Assessment of a multiperiod optimal power flow for power system operation
The optimal power flow is an important tool for power system planning and power system operation. The optimal power flow is used in a 24-hour period to find an economic dispatch of generating units considering network restrictions. The optimal power flow provides valuable information about the opera...
- Autores:
-
Moreno-Chuquen, Ricardo
Cantillo Luna, Sergio Alejandro
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/13288
- Acceso en línea:
- https://hdl.handle.net/10614/13288
- Palabra clave:
- Energía eólica
Producción de energía eléctrica
Electric power production
Generation
Optimal power flow
Wind power
Optimization
Power systems
Renewable energy
- Rights
- openAccess
- License
- Derechos reservados - Praise Worthy Prize, 2020
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Assessment of a multiperiod optimal power flow for power system operation |
| title |
Assessment of a multiperiod optimal power flow for power system operation |
| spellingShingle |
Assessment of a multiperiod optimal power flow for power system operation Energía eólica Producción de energía eléctrica Electric power production Generation Optimal power flow Wind power Optimization Power systems Renewable energy |
| title_short |
Assessment of a multiperiod optimal power flow for power system operation |
| title_full |
Assessment of a multiperiod optimal power flow for power system operation |
| title_fullStr |
Assessment of a multiperiod optimal power flow for power system operation |
| title_full_unstemmed |
Assessment of a multiperiod optimal power flow for power system operation |
| title_sort |
Assessment of a multiperiod optimal power flow for power system operation |
| dc.creator.fl_str_mv |
Moreno-Chuquen, Ricardo Cantillo Luna, Sergio Alejandro |
| dc.contributor.author.spa.fl_str_mv |
Moreno-Chuquen, Ricardo Cantillo Luna, Sergio Alejandro |
| dc.subject.armarc.spa.fl_str_mv |
Energía eólica Producción de energía eléctrica |
| topic |
Energía eólica Producción de energía eléctrica Electric power production Generation Optimal power flow Wind power Optimization Power systems Renewable energy |
| dc.subject.armarc.eng.fl_str_mv |
Electric power production |
| dc.subject.proposal.eng.fl_str_mv |
Generation Optimal power flow Wind power Optimization Power systems Renewable energy |
| description |
The optimal power flow is an important tool for power system planning and power system operation. The optimal power flow is used in a 24-hour period to find an economic dispatch of generating units considering network restrictions. The optimal power flow provides valuable information about the operation cost, transmission flows, generation and congestion in the system. This information is used by generators, planners, operators and regulators to analyze and take decisions about the system at short and long term. At short term corresponds to information for the operation. At long term corresponds to information for the planning. This paper proposes a detailed optimal power flow formulation looking for a minimum cost of generation considering wind generation. Five solvers were used in order to compare differences between them. The solvers used are CBC, CLP, CPLEX, Gurobi and GLPK. These solvers are commonly used to solve the multiperiod DC optimal power flow. An IEEE-24 test system is used to compare the solutions provided by the solvers. The findings reveal significant differences between the solvers when they are used to solve the IEEE-24 test system. Additionally, the computing time for each solver is reported. The solvers CPLEX y Gurobi exhibits the lower computational time to find a solution |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-11 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-29T19:49:09Z |
| dc.date.available.none.fl_str_mv |
2021-09-29T19:49:09Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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18276660 |
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eng |
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eng |
| dc.relation.citationedition.spa.fl_str_mv |
Volumen 15, número 6 (2020) |
| dc.relation.citationendpage.spa.fl_str_mv |
492 |
| dc.relation.citationissue.spa.fl_str_mv |
Número 6 |
| dc.relation.citationstartpage.spa.fl_str_mv |
484 |
| dc.relation.citationvolume.spa.fl_str_mv |
Volumen 15 |
| dc.relation.cites.eng.fl_str_mv |
Moreno Chuquen, R., Cantillo Luna, S. (2020). Assessment of a multiperiod optimal power flow for power system operation. International Review of Electrical Engineering. (Vol. 15 (6), pp. 484-492. DOI: https://doi.org/10.15866/iree.v15i6.18304 |
| dc.relation.ispartofjournal.eng.fl_str_mv |
International Review of Electrical Engineering |
| dc.relation.references.eng.fl_str_mv |
[1] Al Hasibi, R., Hadi, S., Sarjiya, S., Multi-Objective Optimization of Integrated Power System Expansion Planning with Renewable Energy-Based Distributed Generation, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 19-31. doi: https://doi.org/10.15866/iree.v14i1.16082 [2] Sangwato, S., Oonsivilai, A., Optimal Power Flow with Interline Power Flow Controller Using Hybrid Genetic Algorithm, (2015) International Review of Electrical Engineering (IREE), 10 (6), pp. 727-733. doi: https://doi.org/10.15866/iree.v10i6.7568 [3] Lakdja, F., Abdeslam, D., Gherbi, F., Optimal Location of Thyristor-Controlled Series Compensator for Optimal Power Flows, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 465-472. [4] R.A. Jabr. Adjustable robust OPF with renewable energy sources. IEEE Trans Power Syst, vol. 28 n. 4, 2013, pp 4742–4751. [5] A. Castillo, X. Jiang, D.F. Gayme. Lossy DCOPF for optimizing congested grids with renewable energy and storage. Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc. June 4-6, 2014, Portland, OR, United States. [6] T. Geetha, V. Jayashankar. Generation dispatch with storage and renewables under availability-based tariff. IEEE Region 10 Annual International Conference TENCON. 2008. [7] Gourma, A., Berdai, A., Reddak, M., Tytiuk, V., Reliability and Optimization Strategy in an Interconnected Network at a Wind Farm, (2018) International Review on Modelling and Simulations (IREMOS), 11 (2), pp. 76-83. doi: https://doi.org/10.15866/iremos.v11i2.13596 [8] Varanasi, J., Tripathi, M., Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting, (2019) International Review on Modelling and Simulations (IREMOS), 12 (1), pp. 16-23. doi: https://doi.org/10.15866/iremos.v12i1.15781 [9] Srivastava, A., Bajpai, R., An Efficient Maximum Power Extraction Algorithm for Wind Energy Conversion System Using Model Predictive Control, (2019) International Journal on Energy Conversion (IRECON), 7 (3), pp. 93-107. doi: https://doi.org/10.15866/irecon.v7i3.17403 [10] A. Castillo, D. F. Gayme. Evaluating the effects of real power losses in optimal power flow-based storage integration. IEEE Transactions on Control of Network Systems, vol 5, n. 3. Sep 2018, pp 1132–1145. [11] Sharifzadeh H, Amjady N, Zareipour H. Multi-period stochastic security-constrained OPF considering the uncertainty sources of wind power, load demand and equipment unavailability. Electric Power Systems Research, vol. 146, n. 5. May 2017, pp. 33–42. [12] Boonchuay, K. Tomsovic, F. Li, W. Ongsakul. Robust optimization-based DC optimal power flow for managing wind generation uncertainty. AIP Conference Procedings, vol 1499, n. 1. May 2014, pp 31–35. [13] Rahmat Azami MSJ and GH. Economic load Dispatch and DCOptimal Power Flow Problem-PSO versus LR. International Journal of Multidisciplinary Sciences and Engineering, vol. 2, n. 9. Dec 2011, pp 8–13. [14] A. Soroudi. Power System Optimization Modeling in GAMS. (Springer International Publishing, 2017). [15] R. A. Jabr, S. Karaki, J. A. Korbane. Robust Multi-Period OPF with Storage and Renewables. IEEE Transactions on Power Systems, vol. 30 n. 5. Sep 2015, pp. 2790–2799. [16] B. Eldridge, R. O’Neill, A. Castillo. An Improved Method for the DCOPF with Losses. IEEE Transactions on Power Systems vol. 33, n. 4. July 2018, pp. 3779–3788. [17] P. Maghouli, A. Soroudi, A. Keane. Robust computational framework for mid-term techno-economical assessment of energy storage. IET Generation, Transmission & Distribution, vol. 10 n. 3. Feb 2016, pp. 822–831. [18] Hafez, A., AlSadi, S., Nassar, Y., Chaotic Optimization Versus Genetic Algorithm for Optimal Tuning of Static Synchronous Series Compensator Stabilizing Controller, (2019) International Review of Electrical Engineering (IREE), 14 (3), pp. 159-172. doi: https://doi.org/10.15866/iree.v14i3.16163 [19] Mmary, E., Marungsri, B., Multiobjective Optimization of Renewable Distributed Generations in Radial Distribution Networks with Optimal Power Factor, (2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 297-304. doi: https://doi.org/10.15866/iree.v13i4.15069 [20] Adam, K., Miyauchi, H., Optimization of a Photovoltaic Hybrid Energy Storage System Using Energy Storage Peak Shaving, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 8-18. doi: https://doi.org/10.15866/iree.v14i1.16162 [21] Oloulade, A., Moukengue, A., Vianou, A., Multi-Criteria Optimization of the Functionning of a Distribution Network in Normal Operating Regime, (2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 290-296. doi: https://doi.org/10.15866/iree.v13i4.14401 [22] Hassoune, A., Khafallah, M., Mesbahi, A., Benaaouinate, L., ouragba, T., Control Strategies of a Smart Topology of EVs Charging Station Based Grid Tied RES-Battery, (2018) International Review of Electrical Engineering (IREE), 13 (5), pp. 385-396. doi: https://doi.org/10.15866/iree.v13i5.15520 [23] Moreno, R. Identification of Topological Vulnerabilities for Power Systems Networks. In 2018 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5. doi: https://doi.org/10.1109/PESGM.2018.8586143 [24] Moreno-Chuquen, R., Obando-Ceron, J., Network Topological Notions for Power Systems Security Assessment, (2018) International Review of Electrical Engineering (IREE), 13 (3), pp. 237-245. doi: https://doi.org/10.15866/iree.v13i3.14210 [25] Khemmook, P., Khomfoi, S., Transient Stability Improvement Using Coordinated Control of Solar PVs and Solid State Transformers, (2018) International Review of Electrical Engineering (IREE), 13 (6), pp. 486-494. doi: https://doi.org/10.15866/iree.v13i6.15869 [26] Omar, A., Ali, Z., Abdel Aleem, S., Abou-El-Zahab, E., Sharaf, A., A Dynamic Switched Compensation Scheme for Grid- Connected Wind Energy Systems Using Cuckoo Search Algorithm, (2019) International Journal on Energy Conversion (IRECON), 7 (2), pp. 64-74. doi: https://doi.org/10.15866/irecon.v7i2.16895 [27] Mauledoux, M., Valencia, A., Avilés, O., Genetic Algorithm Optimization for DC Micro Grid Design, a Case of Study, (2017) International Review of Electrical Engineering (IREE), 12 (4), pp. 318-323. doi: https://doi.org/10.15866/iree.v12i4.11544 [28] Jabri, M., Aloui, H., Genetic Lagrangian Relaxation Selection Method for the Solution of Unit Commitment Problem, (2019) International Journal on Engineering Applications (IREA), 7 (2), pp. 59-64. doi: https://doi.org/10.15866/irea.v7i2.17022 [29] Prodromidis, G., Tsiaras, E., Coutelieris, F., Autonomous Buildings with Electricity by Renewables, (2018) International Journal on Energy Conversion (IRECON), 6 (5), pp. 153-159. doi: https://doi.org/10.15866/irecon.v6i5.15919 [30] Rizk-Allah, R., Abdel Mageed, H., El-Sehiemy, R., Abdel Aleem, S., El Shahat, A., A New Sine Cosine Optimization Algorithm for Solving Combined Non-Convex Economic and Emission Power Dispatch Problems, (2017) International Journal on Energy Conversion (IRECON), 5 (6), pp. 180-192. doi: https://doi.org/10.15866/irecon.v5i6.14291 [31] Syahputra, R., Robandi, I., Ashari, M., Performance Improvement of Radial Distribution Network with Distributed Generation Integration Using Extended Particle Swarm Optimization Algorithm, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 293-304. doi: https://doi.org/10.15866/iree.v10i2.5410 [32] Muthukumar, K., Jayalalitha, S., Ramaswamy, M., PSO Embedded Artificial Bee Colony Algorithm for Optimal Shunt Capacitor Allocation and Sizing in Radial Distribution Networks with Voltage Dependent Load Models, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 305-320. doi: https://doi.org/10.15866/iree.v10i2.5481 [33] Moreno, R., Obando, J., Gonzalez, G., An integrated OPF dispatching model with wind power and demand response for day-ahead markets, (2019) International Journal of Electrical and Computer Engineering (IJECE), 4 (4), pp. 2794-2802. doi: http://doi.org/10.11591/ijece.v9i4.pp2794-2802 [34] Wongdet, P., Leeton, U., Marungsri, B., Line Loss Reduction by Optimal Location of Battery Energy Storage System for the Daily Operation in Microgrid with Distributed Generations, (2018) International Journal on Energy Conversion (IRECON), 6 (3), pp. 83-89. doi: https://doi.org/10.15866/irecon.v6i3.15095 [35] Obando, J., Gonzalez, G., Moreno, R., Quantification of operating reserves with high penetration of wind power considering extreme values, (2020) International Journal of Electrical and Computer Engineering (IJECE), 10 (2), pp. 1693-1700. doi: http://doi.org/10.11591/ijece.v10i2.pp1693-1700 [36] J. Yi-Xiong, C. Hao-Zhong, Y. Jian-yong, Z. Li, New discrete method for particle swarm optimization and its application in transmission network expansion planning, Electric Power Systems Research, vol. 77 n. 3-4, 2007, pp. 227-233. doi. http://dx.doi.org/10.1016/j.epsr.2006.02.016 [37] COIN-OR Branch and Cut Interface Julia package. Accessed on Aug. 4, 2019. [Online]. Available: https://github.com/JuliaOpt/Cbc.jl [38] COIN-OR Linear Programming Interface Julia package. Accessed on Aug. 5, 2019. [Online]. Available: https://github.com/JuliaOpt/Clp.jl [39] IBM ILOG CPLEX Optimization Studio V12.9.0 documentation. Accessed on Aug. 4, 2019. [Online]. Available: https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/i log.odms.studio.help/Optimization_Studio/topics/COS_home.html [40] The GUROBI Manual. Accessed on August 5, 2019. [Online]. Available: https://www.gurobi.com/documentation/8.1/refman/index.html [41] Julia GNU Linear Programming Kit (GLPK) package. Accessed on August 5, 2019. [Online]. Available: https://github.com/JuliaOpt/GLPK.jl [42] Julia for Mathematical Optimization (JuMP) package. Accessed on Aug. 4, 2019. [Online]. Available: http://www.juliaopt.org/JuMP.jl/v0.19.0 |
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Moreno-Chuquen, Ricardof36efacf1d947d7410ab7d332d414753Cantillo Luna, Sergio Alejandro83f94fa4be72d6c8cf3b1410b45cfe2b2021-09-29T19:49:09Z2021-09-29T19:49:09Z2020-1118276660https://hdl.handle.net/10614/13288The optimal power flow is an important tool for power system planning and power system operation. The optimal power flow is used in a 24-hour period to find an economic dispatch of generating units considering network restrictions. The optimal power flow provides valuable information about the operation cost, transmission flows, generation and congestion in the system. This information is used by generators, planners, operators and regulators to analyze and take decisions about the system at short and long term. At short term corresponds to information for the operation. At long term corresponds to information for the planning. This paper proposes a detailed optimal power flow formulation looking for a minimum cost of generation considering wind generation. Five solvers were used in order to compare differences between them. The solvers used are CBC, CLP, CPLEX, Gurobi and GLPK. These solvers are commonly used to solve the multiperiod DC optimal power flow. An IEEE-24 test system is used to compare the solutions provided by the solvers. The findings reveal significant differences between the solvers when they are used to solve the IEEE-24 test system. Additionally, the computing time for each solver is reported. The solvers CPLEX y Gurobi exhibits the lower computational time to find a solution9 páginasapplication/pdfengPraise Worthy PrizeDerechos reservados - Praise Worthy Prize, 2020https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Assessment of a multiperiod optimal power flow for power system operationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Energía eólicaProducción de energía eléctricaElectric power productionGenerationOptimal power flowWind powerOptimizationPower systemsRenewable energyVolumen 15, número 6 (2020)492Número 6484Volumen 15Moreno Chuquen, R., Cantillo Luna, S. (2020). Assessment of a multiperiod optimal power flow for power system operation. International Review of Electrical Engineering. (Vol. 15 (6), pp. 484-492. DOI: https://doi.org/10.15866/iree.v15i6.18304International Review of Electrical Engineering[1] Al Hasibi, R., Hadi, S., Sarjiya, S., Multi-Objective Optimization of Integrated Power System Expansion Planning with Renewable Energy-Based Distributed Generation, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 19-31. doi: https://doi.org/10.15866/iree.v14i1.16082[2] Sangwato, S., Oonsivilai, A., Optimal Power Flow with Interline Power Flow Controller Using Hybrid Genetic Algorithm, (2015) International Review of Electrical Engineering (IREE), 10 (6), pp. 727-733. doi: https://doi.org/10.15866/iree.v10i6.7568[3] Lakdja, F., Abdeslam, D., Gherbi, F., Optimal Location of Thyristor-Controlled Series Compensator for Optimal Power Flows, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 465-472.[4] R.A. Jabr. Adjustable robust OPF with renewable energy sources. IEEE Trans Power Syst, vol. 28 n. 4, 2013, pp 4742–4751.[5] A. Castillo, X. Jiang, D.F. Gayme. Lossy DCOPF for optimizing congested grids with renewable energy and storage. Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc. June 4-6, 2014, Portland, OR, United States.[6] T. Geetha, V. Jayashankar. Generation dispatch with storage and renewables under availability-based tariff. IEEE Region 10 Annual International Conference TENCON. 2008.[7] Gourma, A., Berdai, A., Reddak, M., Tytiuk, V., Reliability and Optimization Strategy in an Interconnected Network at a Wind Farm, (2018) International Review on Modelling and Simulations (IREMOS), 11 (2), pp. 76-83. doi: https://doi.org/10.15866/iremos.v11i2.13596[8] Varanasi, J., Tripathi, M., Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting, (2019) International Review on Modelling and Simulations (IREMOS), 12 (1), pp. 16-23. doi: https://doi.org/10.15866/iremos.v12i1.15781[9] Srivastava, A., Bajpai, R., An Efficient Maximum Power Extraction Algorithm for Wind Energy Conversion System Using Model Predictive Control, (2019) International Journal on Energy Conversion (IRECON), 7 (3), pp. 93-107. doi: https://doi.org/10.15866/irecon.v7i3.17403[10] A. Castillo, D. F. Gayme. Evaluating the effects of real power losses in optimal power flow-based storage integration. IEEE Transactions on Control of Network Systems, vol 5, n. 3. Sep 2018, pp 1132–1145.[11] Sharifzadeh H, Amjady N, Zareipour H. Multi-period stochastic security-constrained OPF considering the uncertainty sources of wind power, load demand and equipment unavailability. Electric Power Systems Research, vol. 146, n. 5. May 2017, pp. 33–42.[12] Boonchuay, K. Tomsovic, F. Li, W. Ongsakul. Robust optimization-based DC optimal power flow for managing wind generation uncertainty. AIP Conference Procedings, vol 1499, n. 1. May 2014, pp 31–35.[13] Rahmat Azami MSJ and GH. Economic load Dispatch and DCOptimal Power Flow Problem-PSO versus LR. International Journal of Multidisciplinary Sciences and Engineering, vol. 2, n. 9. Dec 2011, pp 8–13.[14] A. Soroudi. Power System Optimization Modeling in GAMS. (Springer International Publishing, 2017).[15] R. A. Jabr, S. Karaki, J. A. Korbane. Robust Multi-Period OPF with Storage and Renewables. IEEE Transactions on Power Systems, vol. 30 n. 5. Sep 2015, pp. 2790–2799.[16] B. Eldridge, R. O’Neill, A. Castillo. An Improved Method for the DCOPF with Losses. IEEE Transactions on Power Systems vol. 33, n. 4. July 2018, pp. 3779–3788.[17] P. Maghouli, A. Soroudi, A. Keane. Robust computational framework for mid-term techno-economical assessment of energy storage. IET Generation, Transmission & Distribution, vol. 10 n. 3. Feb 2016, pp. 822–831.[18] Hafez, A., AlSadi, S., Nassar, Y., Chaotic Optimization Versus Genetic Algorithm for Optimal Tuning of Static Synchronous Series Compensator Stabilizing Controller, (2019) International Review of Electrical Engineering (IREE), 14 (3), pp. 159-172. doi: https://doi.org/10.15866/iree.v14i3.16163[19] Mmary, E., Marungsri, B., Multiobjective Optimization of Renewable Distributed Generations in Radial Distribution Networks with Optimal Power Factor, (2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 297-304. doi: https://doi.org/10.15866/iree.v13i4.15069[20] Adam, K., Miyauchi, H., Optimization of a Photovoltaic Hybrid Energy Storage System Using Energy Storage Peak Shaving, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 8-18. doi: https://doi.org/10.15866/iree.v14i1.16162[21] Oloulade, A., Moukengue, A., Vianou, A., Multi-Criteria Optimization of the Functionning of a Distribution Network in Normal Operating Regime, (2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 290-296. doi: https://doi.org/10.15866/iree.v13i4.14401[22] Hassoune, A., Khafallah, M., Mesbahi, A., Benaaouinate, L., ouragba, T., Control Strategies of a Smart Topology of EVs Charging Station Based Grid Tied RES-Battery, (2018) International Review of Electrical Engineering (IREE), 13 (5), pp. 385-396. doi: https://doi.org/10.15866/iree.v13i5.15520[23] Moreno, R. Identification of Topological Vulnerabilities for Power Systems Networks. 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