In response to the urgent demand for clean heating in rural areas， a heat pump heating system utilizing indirect photovoltaic/thermal （PV/T） components has been proposed. This study focuses on a single household building （64 m²） located in a village in the Lanzhou region. A simulation model was constructed using the TRNSYS dynamic system simulation software platform， analyzing the operational characteristics of the system across three time scales： hourly， daily and during the heating period. The research investigates how variations in heat pump rated thermal power and thermal storage tank volume affect system performance. The results indicate that when the heat pump’s rated thermal power is set at 2.50 kW and the thermal storage tank volume is 0.9 m³， the system effectively reduces the average temperature of the thermal storage tank. Consequently， total electricity consumption during peak periods is lowered to 536.2 kW·h. The average electrical efficiency of PV/T components reaches 12.3%， while their average thermal efficiency stands at 35.35%. Additionally， the solar energy guarantee rate for this system is recorded at 77%， with an overall system efficiency of 49%. This optimized parameter combination demonstrates significant advantages for PV/T heat pump heating systems applied in rural clean heating contexts； it effectively enhances energy utilization efficiency and reduces operating costs， providing a viable solution for clean heating technologies in rural areas.

With the continuous development of the power system and the increasing awareness of environmental protection， the proportion of renewable energy generation in the power system is constantly increasing， and the scheduling of single thermal power generation units has become a coordinated scheduling mode for multi-energy generation. To solve the scheduling optimization problem of multi-energy power systems with energy storage devices， this paper establishes a multi-energy power system scheduling model of wind-solar-thermal-energy storage battery-pumped storage with the goal of minimizing system generation costs and pollution emissions. This paper introduces an adaptive strategy based on the number of iterations to optimize position update factor. Gaussian mutation is used to perturb the algorithm population， and the elite strategy in the ant lion algorithm is combined with the grasshopper algorithm to solve the proposed scheduling model using an improved multi-objective grasshopper algorithm. Real examples are simulated and analyzed on the Matlab platform， and an optimal multi-objective power system scheduling scheme is proposed. Through simulation analysis of test functions and simulation examples， the superiority of the improved algorithm and the rationality of the established model are verified.

Aiming at the challenges of insufficient scheduling flexibility and rising operational costs in multi-park electric-thermal systems under large-scale renewable energy integration，this paper proposes an optimal scheduling model for tiered dual-time-scale distributed electric-thermal system based on edge computing. Firstly，a three-tier collaborative architecture comprising a physical equipment layer，edge computing layer，and cloud layer is constructed. Edge computing facilitates rapid data processing and distributed decision-making among parks. Secondly，the improved analytical target cascading method is employed with a dual-time-scale strategy： the lower layer optimizes electrical energy interactions at a 5 min granularity，while the upper layer coordinates thermal energy interactions at a 1 h granularity. The augmented Lagrangian method is integrated to decouple and iteratively solve multi-time-scale optimization problems. Finally，a benefit redistribution mechanism based on energy contribution degrees is designed，utilizing an asymmetric mapping function to quantify each park’s contributions to electric-thermal exchanges and renewable energy consumption，ensuring equitable profit distribution. Case studies demonstrate that the proposed model reduces comprehensive operational costs by 34.46% compared to conventional methods，significantly improves renewable energy consumption rates，and achieves convergence within eight iterations. The findings confirm that the integration of edge computing and dual-time-scale strategies effectively addresses spatiotemporal disparities in energy flows，providing theoretical and practical insights for coordinated optimization in multi-energy-coupled systems.

To reduce the carbon emissions of the park's integrated energy system， an integrated energy system scheme based on electric energy substitution technology is designed according to the park's energy demand and load characteristics. A full-working condition simulation model is also established. Firstly， the non-dominated sorting genetic algorithm （NSGA） and Gurobi solver are employed to address the multi-objective cooperative optimization problem. Subsequently， the entropy weight-solution distance method is utilized for comprehensive evaluation and decision-making， thereby determining the optimal system capacity allocation and operational strategy. Case analysis demonstrates that the proposed scheme decreases the park's carbon emission intensity by 77%， satisfying the requirements for a near-zero carbon park. Additionally， the annual net energy cost is reduced by 61.2%， while the energy self-sufficiency rate is increased to 71.3%. The optimized integrated energy system significantly enhances energy efficiency and security， reduces carbon emissions， and strengthens the park's energy independence.

Aiming at the problems of single energy product types， high pollutant gas emissions and poor economic efficiency in traditional integrated energy systems， a configuration of electric-thermal combined supply integrated energy system with power to X （P2X） technologies such as power to hydrogen （P2H）， hydrogen to gas （H2G） and hydrogen to ammonia （H2A） is proposed. Firstly， in terms of system modelling， a power to hydrogen/ gas/ ammonia （P2H/G/A） coupled system is constructed by introducing technologies such as carbon capture， utilization and storage （CCUS） and oxygen enriched/ammonia mixed combustion in thermal power units. Secondly， in the low-carbon economic transformation of the power system， a comprehensive energy system objective function is constructed， which considers the carbon reduction effects and improved heating economic benefits of the oxygen enriched combustion- H2G coupling model， as well as the reduction of coal consumption costs and coal-related carbon emissions by the H2A- ammonia mixed combustion coupling model. Finally， an example is constructed based on a demonstration site in Inner Mongolia， the economic benefits and carbon reduction effects of different energy conversion technologies are compared and analyzed. The conclusion shows that the proposed integrated system can significantly optimize the energy structure and achieve multi-energy cooperative low-carbon economic operation. Compared with traditional integrated energy systems， the economic cost is reduced by 7.5 × 10⁵ Yuan （19.5%） and the environmental cost is reduced by 5.0 × 10⁵ Yuan （11.5%）.

In response to the “dual carbon” strategic goals， the interactive linkage of tiered carbon trading， green certificate trading， and demand response holds significant importance. Their interaction can reduce carbon emissions and operational costs in parks. Firstly， a mathematical model for green certificate-tiered carbon trading is constructed. Secondly， a demand response mechanism is incorporated to guide user electricity behavior， promote the integration of renewable energy， and reduce system operating costs. Then， the total system operating cost and total carbon emissions are set as the objective functions for multi-objective optimization scheduling. The model is solved to obtain a Pareto solution set， and the technique for order preference by similarity to ideal solution （TOPSIS） combined with grey relational analysis is employed to determine the ideal solution from the Pareto set. Finally， multiple scenarios are configured for comparative analysis， verifying the practicality and effectiveness of the proposed model.

As the scale of livestock breeding continues to improve， dairy farming is developing in a direction characterized by specialization， intensification and standardization. In light of the “double carbon” objective， the energy supply paradigm of advanced dairy farming has also undergone a transition towards green energy. In order to address the issue of efficient energy utilization and reduction of electricity production costs in the context of large-scale dairy farming operations， an optimal scheduling method for energy systems in such farms is proposed， and this method is based on source-load-storage coordination and interaction. This method classifies and models the primary production energy-using equipment according to the demand and response characteristics of dairy farm production electricity， establishing an optimal scheduling model of a large-scale dairy farm energy system that considers the cost of electricity. In the context of time-of-use electricity pricing， this study proposes an optimal scheduling strategy for dynamic adjustment of the source-load-storage system. Based on the actual production conditions of a large-scale dairy farm， a case study is conducted， comparing the power consumption effects before and after the implementation of the optimal scheduling method. The results of the case study demonstrate that the proposed optimal scheduling strategy can effectively reduce the daily electricity costs of the large-scale dairy farm while meeting production demands， thereby maximizing the economic benefits of electricity in dairy farms.

With the rapid development of renewable energy, small hydropower station, as a kind of clean energy, shows a good development prospect. However, at present, small hydropower stations are faced with problems such as small scale, insufficient mechanism of participating in power grid dispatching and urgent innovation of operation mode of peak and frequency regulation. Based on the operation characteristics of small hydropower station, considering the cost and emission of thermal power units and the stable operation of small hydropower station, a small hydropower station model with multi-level gradient variable speed water control and stability regulation is constructed. At the same time, an improved growth rime algorithm and multi-gradient supply-storage cooperative scheduling strategy are proposed to explore the optimal decision scheme to realize the economic benefit, environmental benefit and scheduling stability of the system. The experimental results show that compared with other algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO) and crested porcupine optimizer (CPO), The improved rime optimization algorithm can improve the economy of the system by 8.7%, reduce the pollution emission by 20.6%, and enhance the operation stability of the small hydropower station by 32.6%. The results validate the effectiveness of the proposed model and algorithm, and provide a new idea for the coordinated operation optimization of multi-source energy complementary systems.

Aiming at the problem of multi-energy collaborative optimization under the power internet of things, an optimization model of collaborative trading of wind and solar storage was established. The excellent regulation performance of pumped storage power (PSP) plant is used to bundle it with wind power plant and photovoltaic power plant in proportion to maximize the absorption of new energy; The other part participates in the main side market regulation to achieve collaborative optimization. Through the construction of landscape storage alliance, it aims to eliminate the deviation of landscape new energy output, so as to solve the security problem of information exchange in the internet of things. In addition, the Shapley value method is used to allocate the surplus value. In this process, considering the carbon emission requirements, the carbon reduction efficiency of electric vehicles(EV) is introduced on the load side, and it is constrained by the output of thermal power. At the same time, the electric energy storage regulation is added to the grid side to ensure the safe operation of the system in the power internet of things environment. This study adopts the volume quotation method on the subject side, so that all the new energy forecasts are cleared, while the load side is cleared following the market price fluctuations. Further considering the fluctuation deviation, the output of the multi-energy subject is optimized. Simulation results under different scenarios verify the feasibility and adaptability of the proposed optimization model.

In order to reduce the influence of uncertainty on both sides of the source and load on the security and economy of the integrated energy system (IES), and to improve the flexibility and stability of IES in the face of uncertainties, various strategies for energy storage participation in smoothing out the uncertainty fluctuations are proposed, and a robust model is established for the day-ahead and real-time two-stage cooperative optimization under multiple uncertainties. A robust adjustable factor is added to the model to comprehensively evaluate the system economy and robustness. In the day-ahead phase, a pre-dispatch plan is determined based on the predicted power of new energy and load to realize the power balance at the minimum operating cost. In the real-time phase, the adjustment power of the secondary flexible adjustment equipment is determined according to the new energy output and the actual simulated power of the load to realize power rebalancing at minimum cost. The case study shows that the real-time adjustment of power supply side and energy storage side can better play the synergistic adjustment function of IES to deal with uncertainty; the introduction of robust adjustable factor to portray the uncertainty better balances the economy and security of system operation.

In order to cope with the uncertainty of renewable energy utilization and customer loads, a multi-timescale prediction method is proposed, where the prediction process is carried out in three phases: day-ahead, intra-day rolling and real-time, with timescales of 1 h, 15 min and 5 min, respectively. First, a prediction method based on difference statistics is used to accomplish the three stages of forecasting meteorological parameters. Second, a regression prediction model combining signal decomposition and machine learning is established for the day-ahead and intraday stages of load prediction, and a machine learning time series prediction model is established for the real-time stage. Next, the best prediction methods for typical daily loads in the day-ahead and intra-day rolling stages are determined based on the prediction accuracy metrics of the test set. Finally, the prediction method is applied to the energy forecast of typical days to verify the feasibility of the method. The results show that the determination coefficient R² of the prediction results of the meteorological parameters for a typical day in all three phases is above 0.8; in the day-ahead and intraday rolling phases, the prediction tasks of multivariate loads should be performed with different signal decomposition methods, and the R² of the load prediction results in the real-time phase is above 0.9, and the mean absolute percentage error (MAPE) is close to 0.

Solar-air source heat pump hot water system (SAHWS) is commonly used in dormitory building heating, through the optimization of system parameters can significantly improve the energy efficiency and environmental friendliness of the system. In order to obtain an optimization method that comprehensively considers the SAHWS economy, energy, environmental protection and energy saving, this paper proposed a new combination optimization design strategy, and used TRNSYS software to build a system simulation model. Taking Xi'an, Xining and Lhasa, which are three cities with different levels of solar energy resources, as examples. Comparative analysis of SAHWS operating conditions. The results show that compared with the common life cycle cost design, the proposed combinatorial optimization design not only reduces the system cost, but also reduces the system energy consumption. The heat pump designed by combination optimization has the shortest energy consumption and working hours, and has the lowest heat loss. It has better performance in investment cost, system seasonal performance factor, solar energy guarantee rate, carbon dust and carbon dioxide emissions.

Multi-energy storage is an effective way to improve the renewable energy consumption level of integrated energy system. However, with the increase of coupled energy storage equipment, the complexity of the system increases. Unstable operation and low energy efficiency caused by the complexity and uncertainty of the system have restricted the development of integrated energy system. Therefore, this paper proposes to integrate electricity storage, heat storage and hydrogen storage into the integrated energy system, and researches on multi-timescale optimization. From the construction of the system model to proposing multi-timescale optimization schemes such as day-ahead optimal scheduling, intra-day rolling optimization, and real-time adjustment, the advantages of multi-energy storage are fully utilized to solve the impact of source-load uncertainty on the system. The results show that the total power storage is adjusted to 12.421 99 MW unbalanced power, the community renewable energy consumption level is increased by 0.42%, the electricity purchase cost is reduced by 3.5%, and the carbon tax is reduced by 1.5%. This shows that the proposed multi-timescale optimal operation method makes the system run more smoothly by flexibly adjusting the output of each device, and improves the stability of the system and the level of renewable energy consumption.

Due to the uncertainties in both the landscape and user demand, traditional optimal scheduling responses can result in imbalances between supply and demand within a single day. In order to address this issue, a comprehensive wind and solar storage energy system is constructed, incorporating fans, photovoltaic panels, batteries, as well as light and heat technologies to fully utilize renewable energy sources. Building upon day-ahead scheduling, a mathematical model for day-rolling optimization is established using the model predictive control algorithm combined with the state space equation of the energy system. A typical daily example simulation is conducted on the Matlab platform. By analyzing the power output and energy supply of each device within the system, simulation results demonstrate that in three typical days there is a reduction of 12.3%, 7.4%, and 11.3% respectively in daily operating costs for the system while also reducing power supply and thermal imbalances rate which enhances both economic efficiency and reliability of this energy supply system.

As the complexity of integrated energy systems increases, so does the demand for component coupling, leading to heightened system uncertainties. Hence, establishing an evaluation index system and assessment model for the flexibility of integrated energy is a crucial prerequisite for enhancing the flexibility of these systems. Initially, this paper constructs evaluation systems for integrated energy system flexibility resources at both the system-wide and localized levels. Then, integrating economic, environmental, and safety factors, this paper employs the analytic hierarchy process, the entropy method, and fuzzy evaluation to develop a comprehensive evaluation model. This model is used for radar chart analysis of index weights and fuzzy comprehensive evaluations based on scenarios implementing eight different flexibility resource scheduling schemes. Results indicates that among the set of flexibility resource scheduling schemes, the highest flexibility score is achieved by the scheme combining gas internal combustion and gas boilers. However, when considering multiple factors such as economic viability, environmental impact, and safety, the two scheduling schemes, gas internal combustion engine combined with heat pump and water energy storage, and gas internal combustion engine combined with heat pump and energy storage station, are more prominent.

In order to improve clean energy consumption and flexible operation of multi energy coupling systems, while reducing the impact of source and load uncertainties on the system, this paper constructs a wind-photovoltaic-electric-heat-hydrogen integrated energy system (WPEHH-IES) including concentrating solar power, and proposes a two-stage robust optimization method with adjustable robustness for spatial polyhedral uncertainty sets. Based on the column-and-constraint generation (C&CG) algorithm, a two-stage three-layer min-max-min robust optimization model is decomposed into a one-layer min main problem and two-layer max-min subproblem. The structure of the two-layer subproblem is simplified by using the strong duality principle, and the optimal solution of the original problem is obtained by alternating the subproblem and the main problem. Finally, the effectiveness of the constructed model and solving algorithm, as well as the flexibility of the output of the multi energy coupling system, were verified through example analysis.

Solid state heat storage technology has positive significance in consuming surplus electricity from the power grid, balancing peak and valley loads, and reducing environmental pollution by converting nonpeak electricity into thermal energy. In response to the problem of uneven heat extraction in existing solid heat storage devices, a new type of structural heat storage body is proposed, which designs an intermediate channel in the ventilation duct. The numerical model of the heat storage body was established by using Fluent. The temperature distribution and the velocity distribution in the ventilation duct of the new heat storage body when releasing heat are studied, and compared with the structure without intermediate connection. The research shows that the middle connected structure improves heat transfer efficiency and reduces heat accumulation phenomenon. Compared with the structure without intermediate connection, the average temperature of the new structure heat storage body is reduced by 37~68 ℃, with a maximum reduction of 22.8%, and the maximum temperature difference is reduced by 27~60 ℃, with a maximum reduction of 48.2%. The middle connected structure reduces the relative standard deviation of flow velocity at the center of the ventilation duct by 45.4%, improving the uniformity of the flow field. The middle connected structure along the vertical direction makes the temperature distribution of the heat storage body more uniform in the vertical direction, and does not deteriorate the temperature uniformity in the horizontal direction.

Intelligent and low carbonization is the inevitable trend of the development of comprehensive energy digital transformation of group enterprises. Combined with the characteristics of the group enterprise, such as multiple energy varieties, long process, large volume, high requirements for energy conservation and emission reduction indicators, and the needs of actual management, aiming at the problems such as multiple energy subsystems, lack of data interconnection, and lack of intelligent decision-making application based on monitoring data, an integrated energy system architecture based on intelligent decision is proposed, which integrates different kinds of energy subsystems and equipment, and realizes the design and integration of intelligent integrated energy system architecture of group enterprises by combining the practical management and decision requirements of energy-saving transformation, energy efficiency evaluation, energy trading, etc. The design scheme conforms to the law of energy management construction of actual group enterprises, effectively uses the stock of software subsystems and equipment to minimize the system investment, and effectively guarantees the compatibility, openness and scalability requirements of the energy management system, providing a reference for the architecture design of the integrated energy management system of group enterprises.

In recent years, under the goal of "carbon peak and carbon neutrality", energy conservation and consumption reduction in industrial enterprises have attracted wide attention. As the second largest power source, the energy saving and efficiency improvement of air compressor is one of the key work at present. In this paper, technical analysis and research are carried out on four different driving forms of air compressors that are electric-driven, steam-driven of back pressure heating unit, steam-driven of condensing heating unit and steam-driven of condensing non-heating unit. Combined with the technical characteristics, the primary energy consumption, carbon emission and energy cost of compressed air are analyzed and compared. The results show that electric air compressor has the lowest carbon emissions and the highest energy costs, and the steam-driven air compressor coupled with the condensing heating unit or condensing non-heating unit has the worst effect than others in the primary energy consumption and carbon emissions. The steam-driven air compressor coupled with the back pressure heating unit is better than the electric air compressor in terms of primary energy consumption and carbon emission of compressed air.

Combined cooling, heating and power (CCHP) system can meet the user's demand for cold and hot power load at the same time, and can realize heat cascade utilization and efficient energy supply. However, the internal energy coupling degree of CCHP system is high, and various energies affect each other, which brings challenges to the capacity configuration and efficient energy supply of CCHP system. Therefore, a CCHP system with energy storage and renewable energy technology is established, two operation strategies for preferential utilization of waste heat are proposed, and the multi-objective genetic algorithm is used to optimize and determine the capacity of the system equipment, and various operating indicators are analyzed. The results show that the "3E" comprehensive index of operation strategy 2 (recovery of waste heat priority heat supply) is 0.344, which is higher than that of operation strategy 1 (recovery of waste heat priority cooling), and the annual value saving rate of operation strategy 2 is 4.5%, which is better than that of operation strategy 1. The emission reduction rate of various pollutants in operation strategy 2 is higher than that of operation strategy 1, showing good environmental benefits.

In this paper, the efficiency and economy of the distributed energy system of the ground source heat pump coupled storage pool bearing the base load were studied by taking the actual project of some energy station as an example.The computer software is used to simulate the terminal cooling and heating load hourly, and the ground source heat pump and energy storage pool are used to undertake the base load, coupled with other traditional energy sources as the operation mode of peak adjustment and guarantee of the system, so as to determine the installed proportion of the project and calculate the energy efficiency and economy of the system.The research shows that the energy system of the energy station adopts different forms of energy mix according to the local energy prices at different times, which realizes the economic, efficient and environmental operation of the project.For the distributed energy system with the base load borne by the ground source heat pump coupled energy storage pool, the intelligent energy management platform is built to realize the operation mode of multi-energy collaboration and intelligent coupling. Compared with traditional energy supply system, distributed energy system can not only improve energy utilization efficiency and system economy, but also improve the utilization rate of renewable energy.

The hybrid energy system that integrates renewable energy technology, energy storage technology with natural gas cogeneration technology is an important way to build a clean, low-carbon, safe and efficient energy system in the future. However, this kind of hybrid energy system is difficult to optimize due to its complex structure, numerous parameters and variables and high coupling degree of multiple supply energy. On the other hand, due to the strong fluctuations of meteorological parameters and user-side demand, the design and optimization of the energy system require detailed simulation and a large number of input data. However, the existing methods can not balance the accuracy and calculation speed in such long-time-scale optimization. Therefore, this study proposes a two-layer global multi-objective co-optimization framework that combines genetic algorithm and the technique for order of preference by similarity to ideal solution based on hourly load throughout a year. The inner layer optimizes the ratio of waste heat distribution and the power of the generating unit with the objectives of operation cost, waste heat utilization rate and grid interaction. The outer layer optimizes the equipment capacity based on the annual cost, primary energy consumption and carbon dioxide emission. The optimal capacity and operation planning of the equipment are determined by the iteration between the inner and outer layers. Furthermore, by comparing with the typical-day optimization scheme, the research finds that the year-round optimization scheme possesses a better overall performance with 3.64% less annual cost, 3.04% less carbon dioxide emission, 17.56% less primary energy consumption and 40.27% less power grid dependence. The above results show that the method this paper proposed can provide effective reference and solution for the study of similar energy system design and operation optimization.

Combining the different indoor heat demands of time-sharing, designing a reasonable control strategy will bring huge energy saving potential to the solar heating system (SHS) with auxiliary heat pump. Based on the existing research on the human body's differential heat demand in northwest China, this study proposes a flexible energy-saving control strategy of a combined solar and air source heat pump (ASHP) heating system based on time-sharing heat demand. Considering the indoor heat demand in different time periods, the corresponding room temperature control range is set and the output sequence of the heat source is optimized. As a result, the control system becomes more flexible. The simulation model of the system is established by TRNSYS, and the operation of the system under different control strategies is studied. A case study of a residential building in three areas with different solar energy resource levels is considered. Compared with the commonly used "thermostatic" control strategy, the proposed control strategy allows the full utilization of the solar energy and a reduction in the total energy consumption by up to 24% through reducing the working time of the ASHP. The results also show that the proposed control strategy is more effective in areas with high radiation intensity.

Based on multi-energy complementation, a variety of different forms of cold sources are intelligently coupled in the regional energy station to meet the demand of terminal cooling load. In order to make the multi-energy complementary regional energy station cooling system run economically, energy-saving, green, environmental protection and efficiently, the installed capacity of the cold source should match the operation mode in the design. Based on the practical case of a regional energy station in Beijing, the late operation mode of the project is analyzed, and the installed capacity of each cold source of the cooling system is designed. The research shows that after coupling the ice storage tank in the cooling system, different operation modes can be adjusted according to the change of terminal load in order to reduce the operating cost of the system and achieve economic and efficient operation. Compared with the traditional single source cooling system, the multi-energy complementary cooling system can improve the matching of cooling and terminal load through intelligent coupling of various energy sources, and reduce the operating cost.

Based on the connotation and construction goal of "electricity as the core" modern energy system, this paper proposes that the modern energy system with "electricity as the core" mainly includes the construction of energy base, the construction of comprehensive energy service system, and the interaction between supply and demand between energy base and smart city. The energy base should be fully coupled with wind, water and heat storage, and give full play to the complementarity of different energy forms in space-time characteristics and energy characteristics. It is suggested that the construction of three typical models should be focused on the integration of wind- photovoltaic - thermal -storage, wind- photovoltaic- hydroelectric- storage and wind- photovoltaic -hydrogen-storage. In order to improve the efficiency of terminal energy consumption, this paper gives three application examples of integrated energy service mode in the industrial field, the construction field and the transportation field.Vigorously promoting the construction of energy base on the supply side, promoting smart energy based on comprehensive energy services on the demand side, and realizing the efficient and high-frequency interaction between supply and demand with the support of industrial Internet technology and the guarantee of business model innovation are the inevitable choices for improving the security and flexibility of power system, ensuring the balance of supply and demand, and building a modern energy system with "electricity as the core" .

For the integrated energy system (IES) constituted of electricity-heat-gas networks, an alternating iterative method was proposed to solve multi-energy flow in this paper. According to the operation mode of energy coupling equipment, the calculation sequence of different energy subsystems was determined. In the process of calculation, interactions between network data and coupling equipment data can be ensured, so that the proposed algorithm can be available to a variety of different integrated energy operation scenarios. Finally, case studies were performed on IEEE 33-bus district electricity network, 32-node district heat network and 36-node district natural gas system to demonstrate the correctness and effectiveness of the proposed method.

For a long time, energy has been a hot and difficult issue in China's economic development, solving energy shortage and environmental pollution plays an important role in realizing sustainable development. With the development of multi energy complementary and collaborative planning technology, multi energy coupling has become the most extensive energy supply mode of the integrated community energy system. How to improve the operation efficiency of the integrated energy system is the main research direction in the future, so it is very important to study the economic operation technology of the integrated community energy system under the premise of environmental protection and energy supply reliability. Firstly, this paper summarized the integrated energy system and operation optimization method, and puts forward the mathematical model of micro grid source and multi energy coupling unit; Secondly, considering the optimal comprehensive cost, the minimum environmental impact and the most reliable operation of the system, the typical objective functions and constraints of the integrated community energy system optimization model were summarized. The NSGA-II and multi-objective particle swarm optimization model solving algorithms were summarized, and the effectiveness of the modeling and optimization methods was verified by simulation examples. Finally, the paper summarized and prospected the operation optimization of the integrated community energy system.

Multi energy complementary system provides safe and reliable energy supply through the optimal coupling of cold, hot and electrical energy, which is the way to achieve the optimal social energy efficiency, promote the utilization of renewable energy and realize sustainable development. The income structure, profit model, optimization objectives and influencing factors of the multi energy complementary system are analyzed. The actual case of an economic development zone in a certain region shows that: different project investors, result in different optimization objectives and operation modes as well as profitability of multi-energy complementary system operators. The profit of source + network + dispatching control center mode is higher than that of network + dispatching control center mode. If the load can participates in the demand-side response, the cost of energy supply can be further reduced. The research results can provide reference for commercial operation of multi energy complementary system in China.

With the rapid development of source-grid-load-storage coordinated and optimized operation mode of comprehensive energy, it has become a trend for large users to adopt new energy power generation, energy storage, ground source heat pump and other forms of comprehensive utilization of energy. Both the energy supply system and the energy consumption system abandon the low efficiency and extensive application mode, and turn to the efficient and intelligent comprehensive energy application mode. This paper introduces in detail the energy consumption system and characteristics of large users, the user-centered integrated energy system architecture, and the integrated energy management system for large users. Taking a large-scale user's comprehensive energy construction scheme as an example, this paper introduces the energy utilization system of large-scale users in detail. A variety of energy efficiency analysis is carried out to verify the rationality of the scheme, which can provide reference for large users' comprehensive energy construction application and strategy control

In order to improve the comprehensive performance of wind-solar hybrid co-generation system for severely cold and high altitude regions, a capacity optimization model was established. A multi-objective optimization was performed using the Non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ) to maximize annual CO₂ emission reduction, minimize system investment cost and loss of power supply probability. According to the annual sunshine and wind conditions in that area of Tibet, the capacity optimal design for wind-solar hybrid co-generation system was carried out using the non-dominated sorting genetic algorithm-Ⅱ(NSGA-II). The results show that the loss of power supply probability, investment cost and annual CO₂ emission reduction are 4.6%, 162 000 ￥ and 95.4 t, respectively. The solar collector accounts for about 50% of the total investment, followed by the photovoltaic cell and battery. While, the investment of fan and heat storage tank is the least. Besides, the hourly loss of power supply probability is mainly concentrated in the period of low outdoor temperature. The multi-objective optimization can balance the requirements of economy, reliability and environment for wind-solar hybrid co-generation system, which verifies the effectiveness of this method. It provides theoretical guidance for the optimal design of the wind-solar hybrid co-generation system in severely cold and high altitude regions.

The supply and consumption of energy is an important factor affecting the operation of enterprises. According to the characteristics of energy consumption of enterprises, this paper put forward a multi-objective and strong coupling comprehensive energy planning optimization design theoretical method and multi-energy coupling algorithm. Combined with practical examples, the simulation calculation and analysis were carried out with Matlab as the data analysis and modeling platform, The load analysis and prediction model, installed capacity allocation model, technical and economic analysis model, risk stress test model and other analysis tools were established, and the optimal comprehensive energy planning optimization scheme was obtained, which proved the effectiveness and practicability of the optimization design method and algorithm, It provides a theoretical basis for enterprises to establish a comprehensive energy system with multi energy complementary, high efficiency and energy saving, green and low carbon, source grid load storage integration