Application of a Novel Modified Hybrid Algorithm for Solving Dynamic Economic Dispatch Problem with Practical Constraints

Hamed, M.N.G.; Mahdad, Belkacem; Srairi, K.; Mancer, Nabil

doi:10.15598/aeee.v16i4.2877

Cited by 3 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (Hamed et at., 2018), a hybrid method called FAPSOTVAC (a combination of Firefly Algorithm and Time Varying Acceleration based Particle Swarm Optimization) was proposed to solve dynamic economic dispatch problem to minimize the total fuel cost considering all practical constraints. It was an extension of static economic dispatch by including highly complex and nonlinear constraints of generating units such as valve point effect, prohibited zones, ramp rate limits and total power losses.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Sangob

Sirisumrannukul

2021

Front. Energy Res.

View full text Add to dashboard Cite

There has been a growing presence of electric vehicles in many countries including Thailand, where many forms of incentives have been provided to build integrated infrastructure, and to encourage drivers to switch to electric vehicles (EVs). Because the immediate entry of EVs unavoidably can alter household load profiles, reinforcement on the existing system based on traditional planning may not be sufficient and can introduce over or under capital and operating expenditure over the time horizon. Therefore, if distribution systems are unreadily prepared for such an uptake, three obvious problems can be expected: 1) voltage regulation, 2) overloads of the distribution feeders and the distribution transformers, and 3) high energy loss. In this paper, an activity-based, time-sequential Monte Carlo Simulation algorithm was comprehensively developed for uncontrollable and smart charging, given annually updated information of EV locations and number of EVs, their energy consumption, hourly average vehicle speed, number of daily trips, travel distance per trip, size of EV batteries, time to arrive home and time to leave home. Minimizing the annual sum of investment and operating costs over a planning period could then be sequentially solved by a Particle Swarm Optimization (PSO) algorithm. The results from a practical 122-bus, 24 kV/400 V distribution system with different scenarios of uncontrollable and smart charging show that the sequential optimization embedded with deterministic decision can help improve customer voltage profile, keep feeder and transformer loading within acceptable operating limits and offer significant cost savings from energy loss. As far as a large number of low-voltage networks, and the associated large sum of cost savings are concerned, the proposed planning framework is practical to be applied and expected to be served as a new guideline for future implementation in Thailand.

show abstract

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Optimal Sequential Distribution Planning for Low-Voltage Network With Electric Vehicle Loads

Sangob

Sirisumrannukul

2021

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Among recent developments are two hybrid techniques, BBO-PSOTVAC and FA-PSOTVAC, developed in 2018 by Hamed et al [27], combining firefly algorithm (FA) and biogeography-based optimization (BBO) with time-varying acceleration-based particle swarm optimization (PSOTVAC) to improve the solution of DED. In the same year, Pan et al [28] solved the DED problem with VPL using a hybrid technique MILP-IPM involving mixed-integer linear programming (MILP) and interior point method (IPM).…”

Section: Earlier Hybridsmentioning

confidence: 99%

“…In Table 13, output of proposed HPSTCO for test case 3 is compared with the output given (for the same case) by other recently published hybrid methods, namely ADE-SA [34], hybrid MILP-IPM [28], FA-PSOTVAC [27], BBOSB [26], HIGA [25], LDISS [24], hybrid GSA-SQP [22], hybrid DE [19], EPSO-GM [33] or hybrid PSO-SQP [17]. The generation cost yielded by these methods (taken in the same order) are 1016412.81$/day,…”

Section: Test Case 3: 10-unit System Without Transmission Lossmentioning

confidence: 99%

“…The fuel cost obtained by the proposed method is 1,051,964.4$/day. In Table 16, the simulation result of proposed HPSTCO is compared with other recently published hybrid methods, namely BBOSB [29], FA-PSOTVAC [27], BBPSO [26], HIGA [25], LDISS [24], HHS [15] and hybrid EP-SQP [17], which are 3,054,190.6032$/day, 3,105,700$/day, 3,062,144$/day, 3,055,435.068$/day, 3,051,259.9486$/day, 3,057,313.39$/day and 3,159,204$/day, respectively. In comparison, the cost yielded by HPSTCO is 3,051,105.813 $/day only.…”

Section: Test Case 4: 30-unit Systemmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic economic dispatch using hybrid metaheuristics

Santra¹,

Mukherjee²,

Sarker³

et al. 2020

Journal of Electrical Systems and Inf Technol

View full text Add to dashboard Cite

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.

show abstract