Algoritma Genetika dalam Penentuan Alokasi Biaya Wheeling Menggunakan LRMC dan MW-Mile
Abstrak
Deregulasi industri listrik telah terjadi di banyak negara. Tujuan utama di balik deregulasi ini adalah memperkenalkan kompetisi yang bertujuan untuk meningkatkan efisiensi dan kualitas layanan di industri pasokan listrik. Perubahan yang signifikan akan terjadi pada nilai pembangkitan dan fungsi saluran transmisi. Pelanggan akan berpartisipasi menyambut pasar bebas dan hal ini menyebabkan banyak perusahaan yang ingin membangun pembangkit sendiri dalam skema operasi wheeling untuk memenuhi kebutuhan sendiri. Wheeling menjadi solusi dari permasalahan tersebut. Metode aliran daya akan digunakan setelah penambahan wheeling pada sistem. Tujuan penggunaan metode ini adalah mengetahui kondisi sistem setelah wheeling ditambahkan karena peta aliran daya akan berubah saat ada pelaku wheeling. Metode studi aliran daya memberikan informasi besarnya daya total yang dibangkitkan oleh generator, tetapi tidak memberikan informasi daya yang dialirkan oleh generator di setiap jaringan transmisi. Untuk mengetahui alokasi daya yang dialirkan oleh generator di setiap jaringan transmisi, metode power tracing akan digunakan. Metode ini dapat memberikan informasi alokasi daya yang dialirkan oleh generator di setiap jaringan transmisi pada sistem. Penelitian ini akan membahas metode power tracing menggunakan metode algortima genetika (AG). AG adalah satu dari beberapa metode optimisasi dan mengasumsikan alokasi daya yang dialirkan oleh generator sebagai masalah yang akan dioptimisasi. Penentuan harga wheeling menggunakan metode Long Run Marginal Cost (LRMC). Metode ini memproyeksikan biaya masa depan dengan memperhatikan perubahan beban yang terjadi setiap saat dalam kurun waktu yang ditentukan. Pada makalah ini metode LRMC dibandingkan dengan metode penetuan biaya wheeling lainnya, yaitu metode MW-Mile. Hasil dari penelitian menunjukkan bahwa metode LRMC lebih murah dibandingkan dengan metode MW-Mile. Dari perspektif ekonomi, penentuan biaya wheeling menggunakan metode LRMC lebih murah 14%-20% daripada metode MW-Mile.
Referensi
Y.R. Sood, N.P. Padhy, and H.O. Gupta, “Wheeling of Power Under Deregulated Environment of Power System - A Bibliographical Survey,” IEEE Trans. Power Syst., Vol. 17, No. 3, pp. 870–878, Aug. 2002, doi: 10.1109/TPWRS.2002.800967.
H.M. Merrill and B.W. Erickson, “Wheeling Rates Based on Marginal-Cost Theory,” IEEE Power Eng. Rev., Vol. 9, No. 11, pp. 39–40, Nov. 1989, doi: 10.1109/MPER.1989.4310379.
K.H. Lalitha and I.K. Kiran, “Comparison of Wheeling Cost Using Power Flow Tracing Methods in Deregulated Electric Power Industry,” Int. J. Eng. Technol. Manag. Appl. Sci., Vol. 5, No. 6, pp. 861–870, 2017.
H.H. Happ, “Cost of Wheeling Methodologies,” IEEE Trans. Power Syst., Vol. 9, No. 1, pp. 147–156, Feb. 1994, doi: 10.1109/59.317547.
S. Larbwisuthisaroj and S. Chaitusaney, “Wheeling Charge Considering Line Flow Differentiation Based on Power Flow Calculation,” 15th Int. Conf. Eletr. Eng. Comput. Telecommun., Inf. Technol., 2018, pp. 293–296, doi: 10.1109/ECTICon.2018.8619951.
S. Riyaz, R. Upputuri, and N. Kumar, “Wheeling Charge Evaluation by Using Proposed MW-Mile Method Considering Transmission Losses and Load Power Factor Variation,” 2020 1st IEEE Int. Conf. Meas. Instrum., Control, Automat. ICMICA 2020, pp. 1–5, 2020, doi: 10.1109/ICMICA48462.2020.9242701.
Hermawan and T. Andromeda, “Comparison of Cost Estimation Methods in Power Wheeling for Java-Bali Interconnection System,” 2017 4th Int. Conf. Inf. Technol. Comput., Elect. Eng. (ICITACEE), 2017, pp. 127–130, doi: 10.1109/ICITACEE.2017.8257689.
X. Gao, P. You, and M. Wen, “Fixed Cost Allocation Based on Current Electromagnetic Fields on Power Market,” 2nd IEEE Conf. Energy Internet, Energy Syst. Integr. (EI2), 2018, pp. 1–4, doi: 10.1109/EI2.2018.8582065.
B. Kharbas, M. Fozdar, and H. Tiwari, “Efficient Transmission Cost Allocation by Composite MVA-Mile Method with Network usage Approach,” Int. J. Comput. Appl., Vol. 2017, No. 2, pp. 15–20, 2017.
S. Ghimire, J. Marasini, and M. Paudyal, “A Case Study of MW-Mile, MVAr-Mile, MVA-Mile and Power Factor Based Transmission Pricing in Integrated Nepal Power System,” 2019 IEEE Int. Conf. Elect. Comput., Commun. Technol. (ICECCT), 2019, pp. 1–5, doi: 10.1109/ICECCT.2019.8869392.
F. Zhou, J. Anderson, and S.H. Low, “The Optimal Power Flow Operator: Theory and Computation,” IEEE Trans. Control Netw. Syst., Vol. 8, No. 2, pp. 1010–1022, Jun. 2021, doi: 10.1109/TCNS.2020.3044258.
Z. Jing and W. Xie, “Distribution Pricing Based on Improved Long-Run Incremental Cost Pricing with Dynamic Security Factor,” 2018 Int. Conf. Power Syst. Technol. (POWERCON), 2019, pp. 763–769, doi: 10.1109/POWERCON.2018.8601852.
Y.S. Wijoyo, S.P. Hadi, and S. Sarjiya, “Review Perhitungan Biaya Wheeling (Wheeling Cost Calculation Review),” J. Nas. Tek. Elekt., Teknol. Inf., Vol. 9, No. 1, pp. 116–122, Feb. 2020, doi: 10.22146/jnteti.v9i1.114.
M.H. Sulaiman, M.W. Mustafa, and O. Aliman, “Transmission Loss and Load Flow Allocations via Genetic Algorithm Technique,” TENCON 2009 - 2009 IEEE Region 10 Conf., 2009, pp. 1–5, doi: 10.1109/TENCON.2009.5396005.
A.N. Afandi et al., “An Opportunity of Artificial Salmon Tracking Algorithm for the Optimal Power Wheeling Considering Open Tariffing Systems of the Transmission Charges,” 2018 Conf. Power Eng., Renew. Energy (ICPERE), 2018, pp. 1–6, doi: 10.1109/ICPERE.2018.8739318.
S.H.M. Kerta, Z.A. Hamid, and I. Musirin, “An Ant Colony-Pollinated Flower Algorithm: A New Approach on Reactive Power Load Tracing for Deregulated Power System,” Int. J. Simul. Syst. Sci., Technol., Vol. 17, No. 41, pp. 3.1–3.8, 2016, doi: 10.5013/IJSSST.a.17.41.03.
Y.S. Wijoyo, S.P. Hadi, and S. Sarjiya, “Opportunity Cost Allocation for Wheeling Using Power Flow Tracing,” 2019 Int. Conf. Technol. Policies Elect. Power, Energy, 2019, pp. 2–6, doi: 10.1109/IEEECONF48524.2019.9102537.
K.S. Ahmed, S.P. Karthikeyan, and M.V. Rao, “Proportional Generation and Proportional Load Based Transmission Loss Allocation Considering Reactive Power Demand in Restructured Environment,” TENCON 2017 - 2017 IEEE Region 10 Conf., 2017, pp. 992–997, doi: 10.1109/TENCON.2017.8228002.
B. Tranberg et al., “Flow-Based Analysis of Storage Usage in a Low-Carbon European Electricity Scenario,” 2018 5th Int. Conf. Eur. Energy Mark. (EEM), 2018, pp. 1–5, doi: 10.1109/EEM.2018.8469951.
M. Hotz and W. Utschick, “hynet: An Optimal Power Flow Framework for Hybrid AC/DC Power Systems,” IEEE Trans. Power Syst., Vol. 35, No. 2, pp. 1036–1047, Mar. 2020, doi: 10.1109/TPWRS.2019.2942988.
J. Hörsch et al., “Flow Tracing as A Tool Set for the Analysis of Networked Large-Scale Renewable Electricity Systems,” Int. J. Elect. Power, Energy Syst., Vol. 96, pp. 390–397, Mar. 2018, doi: 10.1016/j.ijepes.2017.10.024.
P. Kumar, N. Gupta, K.R. Niazi, and A. Swarnkar, “A Circuit Theory-Based Loss Allocation Method for Active Distribution Systems,” IEEE Trans. Smart Grid, Vol. 10, No. 1, pp. 1005–1012, Jan. 2019, doi: 10.1109/TSG.2017.2757059.
B. Li, D.A. Robinson, and A. Agalgaonkar, “Identifying the Wheeling Costs Associated with Solar Sharing in LV Distribution Networks in Australia Using Power Flow Tracing and MW-Mile Methodology,” 2017 Australas. Univ. Power Eng. Conf. (AUPEC), 2017, pp. 1–6, doi: 10.1109/AUPEC.2017.8282392.
P. Muangkhiew and K. Chayakulkheeree, “Unified Optimal Power Flow Incorporating Full AC Control Variables,” 2021 9th Int. Elect. Eng. Congr. (iEECON), 2021, pp. 177–180, doi: 10.1109/iEECON51072.2021.9440375.
Y. Arkeman, K.B. Seminar, and H. Gunawan, Algoritma Genetika Teori dan Aplikasinya untuk Bisnis dan Industri. Bogor, Indonesia: IPB Press, 2012.
H.Y. Heng and F. Li, “Literature Review of Long-Run Marginal Cost Pricing and Long-Run Incremental Cost Pricing,” 2007 42nd Int. Univ. Power Eng. Conf., 2007, pp. 73–77, doi: 10.1109/UPEC.2007.4468923.
The MathWorks Inc. (2018) MATLAB version: 9.7.0.1190202 (R2019b).
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.