The production of hydrogen from offshore wind power is an effective way to fully utilize offshore wind energy and promote low-carbon economic operation in integrated energy system. This paper proposes a multi-level market optimization model for an integrated energy system that includes hydrogen production from offshore wind power. The model considers factors such as wind curtailment penalties and demand response costs. The research focuses on the integration of electrothermal energy systems, hydrogen production, carbon capture storage technology, and a ladder carbon pricing mechanism to establish an optimal operation model for participation in various energy markets and trading platforms. The goal is to maximize economic benefits, ensure system stability, and protect the environment. Simulation results demonstrate that system economic benefits increase by 205.64% when income from hydrogen sales is considered. Additionally, the market price of hydrogen and the capacity of the electrolyzer that meet the system's economic and environmental goals are found to be [1.52 yuan/m3, 3 yuan/m3] and [1500 kW, 1700 kW], respectively. Under the ladder carbon pricing mechanism, the optimal carbon trading parameters are determined to be a base price of 108 yuan/t and a price growth rate of 0.6. The participation of the integrated energy system in multi-level market trading can improve economic efficiency, reduce carbon emissions, utilize offshore wind power fully, and enable the flexible application of energy sources such as hydrogen.

Integrated energy system (IES) can achieve multi energy complementarity, but it still faces the problem of energy surplus or shortage. Therefore, there are demands for energy sharing among different IESs, but how to determine the trading quantity and trading price? To address this confusion, a distributed optimization model for multi-IES participating in peer-to-peer (P2P) transactions based on asymmetric Nash negotiation is innovatively proposed. First, to facilitate the energy sharing of several IESs with different structures, a P2P trading framework is designed. Second, to meet the needs of individual rationality and alliance cooperation at the same time, a distributed transaction optimization model based on Nash negotiation is proposed. Considering the discourse power determined by contribution, an asymmetric bargaining mechanism is designed. Third, to protect the privacy and improve solution performance, the improved adaptive step-size alternating direction multiplier algorithm (ADMM) is used for the distributed sequential solution. Finally, the effectiveness of the proposed trading framework, bargaining mechanism, optimization model, and solution algorithm are verified by the implementation of simulation. The simulation results show that: 1) The proposed model and algorithm can assist managers in determining the quantity and price of P2P electricity transactions. 2) Compared with the independent operation mode, the cooperative operation mode has significantly improved the overall interests and individual interests. 3) In distributed transactions, managers only need to submit limited information, which protects the privacy and security of each agent. 4) The asymmetric Nash negotiation mechanism can measure discourse power based on contribution. 5) The improved ADMM has the advantages of stronger convergence performance and faster solution speed.

Under the research background of China vigorously promoting the planning and construction of multi-energy complementary energy bases as well as the steady advancement of the electricity market, how to better promote the participation of large-scale multi-energy complementary bases in the electricity market has become a new research focus. In this paper, a joint optimization model for the participation of multi-energy systems in the electric energy market and auxiliary service market is proposed based on the Nash negotiation theory with coordinated wind-photovoltaic-pumped-storage-hydropower (WPPSH) generation systems as the research objects. Further, the joint optimization model is equated into two subproblems, namely, maximizing the net return of the alliance and negotiating the payment of the multi-energy complementary transaction, and the alternating direction method of multiplier algorithm (ADMM) is used to solve the above two subproblems separately. The simulation results show that the revenue of WPPSH increased by 25.82%, 30.67%, 49.35% and 3.78% respectively after joint operation, and the absorption rates of wind power (WD), photovoltaic (PV) and hydropower (HY) increased by 17.69%, 21.87% and 9.01% respectively. Through the cooperation of WPPSH generation systems, the income of each entity and the alliance can be improved, the fair distribution of incremental income is basically realized, and the consumption of clean energy can be promoted. In addition, the capacity allocation cap ratio of pumped storage (PS) units in the energy market and auxiliary service market will have a certain impact on the returns of WPPSH generation systems in multi-principal cooperation.

The key to “dual carbon” lies in low-carbon energy systems. The energy internet can coordinate upstream and downstream “source network load storage” to break energy system barriers and promote carbon reduction in energy production and consumption processes. This article first introduces the basic concepts and key technologies of the energy internet from the current situation of energy supply and demand in China. Second, this paper proposes to create an energy internet with coordinated and complementary “source network load storage” and to construct a new type of power system with six new characteristics in this context. Finally, combined with an example of the energy internet demonstration project, this paper analyses and summarizes the value creation and business type innovation of the energy internet from three aspects: power market mechanisms, comprehensive energy services, and low-carbon energy diversification, and it looks ahead to the future direction of energy internet construction.

A day-ahead and real-time two-stage risk economic optimal model of integrated energy system (IES) is established. First, considering the electricity and heating coupling characteristics of combined heat and power, the feasible region is described by mathematical model, and the integrated demand response model is expanded from the traditional demand response model. Second, the objective functions and constraints of two stages are established respectively. The first stage optimal objective is to minimize the pre-scheduled operation cost of day-ahead, which arranges the output power of renewable energy and the startup-shutdown plan, output power and reserve capacity of adjustable equipment. The second stage optimal objective is to minimize the re-scheduled expected cost of real-time, which will call reserve capacity, curtail renewable energy output, implement integrated demand response, and use energy storage to cope with power deviations. In order to quantify the risk cost of multiple uncertainties of power, load, and price, the real-time stage objective function is further improved to a form of conditional value at risk. Finally, simulations implemented on a green park show that: the proposed model can achieve the optimization of energy supply at different time scales and improve scheduling enforceability after considering economics and risk. Shapely Value can fairly and reasonably determines the benefit distribution scheme of different subjects in IES.

To cope with the volatility of renewable energy and improve the efficiency of energy storage investment, a bi-level (B-L) optimization model of an integrated energy system (IES) with multiple types of energy storage is established by considering the uncertainty of wind power. The upper-level optimization model considers the lowest configuration cost of energy storage as the objective function and satisfies the constraints of the energy storage configuration. The lower-level optimization model considers the lowest operation cost of the IES as the objective function and satisfies the constraints of the system operation. Second, to overcome the fluctuation problem of wind power output, a robust optimization theory is introduced to describe the uncertainty. Robust coefficients are set to reflect different risk attitudes, which improves the adaptability of the system to uncertainty. Third, the B-L optimization model is solved using the Karush–Kuhn Tucker condition. Finally, a new park is used to implement the simulation. The conclusions are as follows: (1) The economic configuration strategy and optimal operation scheme can be obtained by applying the B-L optimization model, and the upper- and lower-levels interact with each other. The optimal targets of the upper- and lower-level models are −115,848 ¥ and 57,131,102 ¥, respectively. (2) The robust optimization theory improves the ability of a system to deal with risks. Robust optimization theory improves the ability of a system to deal with risks. With an increase in the robustness coefficient, the profit space of the upper-level model increases; however, the operation cost of the lower-level model increases.

Due to the small scale and few functions of the single integrated energy system, the absorption capacity of wind turbine and photovoltaic is limited, the ability to cope with uncertainties is weak, and the space for optimal allocation of resources is limited. To solve the coordination problem of robustness, economy, environmental protection and efficiency, this paper forms an integrated energy system group (IESG) by means of energy sharing and carbon transfer, and innovatively proposes a two-stage distributionally robust optimization model (TSDRO) based on kernel density estimation (KDE) and Wasserstein metric. Firstly, the structure of IESG with carbon capture, utilization, and storage-power-to-gas (CCUS-P2G) system is introduced. Then, the nonparametric KDE method is applied to fit the probability density functions of the forecast power error of wind turbine and photovoltaic. Wasserstein metric is used to characterize the fuzzy uncertainty set of distributions. The cumulative distribution function of KDE is taken as the center, and the obtained distance is taken as the radius to form the Wasserstein ball of probability distribution. Based on affinely adjustable policy, a correlation model of real-time variables with respect to day-ahead variables is established. Finally, according to the dual theory and convex optimization theory, the TSDRO model is reformulated into a solvable model. The simulation results show that: (1) energy sharing and carbon transfer can improve the ability of IESG to cope with uncertainty and expand the boundary of resource optimal allocation, and the minimum expected operating cost under the worst distribution is $ 40,259.94. (2) CCUS-P2G system strengthens the synergistic relationship between electricity and carbon and reduces the carbon emission of the system by 128.2 t. (3) After testing, the results obtained by nonparametric KDE are closer to the true distribution and more objective. (4) The TSDRO model is data-driven and has the advantages of high solving efficiency and low decision-making conservatism. The solution time of the TSDRO model is 73.03 % less than that of the stochastic optimization model, and the operation cost is 1.69 % less than that of the robust optimization model, which achieves the balance of economy, robustness and environmental protection of IESG.

China has developed a preliminary policy system for the development of new energy vehicles regarding the law, electricity price, grid-connected standards, project management, and financial support, however, defects remain in the policy and market environment, market mechanism, control technology, infrastructure, etc. We analyze new energy vehicles based on the analysis of basic data such as the number of electric vehicles and charging facilities, focusing on industrial development strategies, related subsidies, and tax policies. First, this paper summarizes the development status of China’s new energy vehicles in different scenarios. In 2021, China’s new energy vehicle production was 3545 thousand, and sales amounted to 3521 thousand. According to preliminary estimates, the number of new energy vehicles will exceed 15 million in 2030. The research route for the development of new energy vehicle bottlenecks is proposed. Secondly, we analyze foreign and Chinese policies on different stages and construct the implementation path for the healthy and stable development of China’s new energy vehicles. By comparing the basic indicators, related policies, and related innovation activities of new energy vehicles in China, we conclude that the development of the national electric vehicle industry needs to be increased in terms of government policies, business model innovation, and public awareness.

“World Energy Outlook 2021” has mentioned the fact that with the current pledges announced, the 2050 net-zero carbon emission target would not be realized. To further improve energy efficiency, energy integration will be important. Therefore, this paper introduced virtual power plant (VPP) and power to gas (P2G) technology to analyze the improvement of energy integration. Firstly, the structure of VPP connected with P2G is proposed, and the physical output model is constructed. Secondly, combined with carbon emission and economic operation objectives, a multi-objective operation optimization model of VPP considering electrical interconnection is constructed, and the solution idea of the model is put forward. Finally, through the case study, the contribution of P2G, DR and GST is proven. With DR, P2G involved in VPP, the goal of carbon emission reduction can be achieved. In addition, the example also proves that carbon trading has a positive effect on energy efficiency and generation uncertainty improving.

Aiming at utilizing straw, garbage, domestic sewage and other biomass waste resources in rural areas, this study designed a rural biomass wastes energy conversion system (BWs)-based micro energy grid (BWs-MEG), and the mathematical modeling of BWs-MEG is carried out including multi-energy transforms system (METS) and multienergy demand response (MEDR). Then, a two-stage optimal framework for rural BWs-MEG multi-time scale dispatching is designed. In the day-ahead stage, a multi-objective dispatching optimal model was constructed with the objectives of minimum operating costs and maximum eco-environment benefits. In the intra-day stage, the robust optimization theory was utilized to characterize the uncertainties of wind power plant (WPP) and photovoltaic power generation (PV) and a rolling dispatching optimal model was constructed with the objective of minimum deviation costs. After that, the above dispatching model was fuzzified and linearized, and then converted into a mixed integer linear programming model. Finally, a micro-energy grid in northern China was selected as an example for case study. The results showed: (1) BWs can utilize rural biomass waste resources for pyrolysis power generation (PG) and gas production to achieve energy utilization and provide power, heating and gas output. In the islanded operation mode and grid-connected operation mode, when MEG is configured with BWs, the deviation costs decrease by 25.6 % and 26.33 %, while the operating costs increases only by 7.60 % and 13.52 %, respectively. The power output of WPP and PV increase by 1.03 % and 2.19 % in the gridconnected operation mode, indicating that BWs is conducive to realizing the multi-dimension supply and demand balance of power, heating and gas loads. (2) Multi-time scale dispatching model can give play to the regulation ability of BWs, METS and MEDR and connect the day-ahead dispatching plan with the intra-day dispatching strategy to formulate the optimal dispatching strategy. When there is deviation in the day-ahead dispatching strategy, METS and MEDR could maintain the supply and demand balance of power load. AHP can change the heating period and BWs could maintain the supply and balance of heating and gas loads. Compared with the day-ahead dispatching plan, the output of PG and power-to-gas device in intra-day dispatching strategy increase by 21.22 % and 9.78 %. (3) Robust stochastic optimization method can characterize the uncertainties of WPP and PV and formulate the dispatching decision schemes with different risk attitudes. With the robust coefficient Gamma increasing, MEG operating costs and eco-environment benefits increase and deviation adjustment costs decreases, but the robustness of the dispatching scheme is improved. When 0.25 <=Gamma <= 0.75, the increase of the uncertainty parameter will have a direct impact on the formulation of dispatching scheme. And the decision maker belongs to risk preference type, who is willing to take certain risks to win excess benefits. Besides, if MEG operates in the grid-connected mode, the uncertainty risks will be weakened and the operation mode shall be reasonably selected according to the demand for dispatching decision. Overall, the proposed optimization model can promote the energy utilization of rural biomass waste resources, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.

In 2015, “Several Opinions on Further Deepening the Reform of the Power System” was issued. The new round of power system reforms proposes to give full play to the vitality of the power generation-side bidding market, which will help to establish a fair, reasonable, and active power market. Due to factors such as a lack of peak shaving capacity and imperfect market mechanisms, China's renewable energy participation in the power market faces many problems. To solve these, this paper proposes a two-level optimization model for a wind power plant and thermal power unit to participate in the medium and long-term electricity market, and day-ahead market transactions. First, the paper proposes the electricity quantity and electricity price determination method of annual bilateral negotiated transactions, monthly centralized bidding transactions and listings, and delisting transactions, and briefly describes how to decompose contract electricity into the day-ahead market in the medium and long-term market. Then, two ways for the wind power plant and thermal power unit to participate in the power market are proposed. With maximization of the market benefits as the objective function, and through comprehensive consideration of the system reserve, wind curtailment penalty, green certificate trading, and other issues, the two models of independent participation and joint participation in market transactions were established to study the problem of maximum profit on the power generation side. Finally, the analysis results of the calculation example show that the joint participation of the wind power plant and thermal power unit in the power market has additional benefits compared to independent participation. The wind power plant can complete the assessment index of the renewable energy quota for the thermal power unit, which does not need to purchase green certificates to complete the assessment indicators. The thermal power unit can provide reserve services for the wind power plant, avoiding output whenever necessary, reducing the cost of the curtailment penalty, and overcoming the threat of wind power plant output fluctuations to the system. The two alliances have greater profit margins.

Based on the intermittent output and inverse peak regulation characteristics of wind power, a multisource peak regulation transaction optimization model that considers the feasibility of combining thermal power, energy storage, and demand response for both power generation and consumption is proposed in this paper. First, a multisource peak regulation transaction cost model is established by considering flexible load participation in peak regulation and price- and incentive-based demand responses. Subsequently, with the objective of minimizing both the peak regulation cost and wind curtailment rate, a reduced half gradient membership function is selected to transform the multiobjective model. Finally, by analyzing the roles of the different subjects in multisource peak regulation transactions, a compensation mechanism based on the Shapley method is designed, and a local power grid in northeast China is chosen as the simulation object. The results show the following: (1) When the thermal power generators (TPGs) transit from regular peak regulation to deep peak regulation, the wind curtailment rate decreases by 5.10%, and the peak regulation cost increases by $ 0.793 × 106. This indicates that the peak regulation cost of the TPGs and the income of the on-grid wind power need to be balanced. (2) When the energy storage and the demand response are combined for peak regulation, both the peak load regulation cost and wind curtailment rate reach the optimal values, decreasing by $ 0.642 × 106 and 5.72%, respectively, showing cooperative optimization. However, the TPGs require a higher regulation cost, whereas the other subjects achieve incremental benefits. (3) The compensation mechanism assists each subject in obtaining incremental benefits depending on the contribution rate. With the strengthening of the peak regulation of the TPGs, more energy storage and demand response output are introduced to meet the urgent peak regulation requirement, which leads to increased regulation revenue and optimal dispatching. In summary, the proposed transaction and compensation mechanism of multisource peak regulation can be used to balance the peak regulation ability and contributions of different subjects and establish the optimal peak regulation scheme.

Integrated Energy System (IES) is far-reaching significance for improving energy utilization efficiency and reducing carbon dioxide emissions. However, due to the lack of consideration of construction sequencing, carbon trading development trends and uncertainty risks in the current IES planning scheme, problems such as mismatch between supply and demand, excess carbon emissions and weak renewable energy carrying capacity have emerged. To address these problems, this study proposes a multi-stage time sequence robust planning model for IES that considers carbon trading and extreme scenarios. First, the synergistic and complementary energy framework of IES is introduced, and the operation principle of each unit in the system is briefly shown by matrix. Then, considering the construction sequence of multiple stages and the load difference in different seasons, a life cycle planning model is established, which includes carbon trading cost, renewable energy curtailment penalty cost, integrated demand response cost. In order to improve the system’s ability to withstand the uncertainty of power and load, the extreme scenario is obtained according to the confidence level, and the improved robust optimization model is used to enhance the robustness of the system, which plays a dual role of risk prevention and control. Finally, a green ecological park is used as the object to implement simulation, and a safer planning scheme is obtained under extreme scenarios. The case study shows that: (1) Compared with the single-stage planning method, the method proposed in this study can reduce the depreciation cost in the whole life cycle by 5.44%, and improve the phenomenon that the curtailment rate of wind power exceeds 20%. (2) After participating in the carbon market, the cumulative income generated in the three stages is 17.92 million ¥. (3) After implementing demand response, the cumulative configuration capacity of wind turbine, ground source heat pump and energy storage decreased by 1.02%, 7.85% and 57.6%, respectively. (4) The improved robust optimization model improves the risk resistance of the system.

农村能源互联网作为能源互联网的重要组成部分,是乡村能源革命和能源转型的重要推手.构建了合作博弈-云模型对农村能源互联网建设成熟度进行评价.首先,提出农村能源互联网建设成熟度评价指标体系;其次,采用DEMATEL-G1-CRITIC相结合法计算指标权重,并运用合作博弈理论得到组合权重;然后,构建云模型对农村能源互联网建设成熟度进行评价;最后,通过算例分析证明所提权重计算方法更加精确有效,云模型考虑了部分指标的随机性和模糊性,能够为农村能源互联网建设提供科学可靠的决策依据.

Wind, solar, and other renewable energy sources along with roofs, wastelands, and other spatial resources are abundant in rural areas. This paper presents a rural multi-energy complementary system structure, which establishes the output model of wind power, biogas cogeneration, firewood-saving stoves, photovoltaic heat collectors, and air source heat pumps. Moreover, a flexible charge and discharge load model of electric vehicles is established, and a demand response mechanism is implemented to guide the user's electricity consumption behavior. The multi-objective optimization model of the system is based on the minimum energy cost, lowest carbon emissions, and highest energy consumption satisfaction as the multi-objective function. Subsequently, the epsilon-constraint method is used to obtain the Pareto solution set of the multi-objective optimization model, and fuzzy decision theory is used to obtain a compromised optimal solution of the Pareto front. Finally, a rural area in northern China is considered as the study area. The results of the calculation examples show that biogas cogeneration units and electric vehicles can improve the consumption of clean energy, reduce the system energy cost by 358.9 yuan, reduce carbon emissions by 1605.8 kg, increase energy consumption satisfaction, and improve the economic, environmental, service, and other benefits of the system.

国家电网公司提出建设具有中国特色国际领先能源互联网企业的新战略目标及新型基础设施建设的基本方向,对配电网的精准投资、高效运行、精细管理提出了新的要求.为了提升配电网高质量水平,推进技术创新成果转化,其发展属性有待进一步厘清和界定.基于此,为了分析不同区域配电网发展水平,综合考虑配电网的经济效益、服务效益和社会效益,从成本投入、投资产出、运行效率、安全可靠、用户满意、环境保护等多个维度分析配电网资产综合绩效影响因素.然后通过最小二乘原理优化主观评价与客观评价的权重,构建改进的物元可拓模型确定配电网资产综合绩效所属等级.最后以某城市的3个区域(M、N、P)为例,确定配电网资产综合绩效评估等级分别为较差、良好和适中,根据各个维度隶属等级提出针对性管理措施,对于电网"新基建"的高质、快速推进具有重要意义.

At present, there are still some urgent problems to be solved, such as repeated reserve projects, unreasonable timing arrangement of investment in project execution, poor effect of capital use and poor process connection. In this paper, through the related cluster demonstration of the comprehensive plan projects, we strengthen the pre-management and control of the reserve projects from the source, and improve the quality and efficiency of the comprehensive plan management. Firstly, this paper classifies the projects based on the perspective of business association, establishes the association cluster, and then constructs the priority evaluation model of the project association cluster with the help of the portfolio management theory, so as to maximize the project benefit.

多能源需求响应是实现削峰填谷,缓解能源供应与负荷需求矛盾、提升能源利用效率的重要手段,对促进综合能源系统的可持续发展具有重要意义.首先,在构建耦合电-热的园区互补能源系统的基础上,从弹性矩阵的角度构建了电能与热能需求响应模型,考虑机组出力、能源平衡等约束条件,以系统收益为目标函数的优化模型.以需求响应手段实施为变动因素,设置多情景进行了实例分析.算例结果表明:多能源需求响应模型通过对电价与热价的再设计,在总用能量保持基本不变的基础上,用户负荷需求发生了转移,负荷与系统出力的匹配度增强,与传统情景相比,需求响应手段实施后,系统的能源利用效率提升、系统净收益提升与用户支出降低,实现了“经济-环境-系统-用户”等多方的共赢.

The objective of this paper is to establish a method for directly correlating the thermal properties of working fluid with the thermodynamic performance of ORC (organic Rankine cycle) to explore the effect of the thermal properties of working fluid on the thermodynamic performance of the cycle. On the basis of thermodynamic theory, the thermal properties of the working fluid in the ORC are abstracted into characteristic parameters, T-smax, limiting temperature difference, B and C. Based on the characteristic parameters, x (feature function of the heat addition process), (T) over bar (1)(average temperature of heat addition), (T) over bar (2) (average temperature of heat rejection) and the exergy efficiency of 16 working fluids in the ORC are investigated. The results indicate that the change rules forT(smax) and critical temperature T-cr for different working fluids are generally the same. With the increase in T-g,T-out (temperature of the heat source medium at the evaporator outlet), although the increased evaporation temperature T-1 increases (T) over bar (1), decreased x values weaken the increase in the amplitude of (T) over bar (1). The change rules for x and (T) over bar (1) for different working fluids are opposite at low T-g,T-out while the same at high T-g,T-out. The transition temperature T-z, limiting temperature difference and T-cr are the main indicators for evaluating T-1 of different working fluids. The change rules for7 and B for the different working fluids are generally opposite, and B can be used as the criterion for working fluids with low (T) over bar (2). The effects of (T) over bar (1) on exergy efficiency is stronger than that of (T) over bar (2), while the effect of (T) over bar (2) cannot be ignored at high T-g(,out). The characteristic parameters of the working fluid explain the limitation of using T-cr as the criterion for working fluid selection, and establish the criterion for appropriate working fluid.