Combined economic emission dispatch with new price penalty factors

Ziane, Ismail; Farid, Benhamida; Graa, Amel; Salhi, Y.

doi:10.1109/intee.2015.7416864

Cited by 7 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 7 lists the optimal results obtained using different methods for each load demand level. The SDO algo-rithm outperforms the four comparative algorithms (GWO, MFO, TSO, and WOA) and other previously published techniques such as SA [41] and SCA [40] for all demand values. The table presents the power generation from each thermal generator (P1, P2, and P3), total power, power loss, fuel cost, SO 2 emission, NO x emission, and total cost for case study 2.…”

Section: 22mentioning

confidence: 80%

“…Its ability to efficiently explore and exploit the search space, avoid local optima, and converge at a fast rate makes it a powerful tool for solving a wide range of optimization problems in various fields. The main contributions of this paper could be summarized as: Proposing a new optimizer called SDO for solving economic emission dispatch problems of power systems considering the price penalty factor and variable load demand levels. Demonstrating the effectiveness of SDO in three different scenarios involving three, five, and six units, respectively, where it outperformed many existing algorithms, including GWO, MFO, TSO, and WOA, as well as other established algorithms such asGA [37], PSO [37], quantum‐inspired PSO (QPSO) [38], the firefly algorithm (FA) [39], sine cosine algorithm (SCA) [40], SA [41], Lagrange's method (LR) [42], PSO [43], SA [44], the quantum‐behaved bat algorithm (QBA) [29], modified biogeography‐based optimization (MBO) [45], the grasshopper optimization algorithm (GOA) [46], quantum‐inspired tidal FA (QITFA) [47], and the 4th chaotic artificial ecosystem‐based optimization (CAEO4) [48]. Providing extensive analysis and comparison of the outcomes obtained from SDO with various established algorithms, showing that SDO consistently delivers better results in terms of both accuracy and efficiency. Implying significant implications for power plant management, as the SDO technique has the potential to optimize power plant management and improve energy efficiency, ultimately leading to resource savings and cost reductions. …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: 22mentioning

confidence: 80%