Hybrid renewable energy generation planning for isolated microgrid in Indonesia with metaheuristic approach

• Autorzy: Mansur M. T. , Manjang Salama , Arief Ardiaty , Akil Yusri Syam

Identyfikatory

DOI

10.15199/48.2021.11.08

Warianty tytułu

PL

Hybrydowe planowanie wytwarzania energii odnawialnej dla izolowanej mikrosieci w Indonezji z podejściem metaheurystycznym

• Języki publikacji: EN

Abstrakty

EN

This paper presents an optimal planning for the configuration of a hybrid microgrid generating system based on the results of renewable energy potentials, which are photovoltaic (PV), wind turbine (WT), micro-hydro (MH) and batteries. Battle Star Galactica Starcraft Particle Swarm Optimization (BSG-Starcraft PSO) algorithm is used to determine the optimal generation size based on the local energy potential. The case study is located in an isolated area, namely Tangkeno area of Kabaena Island, Bombana Regency, Southeast Sulawesi, Indonesia which is used to verify the proposed method. The optimization results for this study are number of wind turbine (WT) required is 27 units with capacity of 180 Watt each and total capacity of 4.86 kW the number of solar panel (PV) needed is 231 units with capacity of 195 Watt each and total capacity of 44.85 kW. Hence the total capacity for renewable energy generation is 49.73 kW. The results of the comparison between the BSG-Starcraft PSO algorithm and PSO showed that by using the BSG-Starcraft PSO provided optimal and faster solution compare to PSO.

PL

W artykule przedstawiono optymalne planowanie konfiguracji hybrydowego systemu wytwarzania mikrosieci w oparciu o wyniki potencjałów energii odnawialnej, którymi są fotowoltaika (PV), turbina wiatrowa (WT), mikrohydro (MH) i akumulatory. Algorytm Battle Star Galactica Starcraft Particle Swarm Optimization (BSG-Starcraft PSO) służy do określenia optymalnej wielkości generacji w oparciu o lokalny potencjał energetyczny. Studium przypadku znajduje się na odosobnionym obszarze, a mianowicie obszarze Tangkeno na wyspie Kabaena, Bombana Regency, południowo-wschodnie Sulawesi, Indonezja, który służy do weryfikacji proponowanej metody. Wyniki optymalizacji dla tego badania to liczba wymaganych turbin wiatrowych (WT) to 27 jednostek o mocy 180 W każda i łącznej mocy 4,86 kW liczba potrzebnych paneli słonecznych (PV) to 231 jednostek o mocy 195 W każda i całkowita moc 44,85 kW. Stąd łączna moc wytwarzania energii odnawialnej wynosi 49,73 kW. Wyniki porównania algorytmu BSG-Starcraft PSO i PSO wykazały, że zastosowanie BSG-Starcraft PSO zapewniło optymalne i szybsze rozwiązanie w porównaniu do PSO.

Słowa kluczowe

EN

microgrid; renewable energy; BSG-Starcraft PSO algorithm

PL

mikrosieć; energia odnawialna; hybrydowe wytwarzanie energii

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 11
• Strony: 45--50
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Mansur M. T.

• Department of Electrical Engineering, Hasanuddin University, 92171 Gowa, Indonesia
• Department of Electrical Engineering, Halu Oleo University, 93232 Kendari, Indonesia

• https://orcid.org/0000-0001-6607-6575m

autor

Manjang Salama

• Department of Electrical Engineering, Hasanuddin University, 92171 Gowa, Indonesia

autor

Arief Ardiaty

• Department of Electrical Engineering, Hasanuddin University, 92171 Gowa, Indonesia
• Centre for Research and Development on Energy and Electricity, Hasanuddin University, 90245 Makassar, Indonesia

• https://orcid.org/0000-0002-1721-3948

autor

Akil Yusri Syam

• Department of Electrical Engineering, Hasanuddin University, 92171 Gowa, Indonesia

Bibliografia

• [1] Duchaud J.L., Notton G., Fouiloy A., Voyant C., Wind, solar and battery micro-grid optimal sizing in Tilos Island, Energy Procedia, 159(2018), 22-27.
• [2] Tooryan F., Collins E.R., Ahmadi A., Rangarajan S.S., Distributed generators optimal sizing and placement in a microgrid using PSO, IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), 2017, 614- 619.
• [3] Quoc D.P., Chan V.N., Dinh T.N., The T.N., Chi H.L., Optymalny projekt połączony z zarządzaniem energią dla samodzielnej mikrosieci, IEEE Region 10 Conference (TENCON), 2016,1450- 1455.
• [4] Nappu M.B., Arief A., Bansal, R.C., Transmission management for congested power system: A review of concepts, technical challenges and development of a new methodology, Renewable and Sustainable Energy Reviews, 38(2014), 572-580.
• [5] Bhattacharjee S., Acharya S., PV-wind hybrid power option for a low wind topography, Energy Conversion and Management, 89 (2015), 942-954.
• [6] Dali A., Abdelmalek S., Nekkache A., Bouharchouche, A., Development of a sizing interface for photovoltaic-wind microgrid based on PSO-LPSP optimization strategy, Proc. of International Conference on Wind Energy and Applications in Algeria (ICWEAA), 2018, s. 1-5.
• [7] Aznavi S., Fajri P., Asrari A., Sazehgar R., Energy management of multi-energy storage systems using energy path decomposition, Proc. of IEEE Energy Conversion Congress and Exposition (ECCE), 2019, 1- 6.
• [8] Xie X., Wang H., Tian, S., Liu Y., Optimal capacity configuration of hybrid energy storage for an isolated microgrid based on QPSO algorithm, Proc. of IEEE 5th International Conference on Electricity and Power Technologies, 2015, 2094-2099.
• [9] Baygi S.M.H., Elahi A., Karsaz A., A novel framework for optimal sizing of hybrid stand-alone renewable energy system: A gray wolf optimizer, Proc. of 3rd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 2018, 1- 6.
• [10] Singh G., Baredar P., Sing A., Kurup D., Optimal sizing and location of PV, wind and battery storage for electrification to an island: A case study of Kavaratti, Lakshadweep, Journal of Energy Storage 12(2017), 78-86.
• [11] Nappu M.B., Arief A. i Duhri A.S., Economic emission dispatch for thermal power plant in Indonesia, International Journal of Smart Grid and Clean Energy, 8(2019), No.4, 500-504.
• [12] Nappu M.B., Arief, A., Bahtiar M.I., Strategiczne rozmieszczenie kondensatora i DG w celu poprawy napięcia po dużej penetracji elektrowni energii odnawialnej: Indonezyjskie badanie, Proc. of the 7th International Conference on Renewable Energy Research and Application (ICRERA), 2018, 1-6.
• [13] Sasidhar K., Kumar, B, J., Optimal sizing of PV-wind hybrid energy system using genetic algorithm (GA) and particle swarm optimization (PSO), Intenational Journal of Science, Engineering and Techonolofy Research, 4(2015), No. 2, 354-358.
• [14] Alawani H.S., Kimball J.W., Optimal sizing of a wind/solar/battery hybrid microgrid system using the forever power method, Proc. of Seventh Annual IEEE Green Technologies Conference, 2015, 29-35.
• [15] Samy M.M., Barakat S., Ramadan H.S., A Flower Pollination Optimization Algorithm for an Off-Grid PV-Fuel Cell Hybrid Renewable System, International Journal of Hydrogen Energy, 44(2019), No. 4, 2141- 2152.
• [16] Rahmani R., Khairuddin A., Cherati S.M., Pesaran H.A.M., A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO), Conference Proceedings IPEC, 2010, 1-6.
• [17] Tao Y., Hao B., Chen X., Chen H., Shi J., Optimal capacity design for solar combined cooling heating and power system with energy storage, Proc. of 2nd IEEE Conference on Energy Internet and Energy Sistem Integration, 2018, 1- 5.
• [18] Dawoud M.S., Lin X., Okba I.M., Hibrid renewable microgrid optimization techique review, Renewable and Suistainable Energy Reviews, 82(2018), 2039-2052.
• [19] Arief A., Nappu, M.B., Rachman, S.M., Photovoltaic allocation with tangent vector senstiivity, International Journal on Energy Conversion (IRECON), 8(2020), Nr 3, 1-10.
• [20] Arief A., Nappu, M.B., Rachman S.M., Darusman M., Optimal photovoltaic placement at the South Sulawesi power system for stability improvement, Proc. of the 4th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), 2017, 1- 6.
• [21] Arief A., Dong Z.Y., Nappu M.B., Gallagher M., Under voltage load shedding in power systems with wind turbine driven do podwójnie zasilane generatory indukcyjne, Electric Power Systems Research, 96(2013), 91-100.
• [22] Ajami W. A., Arief A., Nappu M.B., Optymalny przepływ mocy dla połączeń międzysystemowych z uwzględnieniem niekończącej się nieciągłości elektrowni wiatrowych. Intenational Journal of Smart Grid and Clean Energy, 8 (2019), nr 3, 372- 376.
• [23] Akram U., Khalid M., Shafiq S., Optimal sizing of a wind/solar/battery hybrid grid-connected microgrid system, IET Renewable Power Generation, 12(2017), 72-80.
• [24] Kharrich M., Sayouti Y., Akherraz M., Optimal microgrid sizing and daily capacity stored analysis in summer and winter season, Proc. of 4th International Conference on Optimization and Applications (ICOA), 2018, 1- 6.
• [25] Potli M., Damodharam Y., Balachandra J.C., Optimal sizing of wind/solar/hydro in an isolated power sistem using SMUGF based FPA, Proc. of 3rd Internartional Conference on Advanced Computating and Commmunication System (ICACCS), 2016, 1-6.
• [26] Nazir R., Laksono H.D., Waldi E.P., Ekaputra E, Coveria P., Optymalizacja odnawialnych źródeł energii: projekt modelu mikrosieci, Energy Procedia. 52(2014), 316-327.
• [27] Jayachadran M., Ravi G., Design and optimization of hybrid micro-grid system, Energy Procedia 117(2017), 95-103. {28] Kahar B., Study and modeling of energy supply on the island of Moti Ternate city based on renewable energy, 2016, Institut Teknologi Surabaya.
• [29] Pranoto S., Okamoto S., Lee J H., Shiraishi A., Sakane Y., Ohashi Y., Determining frictional characteristics of human ocular surfaces by employing BSG-Starcraft of particle swarm optimization, Journal of Biomedical Engineering and Biosciencees (JBEB), 4(2017), 43-59.
• [30] Ashar A R., Modelowanie i optymalizacja mikrosieci jako alternatywnych źródeł energii na Campus 2 Ujung Pandang State Polytechnic, praca dyplomowa niepublikowana, 2019, Hasanuddin University.
• [31] Fathy A., A realible methodology based on mine blast optimization algorithm for optimal size of hybrid PV-wind-FC System for remote area in Egypt, Renewable Energy, 95(2016), 367-380.
• [32] Pranoto S., Okamoto S., Kataoka ., R., Lee J H., Shiraishi A., Sakane Y., Yamaguchi M., Ohashi Y., Development of ocular surface tribometer and friction characteristics of human ocular surface, International Journal of Bioscience, Biocheimistry and Biofarmatics, 8(2018), No.2, 89-99.
• [33] NASA Atmospheric Science Data Center, dostępne: http//coswe.larc.nasagov/.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).