Jaydev Sharma

Multiobjective, Multiconstraint Service Restoration of Electric Power Distribution System With Priority Customers

In this paper, a technique based on nondominated sorting genetic algorithm-II (NSGA-II) is presented for solving the service restoration problem in an electric power distribution system. Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multiobjective, multiconstraint optimization problem. In contrast to the conventional genetic-algorithm (GA)-based approach, this approach does not require weighting factors for the conversion of such a multiobjective optimization problem into an equivalent single objective function optimization problem. In this work, various practical distribution system operation issues, such as the presence of priority customers, presence of remotely controlled, as well as manually controlled switches, etc. have also been considered. Based on the simulation results on four different distribution systems, the performance of the NSGA-II-based scheme has been found to be significantly better than that of a conventional GA-based method. Besides, to reduce the software runtime of the NSGA-II algorithm, a faster version of NSGA-II has also been implemented.

Evolutionary programming based optimal placement of renewable distributed generators

An evolutionary programming (EP) based technique has been presented for the optimal placement of distributed generation (DG) units energized by renewable energy resources (wind and solar) in a radial distribution system. The correlation between load and renewable resources has been nullified by dividing the study period into several segments and treating each segment independently. To handle the uncertainties associated with load and renewable resources, probabilistic techniques have been used. Two operation strategies, namely “turning off wind turbine generator” and “clipping wind turbine generator output”, have also been adopted to restrict the wind power dispatch to a specified fraction of system load for system stability consideration. To reduce the search space and thereby to minimize the computational burden, a sensitivity analysis technique has been employed which gives a set of locations suitable for DG placement. For the proposed EP based approach, an index based scheme has also been developed to generate the population ensuring the feasibility of each individual and thus considerably reducing the computational time. The developed technique has been applied to a 12.66-kV, 69-bus distribution test system. The solutions result in significant loss reduction and voltage profile improvement.

Analytical Approach for Well-Being Assessment of Small Autonomous Power Systems With Solar and Wind Energy Sources

This paper presents a systematic analytical approach for the well-being assessment of small autonomous power systems (SAPSs) with wind and solar energy sources. The proposed technique accounts for the uncertainties associated with solar irradiance, wind speed, demand, and outages of various generating units. The impact of wind power fluctuation on the system stability is also assessed by limiting the wind power dispatch to a certain percentage of system load. Well-being assessment and production costing simulation for SAPS are performed using proposed analytical approach and Monte Carlo simulation (MCS) method, and then, obtained results are compared in terms of accuracy and computational time. The comparison shows that the developed technique requires less computational time than MCS method, with reasonable accuracy, and thus, validates the usefulness of proposed analytical method. The impact of renewable energy penetration on a SAPS is also analyzed using the proposed method.

Coordination Between OLTC and SVC for Voltage Regulation in Unbalanced Distribution System Distributed Generation

This paper presents a two-stage approach for solving the optimal voltage regulation problem in unbalanced radial distribution system in the presence of photovoltaic (PV) generation. The on-load tap changer (OLTC) and static VAr compensator (SVC) have been considered as the voltage control devices in this work. The formulated voltage control problem is a mixed-integer nonlinear programming problem which remains unsolved even after 8 h due to its computational burden. However, the proposed two-stage approach can solve this problem in less than 10 min. The feasibility of the proposed approach has been demonstrated on a modified IEEE 123-bus radial distribution system.

Influence of Price Responsive Demand Shifting Bidding on Congestion and LMP in Pool-Based Day-Ahead Electricity Markets

This paper investigates the influence of price responsive demand shifting bidding on congestion and locational marginal prices in pool-based day-ahead electricity markets. The market dispatch problem of the pool-based day-ahead electricity market is formulated as to maximize the social welfare of market participants subject to operational constraints given by real and reactive power balance equations, and security constraints in the form of apparent power flow limits over the congested lines. The social welfare objective function of the day-ahead market dispatch problem maximizes the benefit of distribution companies and other bulk consumers based on their price responsive demand shifting bids and minimizes the real and reactive power generation cost of generation companies. The price responsive demand shifting bidding mechanism, which has been recently introduced in the literature, is able to shift the price responsive demand from the periods of high price to the periods of low price in day-ahead electricity markets. The comparisons of the price responsive demand shifting bids with conventional price responsive and price taking bids are presented by solving hourly market dispatch problems on five-bus, IEEE 30-bus, realistic UP 75-bus Indian, and IEEE 118-bus systems for 24-h scheduling period. It has been demonstrated that the proposed approach leads to reduction in congestion and locational marginal prices as compared to price responsive and price taking bids and meets the energy consumption targets of distribution companies/bulk consumers.

A novel approach for sensitivity calculations in the radial distribution system

In this paper, a novel approach for calculating the sensitivities of active/reactive power loss and voltage magnitudes with respect to active/reactive power injection at any bus in the radial distribution system is presented. In a radial distribution system, the changes in bus voltages and branch flows due to active/reactive power injection at any bus depend on the network topology. The key to the present method is the formation of a matrix whose structure is dependent only on the network topology. The proposed method is able to calculate the various sensitivity indices for single as well as multiple sources in the system. It is also able to handle all possible radial distribution system structures regardless of the system size proficiently. The results obtained by the proposed method have been compared with the simulated (conventional load flow) and the adjoint network approach results

Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network

A fuzzy neural network comprising of a screening module and ranking module is proposed for online voltage contingency screening and ranking. A four-stage multioutput parallel self-organizing hierarchical neural network (PSHNN) has been presented in this paper to serve as the ranking module to rank the screened critical contingencies online based on a static fuzzy performance index formulated by combining voltage violations and voltage stability margin. Compared to the deterministic crisp ranking, the proposed approach provides a more informative and flexible ranking and is very effective in handling contingencies lying on the boundary between two severity classes. Angular distance-based clustering has been employed to reduce the dimension of the fuzzy PSHNN. The potential of the fuzzy PSHNN to provide insight into the ranking process, without having to go through the complicated task of rule framing is demonstrated on IEEE 30-bus system and a practical 75-bus Indian system.

Contingency ranking for voltage collapse using parallel self-organizing hierarchical neural network

On-line monitoring of the power system voltage security has become a vital factor for electric utilities. This paper proposes a voltage contingency ranking approach based on parallel self-organizing hierarchical neural network (PSHNN). Loadability margin to voltage collapse following a contingency has been used to rank the contingencies. PSHNN is a multi-stage neural network where the stages operate in parallel rather than in series during testing. The number of ANNs required is drastically reduced by adopting a clustering technique to group contingencies of similar severity into one cluster. Entropy based feature selection has been employed to reduce the dimensionality of the ANN. Once trained, the proposed ANN model is capable of ranking the voltage contingencies under varying load conditions, on line. The effectiveness of the proposed method has been demonstrated by applying it for contingency ranking of IEEE 30-bus system and a practical 75-bus Indian system.

Voltage regulation optimization of compensated self-excited induction generator with dynamic load

The configuration of short-shunt self-excited induction generator feeding induction motor loads (SEIG-IM) suffers from excessive transients during startup of motor load under no load and unstable operation. These problems may be due to subsynchronous resonance as obtained with series compensated transmission line or due to the connected load system. The use of damping resistors across series capacitors is proposed to damp out the starting transients and for the stable operation. The steady-state model of short shunt SEIG-IM with damping resistors and resistive and motor load is developed to obtain the values of shunt and series capacitances for optimum voltage regulation. The simulated annealing like approach is used to solve voltage regulation optimization problem. The values of shunt and series capacitances and damping resistance are obtained for optimum voltage regulation under entire loading range and stable operation during starting and loading. The results are experimentally verified, which establish the effectiveness of damping resistance and developed algorithm.

Fuzzy neural network based voltage stability evaluation of power systems with SVC

Voltage stability has become of major concern for the power utilities. In this paper, multi input, single output fuzzy neural network is developed for voltage stability evaluation of the power systems with SVC by calculating the loadability margin. Uncertainties of real and reactive loads, real and reactive generations, bus voltages and SVC parameters are taken into account. All ac limits are considered. In the first stage, Kohonen self-organizing map is developed to cluster the real and reactive loads at all the buses to reduce the input features, thus limiting the size of the network and reducing computational burden. In the second stage, combination of different non-linear membership functions is proposed to transform the input variables into fuzzy domains. Then a three-layered feed forward neural network with fuzzy input variables is developed to evaluate the loadability margin. The proposed methodology is applied to IEEE-30 bus and IEEE-118 bus systems.