Least cost generation expansion planning with wind plant using differential evolution algorithm

Bhuvanesh, A.; Karunanithi, K.; Karman, Steve L.

doi:10.1109/iccpct.2014.7055025

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…The results of [25,[35][36][37] showed that the optimization results using DE still has a possibility to get stuck in a local optimum value. The value of optimal solution depends on population size, mutation value, and crossover rate.…”

Section: Differential Evolutionmentioning

confidence: 97%

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A Review of Potential Method for Optimization of Power Plant Expansion Planning in Jawa-Madura-Bali Electricity System

Budi

Sarjiya

Hadi

2017

CST

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The paper contains a literature review to obtain an optimization method that potentially can be used to optimize power plant expansion of Jawa-Madura-Bali (Jamali) power system in 2015-2050. An optimization model that can represent auction process and direct appointment of IPP by considering the long term period (multi-period framework) and multi-objective function (economical, reliable, and environmentally friendly), is needed. Based on the literature review that has been done, it is obtained the method potentially can be used for Jamali system. The method is a game theory with multi-period, bi-level and multi-objective optimization method. Game theory is used to represent the auction process and direct appointment of IPP. Multi-period is used to represent the long term period from 2015-2050. Multi-objective optimization method is used to represent the aspects of cost, reliability, and CO2 emission which are considered in the optimization process

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Section: Differential Evolutionmentioning

confidence: 97%

“…After the mutation process, the crossover has been done based on the value of crossover rate (CR) [25,37]. The crossover process follows equation (2) [25,35,36].…”

Section: Differential Evolutionmentioning

confidence: 99%

A Review of Potential Method for Optimization of Power Plant Expansion Planning in Jawa-Madura-Bali Electricity System

Budi

Sarjiya

Hadi

2017

CST

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“…Some selection criteria that have been considered by researchers are overall investment cost, reliability, renewable fraction, etc. Examples of the meta-heuristic techniques in GEP literature include genetic algorithm, 7,12,29,45,74,91,95,118,120,[150][151][152][153][154][155][156][157] PSO, 89,95,136,[158][159][160][161][162] simulated annealing, 29,95,118 differential evolution, [49][50][51][52]55,163 ant colony optimisation, 29 artificial immune system, 87,122 frog leaping algorithm, 119,121,164 bee algorithm, 165,166 fuzzy and expert system, 128,129,[167][168][169][170] and tabu search 29,95,154 ; methods such as harmony search, biogeography, and hill climbing have found application in TEP and offer promising applications in GEP problems.…”

Section: Meta-heuristic Techniquesmentioning

confidence: 99%

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

2019

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Summary Generation expansion planning (GEP) is a power plant mix problem that identifies what, where, when, and how new generating facilities should be installed and when old units be retired over a specific planning horizon. GEP ensures that the quantity of electricity generated matches the electricity demand throughout the planning horizon. This kind of planning is of importance because most production and service delivery is dependent on availability of electricity. Over the years, the traditional GEP approaches have evolved to produce more realistic models and new solution algorithms. For example, with the agitation for green environment, the inclusion of renewable energy plants and energy storage in the traditional GEP model is gradually gaining attention. In this regards, a handful of research has been conducted to identify the optimal expansion plans based on various energy‐related perspectives. The appraisal and classification of studies under these topics are necessary to provide insights for further works in GEP studies. This article therefore presents a comprehensive up‐to‐date review of GEP studies. Result from the survey shows that the integration of demand side management, energy storage systems (ESSs), and short‐term operational characteristics of power plants in GEP models can significantly improve flexibility of power system networks and cause a change in energy production and the optimal capacity mix. Furthermore, this article was able to identify that to effectively integrate ESS into the generation expansion plan, a high temporal resolution dimension is essential. It also provides a policy discussion with regard to the implementation of GEP. This survey provides a broad background to explore new research areas in order to improve the presently available GEP models.

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“…Based on the previous researches by the authors, the following factors are considered in the research: The power plants proposed in “The Vision Tamil Nadu 2023” by Tamil Nadu government are North Chennai Thermal Power Station Stage III and Uppur Thermal Power Plant, Udangudi Thermal Power Station Stage I and II, PPN Gas Power Generation Pvt. Ltd., GMR Vasavi Oil Power Plant, Kudankulam Nuclear Power Plant, Bhavani Barrage, wind, Adani Solar, Biomass, and Kundah pumped storage.…”

Section: Gep Model For Tamil Nadumentioning

confidence: 99%

“…Based on the previous researches by the authors, 7,8,12,14,44 the following factors are considered in the research: 4. The fuel mix ratio for coal 1, coal 2, gas, oil, nuclear, hydro, wind, solar, and biomass are considered as 0% to 30%, 0% to 30%, 0% to 40%, 10% to 40%, 20% to 60%, 30% to 60%, 20% to 30%, 20% to 30%, and 20% to 30% for the base case.…”

Section: Assumptions Made In This Researchmentioning

confidence: 99%

Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission

Bhuvanesh¹,

Christa²,

Subramanian³

et al. 2018

Int Trans Electr Energ Syst

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Summary Developing countries like India necessitates installation of new power plants to meet the elevating electricity demand. Several single‐objective mathematical formulations are modeled and solved previously for short‐term and long‐term power generation expansion planning aiding power system planners to ensure optimal decision. The input data, such as forecasted demands, economic and technical data of the generating units are numerous and unmanageable to be analyzed manually. Modern power systems have faced major challenge in solving multiple objectives such as least cost and minimal greenhouse gas emissions simultaneously. This paper presents an application of multiobjective differential evolution algorithm to multistage, multiobjective generation expansion planning problem for Tamil Nadu, a state in India. The multiobjective generation expansion planning problem has been solved for short‐term (6 years) and long‐term (12 years) planning horizon by considering 8 different cases and also compared with Nondominated Sorting Genetic Algorithm version II.

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Least cost generation expansion planning with wind plant using differential evolution algorithm

Cited by 12 publications

References 23 publications

A Review of Potential Method for Optimization of Power Plant Expansion Planning in Jawa-Madura-Bali Electricity System

A Review of Potential Method for Optimization of Power Plant Expansion Planning in Jawa-Madura-Bali Electricity System

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission

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