Solving the economic dispatch by new hybrid algorithm

• Autorzy: Naama Bakhta , Dahmani Kaouther , Abrouche Amel , Bouzeboudja Hamid

Identyfikatory

DOI

10.15199/48.2024.05.30

Warianty tytułu

PL

Rozwiązanie wysyłki ekonomicznej za pomocą nowego algorytmu hybrydowego

• Języki publikacji: EN

Abstrakty

EN

The problem of economic dispatch is the minimization of the total cost of production by satisfying the demand of the load. The resolution of this problem is a way of managing an electricity production system taking into account the constraints of equalities and inequalities, in other words it is to find the optimal production for a given combination of units in operation. The appearance of meta-heuristic methods which are part of artificial intelligence, has effectively contributed to solving this problem. Bee colony optimization is a very recent family of meta-heuristics. Its principle is based on the behavior of real bees in life. Bees have properties that are quite different from those of other insect species. They live in colonies, building their nests in tree trunks or other similar enclosed spaces. In this paper, we will apply the optimization by colony of bees in test systems of different sizes with the aim of minimizing the cost of production of electrical energy by taking into account the effect of the valve points of the power plants. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature.

PL

Problem ekonomicznej wysyłki polega na minimalizacji całkowitego kosztu produkcji poprzez zaspokojenie zapotrzebowania na ładunek. Rozwiązanie tego problemu to sposób zarządzania systemem wytwarzania energii elektrycznej z uwzględnieniem ograniczeń równości i nierówności, czyli znalezienie optymalnej produkcji dla danej kombinacji pracujących jednostek. Pojawienie się metod metaheurystycznych wchodzących w skład sztucznej inteligencji skutecznie przyczyniło się do rozwiązania tego problemu. Optymalizacja kolonii pszczół to bardzo nowa rodzina metaheurystyk. Jego zasada opiera się na zachowaniu prawdziwych pszczół w życiu. Pszczoły mają właściwości zupełnie odmienne od właściwości innych gatunków owadów. Żyją w koloniach, budując gniazda w pniach drzew lub innych podobnych zamkniętych przestrzeniach. W tym artykule zastosujemy optymalizację przez rodzinę pszczół w układach testowych różnej wielkości w celu minimalizacji kosztów produkcji energii elektrycznej poprzez uwzględnienie wpływu punktów zaworowych elektrowni. Aby sprawdzić skuteczność zaproponowanego algorytmu, porównano go z innymi algorytmami dostępnymi w literaturze.

Słowa kluczowe

EN

economic dispatch problem; genetic algorithm; bee colony algorithm; hybrid algorithm; valve point effect

PL

problem ekonomicznej wysyłki; algorytm genetyczny; algorytm kolonii pszczół; algorytm hybrydowy; efekt punktu zaworowego

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 5
• Strony: 164--169
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Naama Bakhta

• Department of Electrical Engineering, Laboratory of sustainable development of electrical energy (LDDEE), University of Science and Technology, Mohamed Boudiaf ( USTO-MB), Oran, Algeria

autor

Dahmani Kaouther

• Department of Electrical Engineering, Laboratory of sustainable development of electrical energy (LDDEE), University of Science and Technology, Mohamed Boudiaf ( USTO-MB), Oran, Algeria

autor

Abrouche Amel

• Department of Electrical Engineering, Laboratory of sustainable development of electrical energy (LDDEE), University of Science and Technology, Mohamed Boudiaf ( USTO-MB), Oran, Algeria

autor

Bouzeboudja Hamid

• Department of Electrical Engineering, Laboratory of sustainable development of electrical energy (LDDEE), University of Science and Technology, Mohamed Boudiaf ( USTO-MB), Oran, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).