Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system

Lee, Jia-Chu; Lin, Whei-Min; Liao, Gwo-Ching; Tsao, Ta‐Peng

doi:10.1016/j.ijepes.2010.08.014

Cited by 175 publications

(73 citation statements)

References 20 publications

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“…In the literature [3][4][5][6][7][8][9][10][11][12][13], an optimal operation model of wind power with the goal of generation cost or fuel cost of conventional generator is constructed.In order to reflect the randomness of wind power, the literature [3] uses the trapezoidal membership function to apply the fuzzy theory to the problem of optimal operation model of wind power, which solves the problem that the prediction accuracy of wind power is low. Then, it is equivalent to seeking the problem of maximizing satisfaction index; the idea of descending search is introduced into particle swarm optimization to improve the convergence of the algorithm, the example show that, considering the economic scheduling problem of wind power uncertainty, the fuzzy theory can be used to express the wishes of decision makers.…”

Section: Single-objective Modelmentioning

confidence: 99%

“…In [11], the optimal operation model aiming at the purchase cost for the power market is constructed, which makes the optimal operation of the wind power system in the power market environment more reasonable and economical, and has high credibility. In [12][13], the environmental cost is regard as a goal, and a membership function is defined to describe the relationship between the system safety level and wind power penetration or between the system safety level and operating costs.…”

Section: Single-objective Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Optimal Operation of Power System Including Wind Farms

Yang¹,

Wang²

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. In order to comprehensively generalize the research status quo, achievements as well as deficiencies on the optimal operation of power system including wind farms, this paper introduced the optimal operation models for power system including wind farms, according to the difference of optimization objective, the models are divided into the single objective model and the multi-objective model. A variety of traditional optimization algorithms and artificial intelligence optimization algorithms common used were summarized, including dynamic programming, Lagrangian relaxation method, genetic algorithm, particle swarm optimization, ect, the principle, advantages and disadvantages of each algorithm were analyzed in this paper. Finally, some suggestions are given for further studies on the optimal operation of wind power.

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Section: Single-objective Modelmentioning

confidence: 99%

Section: Single-objective Modelmentioning

confidence: 99%

A Review on Optimal Operation of Power System Including Wind Farms

Yang¹,

Wang²

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

View full text Add to dashboard Cite

show abstract

“…Moreover, generating system's constraints such as prohibited operating zones and ramp rate limits increases the complexity of the ELD problems. In previous years, as an alternative to the conventional mathematical approaches, different heuristic methods have been implemented on ELD problem such as Differential Evolution [1], Evolutionary Programming [2], Genetic Algorithm [3,4], Firefly Algorithm [5,6], Harmony Search Algorithm [6,7], Artificial Bee Colony [8], Bacterial Foraging Optimization [5,9,10], Simulated Annealing [6,11] and Particle Swarm Optimization [8,[11][12][13] etc.…”

Section: Introductionmentioning

confidence: 99%

“…Lee has proposed an improved GA called Quantum Genetic Algorithm (QGA) in [3], for solving economic dispatch algorithm with tie line constraints and has also included the case of wind power generation. After comparing it with previous techniques, it has been proved to be the best among those algorithms.…”

Section: Introductionmentioning

confidence: 99%

Solving convex and non-convex static and dynamic economic dispatch problems using hybrid particle multi-swarm optimization

et al. 2017

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Original scientific paper Economic Load Dispatch problem has been previously solved successfully with swarm techniques. However, power systems with complex behaviours still await a robust algorithm to be developed for their optimization more precisely. Economic Dispatch problem with constraints such as generator limits, total power demand, ramp rate limits and prohibited operating zones, makes the problem more complicated to solve even for global techniques. To overcome these complications, a new algorithm is proposed called Hybrid Particle Multi-Swarm Optimization (HPMSO). The proposed algorithm has a property of deep search with quite fast response. Convex and Non-convex cost functions along with equality and inequality constraints have been used to evaluate performance of proposed approach. Moreover, Dynamic Economic Dispatch cases have also been included in statistical studies to test the proposed approach even in real time. Different case studies have been accomplished using standard test systems of Static and Dynamic Economic Dispatch. Comparison of proposed approach and previous techniques show that the proposed algorithm has a better performance. Keywords: convex and non-convex problems; economic dispatch; Hybrid Multi-Particle Swarm Optimization (HPMSO); valve-point effectsRješavanje konveksnih i ne-konveksnih statičkih i dinamičkih problema ekonomične otpreme primjenom više rojne optimizacije Izvorni znastveni članak Problem ekonomične otpreme opterećenja ranije se uspješno rješavalo tehnikama rojeva. Međutim, elektroenergetski sustavi složenog ponašanja još uvijek čekaju razvoj robustnog algoritma za njihovu precizniju optimizaciju. Problem ekonomične otpreme uz ograničenja kao što su ograničenja generiranja, ukupna potražnja energije, ograničenja brzine pristupa i zabranjene operativne zone, čini problem složenijim za rješavanje čak i globalnim tehnikama. Za prevladavanje tih komplikacija, predlaže se novi algoritam pod nazivom Hybrid Particle Multi-Swarm Optimization (HPMSO). Predloženi algoritam ima svojstvo dubokog pretraživanja s prilično brzim odzivom. Vrednovanje učinkovitosti predloženog pristupa ispitivalo se konveksnim i nekonveksnim funkcijama troškova uz ograničenja jednakosti i nejednakosti. Štoviše, slučajevi dinamičke ekonomične otpreme također su bili uključeni u statistička istraživanja za testiranje predloženog pristupa čak i u stvarnom vremenu. Različite studije slučaja provedene su korištenjem standardnih sustava za ispitivanje statičke i dinamičke otpreme. Usporedba predloženog pristupa i prethodnih tehnika pokazala je da se predloženim algoritmom postižu bolji rezultati.Ključne riječi: ekonomična otprema; Hybrid Multi-Particle Swarm Optimization; konveksni i ne-konveksni problemi; valve-point učinci

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“…The traditional multi-objective optimization method is to convert the multi-objective optimization problem into a single objective optimization problem with coefficients in a weighted way, and then use the linear or nonlinear optimization method to solve the problem. With the development of computer and the improvement of artificial intelligence technology, intelligent algorithm is widely used in power system optimization dispatching problem, the quantum computation and genetic algorithm are combined to form a quantum genetic algorithm, which is used to solve the economic dispatch problem (as suggested in [3]), genetic algorithm and bacterial foraging algorithm for solving the economic dispatch problem in [4]. As pointed out in [5], it is a comprehensive improvement of the basic GA algorithm, which is based on the operation of the chaos operator and the quantum operator ; what's more, it has been applied into the environmental economic dispatch of micro grid and the result demonstrates pretty well.…”

Section: Introductionmentioning

confidence: 99%