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This study presents a multi-objective operation optimisation model for urban power grids with flexible switching (FS) stations to comprehensively improve system security and reliability. In the proposed model, uncertainties associated with load variation and component failures are considered. Moreover, constraints after a feeder or transformer N − 1 contingency are preliminarily formulated. The operation problem optimises the apparent power of feeder section loading. The combination of normal boundary intersection and sequential quadratic programming is employed to solve the non-linear optimisation model. A case study carried out on a test 75-feeder distribution grid with FS stations demonstrates the effectiveness of the proposed model and solving method. Compared with conventional distribution loadability, the optimal load distribution solution obtained in this study provides a trade-off between system security and reliability. The coordinated operation optimisation method is suitable for urban smart distribution grids featured by distribution automation and large-scale interconnections. [ABSTRACT FROM AUTHOR]

This study puts forward a probabilistic power flow calculation method based on the Johnson system and Sobol's quasi‐random numbers. The Johnson system is utilised to simulate the distribution function of one dimensional variable and model the correlation of multiple uncertainties with historical data of the uncertainties. The improved Sobol's quasi‐random number generator is adopted to produce the low‐discrepancy samples in Monte Carlo simulation. The accuracy of the Johnson system is compared with other modelling methods of uncertainties and the comparison of Sobol's quasi‐random numbers and other techniques, such as Latin hypercube sampling and simple random sampling are presented for the cases of IEEE 30‐bus system and IEEE 118‐bus system. [ABSTRACT FROM AUTHOR]

Owing to the variations of loads and sustainable energy, the power injections and the load increment direction are of uncertainties, which pose great challenges to the traditional models of power system operation. To determine the effects of those uncertainties on the reactive power reserve (RPR), an interval optimal RPR dispatch with generator rescheduling (GR) is formulated. To evaluate the RPR interval more comprehensively, two sub‐models, namely the optimistic and pessimistic sub‐models, are built. The proposed method is applied to several IEEE systems to illustrate its effectiveness. Case tests show that, proper GR schemes can enhance the RPR of power system by postponing the limit hitting of the generator reactive power. The obtained RPR scheme shows better robustness to the uncertainties of power injections compared with that of the conventional RPR dispatches, and the restriction on the RPR interval widths also suggests the operating risk reduction. [ABSTRACT FROM AUTHOR]

This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, demand side management, on‐load tap changer tap adjustment and reactive power compensation are considered. The optimisation of DESS for peak shaving and operation cost decreasing is also integrated. The expansion model allows alternatives to be considered for new wiring, new substation, substation expansion and DG installation. The distribution network expansion planning (DNEP) problem is a mixed integer non‐linear programming problem. Active management and uncertainties especially with the DG integration make the DNEP problem much complex. To find the suitable algorithm, this study converts the DNEP problem to a second‐order cone programming model through distflow equations and constraints relaxation. A modified 50‐bus application example is used to verify the proposed model. [ABSTRACT FROM AUTHOR]

Intermittent distributed generators (IDGs), such as distributed wind turbine generator (WTG) and photovoltaic generator (PVG), have been developing rapidly in recent years. The output power of WTG and PVG highly depends on the wind speed and illumination intensity, respectively. There always exist correlations among the wind speed, illumination intensity, and bus load, which could have significant influence on the determination of siting and sizing of IDGs in distribution system. Given this background, a chance-constrained-programming-based IDGs planning model, which can take into account the correlations, is developed in this paper. Latin hypercube sampling technique and Cholesky decomposition are introduced to handle the correlations. A Monte Carlo simulation-embedded multi-population differential evolution algorithm is employed to solve the developed model. Case studies carried out on the Baran & Wu 33-bus distribution system verify the feasibility of the developed model and effectiveness of the proposed solving methodology. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]