Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem

Gherbi, Yamina Ahlem; Lakdja, Fatiha; Bouzeboudja, Hamid; Gherbi, Fatima Zohra

doi:10.1007/s00521-019-04151-7

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…The valley hours are 24:00-8:00, whose price is 40 ﹩ /MWh. The relevant parameters of thermal power units [7] are shown in Table 2. The relevant parameters of electrochemical energy storage plants are shown in Table 3.…”

Section: Parameter Settingsmentioning

confidence: 99%

Optimization scheduling of power system under load winter and summer double-peak scenario

Fu,

Zheng,

et al. 2024

J. Phys.: Conf. Ser.

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In recent years, due to people’s need for a better life and the impact of extreme weather factors, the annual load curve shows a winter-summer double-peak pattern in summer and winter. In addition, due to the large-scale grid integration of renewable energy sources such as wind power, the uncertainty of its output has further increased the net load during the double-peak scenario, putting the power system under even greater pressure to supply electricity. In this paper, we study the day-ahead hierarchical optimal scheduling model of the power system under the double-peak scenario, considering the pumped-storage flexibility space and the extreme boundary flexibility demand. We simulate and analyze the impacts of different operation modes of the pumped-storage power plant on the scheduling results. The simulation shows that the proposed method can give full play to the effect of pumped storage peak shaving and valley filling and guarantee the reliability of the power supply under the double-peak scenario.

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Section: Parameter Settingsmentioning

confidence: 99%

Optimization scheduling of power system under load winter and summer double-peak scenario

Fu,

Zheng,

et al. 2024

J. Phys.: Conf. Ser.

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“…Non-convex ELD was solved by ACS in [34]. The authors have hybridized BA and FA for solving ELD and CEED in [35]. A self-adaptive version of Jaya algorithm was used for solving ELD in [35].…”

Section: Related Workmentioning

confidence: 99%

“…The authors have hybridized BA and FA for solving ELD and CEED in [35]. A self-adaptive version of Jaya algorithm was used for solving ELD in [35]. It was observed that the modified version of Jaya algorithm performed better that the basis Jaya algorithm and TLBO in solving the ELD problem.…”

Section: Related Workmentioning

confidence: 99%

“…VOLUME 4, 2016 ELD, ELD with valve point effect, CEED CTO [28] Dynamic ELD PSO TCO [29] ELD considering renewable sources Modified BA [30] ELD with losses QBA [31] Dynamic ELD considering renewable sources PSO [32] ELD with valve point effect QBA [33] ELD with losses PSO [34] Non convex ELD ACS [35] CEED Hybrid FA BA [74] ELD Jaya Algorithm [36] ELD with valve point effect, CEED MBA [37] ELD with valve point effect, ramp rates and prohibited zones Improved PSO [38] Convex and non-convex ELD SHO [39] ELD with ramp rates and prohibited zones WOA [40] Convex, non-convex and dynamic ELD MFO [41] ELD with valve point effect Hybrid GWO [42] ELD with losses Improved PSO [43] ELD with losses AEFA [44] Non-convex ELD ALO [45] ELD with losses ACSS [46] ELD with losses Adaptive PSO [47] ELD with losses Enhanced PSO [48] ELD with losses GWO [49] ELD with losses Enhanced BA [50] CEED QBA [51] Dynamic ELD, CEED Modified ABC [52] CEED Enhanced Cultural algorithm [53] Dynamic CEED Chaotic FA [54] Dynamic CEED TLBO [55] Smooth and non-smooth ELD WCA [56] ELD with renewable resources Hybrid BA [57] Multi-area ELD SSA [75] Multi-area ELD Hybrid Jaya TLBO [58] Multi-area ELD ISFS [76] CEED with renewable sources MOEA/D [59] Multi-area ELD MFO [60] CEED Improved TLBO [61] ELD with losses Modified GWO [62] Dynamic ELD GA PSO…”

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

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Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

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“…These approaches are very important but these approaches are very sensitive to early estimations and they are trapped in local minima [9]. Several optimization techniques, such as the gravitational search algorithm (GSA) [10], Cultural Algorithm (CA) [11], Optimization Without Penalty-based Optimization by Morphological Filter algorithm (OWP-based OMF) [12], the hybridization of the firefly algorithm and bat algorithm [13], cuckoo search algorithm [14], sequential hybridization of ETLBO and IPSO [15], genetic algorithms (GAs) [16], the backtracking search algorithm (BSA) [17], harmony search (HS) [18], simulated annealing (SA) [19], differential evolution (DE) [20], the modified marine predators algorithm (MMPA) [21], non-dominated sorting particle swarm optimization (NSPSO) [22], the modified multi-objective cross entropy technique (MMOCE) [23], particle swarm optimization (PSO) [24], teaching-learning-based algorithm (TLBA) [25], biogeography-based optimizer (BBO) [26], and ant colony optimization (ACO) [27][28], are examples of optimization methods which are applied to solve the EED problem. All the mentioned optimization approaches try to achieve better solutions utilizing various heuristic methods.…”

Section: Introductionmentioning

confidence: 99%

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

Hassan

Kamel

Shaikh

et al. 2023

IET Generation Trans & Dist

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The Economic and Emission Dispatch (EED) method is widely used to optimize generator output in a power system. The goal is to reduce fuel costs and emissions, including carbon dioxide, sulphur dioxide, and nitrogen oxides, while maintaining power balance and adhering to limit constraints. EED aims to minimize emissions and operating costs while meeting power demands. To solve the multi‐objective EED problem, the supply‐demand optimization (SDO) algorithm is proposed, which employs a price penalty factor approach to convert it into a single‐objective function. The SDO algorithm uses a swarm‐based optimization strategy inspired by supply‐demand mechanisms in economics. The algorithm's performance is evaluated on seven benchmark functions before being used to simulate the EED problem on power systems with varying numbers of units and load demands. Established algorithms like the Grey Wolf Optimizer (GWO), Moth‐Flame Optimization (MFO), Transient Search Optimization (TSO), and Whale Optimization Algorithm (WOA) are compared to the SDO algorithm. The simulations are conducted on power systems with different numbers of units and load demands to optimize power generation output. The numerical analyses demonstrate that the SDO technique is more efficient and produces higher quality solutions than other recent optimization methods.

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Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem

Cited by 15 publications

References 24 publications

Optimization scheduling of power system under load winter and summer double-peak scenario

Optimization scheduling of power system under load winter and summer double-peak scenario

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

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