目的 随着需求侧响应资源的不断增长，传统的能源调度模式难以满足新能源大量接入的系统需求。为实现小区内多种能源的合理调配，提出了一种基于用户需求侧响应的能源交易策略，旨在优化智能小区内能源的调度。 方法 针对含多栋楼宇的居民小区，对其中的分布式光伏、储能设备和柔性负荷进行统一调配，并根据小区运营商和用户负荷聚合商的定价交互，采用Stackelberg博弈建立两阶段调度优化模型。 结果 算例仿真模拟结果显示，相比传统的以热定电策略，所提模型可以降低40.22%的运行成本，提高22.57%的光伏消纳水平；相比传统的最优运行成本策略，所提模型可以降低29.66%的运行成本，提高6.78%的光伏消纳水平。 结论 所设计的策略在实现公平利益分配、缓解电力波动、灵活应对调度高峰需求、加强新能源整合及确保电网运行安全方面具有良好效果。

Objectives With the continuous growth of demand-side response resources, traditional energy scheduling models struggle to meet the system requirements of high penetration levels of renewable energy. To achieve the rational allocation of multiple energy sources within a community, this study proposes an energy trading strategy based on demand-side response from users, aiming to optimize energy scheduling in smart community. Methods For a residential community with multiple buildings, this study coordinates distributed photovoltaics, energy storage systems, and flexible loads. A two-stage scheduling optimization model is established using the Stackelberg game framework based on pricing interactions between community operators and user load aggregators. Results Simulation results show that, compared to the traditional heat-determined power strategy, the proposed model reduces operational costs by 40.22% and increases photovoltaic utilization by 22.57%. Compared to the conventional cost-optimal operation strategy, the proposed model results in a 29.66% reduction in operational costs and a 6.78% increase in photovoltaic utilization. Conclusions The proposed strategy demonstrates excellent performance in achieving equitable benefit distribution, mitigating power fluctuations, flexibly meeting peak-load demands, enhancing renewable energy integration, and ensuring grid operational security.

针对环境污染及综合能源系统中多供能主体利益冲突问题，提出了一种碳交易机制下基于合作博弈的综合能源系统经济性分析模型。为控制CO₂排放，引入碳交易机制建立了含可再生能源电厂、碳捕集电厂和燃气热电厂3个供能主体的合作博弈模型，该模型优化目标为运维成本、惩罚成本、碳相关成本和环境成本之和最小。为合理分配多供能主体的合作成本，首先采用Shapley值法进行初次分配，然后引入综合考虑成本贡献系数和环保贡献系数的修正因子对初次分配结果进一步优化，提出了改进Shapley值法分摊机制。仿真结果表明各能源主体合作运行能够有效提升新能源消纳能力、促进多能源互补利用，同时显著提高系统经济性以及降低碳排放量，采用改进Shapley值法可以使各能源主体成本分摊更加公平合理。

Aiming at the problems of environmental pollution and the conflict of interests of multiple energy suppliers in the integrated energy system, an economic analysis model of the integrated energy system based on cooperative game under the carbon trading mechanism is proposed. Firstly, in order to control CO₂ emissions, a cooperative game model with three energy supplying entities, including renewable energy power plant, carbon capture power plant and gas-fired thermal power plant, is established by introducing the carbon trading mechanism, and the optimization objective of the model is to minimize the sum of operation and maintenance cost, penalty cost, carbon related cost and environmental cost. In order to rationally allocate the cooperative costs of multiple energy supply subjects, the Shapley value method is firstly used for the initial allocation. Then, a correction factor that integrates the cost contribution coefficient and environmental contribution coefficient is introduced to further optimize the initial allocation results, and an improved Shapley value method apportionment mechanism is proposed. The simulation results show that the cooperative operation of the energy entities can effectively enhance the new energy consumption capacity, promote the complementary utilization of multiple energy sources, and significantly improve the system economy and reduce carbon emissions, and the improved Shapley value method can make the cost sharing among the energy entities more fair and reasonable.

风电出力的随机性以及电动汽车(electric vehicle,EV)充电需求的不确定性给电力系统调度带来了挑战。在传统确定性机组组合模型的基础上,针对电力系统日前调度面临的不确定问题,提出了充分考虑风电与电动汽车双重不确定性的随机优化调度及备用计算模型。首先,对于风电出力不确定性,采用基于场景分析的两阶段随机优化方法,并使用生成对抗网络(generative adversarial network, GAN)来生成风电场景。其次,对于电动汽车充电需求的不确定性,将其分为可调度与不可调度两类。可调度电动汽车根据其出行规律采用随机模拟的方法,并建立了EV充电聚集商模型;不可调度电动汽车通过K-means聚类分析得到其典型负荷曲线,并将其并入系统常规负荷中。最终建立了基于多场景分析考虑EV充电聚集商的两阶段随机机组组合模型,并通过算例分析证明了所提模型的有效性。

The uncertainty of wind power output and charging demand of electric vehicles (EVs) brings great challenge for power system dispatching. Taking day-ahead dispatch as the target, this paper proposes a stochastic optimization scheduling model and corresponding reserve model on the basis of traditional unit commitment model. Firstly, for the uncertainty of wind power output, a two-stage stochastic unit commitment model is established in this paper according to scenario analysis based on generative adversarial network (GAN), while electric vehicles are divided into two categories: schedulable and non-schedulable EVs. Monte-Carlo method is adopted to simulate the behavior and the dynamic change of schedulable EVs on the basis of probability distribution of the travel patterns, and the model of EV aggregators is established in this paper. As for non-schedulable EVs, K-means cluster analysis is adopted to get a typical load curve, and then the charging demand is viewed as part of conventional load. Case study demonstrates the validity of the proposed model.

随着间歇性分布式电源如风电的大量接入，有必要考虑其随机性和相关性对配电网无功优化的影响。首先，采用秩相关系数表示风速和负荷间的相关性，并利用拉丁超立方采样生成具有相关性的初始样本，再应用同步回代缩减法进行场景缩减，以提高计算效率；其次，建立了基于多场景的配电网无功优化模型，综合考虑了并联电容器、静止无功补偿器、有载调压变压器等电压-无功控制设备；然后，应用二阶锥松弛技术将传统无功优化模型转换为混合整数二阶锥规划模型，降低了求解难度；最后，通过改进的IEEE 33节点配电网验证了本文模型的合理性，并重点研究了相关性对无功优化的影响。

With the integration of a large number of the intermittent distributed generators such as wind turbine generators（WTG） into distributed system， the influences brought by random and correlated variables have to be taken into consideration in the reactive power optimization of distribution network. Firstly， rank correlation coefficient is used to represent the the correlations between wind speed and load demand， and the initial correlated samples are generated by means of Latin hypercube sampling（LHS） techniques. In addition， the simultaneous backward reduction is applied to reduce the scenarios to improve the computation efficiency. Secondly， a reactive power optimization model based on multiple scenarios is proposed， considering the volt/var control devices such as shunt capacitor（SC）， static var compensation （SVC）， on-load tap changer（OLTC）. And then， second order cone（SOC） relaxation is applied to transform the traditional reactive power optimization model to mixed integer second order cone programming（MISOCP） model， which can be tractably solved. Finally， the rationality of the proposed model is verified by the improved IEEE 33 node distribution network， and the effects of correlations on the reactive power optimization are emphatically studied.

为有效应对可再生能源出力波动，实现综合能源系统低碳、经济、鲁棒三者平衡，提出考虑碳交易的电-热综合能源系统两阶段鲁棒优化低碳经济调度模型。首先，构建了配置碳捕获设备的综合能源系统模型。其次，采用可调不确定集合描述风电和光伏的出力波动，并以最恶劣情景下成本最小为优化目标，建立了两阶段鲁棒优化调度模型，然后利用列与约束生成算法迭代求解。最后，在调度模型中计及碳交易成本，来限制系统的电能交易以减少间接碳排放，并避免了调度方案的过度保守。结果表明，两阶段鲁棒优化有效提高了系统抵御风险的能力，减少了经济损失；碳交易机制的引入避免了系统过度鲁棒，能够维持低碳运行，实现了系统低碳、经济、鲁棒三者平衡。

This study aims to handle fluctuations in renewable energy effectively and achieve low-carbon, economic, and robust balance optimization in an integrated energy system. A two-stage robust optimization low-carbon economic dispatch model for a park-level integrated energy system that considers carbon trade was proposed. First, an integrated energy system model with carbon capture and storage was constructed. Next, the adjustable uncertainty set was used to describe the fluctuations of wind and photovoltaic output, and a two-stage robust optimization dispatch model was developed with the optimal objective of solving the minimum cost under the worst-case scenario. Subsequently, the column-and-constraint generation algorithm was employed to iteratively solve. Finally, the carbon trading cost was considered in the dispatch model, which limited electricity purchases in the system to reduce indirect carbon emissions and avoid overconservative dispatching schemes. The results show that the two-stage robust optimization method can effectively improve the ability of the system to resist risks and reduce economic loss, and the introduction of a carbon trading mechanism can prevent excessive robustness and maintain low-carbon operation. The proposed method can effectively achieve robust, economical, and low-carbon-balance optimal scheduling for the system.

冷热电联产(combined cooling, heating and power,CCHP)微网系统能够实现能源的梯级利用,在提高能源利用效率的同时可以促进可再生能源消纳。文章综合考虑CCHP微网在电力市场环境下运行的特殊性和风光可再生能源出力的不确定性,建立两阶段鲁棒优化模型,提出了一种计及储电和储热综合储能系统的CCHP微网调度策略。算例结果表明考虑不确定性的鲁棒优化运行策略可以有效降低可再生能源出力偏差,提高CCHP微网运行成本的风险,并且可再生能源的预测与实际出力偏差越大,效果越明显。

Microgrid with combined cooling, heating and power (CCHP) can realize the cascade utilization of energy, improve the efficiency of energy utilization and promote the consumption of renewable energy. In this paper, considering the particularity of CCHP microgrid operating in the power market and the uncertainty of wind and solar power output, a two-stage robust optimization model is established, which considers the integrated energy storage system of electricity and heat storage. The simulation results show that the robust optimization strategy considering uncertainty can effectively reduce the output deviation of renewable energy and increase the risk of CCHP microgrid operation cost, and the larger the output deviation of renewable energy, the more obvious the effect.