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Stochastic programming approach for energy management in electric microgrids
1. | Siemens AG, Corporate Technology (CT RTC AUC SIM-DE), Otto-Hahn-Ring 6, 81739 Munich |
2. | Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, United States |
3. | Siemens Corporate Technology, Otto-Hahn-Ring 6, 81739 Munich, Germany |
References:
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, Cbc (Coin-or Branch and Cut) Solver,, Available from: , (). Google Scholar |
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, Embest Technology Co.,LTD,, Available from: , (). Google Scholar |
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, Microgrids at Berkeley Lab,, Available from: , (). Google Scholar |
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, Medium-term renewable energy market report 2013,, Report of International Energy Agency, (2013). Google Scholar |
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W. Bernhart, Powertrain 2020. The Li-Ion Battery Value Chain - Trends and implications, Case study Roland Berger Strategy Consultants,, 2011. Available from: , (). Google Scholar |
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J. R. Birge and F. Louveaux, Introduction to Stochastic Programming,, Springer series in operations research and financial engineering, (2011).
doi: 10.1007/978-1-4614-0237-4. |
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P. Bishnoi, W. Klein, R. Kuntschke, R. Speh and M. W. Waszak, A Disruptive Approach for a Green Field Smart Grid Installation,, in, (2012). Google Scholar |
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B. Burger, Electricity Production From Solar and Wind in Germany in 2013,, Technical report, (2013). Google Scholar |
[12] |
G. Cardoso, M. Stadler, A. Siddiqui, C. Marnay, N. DeForest, A. Barbosa-Póvoa, and P. Ferrão, Microgrid reliability modeling and battery scheduling using stochastic linear programming,, Electric Power Systems Research, 103 (2013), 61. Google Scholar |
[13] |
G. B. Dantzig, Linear programming under uncertainty,, Management Science, 50 (2004), 1764. Google Scholar |
[14] |
J. Dupačová, N. Gröwe-Kuska and W. Römisch, Scenario reduction in stochastic programming,, Mathematical Programming, 95 (2003), 493.
doi: 10.1007/s10107-002-0331-0. |
[15] |
C. C. Carøe and R. Schultz, Dual decomposition in stochastic integer programming,, Operations Research Letters, 24 (1997), 37.
doi: 10.1016/S0167-6377(98)00050-9. |
[16] |
D. Gade, G. Hackebeil, S. M. Ryan, J. P. Watson, R. J. B. Wets and D. L. Woodruff, Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs,, Sandia Technical report, (2013). Google Scholar |
[17] |
N. Gröwe-Kuska, H. Heitsch, and W. Römisch, Scenario reduction and scenario tree construction for power management problems,, in Power Tech Conference Proceedings, 3 (2003). Google Scholar |
[18] |
W. E. Hart, C. Laird, J. P. Watson and D. L. Woodruff, Pyomo - Optimization Modeling in Python,, Springer, (2012). Google Scholar |
[19] |
N. Hatziargyriou, H. Asano, R. Iravani and C. Marnay, Microgrids,, Power and Energy Magazine, 5 (2007), 78. Google Scholar |
[20] |
A. M. Gleixner, H. Held, W. Huang and S. Vigerske, Towards globally optimal operation of water supply networks,, Numer. Algebra Control Optim., 2 (2012), 695.
doi: 10.3934/naco.2012.2.695. |
[21] |
H. Jiayi, J. Chuanwen and X. Rong, A review on distributed energy resources and microgrid,, Renewable and Sustainable Energy Reviews, 12 (2008), 2472. Google Scholar |
[22] |
J. J. Justo, F. Mwasilu, J. Lee and J. W. Jung, AC-microgrids versus DC-microgrids with distributed energy resources: A review,, Renewable and Sustainable Energy Reviews, 24 (2013), 387. Google Scholar |
[23] |
M. Kaut and S. W. Wallace, Evaluation of scenario-generation methods for stochastic programming,, Pacific Journal of Optimization, 3 (2007), 257.
|
[24] |
G. Martinez, N. Gatsis and G. B. Giannakis, Stochastic programming for energy planning in microgrids with renewables,, in Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), (2013), 472. Google Scholar |
[25] |
S. Mitra, A white paper on scenario generation for stochastic programming,, White paper, (2006). Google Scholar |
[26] |
T. Niknam, R. Azizipanah-Abarghooee and M. R. Narimani, An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation,, Applied Energy, 99 (2012), 455. Google Scholar |
[27] |
A. Parisio and L. Glielmo, Stochastic model predictive control for economic/environmental operation management of microgrids,, in 2013 European Control Conference (ECC), (2013), 2014. Google Scholar |
[28] |
R. T. Rockafellar and R. J. B. Wets, Scenarios and policy aggregation in optimization under uncertainty,, Math. Oper. Res., 16 (1991), 119.
doi: 10.1287/moor.16.1.119. |
[29] |
M. Riis and R. Schultz, Applying the minimum risk criterion in stochastic recourse programs,, Computational Optimization and Applications, 24 (2003), 267.
doi: 10.1023/A:1021862109131. |
[30] |
W. Römisch, Scenario generation,, in Wiley Encyclopedia of Operations Research and Management Science, (2011). Google Scholar |
[31] |
C. Sagastizábal, Divide to conquer: decomposition methods for energy optimization,, Math. Program., 134 (2012), 187.
doi: 10.1007/s10107-012-0570-7. |
[32] |
A. Shapiro, D. Dentcheva and A. Ruszczyński, Lectures on Stochastic Programming. Modeling and Theory,, MPS/SIAM Series on Optimization, (2009).
doi: 10.1137/1.9780898718751. |
[33] |
W. Su, J. Wang and J. Roh, Stochastic energy scheduling in microgrids with intermittent renewable energy resources,, Smart Grid, 99 (2013), 1. Google Scholar |
[34] |
J. P. Watson and D. L. Woodruff, Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems,, Computational Management Science, 8 (2011), 355.
doi: 10.1007/s10287-010-0125-4. |
[35] |
J. P. Watson, D. L. Woodruff and W. E. Hart, PySP: modeling and solving stochastic programs in Python,, Mathematical Programming Computation, 4 (2012), 109.
doi: 10.1007/s12532-012-0036-1. |
[36] |
Y. Zhou, H. Held, W. Klein, K. Majewski, R. Speh, P. E. Stelzig and C. Wincheringer, SoftGrid: A green field approach of future smart grid,, in 2nd International Conference on Smart Grids and Green IT Systems (SMARTGREENS 2013), (2013). Google Scholar |
show all references
References:
[1] |
, Cbc (Coin-or Branch and Cut) Solver,, Available from: , (). Google Scholar |
[2] |
, Embest Technology Co.,LTD,, Available from: , (). Google Scholar |
[3] |
, Microgrids at Berkeley Lab,, Available from: , (). Google Scholar |
[4] |
, Raspberry Pi Embedded Computer,, Available from: , (). Google Scholar |
[5] |
, Sun blade 1000 and sun blade 2000 product notes,, Available from: , (): 19127. Google Scholar |
[6] |
, Lithium-ion batteries - The bubble bursts,'' Case study Roland Berger Strategy Consultants,, 2012. Available from: , (). Google Scholar |
[7] |
, Medium-term renewable energy market report 2013,, Report of International Energy Agency, (2013). Google Scholar |
[8] |
W. Bernhart, Powertrain 2020. The Li-Ion Battery Value Chain - Trends and implications, Case study Roland Berger Strategy Consultants,, 2011. Available from: , (). Google Scholar |
[9] |
J. R. Birge and F. Louveaux, Introduction to Stochastic Programming,, Springer series in operations research and financial engineering, (2011).
doi: 10.1007/978-1-4614-0237-4. |
[10] |
P. Bishnoi, W. Klein, R. Kuntschke, R. Speh and M. W. Waszak, A Disruptive Approach for a Green Field Smart Grid Installation,, in, (2012). Google Scholar |
[11] |
B. Burger, Electricity Production From Solar and Wind in Germany in 2013,, Technical report, (2013). Google Scholar |
[12] |
G. Cardoso, M. Stadler, A. Siddiqui, C. Marnay, N. DeForest, A. Barbosa-Póvoa, and P. Ferrão, Microgrid reliability modeling and battery scheduling using stochastic linear programming,, Electric Power Systems Research, 103 (2013), 61. Google Scholar |
[13] |
G. B. Dantzig, Linear programming under uncertainty,, Management Science, 50 (2004), 1764. Google Scholar |
[14] |
J. Dupačová, N. Gröwe-Kuska and W. Römisch, Scenario reduction in stochastic programming,, Mathematical Programming, 95 (2003), 493.
doi: 10.1007/s10107-002-0331-0. |
[15] |
C. C. Carøe and R. Schultz, Dual decomposition in stochastic integer programming,, Operations Research Letters, 24 (1997), 37.
doi: 10.1016/S0167-6377(98)00050-9. |
[16] |
D. Gade, G. Hackebeil, S. M. Ryan, J. P. Watson, R. J. B. Wets and D. L. Woodruff, Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs,, Sandia Technical report, (2013). Google Scholar |
[17] |
N. Gröwe-Kuska, H. Heitsch, and W. Römisch, Scenario reduction and scenario tree construction for power management problems,, in Power Tech Conference Proceedings, 3 (2003). Google Scholar |
[18] |
W. E. Hart, C. Laird, J. P. Watson and D. L. Woodruff, Pyomo - Optimization Modeling in Python,, Springer, (2012). Google Scholar |
[19] |
N. Hatziargyriou, H. Asano, R. Iravani and C. Marnay, Microgrids,, Power and Energy Magazine, 5 (2007), 78. Google Scholar |
[20] |
A. M. Gleixner, H. Held, W. Huang and S. Vigerske, Towards globally optimal operation of water supply networks,, Numer. Algebra Control Optim., 2 (2012), 695.
doi: 10.3934/naco.2012.2.695. |
[21] |
H. Jiayi, J. Chuanwen and X. Rong, A review on distributed energy resources and microgrid,, Renewable and Sustainable Energy Reviews, 12 (2008), 2472. Google Scholar |
[22] |
J. J. Justo, F. Mwasilu, J. Lee and J. W. Jung, AC-microgrids versus DC-microgrids with distributed energy resources: A review,, Renewable and Sustainable Energy Reviews, 24 (2013), 387. Google Scholar |
[23] |
M. Kaut and S. W. Wallace, Evaluation of scenario-generation methods for stochastic programming,, Pacific Journal of Optimization, 3 (2007), 257.
|
[24] |
G. Martinez, N. Gatsis and G. B. Giannakis, Stochastic programming for energy planning in microgrids with renewables,, in Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), (2013), 472. Google Scholar |
[25] |
S. Mitra, A white paper on scenario generation for stochastic programming,, White paper, (2006). Google Scholar |
[26] |
T. Niknam, R. Azizipanah-Abarghooee and M. R. Narimani, An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation,, Applied Energy, 99 (2012), 455. Google Scholar |
[27] |
A. Parisio and L. Glielmo, Stochastic model predictive control for economic/environmental operation management of microgrids,, in 2013 European Control Conference (ECC), (2013), 2014. Google Scholar |
[28] |
R. T. Rockafellar and R. J. B. Wets, Scenarios and policy aggregation in optimization under uncertainty,, Math. Oper. Res., 16 (1991), 119.
doi: 10.1287/moor.16.1.119. |
[29] |
M. Riis and R. Schultz, Applying the minimum risk criterion in stochastic recourse programs,, Computational Optimization and Applications, 24 (2003), 267.
doi: 10.1023/A:1021862109131. |
[30] |
W. Römisch, Scenario generation,, in Wiley Encyclopedia of Operations Research and Management Science, (2011). Google Scholar |
[31] |
C. Sagastizábal, Divide to conquer: decomposition methods for energy optimization,, Math. Program., 134 (2012), 187.
doi: 10.1007/s10107-012-0570-7. |
[32] |
A. Shapiro, D. Dentcheva and A. Ruszczyński, Lectures on Stochastic Programming. Modeling and Theory,, MPS/SIAM Series on Optimization, (2009).
doi: 10.1137/1.9780898718751. |
[33] |
W. Su, J. Wang and J. Roh, Stochastic energy scheduling in microgrids with intermittent renewable energy resources,, Smart Grid, 99 (2013), 1. Google Scholar |
[34] |
J. P. Watson and D. L. Woodruff, Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems,, Computational Management Science, 8 (2011), 355.
doi: 10.1007/s10287-010-0125-4. |
[35] |
J. P. Watson, D. L. Woodruff and W. E. Hart, PySP: modeling and solving stochastic programs in Python,, Mathematical Programming Computation, 4 (2012), 109.
doi: 10.1007/s12532-012-0036-1. |
[36] |
Y. Zhou, H. Held, W. Klein, K. Majewski, R. Speh, P. E. Stelzig and C. Wincheringer, SoftGrid: A green field approach of future smart grid,, in 2nd International Conference on Smart Grids and Green IT Systems (SMARTGREENS 2013), (2013). Google Scholar |
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