Hydrogen storage and ice storage are promising, environmentally friendly energy storage technologies. However, there are few investigations on the optimal configuration of hybrid renewable energy systems (HRES) for remote off-grid areas with localized scenarios. This paper proposes a new optimal configuration of an off-grid PV/wind/hydrogen/cooling system. Given three performance indices for evaluating HRES, i.e., the levelized cost of energy (LCOE), the loss of power supply possibility (LPSP), and the power curtailment rate (PCR), we use the epsilon-constraint method that formulates LCOE as the objective, while LPSP and PCR serve as constraints. Furthermore, to solve the optimal size of HRES, an improved salp swarm algorithm (ISSA) is proposed. The simulation results show that for an off-grid remote community, the LCOE, LPSP, and PCR of the optimal HRES configuration can achieve $0.31/kWh, 5.00%, and 7.23%, respectively. The comparison of different systems illustrates that adding ice storage in the HRES with hydrogen storage will decrease the LCOE by 27.12%. In addition, compared with other heuristic algorithms, such as SSA, ISSA offers the configuration with the minimum LCOE. The hydrogen-ice storage system is economically significant to off-grid areas with cooling load demand, and the proposed ISSA has excellent accuracy.

Photovoltaics intelligent edge terminals (PV IETs) are new devices for data acquisition and management of PV power station. However, due to the massive, scattered, and disordered nature of PV stations and the expensive price of PV IET, installing a PV IET at each PV station has prohibited for large-scale adoption. Therefore, to reduce the life cycle cost of PV IET in a region, this paper proposes an optimal configuration method for PV IET. We propose a mathematical model for the optimal configuration of PV IETs. For a given regional grid, the model aims to obtain the optimal number and location of PV IETs and the connection mode between PV IETs and PV power stations. In addition, an improved Harris hawk optimization (IHHO) is proposed to solve the nonlinear mathematical model. A case study is carried out under different problem sizes, boundary and devices parameters. The simulation results show that under different case, the PV IET configuration method in this paper can obtain lower life cycle cost than the conventional method, and IHHO has higher accuracy than other algorithms. The optimal configuration method in this article can effectively solve the problem of the optimal configuration of PV IET.

非侵入式负荷监测（non-intrusive load monitoring,NILM）是研究居民用户负荷信息的常用方法，但存在分解准确度低、算法泛化性能低等系列问题。为此，该文应用图信号处理（graph signal processing,GSP）理论，提出一种基于图信号交替优化的居民用户NILM方法。该方法根据总负荷数据构建图信号模型，并基于图信号模型得到关于功率损耗的约束条件，较好地解决了传统方法缺乏负荷数据相关性研究的问题。相比于传统方法需要对模型参数多次调整，交替优化法可以自动调整参数，提高了实时监测能力，降低了电网运营成本。仿真结果表明，在1min采样率下，基于图信号交替优化法的总负荷分解准确度比NILM-GSP提高了15%，计算时间降低了10%，充分体现了该文算法性能的优越性。

保证数据驱动型配电网短期负荷预测精准的关键是选取合适的相似日数据集和构建合理的日负荷预测模型。文中研究了一种基于孪生网络（SN）和长短时记忆（LSTM）网络相结合的配电网短期负荷预测模型。基于配电网负荷相似日的影响因素具有多样化、强随机性的特点,利用SN两个输入权重共享的特点对历史负荷数据进行分析,进而对待测日的特征进行分类,以完成相似日数据选取。此外,利用灰狼优化算法全局搜索能力强、收敛速度快等特点,对基于LSTM网络的配电网短期负荷预测模型进行参数优化。最后,以某一个区域配电网的实际数据为例,验证上述预测方法的准确性与鲁棒性,与LSTM网络、基于粒子群优化的LSTM网络、支持向量机等方法对比可知,所提方法具有较高的准确度和计算效率。

Integrated energy systems (IES) with cooling, heat, electricity, and natural gas have drawn significant interest recently as we embrace more sustainable energy a midst climate change. However, the uncertain outputs of distributed generators (DGs) make it challenging for IES planning while maintaining low-cost installation and operation under carbon emission constraints. To tackle the challenge, this work proposes an optimal planning model for IES considering both DG output uncertainties and carbon emission punishments. To reduce the conservatism of the widely-adopted interval and affine algorithms, an affine model based on the matrix form is first proposed to model the uncertain DG outputs. A tiered dynamic charging cost model is further developed to introduce and minimize carbon emissions with a punishment mechanism at the planning stage. Based on these two sub-models, an optimal IES planning model is proposed to simultaneously minimize the overall costs of investment, operation, and carbon emissions. To solve the multi-dimensional nonlinear model, an improved quantum particle swarm optimization (IQPSO) algorithm is introduced with enhanced global optimization ability. Simulation results on the IEEE 33-bus and 69-bus IES network have demonstrated that the proposed method can effectively reduce the impacts of DG uncertainty and carbon emissions at the planning stage of IES with a better long-term economy.

负荷聚类特性分析是实现配电网的定制电力、高品质供电、高可靠性供电的重要基础.然而现有的Kmeans聚类分析方法,受限于数据样本集和聚类初始中心的选取等,会出现因初始中心不同造成聚类结果差异大的问题.为此,针对配电网负荷数据特点,

The load clustering analysis of distribution networks is the basis for customized power and high-quality and reliable power supply. However, the existing K-means clustering methods are limited by the data sample set and selection of the initial center of clustering, wherein selecting different initial centers will yield significantly different clustering results. Therefore, according to the characteristics of distribution network load data, a distribution network load clustering analysis method based on the combination of improved firefly and K-means algorithms is proposed. Benefiting from the strong global search ability of the firefly algorithm and considering the intraclass similarity and interclass differences, the initial center of the K-means algorithm is optimized to obtain the minimum value of the clustering effectiveness index of the results. Further, to mitigate the weakness of the firefly algorithm in processing load data by adding excellent first-generation individuals via the density method and using an improved attraction formula and a probability attraction between individuals to optimize the individual movement mode in the iterative process, the early convergence speed is accelerated, and the later convergence is stabilized. The algorithm approaches the extreme value faster with a faster calculation speed. This paper lists several examples to verify the clustering effect of the proposed algorithm and finds the optimal initial center of the K-means algorithm for the power load data of a distribution station area, thus obtaining the minimum Davies-Bouldin index, reducing the intraclass loading clustering differences and increasing the interclass differences. The final clustering center exhibits highly representative characteristics, thus providing a basis for load type division and clustering characteristic analysis and laying the foundation for differentiated power service customization on the demand side. © 2023 Tianjin University. All rights reserved.

可再生能源、电动汽车等大量无序接入使得配电系统运行在越来越复杂和不确定的工况中,迫切需要高精度实时状态感知以实现弹性提升,然而现有的配电量测系统远不能满足新一代配电系统对网络状态进行实时感知的要求。为此,提出了一种面向高精度状态感知的配电系统微型同步相量测量单元（micro-phasor measurement units,μPMU）优化配置方法。首先,在高级量测体系（advancedmeasurement infrastructure,AMI）满足网络可观测的基础上,以状态估计均方误差最小为目标,以任意一致的系统运行状态作为参数代入状态估计误差协方差矩阵,建立了μPMU优化配置模型;其次,通过对增益矩阵进行Cholesky分解,将目标函数采用决策变量显式表达出来,从而可采用成熟的商业求解器快速获得高质量的可行解;最后,采用IEEE33节点和69节点配电系统进行测试,验证了所提方法的有效性。

电动汽车规模化接入配电网已经成为一种趋势,电动汽车的车载充电技术也获得广泛应用。本文分析了含车载充电的电动汽车在高渗透率接入条件下,对小区级配电网可靠性的影响。首先,根据电动汽车车载充电机主要技术参数和小区级配电网的构成,在PSCAD环境下构建车载充电机及小区级配电网的模型;其次,按照电动汽车的充电时间分布,分析电动汽车无序充电和有序充电2种充电模式下小区级配电网负荷的变化情况,得出有序充电能较好地达到小区配电网负荷削峰填谷的目的;最后,采用IEEE RBTS-BUS6测试系统分别对电动汽车负荷的接入点、充电模式、渗透率进行可靠性指标量化评估。结果表明,线路过载是电动汽车规模化接入系统的主要限制因素,增加接入点的容量和采用有序充电模式可有效提高小区级配电网的可靠性。