Narayana Prasad Padhy

Unit commitment-a bibliographical survey

With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. This paper gives a bibliographical survey, mathematical formulations, and general backgrounds of research and developments in the field of UC problem for past 35 years based on more than 150 published articles. The collected literature has been divided into many sections, so that new researchers do not face any difficulty in carrying out research in the area of next-generation UC problem under both the regulated and deregulated power industry.

Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints

Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost and optimal emission of generating units, respectively. Combined economic emission dispatch (CEED) problem is obtained by considering both the economy and emission objectives. This biobjective CEED problem is converted into a single objective function using a price penalty factor approach. A novel modified price penalty factor is proposed to solve the CEED problem. In this paper, evolutionary computation (EC) methods such as genetic algorithm (GA), micro GA (MGA), and evolutionary programming (EP) are applied to obtain ELD solutions for three-, six-, and 13-unit systems. Investigations showed that EP was better among EC methods in solving the ELD problem. EP-based CEED problem has been tested on IEEE 14-, 30-, and 118-bus systems with and without line flow constraints. A nonlinear scaling factor is also included in EP algorithm to improve the convergence performance for the 13 units and IEEE test systems. The solutions obtained are quite encouraging and useful in the economic emission environment.

Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances over a wide range of loading conditions and parameter variations and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.

Application and comparison of metaheuristic techniques to generation expansion planning problem

This work presents both application and comparison of the metaheuristic techniques to generation expansion planning (GEP) problem. The Metaheuristic techniques such as the genetic algorithm, differential evolution, evolutionary programming, evolutionary strategy, ant colony optimization, particle swarm optimization, tabu search, simulated annealing, and hybrid approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods virtual mapping procedure (VMP) and penalty factor approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, intelligent initial population generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional dynamic programming and the effectiveness of each proposed methods has also been illustrated in detail.

Optimal location and controller design of STATCOM for power system stability improvement using PSO

The optimal location of a static synchronous compensator (STATCOM) and its coordinated design with power system stabilizers (PSSs) for power system stability improvement are presented in this paper. First, the location of STATCOM to improve transient stability is formulated as an optimization problem and particle swarm optimization (PSO) is employed to search for its optimal location. Then, coordinated design problem of STATCOM-based controller with multiple PSS is formulated as an optimization problem and optimal controller parameters are obtained using PSO. A two-area test system is used to show the effectiveness of the proposed approach for determining the optimal location and controller parameters for power system stability improvement. The nonlinear simulation results show that optimally located STATCOM improves the transient stability and coordinated design of STATCOM-based controller and PSSs improve greatly the system damping. Finally, the coordinated design problem is extended to a four-machine two-area system and the results show that the inter-area and local modes of oscillations are well damped with the proposed PSO-optimized controllers.

Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

A new load forecasting (LF) approach using bacterial foraging technique (BFT) trained wavelet neural network (WNN) is proposed in this paper. Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned using BFT optimization. With the advantages of global search abilities of BFT as well as the multiresolution and localizing natures of wavelets, the networks are constructed which identifies the inherent non-linear characteristics of power system loads. The proposed approach is validated with Tamil Nadu Electricity Board (TNEB) system, India. The comparison of Delta Rule and BFT-based LF for different periods are depicted with their mean absolute percentage errors (MAPE).

Power-system Stability Improvement by PSO Optimized SSSC-based Damping Controller

Abstract Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this article. The design problem of the proposed controller is formulated as an optimization problem, and the particle swarm optimization technique is employed to search for the optimal controller parameters. By minimizing a time-domain-based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved, stability performance of the system is improved. The performance of the proposed controller is evaluated under different disturbances for both a single-machine infinite-bus power system and a multi-machine power system. Results are presented to show the effectiveness of the proposed controller. It is observed that the proposed SSSC-based controller provides efficient damping to power-system oscillations and greatly improves the system voltage profile under various severe disturbances. Furthermore, the simulation results show that in a multi-machine power system, the modal oscillations are effectively damped by the proposed SSSC controller.

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.

Solution to profit based unit commitment problem using particle swarm optimization

In this paper, an algorithm to solve the profit based unit commitment problem (PBUCP) under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique to maximize the GENCOs profit. Deregulation in power sector increases the efficiency of electricity production and distribution, offer lower prices, higher quality, a secure and a more reliable product. The proposed algorithm has been developed from the view point of a generation company wishing to maximize its profit in the deregulated power and reserve markets. UC schedule depends on the market price in the deregulated market. In deregulated environment utilities are not required to meet the demand. GENCO can consider a schedule that produce less than the predicted load demand and reserve but creates maximum profit. More number of units are committed when the market price is higher. When more number of generating units are brought online more power is generated and participated in the deregulated market to get maximum profit. This paper presents a new approach of GENCOs profit based unit commitment using PSO technique in a day ahead competitive electricity markets. The profit based unit commitment problem is solved using various PSO techniques such as Chaotic PSO (CPSO), New PSO (NPSO) and Dispersed PSO (DPSO) and the results are compared. Generation, spinning reserve, non-spinning reserve, and system constraints are considered in proposed formulation. The proposed approach has been tested on IEEE-30 bus system with 6 generating units as an individual GENCO. The results obtained are quite encouraging and useful in deregulated market. The algorithm and simulation are carried out using Matlab software.

Cost-Benefit Reflective Distribution Charging Methodology

This paper describes the principle and implementation of a new MW+MVAr-Miles charging methodology, which was developed to reflect three key cost drivers in distribution network development: the distance used to support nodal real and reactive power injection/withdrawal; the degree of support offered by the network assets; and the operating condition of the supporting assets in terms of their power factors. The inclusion of the latter driver allows the developed charging methodology to reward network users who are contributing to better power factors and better network utilization, while penalizing customers who worsen power factors and network utilization. As a consequence, the charging model is able to provide forward-looking incentives for network customers to behave in a manner to better the network condition, which will in turn help to reduce the cost of future network development. In addition, the separation of real and reactive power pricing would give network users clear indications of the cost of their reactive power draw from the network, which in turn could help them to evaluate the economics in investing in reactive power compensation devices. The proposed charging methodology is demonstrated on a practical eight-busbar distribution system with a mixed demand and embedded generation (EG). This paper results from work undertaken in a project on distribution charging methodologies for Western Power Distribution. The views in this paper expressed are not those of Western Power Distribution.