Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm

Gong, Dunwei; Zhang, Yong; Qi, Cheng-liang

doi:10.1016/j.ijepes.2009.11.017

Cited by 164 publications

(70 citation statements)

References 19 publications

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“…In this paper, the proposed algorithm is carried out on the IEEE 30-bus test system, and the relevant parameters of the system are found in [16]. The total load of the system is 283.4 MW and the upper and lower bounds of the output of the generator sets, the fuel cost coefficient and the pollutant discharge coefficient are listed in Table 1.…”

Section: Single Objective Optimizationmentioning

confidence: 99%

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

He¹,

Yapeng²,

Jiawei³

et al. 2017

IJHIT

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Section: Single Objective Optimizationmentioning

confidence: 99%

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

He¹,

Yapeng²,

Jiawei³

et al. 2017

IJHIT

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“…The environmental load dispatch problem minimizes the emissions of environment pollutants including sulphur dioxide (SO 2 ) and nitrogen oxides (NO x ) [15]. A quadratic polynomial formulation is associated with an exponential term to model the emissions as…”

Section: Dynamic Environmental Load Dispatch Modelmentioning

confidence: 99%

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Yang

Niu

et al. 2014

J. Mod. Power Syst. Clean Energy

107

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Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

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“…(6) and Eq. (8). If there is any violation in constraint, the repair scheme given in Figure 6 is performed to overcome the violation.…”

Section: Repair Strategy For Constraint Managementmentioning

confidence: 99%

Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem

Koodalsamy¹,

Simon²

2013

Turk J Elec Eng & Comp Sci

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Abstract:The economic dispatch (ED) problem is one of the important optimization problems in power system operation. Recently the power system has stressed the need for reliable, nonpolluting, and economic operation. Hence, 3 conflicting functions of reliability, emission, and fuel cost are considered in the objective function of the proposed ED problem. The problem is formulated as a nonsmooth and nonconvex problem when the valve-point effects of thermal units are considered in the proposed reliable emission and economic dispatch (REED) problem. This paper presents a multiobjective optimization methodology for solving the newly developed REED problem using a fuzzified artificial bee colony algorithm. The artificial bee colony algorithm is used to schedule the optimal dispatch and fuzzy membership approach is used to find the best compromise solution from the Pareto optimal set. The methodology is validated on an IEEE 30-bus system and 3-, 6-, 10-, 26-, and 40-unit systems and the results are compared with the existing literature.The results clearly show that the proposed method is able to produce well-distributed Pareto optimal solutions when compared with other methods reported in the literature.

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Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm

Cited by 164 publications

References 19 publications

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem

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