Dynamic economic dispatch using hybrid metaheuristics

Abstract

Dynamic economic dispatch problem or DED is an extension of static economic dispatch problem or SED which is used to determine the generation schedule of the committed units so as to meet the predicted load demand over a time horizon at minimum operating cost under ramp rate constraints and other constraints. This work presents an efficient hybrid method based on particle swarm optimization (PSO) and termite colony optimization (TCO) for solving DED problem. The hybrid method employs PSO for global search and TCO for local search in an interleaved mode towards finding the optimal solution. After the first round iteration of local search by TCO, the best local solutions are considered by PSO to update the schedules globally. In the next round, TCO performs local search around each solution found by PSO. This paper reports the methodology and result of application of PSO-TCO hybrid to 5-unit, 10-unit and 30-unit power dispatch problems; the result shows that the PSO-TCO (HPSTCO) gives improved solution compared to PSO or TCO (when applied separately) and also other hybrid methods.

Various demand side management techniques and its role in smart grid–the state of art

“…Distributed scheduling is used for optimization in [27]. Heuristic and metaheuristic based optimization is used in [28], [29]. A filter- based weighted average price forecast and a combination of tilting block rates with real-time pricing are discussed [30].…”

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

“…It was observed that CTO performs better than other metaheuristics such as TLBO, DE, GA, PSO etc. Dynamic ELD was solved by hybrid PSO TCO [28]. The hybridization of PSO and TCO favoured faster convergence and produced better quality of solutions.…”

“…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…”

Static and dynamic environmental economic dispatch using tournament selection based ant lion optimization algorithm

“…A new modified artificial bee colony algorithm (Secui, 2015b), particle swarm optimization and genetic algorithm techniques (Hussain et al, 2019), chaotic improved harmony search algorithm Several optimization strategies and procedures have been utilized to solve the dynamic economic dispatch (DED) problem with complex objective functions or constraints since the topic was proposed in the 1980s (Rezaie et al, 2019). Literature survey shows to solve this problem using conventional algorithms (Kamli and Amraee, 2017), Dynamic economic dispatch using hybrid meta heuristics (Santra et al, 2020) and Simulated Annealing method (Bouddou et al, 2020) for non-smooth or non-convex cost functions, the majority of these strategies are ineffective. To solve the DED problem, a variety of heuristic optimization methods have been used, including Ant based optimization (Secui, 2015a;Vennila and Rajesh, 2022), Dragon fly based optimization (Chandrasekaran et al, 2021), Hybrid HB-SA algorithm (Vennila et al, 2013), differential evolution techniques (Li et al, 2022;Zou and Gong, 2022), harmony search algorithm (Rezaie et al, 2019), and bee swarm based optimization algorithm (Hussain et al, 2019).…”