Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model
A mathematical optimization approach for the optimal operation focused on the economic dispatch for dc microgrid with high penetration of distributed generators and energy storage systems (ESS) via semidefinite programming (SDP) is proposed in this paper. The SDP allows transforming the nonlinear an...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9165
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9165
- Palabra clave:
- Convex reformulation
Dc microgrids
Economic dispatch
Energy storage systems
Semidefinite programming
Distributed computer systems
Energy storage
Neural networks
Optimization
Scheduling
Wind
Convex reformulation
Dc microgrids
Economic dispatch
Energy storage systems
Semidefinite programming
Electric load dispatching
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| title |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| spellingShingle |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model Convex reformulation Dc microgrids Economic dispatch Energy storage systems Semidefinite programming Distributed computer systems Energy storage Neural networks Optimization Scheduling Wind Convex reformulation Dc microgrids Economic dispatch Energy storage systems Semidefinite programming Electric load dispatching |
| title_short |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| title_full |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| title_fullStr |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| title_full_unstemmed |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| title_sort |
Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model |
| dc.subject.keywords.none.fl_str_mv |
Convex reformulation Dc microgrids Economic dispatch Energy storage systems Semidefinite programming Distributed computer systems Energy storage Neural networks Optimization Scheduling Wind Convex reformulation Dc microgrids Economic dispatch Energy storage systems Semidefinite programming Electric load dispatching |
| topic |
Convex reformulation Dc microgrids Economic dispatch Energy storage systems Semidefinite programming Distributed computer systems Energy storage Neural networks Optimization Scheduling Wind Convex reformulation Dc microgrids Economic dispatch Energy storage systems Semidefinite programming Electric load dispatching |
| description |
A mathematical optimization approach for the optimal operation focused on the economic dispatch for dc microgrid with high penetration of distributed generators and energy storage systems (ESS) via semidefinite programming (SDP) is proposed in this paper. The SDP allows transforming the nonlinear and non-convex characteristics of the economic dispatch problem into a convex approximation which is easy for implementation in specialized software, i.e., CVX. The proposed mathematical approach contemplates the efficient operation of a dc microgrid over a period of time with variable energy purchase prices, which makes it a practical methodology to apply in real-time operating conditions. A nonlinear autoregressive exogenous (NARX) model is employed for training an artificial neural network (ANN) for forecasting solar radiation and wind speed for renewable generation integration and dispatch considering periods of prediction of 0.5 h. Four scenarios are proposed to analyze the inclusion of ESS in a dc microgrid for economic dispatch studies. Additionally, the results are compared with GAMS commercial optimization package, which allows validating the accuracy and quality of the proposed optimizing methodology. © 2018 Elsevier Ltd |
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2019 |
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2019 |
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2020-03-26T16:33:06Z |
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2020-03-26T16:33:06Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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Artículo |
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publishedVersion |
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Journal of Energy Storage; Vol. 21, pp. 1-8 |
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2352152X |
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https://hdl.handle.net/20.500.12585/9165 |
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10.1016/j.est.2018.10.025 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57191493648 56919564100 57204572827 36449223500 55791991200 |
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Journal of Energy Storage; Vol. 21, pp. 1-8 2352152X 10.1016/j.est.2018.10.025 Universidad Tecnológica de Bolívar Repositorio UTB 57191493648 56919564100 57204572827 36449223500 55791991200 |
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https://hdl.handle.net/20.500.12585/9165 |
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eng |
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eng |
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Recurso electrónico |
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application/pdf |
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Elsevier Ltd |
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Elsevier Ltd |
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2020-03-26T16:33:06Z2020-03-26T16:33:06Z2019Journal of Energy Storage; Vol. 21, pp. 1-82352152Xhttps://hdl.handle.net/20.500.12585/916510.1016/j.est.2018.10.025Universidad Tecnológica de BolívarRepositorio UTB5719149364856919564100572045728273644922350055791991200A mathematical optimization approach for the optimal operation focused on the economic dispatch for dc microgrid with high penetration of distributed generators and energy storage systems (ESS) via semidefinite programming (SDP) is proposed in this paper. The SDP allows transforming the nonlinear and non-convex characteristics of the economic dispatch problem into a convex approximation which is easy for implementation in specialized software, i.e., CVX. The proposed mathematical approach contemplates the efficient operation of a dc microgrid over a period of time with variable energy purchase prices, which makes it a practical methodology to apply in real-time operating conditions. A nonlinear autoregressive exogenous (NARX) model is employed for training an artificial neural network (ANN) for forecasting solar radiation and wind speed for renewable generation integration and dispatch considering periods of prediction of 0.5 h. Four scenarios are proposed to analyze the inclusion of ESS in a dc microgrid for economic dispatch studies. Additionally, the results are compared with GAMS commercial optimization package, which allows validating the accuracy and quality of the proposed optimizing methodology. © 2018 Elsevier LtdDepartamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS Department of Science, Information Technology and Innovation, Queensland GovernmentThis work was partially supported by the National Scholarship Program Doctorates of the Administrative Department of Science, Technology and Innovation of Colombia (COLCIENCIAS), by calling contest 727-2015.Recurso electrónicoapplication/pdfengElsevier Ltdhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85056222022&doi=10.1016%2fj.est.2018.10.025&partnerID=40&md5=58ea9fbf0cbef7e0364b3d51354c7119Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming modelinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Convex reformulationDc microgridsEconomic dispatchEnergy storage systemsSemidefinite programmingDistributed computer systemsEnergy storageNeural networksOptimizationSchedulingWindConvex reformulationDc microgridsEconomic dispatchEnergy storage systemsSemidefinite programmingElectric load dispatchingGil-González W.Montoya O.D.Holguín E.Garces A.Grisales-Noreña L.F.Dragičević, T., Lu, X., Vasquez, J.C., Guerrero, J.M., DC microgrids-Part I: A review of control strategies and stabilization techniques (2016) IEEE Trans. Power Electron, 31 (7), pp. 4876-4891Grisales, L.F., Grajales, A., Montoya, O.D., Hincapie, R.A., Granada, M., Castro, C.A., Optimal location, sizing and operation of energy storage in distribution systems using multi-objective approach (2017) IEEE Latin Am. Trans., 15 (6), pp. 1084-1090Strunz, K., Abbasi, E., Huu, D.N., DC microgrid for wind and solar power integration (2014) IEEE J. Emerging Sel. Top. Power Electron, 2 (1), pp. 115-126Giraldo, O.D.M., González, W.J.G., Ruiz, A.G., Mejía, A.E., Noreña, L.F.G., Nonlinear control for battery energy storage systems in power grids (2018) 2018 IEEE Green Technologies Conference (GreenTech), pp. 65-70Montoya, O.D., Gil-González, W., Garces, A., Optimal Power Flow on DC Microgrids: A Quadratic Convex Approximation (2018) IEEE Transactions on Circuits and Systems II: Express Briefs, , 1-1. doiParhizi, S., Lotfi, H., Khodaei, A., Bahramirad, S., State of the art in research on microgrids: A review (2015) IEEE Access, 3, pp. 890-925Montoya, O.D., Garcés, A., Serra, F.M., DERs integration in microgrids using VSCs via proportional feedback linearization control: Supercapacitors and distributed generators (2018) J. 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Res., 130, pp. 222-229Grant, M., Boyd, S., http://cvxr.com/cvx, CVX: Matlab software for disciplined convex programming, version 2.1 (Mar. 2014)Guimaraes, D.A., Floriano, G.H.F., Chaves, L.S., A Tutorial on the CVX System for Modeling and Solving Convex Optimization Problems (2015) IEEE Latin America Transactions, 13 (5), pp. 1228-1257Data, S.S.R., Time series of solar radiation datahttp://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9165/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD511-s2.0-S2352152X18302962-main.pdf.jpg1-s2.0-S2352152X18302962-main.pdf.jpgGenerated Thumbnailimage/jpeg100121https://repositorio.utb.edu.co/bitstream/20.500.12585/9165/4/1-s2.0-S2352152X18302962-main.pdf.jpg1210b94f897e1fbed305947bbbdf5d25MD54ORIGINAL1-s2.0-S2352152X18302962-main.pdf1-s2.0-S2352152X18302962-main.pdfapplication/pdf1604709https://repositorio.utb.edu.co/bitstream/20.500.12585/9165/2/1-s2.0-S2352152X18302962-main.pdfba0823fe743fbe959603d7ad1da0fb90MD52TEXT1-s2.0-S2352152X18302962-main.pdf.txt1-s2.0-S2352152X18302962-main.pdf.txtExtracted texttext/plain44753https://repositorio.utb.edu.co/bitstream/20.500.12585/9165/3/1-s2.0-S2352152X18302962-main.pdf.txtc436e3c59d8e15cc422f158f66e237eaMD5320.500.12585/9165oai:repositorio.utb.edu.co:20.500.12585/91652022-03-03 02:21:20.95Repositorio Institucional UTBrepositorioutb@utb.edu.co |