随着电力系统的发展,互联电网对各自区域内信息的私密性愈发重视;同时,大气污染日趋严重,对发电提出了更高的要求。为此,提出了考虑发电成本、碳排放和大气污染的多区域电力系统多目标模型。不同于传统的总量控制,采用一种时空分布控制对大气污染物排放进行处理。该方法利用高斯烟团模型描述大气污染物的扩散运动,以此计算机组发电所排放的大气污染物对居民的影响,从而调整发电计划。为在保证区域私密性的前提下,协调多维相互冲突的目标,先用隶属度处理多维目标,然后解耦相关目标和约束,并用交替方向乘子法（alternatingdirectionmethodof multipliers,ADMM）进行分布式求解。最后,通过仿真算例验证模型在降低大气污染物排放对居民影响的优越性以及多目标分布式优化的有效性。

Cyber-physical distribution system (CPDS) handles power grid faults by feeder automation. The reliability assessment of CPDS has been intensively discussed for centralized feeder automation (CFA) in existing researches. However, the inapplicability of CFA reliability models, and the low computation efficiency issue of the mostly used Monte Carlo method greatly limits their application in practical engineering cases of the large-scale CPDS with the distributed feeder automation (DFA) mode. Thus, this paper proposes a region-based analytical reliability assessment method for DFA, which has high computation efficiency in the large-scale CPDS. Firstly, the reliability impacts of cyber system anomalies in the DFA mode are analysed and the cyber reliability models are built considering both cyber device failures and cyber-attacks. Then, the cyber system status is integrated into malfunction probability modelling of switches for reliability assessment. Finally, the load reliability is assessed based on the fault region division by the proposed analytical method and the reliability indices are calculated. The accuracy, efficiency and scalability of the proposed method are verified in case studies.

该文提出了一种考虑大气污染物时空分布控制的多时间尺度协调多目标优化调度策略。首先,构建一种考虑大气边界层影响的污染物时空分布计算模型,可基于气象预报信息估算出火电厂污染物的时空分布情况;然后,采用"日前–日内滚动"的调度框架,提出一种考虑大气污染物时空分布控制的多时间尺度协调多目标优化调度模型,同时减少发电成本、碳排放及火电厂造成的大气污染;此外,充分利用大气环境的自净能力,提出一种考虑大气环境容量裕度的多目标决策方法。改进的IEEE 39节点系统和广东电网的仿真结果表明:该策略不仅可有效减少火电厂造成的大气污染,还可根据气象条件及背景浓度的变化实现多个目标之间的动态权衡,实现了真正意义上的经济、环保电力调度。

本文设计了一款基于扰动观测器的滑模控制（perturbation observer based sliding-mode control, POSMC）来实现永磁同步发电机（permanent magnetic synchronous generator, PMSG）的最大功率跟踪（maximum power point tracking, MPPT）.首先,将发电机非线性、参数不确定、以及随机风速聚合成一个扰动,并通过扰动观测器对其进行在线估计.随后,采用滑模控制（sliding-mode control, SMC）对该扰动估计进行实时完全补偿,从而实现不同工况下的控制全局一致性以及各类不确定环境下的鲁棒控制.同时, POSMC采用扰动实时估计值进行补偿来代替传统SMC中所使用的扰动上限值进行补偿,因此可有效解决传统SMC过于保守的缺点,使得控制成本更为合理.最后, POSMC无需精确的PMSG模型,仅需测量d轴电流和机械转速,易于实现.本文进行了3个算例研究,即阶跃风速、随机风速和发电机参数不确定.仿真结果表明,与矢量控制（vector control, VC）和SMC相比, POSMC在各类工况下均可捕获最大风能并具有较强的鲁棒性.基于d Space的硬件在环实验（hardware-in-loop, HIL）验证了所提算法的可行性.

隐马尔可夫模型(HMM)是非侵入式负荷监测常用的算法.由于电压波动与负荷自身电气特性变化等原因,负荷的测量状态如功率可能持续变化,运行过程中出现新的状态转移,但当前基于HMM的非侵入式负荷监测方法并未考虑如何处理该情况,缺乏状态

建设以新能源为主体的新型电力系统是推动电力清洁低碳发展的必然选择，实现清洁能源充分消纳、满足用户侧灵活接入需求成为新型电力系统技术研究的重要方向。首先对新型储能在改善城市电网供电可靠性的优势进行了分析，阐述了储能模块交直流配电网应用场景、技术和经济优势；提出了新型交直流储能系统的技术方案，该方案支持多元化发展，提供灵活多样的能源有序接入服务，以储能模块的新型交直流配电网物理构架为基础，实现分布式能源和电动汽车灵活接入，可满足数据中心等交直流用户需求，提供稳定高质量高可靠的供能，实现能源梯级利用，有效提升园区电压质量，保障清洁能源充分消纳需求，减少线路损耗和用电成本；最后通过某地区新型交直流储能系统示范项目实际案例，从经济和社会效益等方面，论证了本方案的优势和可行性。为实现碳达峰、碳中和目标，保障能源供应安全，推动能源互联网产业多元化和规模化发展，建设以新能源为主体的新型电力系统提供了参考和经验。

Hidden Markov model (HMM) is a common algorithm for non-intrusive load monitoring. Due to voltage fluctuation and changing load electrical characteristics, the measured state of load, such as power, may continue to change, and new state transition occurs during operation. However, the current non-intrusive load monitoring method based on HMM does not consider how to deal with this situation and lacks the generalization ability of state identification and power decomposition. To solve this problem, this paper proposes and constructs a binary-parameter hidden Markov model (BPHMM). Combined with DBSCAN clustering algorithm, the load state is clustered based on active power and steadystate current to reduce the possibility of interference caused by voltage fluctuation and noise data. Viterbi algorithm is improved to take into account the updating of HMM parameters to improve the generalization performance of load state prediction. Considering the random fluctuation of power, based on the principle of maximum likelihood estimation, the optimal model of active power calculation is constructed to realized the load power decomposition. In this paper, the public data set AMPds2 is used to verify the proposed method, and the test example shows that the effectiveness of the proposed method is verified. © 2022, Editorial Department of Control Theory & Applications. All right reserved.