An evolutionary algorithm for consumer welfare optimisation of a contingent power network
03 Nov 2011-pp 504-508
TL;DR: An evolutionary optimization technique based methodology has been proposed to sustain the total generation cost even in contingent states of a power network for consumer welfare and can improve the operating conditions of the system apart from optimizing the price volatility of electrical power market.
Abstract: An evolutionary optimization technique based methodology has been proposed in this paper to sustain the total generation cost even in contingent states of a power network for consumer welfare. The alteration of generation cost during contingency is quite evident which makes the consumers suffer economically due to rise of the level of congestion and price of electricity. The aim of this proposed methodology is to minimize the deviations of generation cost, during contingency, from a preferred value by re-allocation of generation schedule with a controlled load curtailment technique and hence relieving the lines from overloading for congestion management. It has been demonstrated that, on application, the proposed methodology can improve the operating conditions of the system apart from optimizing the price volatility of electrical power market. The methodology has been tested on a standard benchmark system and the comprehensive simulation results looked promising.
Citations
More filters
09 Oct 2016
TL;DR: In this paper, the authors reviewed some congestion management (CM) methods including the nodal pricing method, differential evolution (DE), addition of renewable energy sources, extended quadratic interior point (EQIP) based OPF, mixed integer nonlinear programming, particle swarm optimization (PSO), cost free methods and genetic algorithm (GA).
Abstract: In a deregulated power market, the optimum power flow (OPF) for an interconnected grid system is an important concern as related to transmission loss and operating constraints of power network. The increased power transaction as related to increased demand and satisfaction of those demand to the competition of generation companies (GENCOs) are resulting the stress on power network which further causes the danger to voltage security, violation of limits of line flow, increase in the line losses, large requirement of reactive power, danger to power system stability and over load of the lines i.e. congestion of power in system. It can be managed by rescheduling of generators or optimal location of distributed generation (DG) at minimum cost with minimum loss without disturbing the operating constraints. This paper reviews some of congestion management (CM) methods including the nodal pricing method, differential evolution (DE), addition of renewable energy sources, extended quadratic interior point (EQIP) based OPF, mixed integer nonlinear programming, particle swarm optimization (PSO), cost free methods and Genetic Algorithm (GA). Each technique has its own significance and potential for promotion of rescheduling of generators in a deregulated power system.
4 citations
References
More filters
01 Jan 1995
2,821 citations
TL;DR: This paper presents a novel algorithm to accelerate the differential evolution (DE), which employs opposition-based learning (OBL) for population initialization and also for generation jumping and results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
Abstract: Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
1,419 citations
"An evolutionary algorithm for consu..." refers background in this paper
...DE has drawn an increasing attention for a wide variety of engineering applications including power engineering applications such as transient stability, economic dispatch etc [12-16]....
[...]
TL;DR: The proposed combined method outperforms other state-of-the-art algorithms in solving load dispatch problems with the valve-point effect.
Abstract: Evolutionary algorithms are heuristic methods that have yielded promising results for solving nonlinear, nondifferentiable, and multi-modal optimization problems in the power systems area. The differential evolution (DE) algorithm is an evolutionary algorithm that uses a rather greedy and less stochastic approach to problem solving than do classical evolutionary algorithms, such as genetic algorithms, evolutionary programming, and evolution strategies. DE also incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. This paper proposes a new approach for solving economic load dispatch problems with valve-point effect. The proposed method combines the DE algorithm with the generator of chaos sequences and sequential quadratic programming (SQP) technique to optimize the performance of economic dispatch problems. The DE with chaos sequences is the global optimizer, and the SQP is used to fine-tune the DE run in a sequential manner. The combined methodology and its variants are validated for two test systems consisting of 13 and 40 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other state-of-the-art algorithms in solving load dispatch problems with the valve-point effect.
587 citations
"An evolutionary algorithm for consu..." refers background in this paper
...DE has drawn an increasing attention for a wide variety of engineering applications including power engineering applications such as transient stability, economic dispatch etc [12-16]....
[...]
TL;DR: An algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels and handles the binding constraints by a technique different from the traditional penalty function method is proposed.
Abstract: Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39 -bus New England system.
291 citations
"An evolutionary algorithm for consu..." refers background in this paper
...Congestion management based on optimum generation rescheduling and load shedding schemes are reported in [5], [6]....
[...]
TL;DR: In this article, a new zonal/cluster-based congestion management approach has been proposed, where the zones have been determined based on lines real and reactive power flow sensitivity indexes also called as real/reactive transmission congestion distribution factors, and generators in the most sensitive zones, with strongest and nonuniform distribution of sensitivity indexes, are identified for rescheduling their real power output for congestion management.
Abstract: In a deregulated electricity market, it may always not be possible to dispatch all of the contracted power transactions due to congestion of the transmission corridors. System operators try to manage congestion, which otherwise increases the cost of the electricity and also threatens the system security and stability. In this paper, a new zonal/cluster-based congestion management approach has been proposed. The zones have been determined based on lines real and reactive power flow sensitivity indexes also called as real and reactive transmission congestion distribution factors. The generators in the most sensitive zones, with strongest and nonuniform distribution of sensitivity indexes, are identified for rescheduling their real power output for congestion management. In addition, the impact of optimal rescheduling of reactive power output by generators and capacitors in the most sensitive zones has also been studied. The proposed new zonal concept has been tested on 39-bus New England system and a 75-bus Indian system.
289 citations
"An evolutionary algorithm for consu..." refers methods in this paper
...Different multi-objective PSO based algorithms for congestion management are presented in [7, 8, 9]....
[...]