Search Results (102)

Against the backdrop of an intensifying global climate change and energy crisis, energy system decarbonization constitutes a primary sector for carbon mitigation. Integrated Energy Systems (IES) of district heating systems (DHS), a critical component of district energy networks (DEN), enable energy cascade utilization and enhance renewable energy integration efficiency when coupled with incremental distribution networks (IDN). However, retrofitting coupled systems necessitates significant capital investment and sustained operational expenditures. To evaluate the economic and environmental benefits of system retrofitting and assess cross-sector coordinated optimization potential, this study develops a multi-objective optimization framework for IES transition planning of DHS. Using an operational DHS energy station as a case study, we establish multi-scenario retrofitting strategies and operational protocols with comprehensive feasibility assessments, incorporating sensitivity analysis of cross-sector optimization potential while evaluating how varying electricity-to-heat load ratios affect optimization performance. Results demonstrate that intelligent operation optimization is essential for coordinating multi-equipment operations and maximizing energy conservation. Significant long-term economic and carbon mitigation potential remains untapped in ground source heat pumps and combined cooling, heating, and power (CCHP) systems. Coordinated optimization with campus incremental distribution networks further enhances energy cascade utilization in urban energy systems. Full article

To assess multi-energy complementarity and commercial development status in thermodynamic energy storage systems, this review systematically examines compressed air energy storage (CAES), compressed CO₂ energy storage (CCES), and Carnot battery (CB), focusing on principles, engineering demonstrations, and thermal integration. Their ability to integrate external heat, conduct combined cooling, heating and power (CCHP), or achieve high round-trip efficiency (RTE) through different pathway positions them as critical enablers for achieving net-zero emissions. Over 240 research articles retrieved from Web of Science and other databases, supplemented by publicly available reports published between 2020 and 2025, were systematically analyzed and synthesized. Current technologies demonstrate evolution from single-function storage to multi-energy hubs, with RTEs reaching 75% (CAES/CCES) and 64% (CB). Thermal integration significantly enhances RTEs. The CCES features a 100 MW/1000 MWh demonstration facility, concurrently necessitating accelerated distributed applications with high efficiency (>70%) and energy density (>50 kWh/m³). All three enable grid flexibility (China’s CAES network), industrial decarbonization (CCES carbon–energy depositories), and thermal integration (CB-based CCHP). These systems require >600 °C compressors and AI-optimized thermal management (CAES), high-pressure turbines and carbon–energy coupling (CCES), as well as scenario-specific selection and equipment reliability validation (CB) to achieve the targets of the Paris Agreement. Full article

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and load sides. Therefore, this paper proposes a two-stage robust optimization for bi-level game-based scheduling of a CCHP microgrid integrated with an HRS. Initially, a bi-level game structure comprising a CCHP microgrid and an HRS is established. The upper layer microgrid can coordinate scheduling and the step carbon trading mechanism, thereby ensuring low-carbon economic operation. In addition, the lower layer hydrogenation station can adjust the hydrogen production plan according to dynamic electricity price information. Subsequently, a two-stage robust optimization model addresses the uncertainty issues associated with wind turbine (WT) power, photovoltaic (PV) power, and multi-load scenarios. Finally, the model’s duality problem and linearization problem are solved by the Karush–Kuhn–Tucker (KKT) condition, Big-M method, strong duality theory, and column and constraint generation (C&CG) algorithm. The simulation results demonstrate that the strategy reduces the cost of both CCHP microgrid and HRS, exhibits strong robustness, reduces carbon emissions, and can provide a useful reference for the coordinated operation of the microgrid. Full article

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

To address the challenges of power generation rights trading and profit distribution in the integrated energy system of multi-park combined cooling, heating, and power (CCHP) with new energy grid integration, we constructed a hierarchical game model involving multi-energy system aggregators. By having aggregators price electricity, heat, cold, and carbon costs, the model establishes a hierarchical game framework with the linkage of the four prices (electricity, heat, cold, and carbon), achieving inter-park peer-to-peer (P2P) multi-energy dynamic price matching for the first time. It aims to coordinate distribution network dispatching, renewable energy, energy storage, gas turbine units, demand response, cooling–heating–power coupling, and inter-park P2P multi-energy interaction. With the goal of optimizing the profits of integrated energy aggregators, a hierarchical game mechanism is established, which integrates power generation rights trading models and incentive-based demand response. The upper layer of this mechanism is the profit function of integrated energy aggregators, while the lower layer is the cost function of park microgrid alliances. A hierarchical game mechanism with Two-Level Optimization, integrating the Adaptive Disturbance Quantum Particle Swarm Optimization (ADQPSO) algorithm and the branch and bound method (ADQPSO-Driven Branch and Bound Two-Level Optimization), is used to determine dynamic prices, thereby realizing dynamic matching of energy supply and demand and cross-park collaborative optimal allocation. Under the hierarchical game mechanism, the convergence speed of the ADQPSO-driven branch and bound method is 40% faster than that of traditional methods, and the optimization profit accuracy is improved by 1.59%. Moreover, compared with a single mechanism, the hierarchical game mechanism (Scenario 4) increases profits by 17.17%. This study provides technical support for the efficient operation of new energy grid integration and the achievement of “dual-carbon” goals. Full article

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article

In this paper, a new type of cogeneration system using LNG cold energy as a cooling source and geothermal energy as a heat source is designed and studied from the perspective of LNG cold energy gradient utilization. The system integrates power generation, cold storage, and district cooling. In order to provide more detailed information, the proposed system was analyzed in terms of energy, exergy, and economy. The effects of separator pressure, LNG pump outlet pressure, the mass flow rate of n-Pentane in ORC-I, liquefaction temperature of R23 in the cold storage module, and pump 5 outlet pressure in the refrigeration module on the performance of the system were also investigated. Additionally, the particle swarm algorithm (PSO) was used to optimize the CCHP system with multiple objectives to determine the system’s optimal operation. The optimization results show that the system’s thermal efficiency, exergy efficiency, and depreciation payback period are 66.06%, 42.52%, and 4.509 years, respectively. Full article

In the context of rapid urbanization in China, many farmers still live in areas far away from urban energy supply networks. To meet the multi-level energy demands of rural communities, this study proposes a combined heat, power, and electricity (CCHP) supply system that uses solar and biomass energy as inputs, tailored to the natural resources and climatic conditions of the northwestern region. A theoretical model of this system was established in Nanan Community, Wuwei City, and its dynamic performance throughout the year was simulated and analyzed using TRNSYS software. The system was also evaluated for its economic viability, energy efficiency, and environmental impact. The results show that compared with the original and traditional energy supply systems, the CCHP system achieves average primary energy saving rates of −9.87% and 41.52% during the heating season, annual cost savings of 50.35% and 64.19%, carbon dioxide emission reduction rates of 32.89% and 66.86%, and a dynamic investment payback period of 3.14 years. This study provides development ideas for constructing modern integrated energy systems in rural areas that are remote from urban energy supply networks and offers references for investors. Full article

As the world’s population grows and energy demand increases, there is a need to switch from fossil fuels to renewable energy. In order to preserve the environment and meet these growing demands, especially for cooling applications, trigeneration systems could be the answer. The aim of this work is to provide a structured overview of the current state of the art in the field of trigeneration (CCHP) systems. Firstly, these systems and their applications are presented. An overview of the different indicators used to describe the performance of these systems is given. A comparison between CCHP (combined cooling, heat, and power) systems is made. Finally, improvements and other concepts found in the literature are presented. This review will later serve as a basis for the exergo-economic optimization of a low-temperature CCHP system based on renewable energy sources. Therefore, more attention is given to the technologies used for such systems. Full article

Within the 21st century, in the Maritime Silk Road, wave energy, a clean renewable source, is drawing more interest, especially in areas with power shortages. This paper investigates wave energy in ships, particularly in a hybrid electric bulk carrier, by designing a system that supplements the existing power setup with oscillating buoy wave energy converters. The system includes diesel generators (DGs), a wave energy generation system, heterogeneous energy storage (consisting of battery storage (BS) and thermal storage (TS)), a combined cooling heat and power (CCHP) unit, and a power-to-thermal conversion (PtC) unit. To ensure safe and reliable navigation despite uncertainties in wave energy output, onboard power loads, and outdoor temperature, a robust coordination method is adopted. This method employs a two-stage robust optimization (RO) strategy to coordinate the various onboard units across different time scales, minimizing operational costs while satisfying all operational constraints, even in the worst-case scenarios. By applying constraint linearization, the robust coordination model is formulated as a mixed-integer linear programming (MILP) problem and solved using an efficient solver. Finally, the effectiveness of the proposed method is validated through case studies and comparisons with existing ship operation benchmarks, demonstrating significant reductions in operational costs and robust performance under various uncertain conditions. Notably, the simulation results for the Singapore–Trincomalee route show an 18.4% reduction in carbon emissions compared to conventional systems. Full article

The combined cooling, heating, and power system is based on the principle of energy cascade utilization, which is conducive to reducing fossil energy consumption and improving the comprehensive utilization efficiency of energy. With the characteristics of a lower expansion ratio and larger recuperation of a supercritical carbon dioxide (SCO₂) power cycle, a combined cooling, heating, and power (CCHP) system is proposed. The system is based on a SCO₂ cycle and is driven by solar energy. The system is located in Qingdao and simulated by MATLAB/Simulink software (R2022b). Firstly, the thermodynamic performance of the CCHP system at the design condition is analyzed. The energy utilization efficiency of the CCHP system is 79.75%, and the exergy efficiency is 58.63%. Then, the thermodynamic, environmental, and economic performance analyses of the system under variable conditions are carried out. Finally, the solar multiple is optimized. The results show that the minimum levelized cost of electricity is 10.4 ¢/(kW·h), while the solar multiple is 4.8. The annual primary energy saving rate of the CCHP system is 85.04%, and the pollutant emission reduction rate is 86.05%, compared with the reference system. Therefore, an effective way to reduce environmental pollution and improve the utilization efficiency of solar energy is provided. Full article

Recently, electric distribution grids supply not only electric loads but also heating and cooling loads simultaneously to increase the efficiency of the system and reduce the emission of greenhouse gases. An energy management system (EMS) to reduce the combined total expense including environmental damage cost of the combined cooling, heating, and power (CCHP) smart distribution grids in a cooperative framework is proposed in this paper. The entire problem is modelled as a unit commitment interval mixed integer quadratic program (UCIMIQP). The UC is developed to respond to the operation of the electric, heating, and cooling systems and takes into consideration the exchange of energy between these systems. In addition, the demand response (DR) is incorporated with the optimization problem as a decision variable to shave the peak load and reduce the total system cost. The environmental damage is converted to expense, and the entire combined problem is converted to a unified function that is possible to solve in one step, where this is suitable for online operation. Furthermore, a set of realistic constraints is considered to make the approach close to a real scenario. To verify the effectiveness and robustness of the proposed model, the analysis is applied to the distribution grids, which include electrical, heating, and cooling systems, where these systems operated cooperatively. The interaction between these systems makes the operation more flexible and economical. The results show that the total cost is reduced through an exchange in energy between the systems. Additionally, the consideration of the demand response reduces the maximum load and decreases the total cost. Full article

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing the CCHP system efficiency by identifying the optimal design parameters and assisting decision makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize the net power and exergy efficiency while minimizing CO₂ emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved a higher net power output, satisfactory exergy efficiency, and lower CO₂ emissions compared to similar studies. The study shows that the maximum net power and exergy efficiency, with reduced CO₂ emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO₂ emissions and improved exergy values. Full article

The power-to-X strategy for passenger car applications offers a viable solution for using the surplus electrical power from renewable energy sources instead of exporting it to the grid. The innovative system proposed in this study allocates surplus electrical power from a building-integrated biomass-based Combined Cooling Heating and Power (CCHP) system to on-site applications and evaluates the energetic and economic benefits. The system comprises two key components: a 50 kW electric vehicle (EV) charging station for EVs and a 50 kW alkaline electrolyzer system for on-site hydrogen production, which is later dispensed to fuel cell electric vehicles (FCEVs). The primary goal is to decrease the surplus of electricity exports while simultaneously encouraging sustainable transportation. The system’s economic viability is assessed through two scenarios of fuel (e.g., biomass) supply costs (e.g., with and without fuel market costs) and compared to the conventional approach of exporting the excess power. The key findings of this work include a substantial reduction in surplus electricity exports, with only 3.7% allocated for EV charging and 31.5% for hydrogen production. The simple payback period (SPB) is notably reduced, enhancing economic viability. Sensitivity analysis identifies the optimal hydrogen system, featuring a 120 kW electrolyzer and a 37 kg daily hydrogen demand. The results underscore the importance of prioritizing self-consumed energy over exports to the national grid, thereby supporting integrated renewable energy solutions that enhance local energy utilization and promote sustainable transportation initiatives. Full article

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition. Full article