Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Nawaz, Usman; Malik, Tahir Nadeem; Ashraf, ⋅Muhammad Mansoor

doi:10.1002/2050-7038.12238

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…Considering the incorporation of RESs into GEP problem, a whale optimization algorithm was carried out with adjustment strategies to decrease the GEP problem search space such as MIIGP, VMP, and PFA [49]. However, wind and solar output power have been studied at certain scenarios of penetration and emission reduction levels.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, there are other PPS methods that are more efficient for estimating the variable costs and the reliability indies with smaller computational time. In this context, the EEF method is one of PPS methods, but it depends on the greatest common factor for all adding capacities of existing and candidate units [42,49]. Also, the ELDC is another method for any additional capacity in the planning [43].…”

Section: Objective Functionmentioning

confidence: 99%

“…The reliability indices such as LOLP and EENS are calculated based on existing and candidate units by PPS methods. FOR of wind plants are calculated, in this study, by sliding window technique as [49,50]. It is assumed that FOR values for wind energy are constant over the study period.…”

Section: Proposed Gep Considering Wind Energy Longterm Uncertaintymentioning

confidence: 99%

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Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Abou El-Ela,

El-Sehiemy,

Shaheen

et al. 2024

Neural Comput & Applic

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Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

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Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Abou El-Ela,

El-Sehiemy,

Shaheen

et al. 2024

Neural Comput & Applic

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“…The proposed technique was applied to 5-unit, 10-unit, and 15-unit test systems [49]. The whale optimization algorithm (WOA) [50] has been consistently applied to power system optimization problems such as generation expansion planning [51], unit commitment [52], optimal reactive power dispatch [53], optimal sizing of renewables [54], variable renewable energy resources [37], radial basis function and improved Hopfield neural network based ED [38,39], and others. Recently, the whale optimization algorithm (WOA) was applied to solve a DEED problem while considering the 5-unit test system.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel FFA‐GWO‐based optimization paradigm for solving a dynamic economic emission dispatch in power plants

Mughal,

Nawaz,

Ahmed

et al. 2023

IET Renewable Power Gen

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Thermal power plants are the primary components of the electrical energy production industry. These plants come with certain challenges such as the production of harmful gases causing environmental pollution. This adds to global warming issues that are dangerous for the earth's ecosystem. Another crucial aim is the least cost operation of these power plants. Combining both of these results in the formulation of the Dynamic Economic Emission Dispatch (DEED). This is a multi‐objective, constrained, dynamic, complex, and non‐linear optimization problem. Traditional methods are insufficient to undertake the complexities of DEED due to challenges such as high computational requirements and local optimal solutions. Hence, for obtaining an optimal or near‐optimal solution to such a complex problem, a metaheuristic technique is greatly needed. Metaheuristic techniques offer several benefits to solving complex problems such as being robust, efficient, and overall, less affected by an increase in problem size. In this work, a novel hybrid Firefly Optimization Algorithm (FFA)—Grey Wolf Optimization Algorithm (GWO) has been proposed to solve the DEED problem. The complexity of the DEED problem is undertaken by involving constraints like power limits, power balance, and transmission losses. FFA‐GWO was applied on 5, 10, and 15‐unit standard test systems to meet 24‐h load demands. The optimal costs obtained in 5, 10, and 15‐unit standard test systems are 32264$, 1394212.615$, and 465373.448$ respectively. The comparison of results shows that the proposed framework surpasses the other methodologies in terms of reduced fuel cost and emissions.

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“…Generation expansion planning (GEP) provides an investment plan for constructing different generating units including both conventional generators as well as renewable generation resources at minimum cost over the planning horizon. 1,2 The installation of the new generating units planned by GEP puts a stress on the transmission lines as the extra generated power has to be transmitted to load centers through transmission corridors. Transmission expansion planning (TEP) aims to locating optimal candidate transmission lines to be installed to mitigate network congestion.…”

Section: Introductionmentioning

confidence: 99%

Impact assessment of aco‐optimizeddynamic line rating and transmission switching topology on network expansion planning

Numan

Feng

Abbas

et al. 2020

Int Trans Electr Energ Syst

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Power system planning needs regular investment in transmission area to cope with the increasing load, network congestion, and large-scale integration of renewable generation. With the growth of renewable energy resources integration, electric utilities are faced with an increasingly complex operating environment. Utilities are in need of a series of cost-effective technologies which allow them to operate power systems more economically and reliably. Optimal transmission switching (OTS) and dynamic line rating (DLR) are such cost-effective technologies which offer potential solution to improve power system operation and reliability. This article develops a co-optimized network expansion planning (CONEP) model to devise an optimal investment plan for building new transmission lines and generating units while simultaneously incorporating OTS and DLR topologies. The optimization problem using CONEP is modeled as a mixedinteger linear programming problem. The objective of the optimal investment

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Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

Cited by 14 publications

References 29 publications

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

A novel FFA‐GWO‐based optimization paradigm for solving a dynamic economic emission dispatch in power plants

Impact assessment of aco‐optimizeddynamic line rating and transmission switching topology on network expansion planning

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