Economic power dispatch with cubic cost models using teaching learning algorithm

Elanchezhian, E.B.; Subramanian, S.; Ganesan, S.

doi:10.1049/iet-gtd.2013.0603

Cited by 24 publications

(18 citation statements)

References 28 publications

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“…As it is understood that the equal incremental rate principle works well if the cost function is a quadratic or a piecewise linear [67], if the cost function is neither linear nor quadratic, this mechanism may be even more complex. Thus, we need other methods to get the optimum solution [68].…”

Section: System Modelmentioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

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Section: System Modelmentioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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“…Generators are connected at bus numbers 2,5,8,11,13. Standard values of coefficients for fuel cost and emission dispatch of CO2 gas, SO2 gas and NOX gas are given in Table ( 4) and Table (5) respectively [22]. Values of price penalty factors given in Table (2) are calculated using equation numbers (6).…”

Section: Simulation Study and Resultsmentioning

confidence: 99%

“…Initialize the parameters as number of host nest, maxiteration (3). Generate initial population of n host nest using equation given bellow = lower bounds of j th component (4). Evaluate the objective function and store the best solution as vector (5).…”

Section: Steps Of Cs Algorithmmentioning

confidence: 99%

“…Some of conventional algorithms viz. Linear programming (LPP), Interior point (IP), Gradient method (GM), have been proposed to solve the different problems of power system successfully [4][5][6][7][8], but there are some problems associated with these algorithms found in literature are, local optimum solution, limited in solving unimodel and smooth objectives only [9][10]. The objective function of CEED is non-differential as well as nonlinear one [11].…”

Section: Introductionmentioning

confidence: 99%

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Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms

Goyal¹,

Vadhera²

2019

JESA

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This paper deals with application of meta-heuristic algorithms to resolve the problem of combined economic emission dispatch (CEED) with peak load management for a mediumsized power system in an efficient manner. The objective is to optimize the fuel cost of generation simultaneously minimizing the environmental pollution caused by fossil fuel based power generating units working at their peak limits. In this paper combined problem of minimizing fuel cost and emission of flue gases (NOx, CO2, SO2), is solved using Cuckoo search (CS) and Grasshopper optimization algorithm (GOA) via composite function of all four objectives with help of weight ratios and price penalty factors. Demand Side Management (DSM) measure is also applied at least expensive areas to manage the peak load condition at generating units. The problem is implemented on IEEE 30-bus system with 6 generator units. The simulation results of the CS algorithm for CEED with and without DSM have been compared with the results of GOA algorithm. The compound results obtained by CS algorithm for the problem of CEED with DSM validated its potential.

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“…In recent years, artificial intelligence algorithms have been widely used in dynamic economic dispatching models. For example, genetic algorithm (GA) [3][4][5][6][7][8][9], simulated annealing algorithm (SA) [10], tabu search algorithm (TS) [11,12], differential evolution algorithm (DE) [13][14][15][16][17][18][19], particle swarm optimization (PSO) [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], artificial bee colony algorithm (ABC) [35], artificial immune system algorithm (AIS) [36,37], evolutionary programming algorithm (EP) [38][39][40], complementary quadratic programming algorithm (cQP) [41], biogeography-based optimization algorithm (BBO) [42,43], teaching learning-based optimization algorithm (TLBO) [44,45], charged system search algorithm (CSSA) [46], flower pollination algorithm (FPA) [47], rooted tree optimiz...…”

Section: Introductionmentioning

confidence: 99%

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

Chen

Han

Wang

et al. 2019

Mathematical Problems in Engineering

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A modified antipredatory particle swarm optimization (MAPSO) algorithm with evasive adjustment behavior is proposed to solve the dynamic economic dispatch problem of wind power. The algorithm adds the social avoidance inertia weight to the conventional antipredatory particle swarm optimization (APSO) speed update formula. The size of inertia weight is determined by the distance between the global worst particle and other particles. After normalizing the distance, the inertia weight is controlled within the ideal range by using the characteristics of sigmoid function and linear decreasing method, which improves the ability of particles to avoid the worst solution. Then, according to the characteristics of the acceleration coefficient which can adjust the local and global searching ability of particles, acceleration coefficients of nonlinear change strategy is proposed to improve the searching ability of the algorithm. Finally, the proposed algorithm is applied to several benchmark functions and power grid system models, and the results are compared with those reported using other algorithms, which prove the effectiveness and superiority of the proposed algorithm.

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Economic power dispatch with cubic cost models using teaching learning algorithm

Cited by 24 publications

References 28 publications

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

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