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SUMMARY The well-established under-frequency load shedding (UFLS) is deemed to be the last of effective remedial measures against a severe frequency decline of a power system. With the ever-increasing size of power systems and the extensive penetration of distributed generators (DGs) in power systems, the problem of developing an optimal UFLS strategy is facing some new challenges. Given this background, an optimal UFLS strategy for a distribution system with DGs and load static characteristics taken into consideration is developed. Based on the frequency and the rate of change of frequency, the presented strategy consists of several basic rounds and a special round. In the basic round, the frequency emergency can be alleviated by quickly shedding some loads. In the special round, the frequency security can be maintained, and the operating parameters of the distribution system can be optimized by adjusting the output powers of DGs and some loads. The modified IEEE 37-node test feeder is employed to demonstrate the essential features of the developed optimal UFLS strategy in the MATLAB/SIMULINK environment. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new comprehensive planning methodology is proposed for implementing distribution network reinforcement. The load growth, voltage profile, distribution line loss, and reliability are considered in this procedure. A time-segmentation technique is employed to reduce the computational load. Options considered range from supporting the load growth using the traditional approach of upgrading the conventional equipment in the distribution network, through to the use of dispatchable distributed generators (DDG). The objective function is composed of the construction cost, loss cost and reliability cost. As constraints, the bus voltages and the feeder currents should be maintained within the standard level. The DDG output power should not be less than a ratio of its rated power because of efficiency. A hybrid optimization method, called modified discrete particle swarm optimization, is employed to solve this nonlinear and discrete optimization problem. A comparison is performed between the optimized solution based on planning of capacitors along with tap-changing transformer and line upgrading and when DDGs are included in the optimization. [ABSTRACT FROM PUBLISHER]

Abstract: An iterative based strategy is proposed for finding the optimal rating and location of fixed and switched capacitors in distribution networks. The substation Load Tap Changer tap is also set during this procedure. A Modified Discrete Particle Swarm Optimization is employed in the proposed strategy. The objective function is composed of the distribution line loss cost and the capacitors investment cost. The line loss is calculated using estimation of the load duration curve to multiple levels. The constraints are the bus voltage and the feeder current which should be maintained within their standard range. For validation of the proposed method, two case studies are tested. The first case study is the semi-urban 37-bus distribution system which is connected at bus 2 of the Roy Billinton Test System which is located in the secondary side of a 33/11kV distribution substation. The second case is a 33kV distribution network based on the modification of the 18-bus IEEE distribution system. The results are compared with prior publications to illustrate the accuracy of the proposed strategy. [Copyright &y& Elsevier]

Abstract: A new technique for optimal planning of MV and LV segments of a distribution system is presented in this paper. The main goal is to find optimally distribution transformer and substation locations and ratings, as well as, the route and type of Medium Voltage (MV) and Low Voltage (LV) feeders. The proposed technique is applicable to both uniform and non-uniform load densities areas. In this method, the planning area is divided into regions with relatively uniform load density such as urban, semi-urban, sub-urban. Each of regions is divided into zones, called LV zone. Each LV zone is supplied by an MV/LV transformer. The dimensions of LV zones are found based on the average load of each region. The placement and rating of MV/LV transformers, the type and route of LV conductors in an LV zone all depend on its loads’ location and power. Regarding the placement and rating of MV/LV transformers in planning area and the space of regions, the dimensions of a zone which is supplied by a HV/MV transformer, called MV zone, is determined. Additionally, the location and rating of HV/MV transformers as well as the feeder''s routes and types are calculated. Since the dimensions of an LV zone influence the associated length of MV feeder, the MV feeder cost needs to be included in the total cost associated with the LV zone. This requires the MV feeder type to be known to calculate the corresponding cost. However, the MV feeder type is determined as an output from MV zone planning. As a result, an iterative based method is proposed to consider this common element in computations to develop integrated planning of both LV and MV zones. It is observed that the iterative technique quickly converges to the same results as the exhaustive search method. Discrete particle swarm optimization (DPSO) method is employed for solving the planning problem. The results are compared with nonlinear programming, genetic algorithm and exhaustive search methods. It is observed that DPSO is as accurate as the exhaustive search method for integrated planning of MV–LV distribution systems while its computation time is significantly lower. [Copyright &y& Elsevier]

Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced. [ABSTRACT FROM AUTHOR]

Highlights • Updating pseudo data considering the impact of spatial-temporal correlation. • Studying the impact of load aggregation and PV penetration on spatial-temporal correlation. • Developing a non-iterative computationally efficient DSSE algorithm. • Proposing a time series state estimation framework suitable for unbalanced LV distribution networks. Abstract The increasing complexity of distribution networks calls for advancement in distribution system state estimation (DSSE) to monitor the operating conditions more accurately. A sufficient number of measurement devices is imperative for a reliable and accurate state estimation. The limitation on the measurement devices is generally tackled with using the so-called pseudo measured data. However, the errors in pseudo data by current techniques are quite high leading to a poor DSSE. As customer loads in distribution networks show high cross-correlation in various locations and over successive time steps, it is plausible that deploying the spatial-temporal dependencies can improve the pseudo data accuracy and estimation. Although the role of spatial dependencies in DSSE has been addressed in the literature, one can hardly find an efficient DSSE framework capable of incorporating temporal dependencies present in customer loads. Consequently, to obtain a more efficient and accurate state estimation, we propose a new non-iterative DSSE framework to involve spatial-temporal dependencies together. The spatial-temporal dependencies are modeled by conditional multivariate complex Gaussian distributions and are studied for both static and real-time state estimations, where information at preceding time steps are employed to increase the accuracy of DSSE. The efficiency of the proposed approach is verified based on the quality and accuracy of the indices, standard deviation and computational time. The method applied to a combination of residential and industrial customer loads in three different balanced medium voltage (MV), and one unbalanced low voltage (LV) distribution case studies for evaluations. [ABSTRACT FROM AUTHOR]