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Energy Systems
Erschienen in: Energy Systems 3/2014

01.09.2014 | Original Paper

A stochastic dynamic programming model for co-optimization of distributed energy storage

verfasst von: Xiaomin Xi, Ramteen Sioshansi, Vincenzo Marano

Erschienen in: Energy Systems | Ausgabe 3/2014

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Abstract

We develop a stochastic dynamic programming model that co-optimizes the use of energy storage for multiple applications, such as energy, capacity, and backup services, while accounting for market and system uncertainty. Using the example of a battery that has been installed in a home as a distributed storage device, we demonstrate the ability of the model to co-optimize services that ‘compete’ for the capacity of the battery. We also show that these multiple uses of a battery can provide substantive value.
Vorheriger Artikel Mathematical optimization for challenging network planning problems in unbundled liberalized gas markets
Nächster Artikel An empirical model of power curve of a wind turbine

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Fußnoten
1
Pumped hydroelectric storage (PHS) accounts for most currently installed storage capacity [21] and interest in compressed-air energy storage (CAES) is increasing [22]. Due to their physical attributes and geological requirements, PHS and CAES are both utility-scale storage technologies.
 
2
A kW-h, which is a unit for one kW of AS capacity provided for 1 h, should be distinguished from a kWh, which is a unit of energy.
 
3
This is also why we define \(\delta _t^u\) and \(\delta _t^d\) as the hour-\((t-1)\) ratios, due to our time definition convention.
 
4
The scenario tree only has 20 sample paths because the resulting two-stage model is computationally intractable with a larger tree.
 
Literatur
1.
Assessment of energy storage systems suitable for use by electric utilities. Tech. Rep. EPRI-EM-264. Electric Power Research Institute, Palo Alto (1976)
2.
EPRI-DOE handbook for energy storage for transmission and distribution applications. Tech. Rep. 1001834, Electric Power Research Institute and the U.S. Department of Energy, Palo Alto (2003)
3.
Eyer, J.M., Corey, G.: Energy storage for the electricity grid: benefits and market potential assessment guide, Tech. Rep. SAND2010-0815. Sandia National Laboratories (2010)
4.
Graves, F., Jenkin, T., Murphy, D.: Opportunities for electricity storage in deregulating markets. Electr. J. 12, 46–56 (1999)CrossRef
5.
Butler, P.C., Iannucci, J., Eyer, J.M.: Innovative business cases for energy storage in a restructured electricity marketplace, Tech. Rep. SAND2003-0362. Sandia National Laboratories (2003)
6.
Eyer, J.M., Iannucci, J.J., Corey, G.P.: Energy storage benefits and market analysis handbook, Tech. Rep. SAND2004-6177. Sandia National Laboratories (2004)
7.
Mokrian, P., Stephen, M.: A stochastic programming framework for the valuation of electricity storage. In: 26th USAEE/IAEE North American Conference. International Association for Energy Economics, Ann Arbor (2006)
8.
Sioshansi, R., Denholm, P., Jenkin, T., Weiss, J.: Estimating the value of electricity storage in PJM: arbitrage and some welfare effects. Energy Econ. 31, 269–277 (2009)CrossRef
9.
Sioshansi, R., Denholm, P., Jenkin, T.: A comparative analysis of the value of pure and hybrid electricity storage. Energy Econ. 33, 56–66 (2011)CrossRef
10.
Walawalkar, R., Apt, J., Mancini, R.: Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy 35, 2558–2568 (2007)CrossRef
11.
Drury, E., Denholm, P., Sioshansi, R.: The value of compressed air energy storage in energy and reserve markets. Energy 36, 4959–4973 (2011)CrossRef
12.
Paatero, J.V., Lund, P.D.: Effect of energy storage on variations in wind power. Wind Energy 8, 421–441 (2005)CrossRef
13.
Black, M., Strbac, G.: Value of bulk energy storage for managing wind power fluctuations. IEEE Trans. Energy Convers. 22, 197–205 (2007)CrossRef
14.
García-González, J., de la Muela, R.M.R., Santos, L.M., González, A.M.: Stochastic joint optimization of wind generation and pumped-storage units in an electricity market. IEEE Trans. Power Syst. 23, 460–468 (2008)CrossRef
15.
Denholm, P., Kulcinski, G.L., Holloway, T.: Emissions and energy efficiency assessment of baseload wind energy systems. Environ. Sci. Technol. 39, 1903–1911 (2005)CrossRef
16.
Greenblatt, J.B., Succar, S., Denkenberger, D.C., Williams, R.H., Socolow, R.H.: Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation. Energy Policy 35, 1474–1492 (2007)CrossRef
17.
Sioshansi, R.: Increasing the value of wind with energy storage. Energy J. 32, 1–30 (2011)CrossRef
18.
Sioshansi, R.: Emissions impacts of wind and energy storage in a market environment. Environ. Sci. Technol. 45, 10728–10735 (2011)
19.
Denholm, P., Sioshansi, R.: The value of compressed air energy storage with wind in transmission-constrained electric power systems. Energy Policy 37, 3149–3158 (2009)CrossRef
20.
Sioshansi, R., Denholm, P.: Benefits of colocating concentrating solar power and wind. IEEE Trans. Sustain. Energy 4, 877–885 (2013)CrossRef
21.
Deane, J.P.: Ó Gallachóir, B.P., McKeogh, E.J.: Techno-economic review of existing and new pumped hydro energy storage plant. Renew. Sustain. Energy Rev. 14, 1293–1302 (2010)
22.
Succar, S., Williams, R.H.: Compressed air energy storage theory, resources and applications for wind power, Tech. rep. Princeton Environmental Institute, Princeton (2008)
23.
Nourai, A.: Installation of the first distributed energy storage system (DESS) at American Electric Power (AEP), Tech. Rep. SAND2007-3580. Sandia National Laboratories (2007)
24.
Espinosa, J.R.: Implementation and integration of air conditioner, cycling at Southern California Edison. IEEE Trans. Power Syst. 2, 792–798 (1987)
25.
Tomić, J., Kempton, W.: Using fleets of electric-drive vehicles for grid support. J. Power Sources 168, 459–468 (2007)CrossRef
26.
Joo, S.K., Kim, J.C., Liu, C.C.: Empirical analysis of the impact of 2003 blackout on security values of U.S. utilities and electrical equipment manufacturing firms. IEEE Trans. Power Syst. 22, 1012–1018 (2007)CrossRef
27.
Mohseni, P., Stevie, R.G.: Electric vehicles: Holy grail or Fool’s gold. Power & Energy Society General Meeting, 2009, pp. 1–5. Institute of Electrical and Electronics Engineers, Calgary (2009)
28.
Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley-Interscience, Hoboken (2007)CrossRef
29.
Kempton, W., Tomić, J.: Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J. Power Sources 144, 268–279 (2005)CrossRef
30.
Shapiro, A.: Inference of statistical bounds for multistage stochastic programming problems. Math. Methods Oper. Res. 58, 57–68 (2003)CrossRefMATHMathSciNet
31.
Kariuki, K.K., Allan, R.N.: Evaluation of reliability worth and value of lost load. IEE Proc. Gener. Transm. Distrib. 143, 171–180 (1996)
32.
Knittel, C.R., Roberts, M.R.: An empirical examination of restructured electricity prices. Energy Econ. 27, 791–817 (2005)CrossRef
33.
Shrestha, G.B., Songbo, Q.: Statistical characterization of electricity price in competitive power markets. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). Institute of Electrical and Electronics Engineers, Singapore (2010)
34.
Seppälä, A.: Statistical distribution of customer load profiles. In: Proceedings of 1995 International Conference on Energy Management and Power Delivery. Institute of Electrical and Electronics Engineers (1995)
35.
DeSieno, C.F., Stine, L.L.: A probability method for determining the reliability of electric power systems. IEEE Trans. Reliab. R-14, 30–35 (1965)
36.
Brown, R.E., Gupta, S., Christie, R.D., Venkata, S.S., Fletcher, R.H.: Distribution system reliability assessment using hierarchical Markov modeling. IEEE Trans. Power Deliv. 11, 1929–1934 (1996)CrossRef
37.
van Casteren, J.F.L., Bollen, M.H.J., Schmieg, M.E.: Reliability assessment in electrical power systems: the Weibull–Markov stochastic model. IEEE Trans. Ind. Appl. 36, 911–915 (2000)CrossRef
38.
Midlam-Mohler, S., Ewing, S., Marano, V., Guezennec, Y., Rizzoni, G.: PHEV fleet data collection and analysis. 2009 IEEE Vehicle Power and Propulsion Conference, pp. 1205–1210. Institute of Electrical and Electronics Engineers, Dearborn (2009)
39.
Tulpule, P., Marano, V., Rizzoni, G.: Energy management for plug-in hybrid electric vehicles using equivalent consumption minimisation strategy. Int. J. Electr. Hybrid Veh. 2, 329–350 (2010)CrossRef
Metadaten
Titel
A stochastic dynamic programming model for co-optimization of distributed energy storage
verfasst von
Xiaomin Xi
Ramteen Sioshansi
Vincenzo Marano
Publikationsdatum
01.09.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Energy Systems / Ausgabe 3/2014
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI
https://doi.org/10.1007/s12667-013-0100-6

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