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Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining‐sharing knowledge‐based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0‐1 integer programming FSD problem. In LOBGSK, individuals are encoded by binary numbers and logical operations instead of real arithmetic operations are designed to update the individuals. By this, LOBGSK can avoid transcoding in solving the FSD problem. To validate the effectiveness of LOBGSK, it is first applied to a 4‐substation test system considering various fault scenarios. Then it is further implemented to the IEEE 118‐bus system and an actual fault event occurred in a practical power grid in Jilin province of China. In addition, the influence of three key parameters of LOBGSK is also investigated. Simulation results show that LOBGSK is robust against its key parameters and can offer a 100% successful rate to diagnose different faults quickly, which is demonstrated by the reported results of some published FSD methods. Furthermore, it outperforms seven state‐of‐the‐art metaheuristic algorithms and the original GSK in solving the FSD problem of power systems. [ABSTRACT FROM AUTHOR]

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a series‐parallel photovoltaic (PV)‐storage independent microgrid (MG), where low‐voltage distributed generation units could be connected to power grid conveniently. Moreover, it provides a promising way to form medium voltage MG. For this proposed structure, a decentralized control is proposed. The PV units are controlled by a maximum power point tracking (MPPT)‐based droop control, where MPPT of the PV units is achieved. Storage units are regulated via a "droop" controller dependent its reactive power polarity and take the responsibility of keeping the power supply‐demand balance of the MG. Since all these controllers make decisions only based on their local information, the proposed control is reliable and cost‐effective. Moreover, the small‐signal stability of the system is proved. And simulation results are presented to verify the effectiveness. [ABSTRACT FROM AUTHOR]