This paper presents a distributed coordinated clearing strategy to facilitate the procurement of energy and flexibility in transmission and distribution networks. This strategy targets 15-min flexibility requirements of energy systems. Inspired by European market practices, a two-stage distributed clearing framework coordinated the transmission system operator (TSO) with distribution system operators (DSOs) is established. TSO and DSOs are responsible for transmission-level and distribution-level energy and flexibility markets, respectively. In the proposed framework, the energy and flexibility markets operate in a coordinated manner and are cleared sequentially, thereby optimizing flexibility procurement for both the transmission network (TN) and active distribution networks (ADNs) to meet the 15-min flexibility requirements of the power system. The alternating direction method of multipliers (ADMM) is used to solve the proposed distributed model while protecting the privacy of all stakeholders. Numerical simulations on a revised IEEE 30-bus transmission with two 33-node ADNs demonstrate that the proposed strategy improves system flexibility provision while enhancing the economic performance of both the TSO and DSOs. Specifically, compared with the decoupled transmission–distribution operation mode, the proposed method can not only reduce the TSO’s flexibility procurement cost by 22.4% but also increase the profits of DSOs by $3066.5.

To address the issue that substations are prone to failures during flood disasters, which further cause large-scale and prolonged power outages, a coordinated optimal allocation strategy of flood control resources is proposed to enhance power grid resilience. Firstly, the failure and network features for substations are constructed considering the uncertainty of flood depth. Subsequently, a representative set of failure scenarios for transmission and distribution (T&D) substations is generated based on feature selection. Secondly, accounting for the coupling relationship between the availability status of T&D substations and the operation strategies of active distribution networks, a transmission-distribution coordinated stochastic optimization model is established to optimize the allocation of flood control resources. The objective is to minimize the system’s expected comprehensive costs incurred by substation structural damage and load shedding constrained by the pre-disaster substation protection constraints and the operation constraints in T&D networks during flood disasters. Finally, numerical case studies based on the improved T24D40 system are conducted. The results demonstrate that the feature-selection-based scenario generation method enables the critical substations with high failure rates and network importance to gain higher protection priority. More importantly, compared with separate decision-making and with limited coordinated decision-making for T&D substation protection schemes, the proposed model, which could effectively maximize the utilization efficiency of flood control resources, reduces the system’s expected comprehensive costs by 32.1% and 8.9%, respectively.

Earthquake disasters could cause power outages and natural gas leaks in urban integrated energy systems, severely impacting residents' normal production and daily life. Enhancing the resilience of urban integrated energy systems can strengthen the system's ability to withstand earthquake disasters. In this paper, a joint maintenance strategy for urban integrated energy systems considering distribution network reconfiguration under earthquake disasters is proposed. The strategy analyzes the effects of distribution network topology reconfiguration on the integrated energy system, focusing on the chain reactions of faults between different subsystems. Case studies validate the effectiveness of the proposed strategy in load recovery through an urban integrated energy system composed of the IEEE-13 power system and a 7node natural gas system.

The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load predictionmethod is proposed in coupled urban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is formulated considering both nodal and path resistance. Then, a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method is applied to predict the distribution of EVcharging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model.Moreover, a dynamic charging pricing scheme for EVs isdevised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks. Finally, the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model. The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87% to 37.21% compared to the BPR model. Additionally, the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from 27.2 to 31.49MWh by considering the influence of ambient temperature and speed on power energy consumption.

Market power identification is an important issue for spot electricity market analysis and operation. Market power is more complicated than that in other market to identify due to the specific properties and mechanisms of electric market. This paper proposes a method to identify different type of market power, such as generation dominance, transmission congestion and collusion between electricity suppliers. Structural indices and indices base on market simulation method are presented to identify three type of market power. A three-bus test system is presented to verify the correctness and effectiveness of the proposed method.

The problem of insufficient inertia and frequency security becomes a critical concern in generation maintenance scheduling due to the increasing penetration of renewable energy sources (RESs). To address the issue, this paper presents a stochastic generation maintenance scheduling with inertia-dependent primary frequency regulation constraints. First, inertia-dependent primary frequency regulation constraints consisting of the minimum inertia requirement, frequency nadir, and quasi-steady-state frequency deviation are formulated based on the dynamic frequency characteristic of power systems in response to large disturbances. Then, a two-stage stochastic optimization model for generation maintenance scheduling including frequency security constraints is proposed. Finally, a solution methodology based on Benders decomposition (BD) is used to promote the calculation speed of the proposed model. Numerical simulations demonstrate that the proposed generation maintenance scheduling could address the potential frequency security issues and promote integration of RESs associated with calculation speed.

The accurate condition assessment of wind turbines greatly influences the refined asset management and maintenance scheduling of wind farms. To address the challenges of existing assessment methods in selecting the reliability value and determining wind turbine status levels of being in transition, this study proposes a wind turbine condition evaluation method based on asymmetric proximity. Firstly, the state evaluation index system consisting of the wind turbine performance and output state indices is constructed, and the weighting factors are calculated comprehensively by integrating the subjective and objective weights. Then, the membership function of the index layer is established based on the set pair analysis, and the membership of the target layer is deduced by the weighted average operator. Finally, the proximity degrees between status levels and target membership degrees are calculated, and the wind turbine state is determined based on the proximity principle. Case studies demonstrate that the accuracy rate of the proposed method is up to 97%, which is 6% and 8% higher than the maximum membership principle and the reliability criterion, respectively.

为深入分析不确定性条件下具有相关性的不同地理位置风电场输出功率对电-气综合能源系统安全运行的影响,提出一种计及风电相关性的电-气综合能源系统概率能量流计算分析方法.首先建立了电力系统与天然气系统的稳态能量流模型以及风电功率和电-气负荷的概率分布模型;其次利用Cholesky分解结合参数变换得到计及相关性的风电样本,并采用Gram-Charlier展开级数法拟合状态变量的概率分布曲线;最后将文中所提方法置于IES30-20节点电-气综合能源测试系统中验证其准确性和有效性.算例分析表明文中所提算法能够快速准确的计算电-气综合能源系统概率能量流.

设备状态检修背景下,随着风电等高比例新能源并网发电,电网检修计划决策中输变电设备个体与电网运行整体间的矛盾和冲突越来越难以平衡,使电网检修计划决策面临严峻挑战.对此,文中提出考虑风电不确定性的电网状态检修策略.首先,基于设备状态监测信息和状态评价技术,建立设备故障率及其与检修策略间关系的数学表达;其次,依据风险理论给出电网故障风险和检修风险的数学模型,并基于次时间尺度的安全约束机组组合模型评估其中的电网运行损失;最后,提出以电网故障风险和检修风险之和最小为目标的考虑风电不确定性的电网状态检修模型,并采用修改的IEEE-30节点系统算例验证所提模型的有效性.

With the increasing capacity of wind power in power systems, wind power volatility and uncertainty pose a great threat to the operation of the electricity system, especially for areas with high heating demand. Integrated electricity and district heating system (IEDHS) is a promising option to improve power system flexibility to accommodate more wind power. This paper excavates the potentiality of coordinated operation of IEDHS to alleviate the effects of wind power uncertainty with an adjustable robust interval approach. Firstly, the structure of IEDHS and operation models of district heating network are presented. An adjustable robust interval optimal dispatch model for IEDHS is established considering wind power uncertainty. The wind power mismatches in the worst-case scenarios are balanced by adjustable thermal units based on the time-varying participation factors. Then, the nonlinear mathematical formulation of the scheduling model is proposed and converted into a mixed-integer linear programming (MILP) problem utilizing a binary expansion method. By solving this model, the maximum allowable output intervals of wind power and optimal economic dispatch results of IEDHS can be obtained. Finally, case studies performed on two IEDHS are applied to verify the validity of the proposed model.

针对市场环境下如何协调主动配电网与输电网的运行策略以实现二者共赢的问题,提出一种基于主从博弈的输配电网协同经济调度策略.首先,采用主从博弈架构建立了输配电网双层经济调度优化模型,其中上层为输电网价格出清模型,下层为考虑二阶锥交流潮流约束的配电网经济调度模型.然后,应用二阶锥重构技术、KKT(Karush-Kuhn-Tucker)条件、强对偶定理和线性松弛技术将所提双层模型转化为混合整数二阶锥单层规划模型进行求解.最后通过算例验证了模型和方法的有效性.结果表明,所提输配协同经济调度策略可以有效降低输电网电能及备用出清价格,同时提高了配电网运行经济性.

The penetration of a high proportion of renewable energy sources (RES) into the power grid intensifies the source–load imbalance, which greatly weakens the network transmission performance and power supply quality, and the effect of relying only on individual regulation within the region is negligible. To enhance the capacity of interconnection and coordination among different areas of power systems and improve the accommodation level of RES and low-carbon efficiency, an optimal transmission switching model based on the bus tearing method is proposed in this article. Firstly, the complex power system is decomposed based on the bus tearing method, and thus, the interconnected power grid structure of the multiarea system is constructed. Secondly, the optimal model of interconnected power grid decomposition and coordination structure considering renewable energy generation is constructed, based on exquisite modeling, to reduce the difficulty of unified analysis and decision-making of the multiarea interconnected power system, and the expression of the model is simplified in the form of the matrix. Then, the analytical target cascading (ATC) method is used to decouple the complex model from the main problem and subproblem and solve the distributed parallel problem, to understand the optimization of the decomposition and coordination structure of the interconnected power grid with source–load coordination. Finally, based on the case studies of the IEEE 14-bus system and IEEE 118-bus system, the effectiveness of the proposed model and method is verified, the coordinated operation of the interconnected power grid and the optimal allocation of network resources are achieved, and the economy of power system operation is improved.

With the increasing penetration of renewable energy generation (REG) (i.e. wind power) and energy storage system (ESS) connected to the active distribution network (ADN), which breaks the traditional transmission-distribution-micro (TG -ADN-MG) grid hierarchical operation pattern. ADN is the intermediate link between transmission grid (TG) and microgrid (MG), based on the correlation and interaction between TG, ADN and MG, a unit commitment (UC) decision-making model of the synergestic TG-ADN-MG power system is proposed in this paper, based on the meticulous modeling of TG-ADN-MG scheduling, by introducing the analytical target cascading (ATC) algorithm, the power system at three layers are taken as different stakeholders, through the tie-line exchange power equivalent to the virtual generator and virtual load to achieve decoupled model, and synergetic source-network-load schedule of a complex system is realized. Concurrently, to enhance the capability of each layer of active momentum in the TG, ADN and MG to deal with wind power uncertainty, through the synergistic optimization of the decision-making variable of each layer, the economic goal optimization and the resource complementarity of the whole interconnected system are realized. Finally, case studies on the modified IEEE 6-bus system verify the effectiveness of the proposed model and method.

大容量直流输电密集接入负荷中心加剧了受端电网交流故障的失稳风险,为准确、客观地实现交流故障筛选与排序,将改进模糊层次分析法(improved fuzzy analytic hierarchy process,IFAHP)应用到交直流受端电网交流故障的筛选与排序.首先,构建表征受端电网稳定性的综合评价指标,包括暂态电压稳定、功角稳定、网架支撑能力、极限切除时间、短路电流、潮流转移和网损指标,多层次多角度反映交流故障的严重程度,克服采用单一评价指标的局限性.然后,通过层次分析和梯形模糊数互补判断矩阵来确定准则层和综合评价指标的各项权重,实现定性评价与定量分析的有效结合,克服专家决策主观性的影响,从而较为准确、全面、客观地实现交直流受端电网交流故障的筛选与排序.最后,通过广东电网实际算例的仿真分析,验证了所提方法的有效性.

随着配电网逐渐接入越来越多的分布式可再生能源,以及为接纳这一新能源而配置储能等主动应对措施,传统的输电网与配电网割裂的机组组合模式容易因二者发电计划协调性不足而影响可再生能源发电的充分消纳.对此,本文以风储接入配电网为例,计及电力系统一次调频特性和二次频率调整策略,提出了含风储主动配电网与输电网协同的机组组合优化决策模型及对应的分布式求解方法.该方法以主动配电网与输电网间联络线为分解协调点,解耦构造相对输电网的虚拟负荷和相对主动配电网的虚拟电源.在此基础上,依据增广拉格朗日松弛技术,将协同优化模型分解为输电网机组组合优化决策模型和多个含风储主动配电网运行优化决策模型,然后借助拉格朗日乘子修正策略,驱使含风储主动配电网与输电网机组组合决策以交替迭代的方式趋向可行到最优,完成协同决策.最后,通过简单6节点算例系统验证了本文所提模型和方法的有效性.

随着零售电力市场的逐步开放,代理商将在工业园区的用电管理中扮演越来越重要的角色.合理设计定价策略,增加代理商利润,同时有效降低园区用户空调系统用电费用,是重要的运营问题.为此,文中提出一种考虑建筑热惯性的代理商-用户主从博弈电价双层优化模型.上层以代理商利润最大为目标,依据用户反馈的用电策略,决定代理商向电网购、售电的策略,以及面向园区用户的定价策略;下层以用户购电费用最小为目标,基于代理商定价策略,充分利用建筑热惯性,优化用户空调系统用电策略.该双层模型是相互嵌套的非线性规划问题.应用KKT(Karush-Kuhn-Tucker)条件、强对偶定理及线性松弛技术,将模型转化为可利用商业求解器YALMIP/CPLEX求解的混合整数线性规划模型.最后,以中国广东某工业园区为例验证所提模型和方法的有效性.算例结果表明,在主从博弈模型中充分利用建筑热惯性,能够有效地减小用户空调系统运行费用,并可增加代理商利润.

Abstract With the increasing building energy consumption, building integrated photovoltaic has emerged. However, this method has problems such as low photovoltaic absorption rate and large load peak–valley difference. For this reason, the authors have constructed a building integrated photovoltaic‐phase change material system considering the demand response. Under the demand response at the time of use, the system was powered by building photovoltaic power generation. The thermostatically controlled load demand inside buildings was satisfied jointly by the phase change energy storage and the air conditioning. The system can run offline or connected to the grid through surplus electricity. When electricity is insufficient, the system can purchase it from the grid. The system dispatching strategy is also given. Based on the principles of minimising the daily cost of system operation, maximising the photovoltaic absorption rate, and minimising the peak–valley difference, a multi‐objective optimisation model is established, and the particle swarm algorithm is used to perform the capacity configuration on the energy storage system. Finally, case analysis was conducted to verify that the scheme is able to reduce the system operating cost by over 50%. In addition, it can greatly increase the photovoltaic absorption rate and reduce the peak–valley difference to achieve peak load shifting. The scheme has significant economic and technical benefits.

为提高大型区域互联系统连续潮流的计算效率,提出一种改进的基于快速解耦电力系统连续潮流并行计算方法.通过在校正阶段采用快速解耦法求解潮流方程,根据系统的阻抗参数和功率增长方向构造修正方程组的系数矩阵,对潮流方程修正方程组进行预处理,并采用基于CPU-GPU混合架构加速的稳定双共轭梯度法进行求解.基于IEEE-118节点系统、Case13802等多个不同规模测试系统的算例分析表明,该改进算法有效提高了连续潮流的计算速度.

Abstract Weak inertia characteristics of power systems with high penetrations of renewables have become a prominent problem for frequency security. To solve this problem, a convolutional neural network (CNN)‐based deep learning approach is applied to realize rapid frequency security assessment (FSA). First, the time series frequency security feature is autonomously mined from the wide‐area measurement data to serve as the input data. By doing so, the complex construction process of frequency security feature quantity is avoided. A deep learning structure is then used to establish a non‐linear mapping relationship between time series features and frequency security indicators to realize end‐to‐end power system frequency security prediction. Next, the evaluation accuracy of the proposed approach is optimized by tuning the key parameters in the CNN‐based evaluation model. Through data measurement error analysis and a wind penetration sensitivity study, the anti‐interference performance of the proposed evaluation model is demonstrated. Finally, the effectiveness of the CNN‐based FSA is verified by case studies of a modified 16‐machine 68‐node system and the China Southern Power Grid.

在量测、通信技术具备的前提下,在常规控制设施给定条件下,针对快速电力电子化技术中,大量逆变或整流控制点设定与控制点设定的优化在速度上难以跟随的问题,提出含分布式光储配电网时变最优潮流追踪的模型和分布式在线的算法。在建立时变优化模型和分析在线优化原理的基础上,就已有算法存在信息统一获取和集中计算这一弊端,将其改进为在配电网各区域仅局部及边界信息可知条件下,实现对配电网时变最优潮流分布式的追踪求解。首先,基于配电网开环运行的辐射状结构特点将配电网划分为若干区域,并对时变最优潮流的追踪求解在区域之间进行解耦。进一步,各区域可完全基于局部及边界信息,分布式的优化计算各自内部分布式光储的控制设定点,在设定点不断下发执行的过程中,实现对时变最优潮流快速分布式的追踪求解。文中对提出算法的收敛性进行了证明,并通过IEEE 123节点配电网算例分析验证了这一算法的有效性。