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This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. First, based on duality theory, the max min form of the model is converted to a max form. Second, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.

Distributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network. The authors strongly recommend this review to researchers, scientists and engineers who are working in this field of research work.

In this study, a new methodology to optimally allocate wind turbines (WTs) in distribution networks is proposed. A market-based optimal power flow is used to determine the optimal numbers and capacities of WTs in a way that maximises the social welfare. The method is conceived for distribution network operators to strategically allocate WTs in distribution networks. The proposed method by yielding location-specific WTs capacity settlement both in terms of cost reduction and consumers' benefits is consistent with distribution network topology and constraints. The method is solved by using step-controlled primal dual interior point method considering network constraints. The effectiveness of the proposed method is demonstrated with two radial distribution systems including an 84-bus 11.4 kV and a 69-bus 11 kV network.

In this paper, a probabilistic method to evaluate the impact of wind turbines (WTs) integration into distribution systems within a market environment is proposed. Combined Monte Carlo simulation (MCS) technique and market-based optimal power flow (OPF) are used to maximize the social welfare considering different combinations of wind generation and load demand over a year. MCS is used to model the uncertainties related to the stochastic nature of wind and the volatility of WTs’ offers. The market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The method is conceived for distribution network operators (DNOs) in order to evaluate the effect of WTs integration into the grid. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4 kV radial distribution system.

© The Institution of Engineering and Technology 2016.This study proposes a probabilistic multi-objective optimisation method for the operation of three-phase distribution networks incorporating active network management (ANM) schemes including coordinated voltage control and adaptive power factor control. The proposed probabilistic method incorporates detailed modelling of three-phase distribution network components and considers different operational objectives. The method simultaneously minimises the total energy losses of the lines from the point of view of distribution network operators and maximises the energy generated by photovoltaic (PV) cells considering ANM schemes and network constraints. Uncertainties related to intermittent generation of PVs and load demands are modelled by probability density functions (PDFs). Monte Carlo simulation method is employed to use the generated PDFs. The problem is solved using ϵ-constraint approach and fuzzy satisfying method is used to select the best solution from the Pareto optimal set. The effectiveness of the proposed probabilistic method is demonstrated with IEEE 13- and 34-bus test feeders.

A novel method for Ferroresonance detection is presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching and transformer switching. Wavelet transform is used for decomposition of signals and Learning Vector Quantizer(LVQ) neural network used for classification. Ferroresonance data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events. Copyright © 2008 John Wiley & Sons, Ltd.

This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.

This paper provides a probabilistic method to assess the impact of wind turbines (WTs) integration into distribution networks within a market environment. Combined Monte Carlo simulation (MCS) technique and market-based optimal power flow (OPF) are used to maximize the social welfare by integrating demand side management (DSM) scheme considering different combinations of wind generation and load demand over a year. MCS is used to model the uncertainties related to the stochastic variations of wind power generation and load demand. The market-based OPF is solved by using step-controlled primal dual interior point method considering network constraints. The method is conceived for distribution network operators (DNOs) in order to evaluate the effect of WTs integration into the network. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4 kV radial distribution system.

A hybrid optimization method for optimal allocation of wind turbines (WTs) that combines particle swarm optimization (PSO) and market-based optimal power flow (OPF) with security constraints is proposed in this paper. The method maximizes the net present value (NPV) associated with WTs investment in a distribution market environment. The PSO is used to choose the optimal size while the market-based OPF to determine the optimal number of WTs at each candidate bus. The stochastic nature of both load demand and wind power generation is modeled by hourly time series analysis considering different combinations of wind generation and load demand. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.

This paper presents a fuzzy controller for improving Fault Ride-Through (FRT) capability of variable speed wind turbines (WTs). The controller is designed in order to compensate the voltage at the point of common coupling by controlling the active and reactive power generated by WTs. Simulations are carried out on a real 37-bus Italian weak distribution system. The results show that the proposed controller can improve the FRT capability in several real cases.

One of the most important topics associated to the integration of wind turbines (WTs) into distribution systems is their siting and sizing. Inappropriate allocation of WTs in distribution systems not only leads to threaten the system operation but also to decrease consumers' benefits. In this paper, a new methodology is proposed to allocate WTs in distribution networks. An optimal power flow (OPF) is used to determine the optimal placement of WTs that maximizes the system Social Welfare (SW). The method is solved by using Step-Controlled Primal Dual Interior Point Method (SCPDIPM) considering network constraints and its effectiveness has been verified on an 83-bus 11.4 kV radial distribution system. By yielding location-specific WTs capacity settlement both in terms of cost reduction and consumers' benefits is consistent with distribution network topology and limitations and can help resource developers to better allocate WTs.

In this paper, a hybrid optimization method that combines Particle Swarm Optimization (PSO) and Optimal Power Flow (OPF) is proposed in order to allocate Wind Turbines (WTs) in distribution systems. The OPF is used to determine the optimal number of WTs in order to maximize the Social Welfare (SW) of the network while the PSO is used to choose the best type of WTs at each candidate bus. The OPF problem is solved using Step-Controlled Primal Dual Interior Point Method (SCPDIPM) considering network constraints. The effectiveness of the proposed method is verified on an 83-bus 11.4 kV radial distribution system. (6 pages)

Smart Grid is a concept for transforming the electric power grid by using advanced automatic control and communications techniques and other forms of information technology. It integrates innovative tools and technologies from generation, transmission and distribution all the way to consumer appliances and equipment. This concept integrates energy infrastructure, processes, devices, information and markets into a coordinated and collaborative process that allows energy to be generated, distributed and consumed more effectively and efficiently. This paper reviews some researches and studies on Smart Grids (SGs) technology.

In this paper an efficient method for detection of inrush current in distribution transformer based on wavelet transform is presented. This method uses Wavelet Transform (WT) and Probabilistic Neural Network (PNN) to discriminate inrush current from other transients such as capacitor switching, load switching and single phase to ground fault. WT is used for decomposition of signals and PNN for classification. Inrush current data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying inrush current from other events.

Transformer inrush currents are high magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. This paper presents an S-Transform based Probabilistic Neural Network (PNN) classifier for recognition of inrush current. Using this method inrush current can be discriminate from other transients such as capacitor switching, load switching and single phase to ground fault. S-transform is used for feature extraction and PNN is used for classification. Inrush current data and other transients are obtained by simulation using EMTP program. The simulation results reveal that the combination of S-Transform and PNN can effectively detect inrush current from other events.

Transformer inrush currents are high magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. In this paper an efficient method for detection of inrush current in distribution transformer based on wavelet transform is presented. Using this method inrush current can be discriminate from other transients such as capacitor switching, load switching and single phase to ground fault. Wavelet transform is used for decomposition of signals and Learning Vector Quantizer(LVQ) neural network used for classification. Inrush current data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying inrush current from other events.

Ferroresonance has more damaging effects on transformers and other equipments in distribution networks such as oscillating over voltages and over currents, distortion in voltage and current waveforms, transformer heating, loud noise due to magnetostriction and mal-operation of the protective devices. In this paper the factors that affect the ferroresonance over-voltages are checked to find the method that can mitigate the overvoltages in the 20kV distribution system. The main factors mainly include; energization and deenergization phase angle, loads of transformer, multi-transformers in parallel, single-phase and multi-phase switching operation, line capacitance, connection of transformer primary winding and switching time duration. The purposed solutions of this problem are determined on the simulation results.

In this paper an efficient method for detection of ferroresonance in distribution transformer based on wavelet transform is presented. Using this method ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Multi Layer Perceptron (MLP) neural network used for classification. Ferroresonance data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying ferroresonance from other transients.

A novel method for Ferroresonance detection is presented in this paper. Using this method Ferroresonance can he discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and competitive neural network used for classification. Ferroresonance data and other transients are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.