Application of optimization algorithms to generation expansion planning problem

Bhuvanesh, A.; Christa, S.T. Jaya; Kannan, S.; Pandiyan, M. Karuppasamy; Gangatharan, K.

doi:10.3233/jifs-169681

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…n×r×T . For the incommensurability between operation and maintenance cost indicator, comprehensive energy efficiency indicator and environmental indicator, according to the method of [18], the indicators can be normalized by means of relative state eigenvalues. The formula is as follows:…”

Section: Acquisition and Normalization Of Uncertain Indicator Decisiomentioning

confidence: 99%

“…The output power of a photovoltaic power station is determined by factors such as luminous intensity, photovoltaic array area, and photoelectric conversion efficiency. The specific calculation formula is as Formula (18).…”

Section: Renewable Energy Power Generation Modelmentioning

confidence: 99%

“…On the other hand, current common GEP models including linear programming, nonlinear programming, multi-objective programming, integer programming, multilevel programming, and dynamic programming [13][14][15][16][17][18] are based on some important assumptions. One of the assumptions is that the three elements in decision analysis-alternative scheme, constraint space, and utility function-can be expressed with absolute accuracy and good mathematics, so the solution space is clear and the result of the solution also strives towards "optimal".…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources

et al. 2019

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In generation expansion planning, sustainable generation expansion planning is gaining more and more attention. Based on the comprehensive consideration of generation expansion planning economics, technology, environment, and other fields, this paper analyzes the sustainable development of power supply planning evaluation indicators and builds a multi-objective generation expansion planning decision model considering sustainable development. According to the target variables in the model, the variables such as attribute variables are divided into different subsets, and the logical relationship analysis method between different nodes is obtained based on Dynamic Bayesian network theory, which reduces the complexity of the planning model problem. The application examples show the feasibility and effectiveness of the proposed model and the solution method.

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Section: Acquisition and Normalization Of Uncertain Indicator Decisiomentioning

confidence: 99%

Section: Renewable Energy Power Generation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources

et al. 2019

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“…The applied model for expanding generation capacity is national electricity market optimizer (NEMO) through the covariance matrix adaptation evolution strategy (CMA-ES) algorithm considered for all candidates in the case study of Indonesia's Java-Bali. Differential evolution (DE), opposition-based differential evolution (ODE) and self-adaptive differential evolution (SaDE) algorithms are considered in [16] to find the optimized plan for generation expansion of the case study in Indian state Tamil over a 6-year and a 12-year period. The key point about this study is considering the penalty costs on emissions and new technologies for generating the electricity and comparing them in different strategies.…”

Section: Introductionmentioning

confidence: 99%

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Jalalzad¹,

Yektamoghadam

Haghighi

et al. 2022

Energies

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In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

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“…After all the above aspects are analyzed, it is necessitated to modify DE in a new direction. Recently, DE and its variants such as SaDE and ODE have been utilized to solve generation expansion planning problem 16 . Hence, in this study, the applications of DE and its variants have been presented to find the optimal schedule of the DGs.…”

Section: Introductionmentioning

confidence: 99%

Implementation of differential evolution algorithm and its variants for optimal scheduling of distributed generations

Shilaja

2020

Int J Communication

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Summary Modern power systems are being shifted its attention towards distributed generations (DGs). DGs have allured more concern as an auspicious opportunity with considerable economic profits. To minimize the power generation costs of DGs, day‐to‐day operation scheduling is essential. The role of this study is to offer an optimal operation schedule for DG with several energy sources including renewable energy sources (RES), considering economic facets. In order to achieve the cost minimization along with optimal scheduling, an objection function has been formulated and solved using the optimization algorithms. This study aims to present the applications of differential evolution (DE) algorithm and its variants such as opposition‐based differential evolution (ODE), self‐adaptive differential evolution (SaDE), improved differential evolution (IDE), and cultivated differential evolution (CuDE) for scheduling DG optimally. A scheme for optimal scheduling of thermal, wind power, and solar PV generators has been evaluated. The simulations have been carried out on IEEE 14 bus system, IEEE 30 bus system, IEEE 57 bus system, and Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO), as a real part of 62 bus Indian utility system (IUS). The novelty of this study lies in simulating a real‐time system for solving optimal scheduling problem, in that way helping decision makers to choose the optimal operation points. The results indicate that the SaDE outperformed other DE variants by giving the best fitness value and convergence rate.

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Application of optimization algorithms to generation expansion planning problem

Cited by 14 publications

References 17 publications

Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources

Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Implementation of differential evolution algorithm and its variants for optimal scheduling of distributed generations

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