Gencos wind–thermal scheduling problem using Artificial Immune System algorithm

Lakshmi, K.; Vasantharathna, S.

doi:10.1016/j.ijepes.2013.06.036

Cited by 35 publications

(16 citation statements)

References 29 publications

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“…An Artificial Immune System approach for solving generation scheduling problem of a Genco comprised of thermal and wind energy systems is presented in [15]. This wind-thermal scheduling problem determines the time of instant to start-up and shut-down the generating units over a scheduling period, while satisfying system and generators constraints.…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization

Reddy

2016

Electr Eng

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This paper proposes an optimal scheduling/ allocation of energy and spinning reserves for a wind-thermal power system. There is a considerable need for the renewable energy resources in the modern power system; therefore, in this paper, wind energy generators are used. Here, two different market clearing models are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators, and demand/consumers. The stochastic behavior of wind speed and wind power generation is represented by the Weibull probability density function. The objective function considered in this paper includes cost of energy provided by conventional thermal and wind generators, cost of reserves provided by conventional thermal generators and load demands. It also includes costs due to under-estimation and over-estimation of available wind power generation. Clustered adaptive teaching learning based optimization algorithm is used to solve the proposed optimal scheduling problem for both conventional and wind-thermal power systems considering the provision for spinning reserves. To show the effectiveness and feasibility of the proposed frame work, various case studies are presented for two different test systems.

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Section: Introductionmentioning

confidence: 99%

Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization

Reddy

2016

Electr Eng

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“…In our case study, this limitation is set to 1°C more or less than the desired temperature. Finally, due to physical factors, the loads and the load sheddings are both limited by their maximum and minimum constraints (12) and (13).…”

Section: Home Energy Management Problemmentioning

confidence: 99%

“…However, to the authors' knowledge, AIS has only been implicated in some preliminary achievements in power management, such as solving power supply problems, or electrical reconfigurations. For example, in [12], an AIS is used to control thermal units in residential buildings, and in [13], the authors optimize a wind energy-generating system also with an AIS.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

et al. 2018

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Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints.

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“…The second solution for uncertainty reduction in renewable units including renewable resources is to coordinate other energy resources which are quite expensive, but available and more reliable, such as pump-storage unit, hydro unit, gas turbines, combined cycle power plants, and energy storage batteries [10][11][12][13][14][15][16][17][18][19][20][21]. However, the share of these energy sources should diminish for many reasons [10].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the expected profit is limited by FR and the required reserve is determined due to the error prediction in WF power generation. In [14], the coordinated planning problem of WF and thermal power plants are solved by artificial immune optimization method. This optimization method is implemented on a system including ten thermal power plants and two wind farms (WFs).…”

Section: Introductionmentioning

confidence: 99%

Scenario-based stochastic optimal operation of wind/PV/FC/CHP/boiler/tidal/energy storage system considering DR programs and uncertainties

et al. 2018

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Gencos wind–thermal scheduling problem using Artificial Immune System algorithm

Cited by 35 publications

References 29 publications

Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization

Optimal scheduling of wind-thermal power system using clustered adaptive teaching learning based optimization

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

Scenario-based stochastic optimal operation of wind/PV/FC/CHP/boiler/tidal/energy storage system considering DR programs and uncertainties

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