Convex optimization model for Network Reconfiguration of Smart Grids

• Autorzy: Suryawati Indri , Penangsang Ontoseno , Wibowo Rony Seto

Identyfikatory

DOI

10.15199/48.2023.10.26

Warianty tytułu

PL

Model optymalizacji wypukłej dla rekonfiguracji sieci inteligentnych sieci

• Języki publikacji: EN

Abstrakty

EN

This study proposed a smart grid reconfiguration strategy that takes technical aspects into account. Convex optimization is used to answer the strategy. We find original quadratically constrained and second-order cone approximations to power flow in radial networks during the derivation of each model. Using standard commercial software, the proposed formulation guarantees global optimality with reliable and efficient outcomes. We use IEEE 33 and add DGs to model active distribution systems to evaluate the proposed method. The simulation findings show that the proposed method is capable of solving reconfiguration efficiently.

PL

W badaniu tym zaproponowano strategię rekonfiguracji inteligentnej sieci, która uwzględnia aspekty techniczne. Optymalizacja wypukła służy do odpowiedzi na strategię. Znajdujemy oryginalne kwadratowe ograniczenia i przybliżenia stożka drugiego rzędu do przepływu mocy w sieciach promieniowych podczas wyprowadzania każdego modelu. Przy użyciu standardowego oprogramowania komercyjnego proponowana formuła gwarantuje globalną optymalizację z niezawodnymi i wydajnymi wynikami. Używamy IEEE 33 i dodajemy DG do modelowania aktywnych systemów dystrybucji w celu oceny proponowanej metody. Wyniki symulacji pokazują, że proponowana metoda jest w stanie skutecznie rozwiązać problem rekonfiguracji.

Słowa kluczowe

EN

network reconfiguration; convex optimization; second order cone programming; smart grid

PL

rekonfiguracja sieci; optymalizacja wypukła; programowanie stożkowe drugiego rzędu; inteligentna sieć

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 10
• Strony: 134--137
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Suryawati Indri

• Department of Electrical Engineering Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

• https://orcid.org/0009-0004-2080-6156

autor

Penangsang Ontoseno

• Department of Electrical Engineering Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

• https://orcid.org/0000-0002-3058-1972

autor

Wibowo Rony Seto

• Department of Electrical Engineering Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

• https://orcid.org/0000-0002-6774-988X

Bibliografia

• [1]. M. Baran and F.Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Trans. Power Del., vol. 4, no. 2, pp. 1401–1407, Apr. 1989.
• [2]. H.-D. Chiang and R. Jean-Jumeau, “Optimal network reconfigurations in distribution systems—I: A new formulation and a solution methodology,” IEEE Trans. Power Del., vol. 5, no. 4, pp. 1902–1909, Oct.1990.
• [3]. Owaifeer, M.A., Al-Muhaini, M.: ‘MILP-based technique for smart-healing grids’, IEEE Trans. IET Generation, Transmission & Distribution., vol. 12 n. 10, 2018, pp. 2307 – 2316
• [4]. McDermott, T.E., Drezga, I., Broadwater, R.: ‘A heuristic nonlinear constructive method for distribution system reconfiguration’, IEEE Trans. Power Syst., 1999, 14, (2), pp. 478–483
• [5]. Gomes, F.V., Carneiro, S., Pereira, J.L.R., et al.: ‘A new heuristic reconfiguration algorithm for large distribution systems’, IEEE Trans. Power Syst., 2005, 20, (3), pp. 1373– 1378
• [6]. A. Augugliaro, L. Dusonchet, S. Mangione, An efficent greedy approach for minimum loss reconfiguration of distribution networks, Electric Power System Research, vol. 35, 1995, pp. 167 – 176.
• [7]. Kumar, Y., Das, B., Sharma, J.: ‘Genetic algorithm for supply reconfiguration in distribution system with priority customers’, 2006 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Stockholm, Sweden, June 2006
• [8]. Huang, C.M.: ‘Multiobjective service reconfiguration of distribution systems using fuzzy cause-effect networks’, IEEE Trans. Power Syst., 2003, 18, (2), pp. 867–874
• [9]. J.Z. Zhu, Optimal reconfiguration of electrical distribution network using the refined genetic algorithm, Electric Power Systems Research, vol. 62, 2000, pp. 37 – 42.
• [10]. Oliveira, L.W., Oliveira, E.J., Silva, I.C., et al.: ‘Optimal reconfiguration of power distribution system through particle swarm optimization’. 2015 IEEE Eindhoven Power Tech, Eindhoven, Netherlands, July 2015
• [11]. Souza, S.S., Romero, R., Pereira, J., et al.: ‘Reconfiguration of radial distribution systems with variable demands using the clonal selection algorithm and the specialized genetic algorithm of Chu–Beasley’, J. Control Autom. Electr. Syst., 2016, 27, (6), pp. 689–701
• [12]. Del Pizzo, A., Meo, S., Brando, G., Dannier, A., Ciancetta, F., ‘An energy management strategy for fuel-cell hybrid electric vehicles via particle swarm optimization approach’, International Review on Modelling and Simulations (IREMOS), (2014) 7 (4), pp. 543-553.
• [13]. Subramaniyan M., Subramaniyan S., Jawalkar V, Veerasamy M.:‘Fuzzy Satisfied Multiobjective Distribution Network Reconfiguration: an Application of Adaptive Weighted Improved Discrete Particle Swarm Optimization’, International Review on Modelling and Simulations (IREMOS), (2017) vol 10 no 4.
• [14]. Nagata, T., Hatakeyama, S., Yasouka, M., et al.: ‘An efficient method for power distribution system reconfiguration based on mathematical programming and operation strategy’, Power Syst. Technol., 2000, 3, pp. 1545–1550
• [15]. Cavalcante, P.L., López, J.C., Franco, J.F., et al.: ‘Centralized self-healing scheme for electrical distribution systems’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 145–155
• [16]. M. Lavorato, J. F. Franco, M. J. Rider, and R. Romero, “Imposing radiality constraints in distribution systems optimization problems,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 172–180, Feb. 2012.
• [17]. R. A. Jabr, “Radial distribution load flow using conic programming,” IEEE Trans. Power Syst., vol. 21, no. 3, pp. 1458–1459, Aug. 2006.
• [18]. M. Farivar and S. H. Low, “Branch flow model: Relaxations and convexification—Part I,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2554–2564, Aug. 2013.
• [19]. N. Li, L. Chen, and S. H. Low, “Exact convex relaxation of OPF for radial networks using branch flow model,” in Proc. IEEE 3rd Int. Conf. Smart Grid Communications (SmartGridComm), Nov. 2012, pp. 7–12.
• [20]. J. F. Franco, M. J. Rider, and R. Romero, “A mixed-integer quadratically-constrained programming model for the distribution system expansion planning,” Int. J. Elect. Power Energy Syst., vol. 62, pp. 265–272, 2014.
• [21]. S, H Dolatabadi ., et al :‘An Enhanced IEEE 33 Bus Benchmark Test System for Distribution System Studies ‘, IEEE Trans. Power Syst., vol. 36, no. 3, May 2021