A mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones

Papageorgiou, Lazaros G.; Fraga, Eric S.

doi:10.1016/j.epsr.2006.09.020

Cited by 142 publications

(77 citation statements)

References 14 publications

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“…The conventional methods consist of Linear Programming (LP) [2], Non-Linear Programming (NLP) [3], Quadratic Programming (QP) [4], Lagrangian Programming (LP) [5] and Mixed Integer Quadratic Programming (MIQP) [6]. The main drawbacks of these methods are only need to run in linear problem and not applicable to large scale system.…”

Section: Introductionmentioning

confidence: 99%

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

Venkadesh¹

2017

IOSR JEEE

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Section: Introductionmentioning

confidence: 99%

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

Venkadesh¹

2017

IOSR JEEE

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“…PED is non-convex in nature because of Valve Point Loading Effects (VPLEs), Multiple Fuel Options (MFOs), and Prohibited Operating Zones (POZs) [1]. However, most of the time it is addressed as a convex optimization problem solved by conventional techniques; e.g., equal incremental cost criterion, gradient search method [2], Newton's Method (NM), Lambda Iteration Method (LIM), Lagrange Relaxation (LR) [3], Dynamic Programming (DP) [4], and Quadratic Programming (QP) [5], etc. In such techniques, a simple quadratic function represents the machine curve that ignores the practical constraints; e.g., MFOs, POZs, and VPEs.…”

Section: Introductionmentioning

confidence: 99%

Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

et al. 2018

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This paper presents an optimization method for solving the Power Economic Dispatch (PED) problem of thermal generation units with multiple fuels and valve point loadings. The proposed optimizer is a variant of Differential Evolution (DE) characterized as a Stud Differential Evolution (SDE), which has been proposed earlier and implemented on a hydrothermal energy system. In SDE, an operator named Stud Crossover (SC) is introduced in the conventional DE during the trial vector updating process. In SC operator, a best vector gives its optimal information to all other population members through mating. The proposed algorithm's effectiveness to solve Multiple Fuel PED problem, with and without Valve Point Loading Effects (VPLEs), has been validated by testing it on 10 machine multiple fuel standard test systems having 2400 MW, 2500 MW, 2600 MW, and 2700 MW load demands. The results depict the strength of SDE over various other methods in the literature.

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“…For any specified load condition, ELD determines the power output of each plant which will minimize the overall cost of fuel needed to serve the system load .Many traditional optimization methods have been developed for solving the ELD problems. The major methods include Lagrange relaxation (LR) [1,2], linear programming (LP) [3], mixed integer quadratic programming (MIQP) [4], decomposition approach (DA) and dynamic programming (DP) [6]. However, most of these methods cannot lead optimal solutions due to their shortcomings in terms of problem formulation, solution accuracy and computational efficiency.…”

Section: Introductionmentioning

confidence: 99%