Multi-energy System Research Articles

Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

In the context of the expanding diversity of energy demands, an increasing number of heterogeneous Multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the Multi-energy Multi-microgrid System (MEMMG). However, following this trend, the existing centralized Integrated Energy Management System (IEMS) control strategy is unreliable for future energy systems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical Multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi-agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is adopted to overcome the problems of unstable training environments and large-scale agent training, and each heterogeneous microgrid can develop local strategies independently with the assurance that their internal data will not be overly exposed. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method markedly reduces reliance on exact physical modeling systems. Comparative simulations validate the proposed control scheme’s efficacy.

Quantitative assessment methods and qualitative evaluation system for the complementary characteristics of multi-energy

Assessing and analyzing the complementary characteristics of renewable energy (RE) is crucial for designing, operating, and optimizing multi-energy complementary systems (MECSs). However, unified and precise quantitative descriptions of the complementary and stability characteristics among various energy outputs in MECSs have lacked attention and research. Here, this study innovatively proposed a mathematical model for the multi-energy complementarity index (MECI), which considers the complementarity rates of multiple energy outputs during zero and non-zero output periods, and a mathematical model for the multi-energy volatility index (MEVI), which accounts for fluctuation thresholds and the overall volatility of output processes. An evaluation system for multi-energy complementarity characteristics qualitative analysis has been established. The natural output processes of RE at three MECSs in China were applied in the case calculations and verification. Results show that the hydropower rated discharge (Qrating) has a significant negative correlation with MECI, with the MECI decreasing by an average of 0.0046 for every 5 m³/s increase in Qrating. The relationship between the Qrating and MEVI shows an overall negative correlation with local fluctuations. Notably, The MECI of the BeiPan River MECSs exhibits significant seasonal characteristics, with the MEVI in summer (0.378) and autumn (0.395) higher than those in spring (0.132) and winter (0.160), closely related to the natural seasonal variations of the three energy sources: water, wind, and solar. We believe that the study can assist in evaluating and making decisions on the multi-energy complementarity characteristics of RE bases in the future, making a significant contribution to achieving dual carbon goals.

Distributed multienergy and low-carbon heating technology for rural areas in northern China

In the context of China's “double carbon” policy and “rural revitalization” strategy, clean and low-carbon heating in rural areas of the northern China has become a key problem that needs to be urgently addressed. Practice has proved that “coal-to-gas” and “coal-to-electricity” projects have various limitations, such as high operating cost, high carbon emission, and high cost of distribution network upgrade, making them unsuitable for nationwide promotion. To address this issue, this research proposes a distributed multienergy and low-carbon heating (DMLH) system, which integrates the photovoltaic power generation, combined heat and power (CHP) system, heat storage, and newly developed multisource efficient heat pump (MEHP). The MEHP operates by simultaneously absorbing heat from air and waste heat sources of the CHP generation, providing a stable and efficient heat output. The multienergy complementary operating strategy of the DMLH system has been presented, considering variations of heating demand and environmental temperature. Comprehensive case studies were performed under typical and extremely cold winter scenarios to examine the effectiveness of the DMLH system. Simulation results revealed that 66.7 % of the operating cost can be reduced by the proposed system in typical scenario and the heat supply stability of MEHP has been considerably enhanced compared to a single-stage heat pump system. Furthermore, the DMLH system has low-level requirement of distribution network capability, which is conducive to promotion in the rural areas of northern China.

A Real-Time Power Sharing Strategy for a Multi-Energy System While Considering Individual System Dynamics

For a sustainable and resilient energy supply, multi-energy systems (MES) are becoming more prominent. Real-time efficient power-sharing in an MES consisting of different generation sources, uncontrollable loads, multiple storage options, and Power-to-X technologies is a challenging task. The challenge arises due to constantly fluctuating generation and load, as well as the different system dynamics of each element in the setup. Optimizing the power-sharing among the various controllable elements in an MES could be broken down into a two-level process. The top-level process, commonly known as super-ordinate control, defines the power-sharing over a longer timeframe based on load and generation forecasts and multiple other factors. The lower-level process, commonly known as sub-ordinate control, based on the inputs from the super-ordinate control and live sensor data refines the control signals of the individual elements. This work focuses on developing a control framework for the sub-ordinate control process while considering the individual element dynamics such as dead time and rise time to a control signal. This is important as not considering differences in the system dynamics results in sub-optimal control, causing dynamic mismatches. This work proposes a simple rule-based power-splitting method backed up with individual PID regulators and Smith Predictor for each element in the MES, which are further coupled to each other for more precise and efficient control. The proposed method induces a cooperative behavior among the MES elements and improves the reaction time to a control signal while also improving the target tracking by 3-8%.

Analysis of power load tracking and regulation performance in a distributed multi-energy coupled system with nuclear and solar sources

This paper presents a novel distributed multi-energy coupled system that combines solar PV, nuclear power, and energy storage systems to address the power supply challenges in remote regions. Through parameter analysis and multi-objective optimization, the study aims to minimize the need for frequent reactor adjustments during system operation. The key findings indicate that by regulating the rotational speed of the main-compressor, it is possible to meet the power load requirements for a single nuclear power system. The battery capacity and the number of PV panels have a significant impact on the adjustment frequency of the reactor and the power output of the nuclear power system, respectively. The optimal battery capacity is determined to be 183 kWh, while the optimal number of PV panels is 327. These configurations result in an average power output of 20.86 kW for the PV system and 3458.28 kW for nuclear power system. To maximize the utilization of PV energy and minimize reactor adjustments, an energy dispatch strategy is proposed. With the optimal configuration, the adjustment frequency of the reactor decreases to 339 and 276 during the winter and summer months, respectively. Overall, this paper offers a feasible configuration and energy dispatch method for regional power supply.

Optimizing water resources for sustainable desalination: The integration of expert systems and solar energy in experimental applications

The integration of renewable energy sources with multi-energy systems present challenges and opportunities to enhance sustainability. Among these, solar stills have emerged as a solution for water desalination. With the advent of expert system technologies, avenues are opened for improving the operational efficiency of solar distillers. This paper presents an innovative approach utilizing correlation analysis, ReliefF for feature selection, and a k-Nearest Neighbor (kNN) algorithm for forecasting the cumulative distillate output of a double slope solar still. The analysis is based on a 6-cases-based dataset, which includes variations in distillate output relative to different operational-environmental conditions. Key features that significantly impact overall performance were identified to manage the solar distiller productivity. The findings reveal that the maximum distillate output was 1610 ML/m2.day due to incorporating reflective materials and phase change materials (PCM) in enhancing distillation rates. The kNN model was evaluated based on its R2, RMSE, and CVRMSE, with the best models achieving scores of 0.995, 0.0033, and 0.1666, respectively. These metrics underscore the effectiveness of the proposed machine learning approach in predicting distillate output, thereby enabling informed management of solar distillation processes. Combining renewable energy technologies and computational intelligence holds significant promise for sustainable environmental management, as the study presented.

Performance analysis and optimization of multi-energy complementary combined heat and power system considering the influence of time-of-use price

Abstract The multi-energy complementary system can meet the increasingly abundant and diversified energy consumption needs of users and, at the same time, help to improve energy utilization efficiency, reduce environmental pollution, and optimize the balance of energy supply and demand. In this paper, a multi-energy complementary cogeneration system with a gas turbine distributed photovoltaic, gas boiler, and heat storage tank is proposed, and the operation scheduling optimization model of the system is constructed. Aiming to minimize the comprehensive cost of economy and carbon emission, the model adopts a linear programming method to optimize the operation scheduling of multi-energy complementary systems. Taking a multi-energy complementary cogeneration system as an example, the key equipment of the system is optimized under three different scenarios with different time-of-use prices, and the influence of time division of time-of-use electricity price on the comprehensive cost of the system is analyzed. The results show that the time division method, which is more in line with the system net load curve, can effectively reduce the comprehensive cost of the complementary system and increase the consumption space of new energy.

The optimization and dispatch of regional multi-energy complementary power system in green certificate Trading - Carbon emission trading

Abstract This proposal of the “double carbon” goal underscores the pressing need to reduce carbon emissions. To address this issue, the utilization of an optimal scheduling model for an integrated multi-energy complementary system, comprising wind, solar storage, and thermal power generation, is proposed. Two renewable energy generating units, wind and photovoltaic power, cooperate with thermal power units to generate electricity to meet urban base load requirements. Energy storage units compensate for the instability of power generation from renewable energy units. To significantly decrease system carbon emissions, a green certificate trading-carbon emission trading system is introduced. The carbon emission reduction facilitated by the green certificate can offset a portion of the carbon emissions, and the quantities of tradable green certificates and carbon emissions in their respective markets are adjusted accordingly. This model aims to maximize the system’s comprehensive operating earnings while considering the low-carbon economy aspect. The proposed model undergoes simulation analysis, and the results demonstrate its reasonableness and efficacy in terms of carbon reduction and economic viability.

A Cooperative Operation Strategy for Multi-Energy Systems Based on the Power Dispatch Meta-Universe Platform

To meet the challenges of renewable energy consumption and improve the efficiency of energy systems, we propose an intelligent distributed energy dispatch strategy for multi-energy systems based on Nash bargaining by utilizing the power dispatch meta-universe platform. First, the operational framework of the multi-energy system, including wind park (WP), photovoltaic power plant (PVPP), and energy storage (ES), is described. Using the power dispatch meta-universe platform, the models of WP, PVPP, and ES are constructed and analyzed. Then, a Nash bargaining model of the multi-energy system is built and transformed into a coalition profit maximization problem, which is solved using the alternating direction multiplier method (ADMM). Finally, the effectiveness of the proposed strategy is verified. The results show that the strategy greatly improves the consumption of renewable energy sources and the profit of the overall system.

Thermo-economic assessments on building heating by a sewage source heat pump coupled with heat storage system

To enhance the economic feasibility of building heating systems, phase change heat storage materials are often utilized to utilize renewable energy and address system peak loads. This article presents an innovative method of integrating a sewage source heat pump with a heat storage device. Upon an industrial building in Xilinhot as a case study, numerical models for the heat accumulator coupled with the energy consumption of buildings are established, taking into account dynamic hourly loads and meteorological conditions. Through a comprehensive analysis, the thermophysical properties of the heat accumulator as well as the economic implications of the multi-energy complementary heating system are investigated. Results demonstrate that the complete melting time for a 95 % filling rate is significantly reduced, 87.11 % shorter than that of pure phase change material, achieving the highest heat storage efficiency. Furthermore, the phase change storage device with a 75 % filling rate had the shortest investment payback period, requiring only 6 years. These findings have significant implications for guiding green renovation projects in building heating systems.

A Cloud–Edge Collaborative Multi-Timescale Scheduling Strategy for Peak Regulation and Renewable Energy Integration in Distributed Multi-Energy Systems

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management.

Effect of decision-making principle on P2G–CCS–CHP complementary energy system based on IGDT considering energy uncertainty

As one of the most promising large-scale de-fossilization technologies currently available, Power-to-Gas (P2G) is expected to promote utilization of renewable sources and mitigate the effects of greenhouse gas. Through the coupling of P2G, Combined Heat and Power (CHP) as well as Carbon Capture and Storage (CCS), the P2G–CCS–CHP microgrid energy system could achieve hierarchical energy transfer and green-powered interconversion of gas, electricity and heat flows. However, the conversion of multiple energy shapes between different facilities is deeply intertwined with renewable energy sources, greatly increasing risk of unbalance operation and impacting the overall robustness and economic viability of system. To address these challenges, this study proposes a decision-making framework based on Information Gap Decision Theory (IGDT) method for a P2G–CCS–CHP system. The real-time balancing of heat & power loads and supply, the mechanism of gas-electricity-heat interaction, as well as recycling pattern of synthetic fuel within the system are carefully addressed to achieve optimal operation. Different scheduling schemes are obtained by setting up two separating decision principles, namely risk-seeking (RS) and risk-avoiding (RA), to solve these uncertainties and ensure robust decision making. Meanwhile, adaptability of decision-making methodology is analyzed based on several criteria including share of recycled green-powered fuel, ecological friendliness, carbon trading benefit, etc. Results show that adding of P2G module enables a 12.6% reduction in carbon emissions and generates 1252.2 kg of green powered gas, highlighting the eco-friendly nature of the multi energy system (MES). About 21.5% of the renewable energy flows participate the interconversion of electric-heat-gas and about 17.7% of total carbon flow is circulated as synthetic fuel within the system. In addition, conservative tendency is witnessed in the case of risk-seeking decision mode, leading to an approximate 13.22% increase in the operation costs, compared with risk-avoiding mode. The proposed IGDT decision strategy has shown significant compatibility in dealing with the impact of multiple uncertainties.

Detailed modeling of droop-controlled multi-energy systems under unbalanced operation

Considering the residential sector, greenhouse gas emissions can be effectively reduced by the widespread deployment of distributed renewable energy sources (DRESs) in low-voltage (LV) electrical networks (ENs). Through the electrification and the integration of different energy systems, the exploitation of the locally generated renewable energy can be further increased, acting also as an efficient countermeasure to the technical challenges in ENs posed by the intermittent nature of DRESs. This work deals with the modeling of multi-energy systems (MESs) consisting of unbalanced ENs and district heating networks (DHNs), formulated based on existing EN and DHN models. The developed model is enhanced by the incorporation of a thermal droop control scheme into the controllable sources of DHN, i.e., heat pumps (HPs). The validity of the proposed model is assessed via time-series simulations on a MES composed of a benchmark unbalanced LV EN and a real DHN. It is shown that the integrated droop control scheme can act as a means towards overvoltage mitigation, temperature control and reduced MES losses, improving the operational reliability and exploitation of the DRES potential. Therefore, the proposed model could be useful for system operators and decision makers for the efficient planning and operation of MESs.

Exploring the role of hydrogen in decarbonizing energy-intensive industries: A techno-economic analysis of a solid oxide fuel cell cogeneration system

Industry is nowadays one of the most energy-demanding sectors representing a major contributor of global greenhouse gas emissions. The simultaneous need for electricity and high-temperature heat is what makes some industrial processes difficult to decarbonize via current commercially available technologies. As the demand for materials and goods is expected to grow in the upcoming years, it is crucial to define which strategies and technologies will serve as the cornerstone of sustainable development. This study addresses the imperative need for emission reduction of energy-intensive sectors by proposing a novel hydrogen-based cogeneration system in the framework of the paper and pulp industry, with the aim of providing general insights relevant to a broader spectrum of similar applications. The comparative analysis presented in this work focuses on three cogeneration options aimed at satisfying the paper mill energy needs: a conventional natural gas-fuelled gas turbine, a Solid Oxide Fuel Cell (SOFC) fed with grey hydrogen produced via steam methane reforming, and a SOFC operating using green hydrogen produced on-site. The latter involves an integrated multi-energy system with photovoltaic panels, electrolyzers, compressors, and storage tanks. Indeed, the SOFC potential of supplying electricity and high-temperature heat in the form of pressurized steam for industrial applications has not been well investigated yet and represents one of the main objectives of this work. Building on the real consumption profiles of a paper mill facility, techno-economic analyses are carried out for many system configurations, varying components size and layout to assess their performance with respect to CO2 emissions and two key economic parameters, the Levelized Cost of Hydrogen (LCOH) and the net present cost. An in-house-developed flexible simulation framework is presented and expanded for the purposes of this study, including a detailed model that accounts for design and off-design performance of a SOFC cogeneration unit. Results demonstrate that integrating a SOFC with a heat recovery steam generator result in a 75% reduction in the mass flow of generable pressurized steam in comparison to a gas turbine. Additionally, in the cost-optimal scenario, CO2 emissions are 25% lower than the conventional gas turbine-based configuration, achieving complete independence from the electricity grid and an LCOH of 5.81€/kg without considering revenues from electricity sold.

Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems.

Explorations of Integrated Multi-Energy Strategy under Energy Simulation by DeST 3.0: A Case Study of College Dining Hall

Buildings characterized by high energy consumption necessitate the implementation of efficient multi-energy complementary systems to achieve energy conservation and emission reduction objectives. College dining halls use a lot more electricity than typical residential buildings, despite their relatively small size. The dining hall at the Dongshan Campus of Shanxi University is employed as a representative case study in this research. By utilizing DeST 3.0 software, a comprehensive dynamic load analysis is conducted to estimate the annual energy consumption of the dining hall, with the ultimate goal of an energy-saving system being proposed based on the analysis results. Leveraging DeST 3.0 software, dynamic load characteristics were assessed, revealing an annual energy consumption of 2.39 × 106 kWh for the dining hall. Cooling accounted for 0.91 × 106 kWh, while heating requirements amounted to 0.24 × 106 kWh. These findings illustrate peak power consumption trends, seasonal variations, and potential avenues for energy conservation. To satisfy the heating, cooling, and electricity demands of the dining hall, an integrated energy system incorporating solar and wind energy, as well as utilizing restaurant kitchen garbage as a biomass source, was proposed. This study compares two solar energy utilization systems: photothermal and photovoltaic, with total capacities of 2.375 × 106 kWh and 2.52 × 106 kWh, respectively. The research outcomes underscore that Strategy 2, which integrates a photovoltaic system with wind and biomass energy, emerges as the optimal approach for load management. Ultimately, this investigation demonstrates the feasibility and promise of constructing a hybrid renewable energy system within a college dining hall setting, aligning with sustainability objectives and global trends toward environmentally responsible energy solutions.

A direct and analytical method for inverse problems under uncertainty in energy system design: combining inverse simulation and Polynomial Chaos theory

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC.

Coordinated Operation of Multienergy Systems With Uncertainty Couplings in Electricity and Carbon Markets

Coordinated Operation of Multienergy Systems With Uncertainty Couplings in Electricity and Carbon Markets

A novel exergy-based cost and carbon footprint allocation method in the multi-energy complementary system

Energy efficiency, economic cost and carbon emission of multi-energy complementary systems are three key indicators to sustainable development in the low-carbon background. This paper analyzes the coupling relationships between energy, cost, and carbon emission flows in a combined cooling, heating, and power (CCHP) system. The CCHP system consists of biomass gasification, co-firing internal combustion engine by product gas and natural gas, concentrated photovoltaic thermal solar collector, and absorption heat pump. A novel exergo-carbon method with exergo-economic analyses is proposed to determine the exergy cost and carbon footprint rate of each stream in the CCHP system, and the unit cost and carbon footprint of multiple products of electricity, cold energy and heat energy are obtained by the proposed method. The impacts of co-firing ratio and photovoltaic coverage ratio on energy and exergy efficiencies and cost and carbon footprint of products are analyzed. The sensitivity of biomass carbon footprint is performed to show its influences. The total cost of products including economic and carbon costs, is discussed to guide the carbon market's pricing strategies. Under the design conditions, the unit exergy cost of electricity, chilled water and hot water are 0.1094, 0.7659 and 0.466 $ (kWh exergy)−1, and their unit exergy carbon footprints are 0.3651, 3.9050, and 2.3780 kg CO2-eq (kWh exergy)−1, respectively.

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.