在“双碳”目标的背景下，电力行业已成为碳减排中的重要组成部分。虚拟电厂(virtual power plant，VPP)通过整合和聚集分布式资源等参与碳市场，可进一步提升整体效益；然而，分布式新能源出力的不确定性给其运营管理带来了许多挑战。为此，在利用基于拉丁超立方抽样的场景生成与场景削减法处理风电、光伏出力不确定性问题的基础上，将多单元聚合并考虑到用户侧需求响应的VPP作为一个整体去参与电能量市场以及碳市场，构建VPP总成本最小的优化调度模型，然后利用改进灰狼优化算法对其进行求解。通过对不同场景算例进行对比分析，可以得出：碳市场及需求响应的存在，加强了风光等清洁能源的消纳，减少了温室气体的排放，也减少了VPP的运行成本，经济性和环保性得到了兼顾。

Against the backdrop of the "dual carbon" target, the power sector has become an important part of carbon reduction. Virtual power plants (VPP) can further improve their overall efficiency by integrating and aggregating distributed resources to participate in the carbon market. However, the uncertainty of distributed new energy output poses many challenges for their operation and management. Therefore, on the basis of using the scenario generation and scenario reduction method based on Latin hypercubic sampling to deal with the uncertainty problem of wind power and photovoltaic output of distributed energy, the VPP, which aggregates multiple units and takes into account the user-side demand response, participates in electric energy market as well as the carbon market as a whole, and the optimal scheduling model with the minimum total cost of the VPP is constructed, which is finally solved by using the improved gray wolf optimization algorithm. Through comparative analysis of different scenarios, it can be concluded that the existence of carbon market and demand response enhances the consumption of clean energy such as wind power and photovoltaic, and reduces greenhouse gas emissions, and reduces the operating cost of the VPPs, and takes into account its economy and environmental protection.

为促进电力系统运行过程中的节能减排，提出碳流排放追踪下的电力系统源荷多时间尺度节能调度方法。分析碳流排放追踪下电力系统固定运行模式与灵活运行模式，通过灵活运行模式调节碳捕集设备的捕获水平，基于碳流排放追踪下电力系统的各运行模式，构建日前-日内-实时多时间尺度节能调度模型。该模型以电力系统成本最优为各时间尺度调度模型的目标函数，并设定约束条件；采用改进的多目标粒子群算法或标准粒子群算法，逐级求解所构建的多时间尺度调度模型，完成多时间尺度节能调度。实验结果显示，在不同运行场景下，该方法能基于电力系统源荷可调节的资源调度优势，实现电力系统节能、减碳的调度目标。

To promote energy saving and emission reduction during power system operation, the source-load multi-timescale energy-saving scheduling approach of power systems under carbon flow emission tracking is proposed. The fixed operation mode and the flexible operation mode of power systems under carbon flow emission tracking are analysed, and the capture level of the carbon capture equipment is adjusted through the flexible operation mode. Based on the operation modes of power systems under carbon flow emission tracking, a multi-timescale energy-saving scheduling model with pre-daily, intraday, and real-time timescale is constructed. The objective function of the scheduling model is the optimal cost of power systems, and the constraints are set. The improved multi-objective particle swarm algorithm or the standard particle swarm algorithm is adopted to solve the constructed multi-timescale scheduling model step by step, and the multi-timescale energy-saving scheduling is completed. The experimental results show that the method can effectively achieve the energy-saving and carbon reduction scheduling objectives of power systems based on resource scheduling advantages of power system source-load adjustability under different operating scenarios.

分布式可再生能源发电设备在城市中日益普及,配置储能是应对其出力不确定性的有效措施,但楼宇各自配置储能仍存在成本高、利用率低等问题。在自有储能共享模式下,采用合作博弈理论设计了一种城市楼宇以合作联盟形式进行储能容量共享的机制,并在考虑储能初始投入差异的前提下基于改进Raiffa解法设计了一种合作剩余分配方式。算例结果表明:通过提出的储能共享机制,城市楼宇集群的储能资源利用率提高了18.12%,运行成本降低了10.22%,实现了参与储能共享楼宇的“降本增效”;通过提出的合作剩余分配方式,楼宇集群中配置储能楼宇的“降本”幅度远高于未配置储能楼宇,体现了分配方式的公平性,且计算量较Shapley值法有明显减少。

Distributed generation using renewable energy is prevalent in cities, and energy storage is an effective method for addressing the uncertainty of its output. However, problems such as high costs and low utilization rates may be encountered in buildings that use energy storage. In the self-owned energy-storage sharing mode, an alliance mechanism based on a cooperative game was designed for urban buildings. Furthermore, a cooperative surplus distribution method, which considers the difference in energy storage investment, was developed based on an improved Raiffa solution algorithm. The simulation results demonstrate the applicability of the proposed alliance mechanism in an urban building cluster with different types of buildings, such as residential, office, and commercial buildings. Compared with the noncooperative mechanism, the cost was reduced by 10.22%, and the energy storage utilization rate increased by 18.12%. Moreover, the reduced amplitude of the cost of buildings with energy storage was much higher than that of buildings without energy storage. Therefore, the proposed cooperative surplus distribution method is reasonable, and the calculation volume is significantly reduced compared with that of the Shapley value method.

新能源发电的随机性与间歇性给电力系统运行与控制带来巨大挑战,储能技术是解决该问题的重要手段之一。聚焦于新背景“零碳园区”与新模式“共享储能”,研究了考虑共享储能容量衰减的零碳园区优化调度与经济性评估。首先,提出了共享储能成本效益分析模型,接着建立了以日运行成本最小为目标,同时考虑共享储能服务与电网购电的优化调度模型,然后,提出了经济性评估指标。在算例分析中考虑无配置储能电站、自配储能电站、共享储能电站三种配置方案,以及新电池与退役电池两种选型方案,充分对比了各方案下优化调度与经济性评估结果,并分析了各方案下的场景适用性。最后,通过敏感性分析突出共享储能服务价格对其经济性的影响,并给出服务价格的建议取值。算例结果验证了共享储能电站的经济性优于其他方案。此外,退役电池性能虽低于新电池,但成本较低,参与调度的经济性更好。

Renewable energy generation has a strong random and intermittent nature, which poses a great challenge to power system operation and control. Energy storage is one of the most important means to solve this problem. This paper focuses on the new background of zero-carbon park and the new economic model of shared energy storage, and conducts a study on the optimal dispatch and economic evaluation of zero-carbon park considering the capacity attenuation of shared energy storage. Firstly, the cost-benefit model of zero-carbon park is established, which takes the operation cost as the optimization objective. Then, an economic evaluation index of shared energy storage is put forward. Afterwards, three allocation options, namely, no energy storage, self-assigned energy storage and shared energy storage, and two battery options, namely, new battery and retired battery, are considered in the case study. The results of optimal dispatch and economic evaluation are compared with different options. Finally, the impact of shared energy storage service price on its economy is highlighted through sensitivity analysis, and the suggested values of service price are given. The results of the example verify that the economy of the shared energy storage is superior to other schemes. In addition, although the performance of retired batteries is lower than that of new batteries, the cost is far lower than new batteries, so the economy of retired batteries participating in this study case is better.

为了实现“碳达峰”和“碳中和”战略目标，中国电网将逐步建设成为一个以新能源为主、多类型电源共存的绿色智慧电网。然而，传统常规电源有限的调峰能力难以满足未来电力系统接入高比例新能源后的调峰需求，制约了新能源的消纳能力，并降低了系统运行的安全性与经济性。因此，需要构建抽水蓄能、电化学储能、电动汽车(electric vehicle, EV)虚拟储能等多类型储能模型，并结合某省级电力系统4种典型场景和极端场景，在现有储能调峰辅助服务补偿机制基础上提出站在调度机构角度下的最优调峰效益模型。通过对某省级电网历史数据进行仿真验证，证明该模型在减少火电机组频繁启停、提升调峰经济性方面发挥着积极作用，促进未来新型电力系统的经济性和安全性运行。

In order to achieve the strategic goals of "carbon peak" and "carbon neutral", China's power grid will gradually be built into a green smart grid with new energy as the main power source and multiple types of power sources coexisting. However, the limited peak regulation capacity of traditional conventional power sources is difficult to meet the peak regulation demand of the future power system after accessing high proportion of new energy, which restricts the absorption capacity of new energy and reduces the safety and economy of system operation. Therefore, it is necessary to build multiple types of energy storage models, such as pumped storage, electrochemical energy storage, and electric vehicle virtual energy storage. Combined with four typical scenarios and extreme scenarios of a provincial power system, an optimal peak regulation efficiency model from the perspective of dispatching agency is proposed based on the existing energy storage peak regulation auxiliary service compensation mechanism. Through simulation verification using historical data from a provincial power grid, it has been demonstrated that this model plays a positive role in reducing frequent start-stop cycles for thermal power units and improving economic efficiency in peak regulation. This promotes both economic viability and safe operation for future advanced electricity systems.

受制氢机单机容量限制,电制氢厂站由多台制氢机构成集群,以满足光伏制氢一体化工程的规模化需求。考虑到制氢机启停、爬坡调节惯性,提出集群调度方法,动态调整启停状态及功率分配,以适应光伏出力的时序不确定性。首先,基于伊藤理论建模光伏出力的连续时间随机过程;而后,构建制氢机集群调度的随机优化模型;继而,基于随机微分方程的轨迹灵敏度展开,将随机规划变换为确定性优化求解;最后,根据所得制氢机启、停指令及功率分配的仿射控制律,实现滚动优化。基于内蒙古自治区某在建工程的算例分析表明,对比确定性优化方法,所提方法可提升光伏消纳和制氢收益。

Limited by the individual capacity of the electrolyzer, a hydrogen production plant requires multiple electrolyzers to form a cluster and meet the scale requirements of the integrated photovoltaic hydrogen project. Considering the startup/shutdown and ramping inertia of the electrolyzers, it is necessary to propose a scheduling method for the electrolyzer cluster system. The proposed method dynamically adjusts the startup/shutdown states and power allocation to adapt to the temporally correlated uncertainty of photovoltaic power. First, the continuous-time stochastic process of photovoltaic power is modeled based on It?’s theory, whereby, a stochastic optimization model is constructed for the scheduling of the electrolyzer cluster. Subsequently, by utilizing trajectory sensitivity decomposition based on the stochastic differential equations, the stochastic optimization is transformed into a deterministic one. Finally, a rolling-horizon optimization scheme of the startup/shutdown commands is proposed for the affine control law of power allocation. The case studies based on a demonstration project under construction in Inner Mongolia show that, compared to deterministic optimization, the proposed approach improves the utilization of photovoltaic power, increasing the benefits from hydrogen production.

太阳能具有清洁、安全、可再生的优点，光伏发电可减轻资源消耗，助力可持续发展，然而光伏功率易受天气影响，针对不同天气类型下光伏功率的预测也是一个研究难点。该研究着手于在不同天气类型下应用人工少数类过采样法(synthetic minority over-sampling technique，SMOTE)和机器学习进行光伏功率预测。首先，通过皮尔逊相关系数法选择出对光伏功率影响最大的气象因子；然后，根据重要程度较大的气象因子计算日照时数，通过给日照时数设定阈值进行划分，将天气分类为晴天、多云或阴天、覆雪，再通过SMOTE技术对各种天气类型下的样本进行扩充；最后，通过多种机器学习算法分别针对不同天气场景以及数据扩充前后构建光伏功率预测模型。通过案例分析可知，所提算法能对不同天气类型进行划分，并为不同天气类型下光伏功率预测存在的样本不平衡问题提供了一种解决方案，提升了不同天气场景下光伏功率的预测精度。

Solar energy has the advantages of being clean, safe, and renewable, and photovoltaic (PV) power generation can reduce resource consumption and contribute to sustainable development. However, PV power is easily affected by weather, and the prediction of PV power for different weather types is also a research difficulty. This study proceeds to apply synthetic minority over-sampling technique (SMOTE) and machine learning for PV power prediction under different weather types. Firstly, the meteorological factors that have the greatest impact on PV power are selected by the Pearson's correlation coefficient method. Then the sunshine duration is calculated based on the meteorological factors with a greater degree of importance, and the weather is classified as sunny, cloudy or cloudy, and snow-covered days by setting a threshold for the number of hours of sunshine, and then the samples under various weather types are expanded by the SMOTE technique. Finally, the PV power prediction model is constructed by various machine learning algorithms for different weather scenarios and before and after data expansion. Through case validation, it can be seen that the algorithm proposed in this paper is able to classify different weather types, and provides a solution to the sample imbalance problem of PV power prediction under different weather types, which improves the prediction accuracy of PV power under different weather scenarios.

综合能源负荷场景生成是研究能源计量、规划运行等领域问题的基础,具有重要意义。但由于数据采集困难、综合能源负荷多能耦合等因素的限制,综合能源负荷场景的多样化生成仍是一大难题。提出了一种基于生成对抗网络(generative adversarial networks, GAN)的综合能源负荷场景生成方法。首先建立梯度惩罚优化的Wasserstein生成对抗网络模型,解决综合能源负荷的高随机性可能带来的不收敛或模式崩溃问题。其次,基于深度长短期记忆(long short-term memory, LSTM)的循环神经网络构建生成对抗网络的生成器和判别器,使模型更适用于复杂综合能源负荷数据生成。算例结果表明,所提模型的生成负荷场景在概率分布、曲线标志性特征和冷热电负荷之间相关性等方面相较于蒙特卡洛法和原始生成对抗网络均获得了较好结果,可以在不同模式下生成具有多样性且逼真的负荷场景。

针对三北地区现有能源结构调节能力不足而导致的弃风问题,将风电场、光热电站、火电机组和热电联产机组聚合为虚拟电厂。采用随机优化处理风光不确定性问题,通过拉丁超立方抽样生成大量随机风光场景,并在充分考虑风光相关性和分布随机特性的基础上,利用Kantorovich距离削减与K-均值聚类算法对随机场景进行降维优化,获得风电、太阳直接辐照度典型预测场景。结合光热电站的灵活性与供能惯性,构建含光热虚拟电厂热电联合优化调度模型,并建立系统总运行成本最小的目标函数。最后在算例部分验证所提随机优化方法在计算效率、预测精度和处理风光随机问题的优越性;对不同运行模式下的目标函数进行求解,验证所提出的优化调度策略能够在满足系统运行经济性的同时实现风电的最大消纳。

Aiming at the problem of wind curtailment caused by the energy structure lacked the adjustment ability in the three north area, this paper aggregated wind farm, concentrating solar power plant（CSPP）, thermal power units and combined heat and power（CHP） plant into virtual power plant（VPP）. Using stochastic optimization to deal with the uncertainty of wind-solar, Latin hypercube sampling （LHS） was used to generated a large number of random scenes, and based on considering the random characteristics and correlation of wind-solar distribution fully,Kantorovich distance reduction and <em>K</em>-means clustering algorithm were used to optimized and reduced the dimension of random scenes, for obtaining typical prediction wind-solar scenes. Combined with the flexibility and energy supply inertia of CSPP, the optimal dispatching model of the VPP contained photothermal was constructed, and the objective function of minimizing the total operation cost of the system was established. Finally, an example was given to verify the superiority of the proposed stochastic optimization method in computational efficiency and prediction accuracy; The objective functions under different operation scenarios were solved to verify that the optimal dispatching model could improve the wind power consumption capacity while reducing the system operation cost effectively.

可再生能源的大规模渗透给电力系统的稳定运行带来极大挑战。在供需两侧双重不确定性叠加驱动下,基于终端柔性负荷的需求响应资源亟待挖掘。考虑不同类型用户负荷差异化特性,引入基于合作共赢的多类型负荷聚合商,基于异类负荷响应行为互补特点参与电力系统灵活调度;同时,赋予各负荷聚合商碳交易集成商的双重身份进入碳交易市场,采用预测电负荷法为系统无偿分配碳排放配额,构建奖惩阶梯型碳交易模型。以多个负荷聚合商合作联盟运营成本之和最小为目标,构建多聚合商间交互合作的日前优化模型并进行求解;引入合作博弈Shapley值法,根据各参与者对合作联盟运营的贡献度,进行成本分摊。结果表明,合作运营机制下,联盟整体和个体的运营成本及碳排放量均大幅降低。

The large-scale penetration of renewable energy sources poses significant challenges to the stable operation of power systems. Driven by double uncertainties on both the supply and demand sides, demand response resources based on terminal flexible loads need to be explored. Considering the load differentiation characteristics of different types of users, multitype load aggregators based on cooperation and win-win were introduced. Flexible dispatching of the power system was performed based on the complementary characteristics of the heterogeneous load response behaviors. Moreover, each load aggregator was assigned the dual status of a carbon trading integrator to enter the carbon trading market. A carbon trading model based on a reward-punishment ladder was constructed using the electricity load forecasting method to allocate carbon emission quotas for a system free of charge. Based on this, to minimize the sum of the operating costs of a cooperative alliance of multiple load aggregators, a pre-day optimization model of the interaction and cooperation among multiple aggregators was developed and solved. The Shapley value method was introduced for the cooperative game, and the cost was shared according to the contribution of each participant to the operation of the cooperative alliance. The results show that the overall and individual operational costs and the carbon emissions of the alliance are significantly reduced under the cooperative operation mechanism.