Synopsis of Dissertation

M. Tech EE

Proposed Topic

Optimal Scheduling ofGenerators considering valve point effect using Grey wolf optimization

By
Name

Univ.Roll No-

Supervisor:

Department of Electrical Engineering

Co supervisor

Department of Electrical Engineering

College ***

1.INTRODUCTION

As Unit Commitment & Load Forecasting, the economic load dispatch (ELD) is also a crucial part of modern power system. The purpose of the ED is to find the optimum generation among the existing units, such that the total cost of generating units is minimized while satisfying the power balance equations and various other constraints in the system simultaneously [1]. For any load demand the ELD problem provide power output of each unit in MW and also calculate the overall fuel cost of generating units. Economic load dispatch is specified as the procedure of allowing generation levels to the generating units, so that the system load is furnished exclusively and most frugal. For interrelated system, it is requisite to minimize the expenses.

Fig. 1.1 shows a simple schematic diagram of a thermal power station. Fossil fuel (i.e. coal) is supplied to the boiler in which superheated steam is produced, which comes in contact with the turbine blades. The turbine starts rotating whose shaft is coupled to that of the generator, which in turn rotates and generates an electric power at its output terminals. Now, the key concern is to make this generation economic, subject to several constraints which will be discussed later. The operating cost of a generating unit includes the fuel cost, cost of labor, supply & maintenance. Cost of labor, supplies & maintenance are generally fixed with respect to incoming fuel costs.

Fig 1.1 Simple Model of a Boiler-turbine-generator unit

An electrical generation system comprises of one or more generators linked to the load. Now, the objective is to dispatch the generated power to the connected load securely & economically, satisfying all the operational constraints. Economic load dispatch problem can be classified into two types convex & non-convex. Convex/Smooth ED problems neglect transmission losses& other constraints while non-convex/non-smooth ED problems deviate from idealities & take them into consideration. Thus the complexity or dimensionality of a non-convex ED problem increases and so does the computation time.

The system shown in Fig1.2 comprises of N thermal generating units linked to a single bus bar serving connected electrical load PD. Firepresents the fuel cost of the ith unit and the amount of each unit is Pi (electrical power output). The total fuel cost is the summation of the costs of the individual units.

Fig 1.2: N-Thermal units committed to serve a total load PD

Mathematically, the problem may be expressed as to reduce the objective function, FT which is equal to the total of the individual cost of various generating units.

FT = F1 + F2 + F3 + ------+ FN …………… (1.2)

= ……… (1.3)

Subject to the constraint that sum of power generated must be equal to load demand

PD+PL…….. (1.4)

Pi,min≤ Pi ≤ Pi,max.…… (1.5)

where,

Pi = real power output of ith generator

PD= total load demand, PL= transmission losses

T = time interval number

N = total numbers of committed units

2.LITERATURE SURVEY

Zi-Xiong Liang & J. Duncan Glover (1992) proposed the conventionalmethod named as Dynamic Programming with a zoom feature for resolving economic dispatch problem of thermal generating units. It is the system including transmission line losses. In dynamic programming method there are no restrictions on generator cost functions. Also it is the short-term load forecasting along with generator rate limits and various other constraints. This metod is tested on 3 unit system and results obtained showed that zoom feature provides much faster convergence rate and low computer requirements.

Ahmed Farag et al. (1995)described a new and efficient algorithm to obtain the optimal change in power dispatch related to contingency states or overload situations in power system operation and planning phases under various objectives such as economy, consistency and environmental conditions. The optimization procedures make use of linear programming with bounded variables and it incorporates the techniques of the Section Reduction Method and the Third Simplex Method to solve ELD.

W.Ongsakul & N.Ruangpayoongsak (2001)proposed a genetic algorithm based on simulated annealing solutions (GA-SA) to solve ramp rate constrained dynamic economic dispatch (DED) problems for generating units with non-monotonically and monotonically increasing incremental cost (IC) functions. This method is superior to SA, LS, GA-MOL, SA-GA-MOL, and MOL methods in terms of the accuracy & quality of solution, leading to considerable fuel cost savings.

Hamid Bouzeboudja et al. (2005)put forward a proficient and practical real-coded genetic algorithm (RCGAs) for solving the economic dispatch problem. This feasibility of proposed technique is tested on IEEE-25 bus system and results obtained are compared with a binary-coded genetic algorithm (BCGAs) and classical optimisation technique of Quasi- Newton.Simulation results reveal that solution obtained is of high quality and converges at a faster rate

M.A. Abido (2006) proposed 3 multiobjective evolutionary algorithms (MOEAs) to solve environmental/ economic power dispatch problem. The problem is expressed as a multiobjective optimization problem considering botheconomic and environmental constraints. Three MOEAs techniques are- 1) Nondominated Sorted Genetic Algorithm (NSGA) 2) Niched Pareto Genetic Algorithm (NPGA) 3) Strength Pareto Evolutionary Algorithm (SPEA). MOEAs have been compared to each other and to those reported in the literature. The solution indicates that MOEAs outperform the traditional techniques. Moreover, the SPEA has better variety characteristics, more efficient when compared to other MOEAs and has the best computational time.

T.Thakur et al. (2006)suggested a reliable technique to solve combined economic and emission dispatch (CEED) problem. The technique is termed as particle swarm optimisation (PSO). The results are obtained for a test system with three generating units. The performance of the PSO is compared with Tabu Search-II and Non- Dominated Sorting Genetic Algorithm-II (NSGA-II). The results proved that the proposed method is robust efficient and lead to optimal solution in ELD problem.

K.Chandram, N.Subrahmanyam(2007) coined Brent method for solving economic dispatch problem with transmission losses. This metod is based on selection of lambda values and then optimal lambda is obtained from Brent metod with the help of power balance equation. The test system comprises of 3, 6, 15, 20 generating units and results obtained were compared in terms of their solution quality, accuracy, efficiency and convergence characteristics with the conventional lambda iterative method, genetic algorithm and particle swarm optimization. Results revealed that Brent metod provides more accurate results.

Leandro dos Santos Coelho, Chu-Sheng lee (2008)presented improved PSO approaches for solving ELD problem that takes into account non-linear generator functions such as ramp-rate limits and prohibited operating zones in the power system operation. Improved PSO method includes the combination of PSO, Gaussian probability distribution function or chaotic sequences. The proposed metod is tested on 15 and 20 unit system and results proved that it outperforms than other metaheuristic optimisation methods.

Bhattacharya & P.K. Chattopadhyay (2009) presented Biogeography-Based Optimization (BBO) algorithm to solve combined Economic Emission Load Dispatch (EELD) problem. Biogeography deals with the geographical distribution of biological organisms. Mathematical models of Biogeography describe how species migrate from one habitat to another, how species arise, and how species become extinct. This algorithm is applied for multi­ objective EELD problem in a 3 Generator system with NOx and SOx emission and in a 6 Generator system having valve-point loading and NOx emission. Results obtained for 3 and 6 unit cleared that it provides global solution when compared to others techniques mentioned in literature.

Yang XS, Deb S (2009) formulated a new metaheuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. The proposed algorithm is tested against test functions and then compares its performance with those of genetic algorithms and particle swarm optimization. This method outperforms various heuristic algorithms.

S. Duman et al. (2009) solved ED problem considering the valve point effect by using new gravitational searchalgorithm(GSA). The proposed approach has been applied to 3 & 13 unit systems. The results showed that performance of the proposed approach is more efficient and robust when compared toother optimization algorithms reported in literature.

S.Prabhakar et al. (2009) compared four different evolutionary techniques i.e. genetic algorithm, evolutionary programming, particle swarm optimisation and differential evolution to solve economic load dispatc problem considering line flow constraints. The four techniques are tested on IEEE 30 Bus System and 15 unit system. Among all, PSO is found to provide better quality solution along with faster convergence rate.

K.Senthil and K.Manikandan (2010) proposed an improved tabu search algorithm to solve ELD problem on 3 unit system, 6 unit system with emission constraint and 13 unit system with valve loadin effect. The results are compared with genetic algorithm, tabu search algorithm, distributed tabu search algorithm and Hopfield neural network. Improved tabu search method provides better quality of solution.

S. Krishnamurthy, R. Tzoneva (2011) solved the combined economic emission dispatch problem by using Min-Max and Max-Max price penalty factor approaches using Lagrange’s method and comparison of both techniques. The price penalty factor for the combined economic emission dispatch (CEED) is the ratio of fuel cost to emission value. The viability of proposed metod is tested for IEEE 30 bus system with six generating units. The total fuel cost obtained using Min-Max price penalty factor approach is less by 56.90% in comparison to Max-Max price penalty factor and it leads to better optimisation results.

Shaik Affijull and Sushil Chauhan (2011) recommended Gravitational Search algorithm (GSA) to solve economic load dispatch problem with valve point loading and Kron’s loss. The feasibility of proposed method is tested on 3, 6, 13 and 40- unit test systems. Simulation results revealed thatGSA method lead to optimal solution of ELD problem and has greater potential in handling complex optimization problems for large scale systems.

Saol Babari Nejad et al. (2012) proposed a new method which is derived from the hybrid combination of two different algorithms, CLONAL as the basic algorithm and PSO. The proposed method has been tested on two different systems containing thirteen and forty generators and obtained results have been compared with the results of other stochastic search algorithms.

Senthil Krishnamurthy and Raynitchka (2012) presented the optimization approach (Lagrange’s) and random variables selection approach (Particle Swarm Optimisation) to solve the dispatch problem with transmission constraints. The computational time of the Lagrange’s algorithm depends on the selection of the initial values of the Lagrange’s variable (λ), and on the swarms, positions, and velocity selection in PSO algorithm. The proposed metod is tested on IEEE 30 bus system. Results conclude that Lagrange’s algorithm provides better results for CEED problem in comparison to the PSO algorithm.

Nagendra Singh, Yoendra Kumar (2012) proposed a novel PSO with a moderate random search strategy called as moderate-random particle swarm optimization (MRPSO) for solving ELD problem along with emission constraints. PSO techniques used for ED problem gave wide range of solution but it lack global search ability in the last stage. Thus this technique is modified further. MRPSO enhanced the ability of particles to explore the solution spaces more effectively and increases their convergence rates.

Sangita Roy, Sheli Sinha Chaudhuri (2013) presentedCuckoo Search (CS) method. It is a new metaheuristic algorithm used for solving optimization problem. It was developed in 2009 by Xin- She Yang and Susah Deb. It is based on obligate brood parasitism behavior of some cuckoo species along with the Levy Flight behavior of some birds and fruit flies. CS is also validated using some test functions. After that CS performance is compared with those of GAs and PSO. It has been shown that CS is superior with respect to GAs and PSO. At last, the effect of the experimental results are discussed and proposed for future research.

H.T. Jadhav et al. (2013)presented an artificial bee colony algorithm (MABC) for solving economic emission load dispatch problem. The EELD problem is formulated as a biobjective problem for minimization of fuel cost and emission cost both. This biobjective problem can be converted into single objective by usingweighting factor. The feasibility of presented method is tested on various systems and results are compared with other techniques.

K. Thenmalar, Dr. A.Allirani (2013) coined Firefly Algorithm (FA) to solve economic load dispatch problem. This algorithm is tested on 3 & 6 unit systems. Results obtained clearly revealed that the algorithm is more efficient, practical and valid for real time applications. The results are compared with other techniques such as Artificial Bee colony algorithm(ABC),Particle Swarm Optimization Algorithm (PSO), Simulated Annealing Algorithm (SA).It was proved to be more useful for constrained optimization problem in terms of solution accuracy.

S. Usha Rani And C. Padmanabha Raju (2013) proposed Dynamic programming (DP) and Particle swarm optimization (PSO) techniques to solve the unit commitment problem, for committing the units optimally. The effectiveness of the algorithm was tested on two test systems. The first system comprising of three units and the second system is an IEEE 30-bus system and the attained results using the two methods are compared for total operating cost. PSO algorithm is proved to be more proficient and time saving.

S. Khaleghi et al. (2014) proposed a novel application of the improved particle swarm optimization (IPSO) in an economic dispatch problem (EDP) that consists of valve-point loading, power balance, and generators constraints. This procedure is suitable for solving large-scale and complex economic dispatch problems. IPSO algorithm is tested on three unit system and experimental results are compared with other efficient methods. Simulation results demonstrate the efficiency of proposed algorithms for solving economic dispatching problems.

Navpreet Singh Tung & Sandeep Chakravorty (2014) coined pattern search (PS) algorithm to solve ED problem. This method is tested on 5 unit system (IEEE 25 bus system) and results obtained are compared with other techniques presented in literature. It out-performed other techniques in terms of computation speed, fuel cost and power generation. Thus it proved to be more robust and lead to optimal solution in economic dispatch problem.

M.N.Abdullah et al. (2014) presented Evolutionary Particle Swarm Optimization (EPSO) method to solve economic emission dispatch problem. Due to environmental issues, the environmental pollution releases by thermal power generation should be considered in power dispatch planning instead of minimizing the total fuel cost only. The EPSO algorithm worked based on the EP and PSO algorithms. PSO algorithm is used as the main optimizer while the EP operator is utilized to enhance the searching capability.The performance of the EPSO algorithm has been tested on the IEEE 30 bus with 6 generator unit system. The results revealed that EPSO have shorter computation time as compared to PSO.

3.GAPS

From the above Literature Review we conclude the following:

1. The grey wolf optimization has not been developed for constrained economic emission problem

2. The proposed algorithm is simple and less time consuming as it has only two parameters.

3. The proposed algorithm is much more efficient in finding global optimum with high

success rate.

4. OBJECTIVES

1) To develop an algorithm for grey wolf optimization for optimal power generation

2) To implement the proposed method on different IEEE systems(Optimal Power generation, Total operating cost minimization, minimization of valve point effect)

3) Compare the proposed method with other methods in literature

5.METHODOLOGY

Methodology to reach the objectives has following steps:

  1. Study of all previous papers.
  2. Study of various methods to solve the economic dispatch problem considering valve point effect.
  3. To determine the various improvements required for economic dispatch problem considering valve point effect.
  4. To apply that technique for various combinations of generating units.

6. PRIME REQUIREMENT

  1. Matlab Software (Version R2009b)
  2. IEEE and Science Direct journals
  3. E-Journals
  4. Internet facility

7. PROPOSED PLACE OF WORK

REFERENCES

1)[1] Hamid Bouzeboudja, Abdelkader Chaker, Ahmed Allali, “Economic dispatch solution using a real coded genetic algorithm”, Acta electrotechnia et informatica, vol 5, no. 4, 2005, pp 1335-8243

2)J.Wood & B.F.Wollenberg, “ Power Generation Operation & Control”, 2nd edition, new york, willey,1996

3)Zi-Xiong Liang, J. Duncan Glover, “ A zoom feature for a dynamic programming solution to economic dispatch including transmission losses”, IEEE transaction on power system, vol 7, no. 2, may 1992

4)Sangita Das Biswas and Anupama Debbarma, “ Optimal operation of large power system by GA metod”, Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS), 2012

5)H.T. Jadhav, Shubham Raj, Ranjit Roy, “ Solution to economic emission load dispatch problem using modified artificial bee colony algorithm”, International conference on electric power & enery conversion systems, oct 2013

6)S. Duman, U.Guvenc, N. Yorukeren, “ Gravitational search algorithm for economic dispatch with valve point effect”, International review of electrical engineering (IREE), vol 5, no. 6, Nov-Dec 2010, pp 2890-2895