum total exergy cost rate, and maximum annual equivalent carbon dioxide (CO2) emission reduction as objective functions. MATLAB software (combined with REFPROP software) is utilized for simulation and multi-objective optimization. The performance of each component under specific conditions is assessed, and a comparison with the simple absorption refrigeration/Kalina cogeneration system (AR/KC) highlights the advantages of the DPKC-AR-CCP system; an investigation into the effects of different operating parameters on performance is conducted. The results indicate that, under identical operational parameters, the DPKC-AR-CCP system exhibits a substantial increase in net output power (230.2 %), cooling capacity (98.7 %), and exergy efficiency (148.8 %) compared to the simple AR/KC system; payback period is reduced by 64.8 %; and annual equivalent CO2 emissions are decreased by 202.2 %. However, there is an increase in the total exergy cost rate by 90.5 %. The optimal concentration of ammonia/water is determined to be 0.816/0.184, with a heat exchanger outlet temperature of 341.07 K, driving pressure of 0.654 MPa, and low-pressure expander inlet pressure of 5.064 MPa. Correspondingly, the optimal exergy efficiency is 87.2 %, the annual reduction in CO2 emissions is 5.53 × 107 kg, and the total exergy cost rate is 288.48$/h.

Evaluation of dimethyl carbonate production process from CO2 by rigorous simulation and detailed optimization

AbstractThe CO2‐derived dimethyl carbonate (DMC) synthesis process becomes greatly attentive but suffers high energy consumption in DMC distillation process. In this work, the DMC‐MeOH azeotropes separation process by pressure swing distillation and extractive distillation was compared, and key operating parameters, including the total number of trays and the feeding position of the mixture liquid, were optimized with the minimum total annual cost (TAC) as the objective function. On the basis of this optimization, economic evaluation of different distillation processes was conducted, and it was found that extractive distillation was more economical than pressure swing distillation. The application of the dividing‐wall distillation process upgraded by extractive distillation can significantly reduce the minimum annual total cost by 37.4% and 10.7% compared to the original pressure swing distillation and extractive distillation process, respectively. The optimization of relevant heat exchange network based on pinch technology resulted in energy consumption reduction by 27.2% and 25.9% for its hot and cold utilities, respectively. Carbon life cycle assessment (LCA) on the DMC distillation process revealed over 50% of energy as well as carbon emissions from steam consumption, whose reduction can significantly minimize CO2 emissions, energy consumption, and ultimate cost. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Optimal design of a combined Power-Heating-Oxygen system for plateau hotel buildings

The Qinghai–Tibet Plateau of China is a region of low atmospheric pressure and oxygen deficiency, with a high heating demand in the winter; therefore, plateau hotel buildings have a demand for comprehensive power-heating-oxygen loads. Solar energy resources are particularly abundant in plateau areas, where a combined multi-energy system based on solar energy can be designed to meet the demand of plateau hotel buildings for multiple loads. However, currently, there is a relative lack of methods for the optimal design and performance evaluation of combined power-heating-oxygen (CPHO) systems. Thus, this study proposes a CPHO system model for plateau hotels, and provides two operation strategies, i.e., power priority and oxygen priority. Taking the minimum total annual cost of the combined system as the optimization objective, an optimized CPHO system model for plateau hotels has been proposed, followed by a case study of a hotel in Qilian County, Qinghai Province. Results showed that: 1) Compared with the conventional combined power-heating (CCPH) system, the CPHO system saw reductions of 0.8% and 1.3% in its total annual cost under the operation strategies of power priority and oxygen priority, respectively. 2) The energy self-consumption (SC) rates of the CCPH system and CPHO system under the operation strategies of power priority and oxygen priority were 84.8%, 64.2%, and 89.8%, respectively; with power self-sufficiency (PSS) rates of 8.3%, 49.9%, and 29.5%, and oxygen self-sufficiency (OSS) rates of 0.0%, 3.2%, and 4.7%, respectively. 3) The total annual cost of the CPHO system was affected slightly by the cost of photovoltaics (PVs) and energy storage devices, and significantly by the price of oxygen. When the price of oxygen was 8 yuan per normal cubic meter(Nm3) or above, the CPHO system was economically efficient under the operation strategy of oxygen priority. When the price of oxygen was 10yuan/Nm3 or above, the system was economically efficient under both operation strategies.