nach oben

Erschienen in:

2014 | OriginalPaper | Buchkapitel

Energy Hub Management with Intermittent Wind Power

verfasst von : Alireza Soroudi, Behnam Mohammadi-Ivatloo, Abbas Rabiee

Erschienen in: Large Scale Renewable Power Generation

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The optimal energy management in energy hubs has recently attracted a great deal of attention around the world. The energy hub consists of several inputs (energy resources) and outputs (energy consumptions) and also some energy conversion/storage devices. The energy hub can be a home, large consumer, power plant, etc. The objective is to minimize the energy procurement costs (fuel/electricity/environmental aspects) subject to a set of technical constraints. One of the popular options to be served as the input resource is renewable energy like wind or solar power. Using the renewable energy has various benefits such as low marginal costs and zero environmental pollution. On the other hand, the uncertainties associated with them make the operation of the energy hub a difficult and risky task. Besides, there are other resources of uncertainties such as the hourly electricity prices and demand values. Hence, it is important to determine an economic schedule for energy hubs, with an acceptable level of energy procurement risk. Thus, in this chapter a comprehensive multiobjective model is proposed to minimize both the energy procurement cost and risk level in energy hub. For controlling the pernicious effects of the uncertainties, conditional value at risk (CVaR) is used as risk management tool. The proposed model is formulated as a mixed integer nonlinear programming (MINLP) problem and solved using GAMS. Simulation results on an illustrative test system are carried out to demonstrate the applicability of the proposed method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Control of Emerging Brushless Doubly-Fed Reluctance Wind Turbine Generators

Nächstes Kapitel Adopting the IEC Common Information Model to Enable Smart Grid Interoperability and Knowledge Representation Processes

Nur mit Berechtigung zugänglich

Krause T, Andersson G, Frohlich K, Vaccaro A (2011) Multiple-energy carriers: modeling of production, delivery, and consumption. Proc IEEE 99(1):15–27CrossRef

Koeppel G, Andersson G (2009) Reliability modeling of multi-carrier energy systems. Energy 34(3):235–244CrossRef

del Real AJ, Arce A, Bordons C (2009) Optimization strategy for element sizing in hybrid power systems. J Power Sources 193(1):315–321CrossRef

Kienzle F, Ahcin P, Andersson G (2011) Valuing investments in multi-energy conversion, storage, and demand-side management systems under uncertainty. IEEE Trans Sustain Energy 2(2):194–202CrossRef

Kienzle F, Andersson G (2010) Location-dependent valuation of energy hubs with storage in multi-carrier energy systems. In: IEEE 7th International Conference on the European Energy Market (EEM). pp 1–6

Fabrizio E, Corrado V, Filippi M (2010) A model to design and optimize multi-energy systems in buildings at the design concept stage. Renew Energy 35(3):644–655CrossRef

Arnold M, Andersson G (2011) Model predictive control of energy storage including uncertain forecasts. In: Power Systems Computation Conference (PSCC). Stockholm, Sweden

Almassalkhi M, Hiskens I (2011) Optimization framework for the analysis of large-scale networks of energy hubs. In: Power Systems Computation Conference (PSCC). Stockholm, Sweden

Krause T, Kienzle F, Art S, Andersson G (2010) Maximizing exergy efficiency in multi-carrier energy systems. In: IEEE Power and Energy Society General Meeting. pp 1–8

10.

Morales J, Conejo A, Perez-Ruiz J (2010) Short-term trading for a wind power producer. IEEE Trans Power Syst 25(1):554–564

11.

Catalao JP, Pousinho HM, Mendes VM (2012) Optimal offering strategies for wind power producers considering uncertainty and risk. IEEE Syst J 6(2):270–277CrossRef

12.

Rabiee A, Soroudi A (2013) Stochastic multiperiod OPF model of power systems with HVDC-connected intermittent wind power generation. IEEE Trans Power Delivery 99:1–1 (Early access)

13.

Soroudi A, Ehsan M (2011) A possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks–a case study. Renew Sustain Energy Rev 15(1):794–800CrossRef

14.

Wang Y, Xia Q, Kang C (2011) Unit commitment with volatile node injections by using interval optimization. IEEE Trans Power Syst 26(3):1705–1713CrossRef

15.

Wu L, Shahidehpour M, Li Z (2012) Comparison of scenario-based and interval optimization approaches to stochastic SCUC. IEEE Trans Power Syst 27(2):913–921CrossRef

16.

Koonce A, Apostolakis G, Cook B (2008) Bulk power risk analysis: ranking infrastructure elements according to their risk significance. Int J Electr Power Energy Syst 30(3):169–183CrossRef

17.

Soroudi A, Amraee T (2013) Decision making under uncertainty in energy systems: state of the art. Renew Sustain Energy Rev 28:376–384CrossRef

18.

Soroudi A (2012) Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment. IEEE Trans Power Syst 27(3):1283–1293CrossRef

19.

Soroudi A, Afrasiab M (2012) Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty. IET Renew Power Gener 6(2):67–78CrossRef

20.

Soroudi A, Caire R, Hadjsaid N, Ehsan M (2011) Probabilistic dynamic multi-objective model for renewable and non-renewable distributed generation planning. IET Gener Transm Distrib 5(11):1173–1182CrossRef

21.

Soroudi A, Aien M, Ehsan M (2012) A probabilistic modelling of photo voltaic modules and wind power generation impact on distribution networks. IEEE Syst J 6(2):254–259CrossRef

22.

Mohammadi-Ivatloo B, Zareipour H, Amjady N, Ehsan M (2013) Application of information-gap decision theory to risk-constrained self-scheduling of GenCos. IEEE Trans Power Syst 28(2):1093–1102CrossRef

23.

Soroudi A, Ehsan M (2012) IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty. IEEE Trans Smart Grid 4(2):886–895CrossRef

24.

Soroudi A (2013) Smart self-scheduling of GenCos with thermal and energy storage units under price uncertainty. Int Trans Electr Energy Syst 1–11. http://dx.doi.org/10.1002/etep.1780

25.

Conejo AJ, Carrion M, Morales JM (2010) Decision making under uncertainty in electricity markets. Springer, New YorkCrossRefMATH

26.

Albrecht P (1993) Shortfall returns and shortfall risk. Institut für Versicherungswissenschaft

27.

Whitmore GA, Findlay MC (1978) Stochastic dominance: an approach to decision-making under risk. Lexington Books, Lexington

28.

Linsmeier TJ, Pearson ND (2000) Value at risk. Financ Anal J 56:47–67

29.

Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21–42

30.

Agarwal V, Naik NY (2004) Risks and portfolio decisions involving hedge funds. Rev Financ Stud 17(1):63–98CrossRef

31.

Mohammadi-Ivatloo B, Zareipour H, Ehsan M, Amjady N (2011) Economic impact of price forecasting inaccuracies on self-scheduling of generation companies. Electr Power Syst Res 81(2):617–624CrossRef

32.

Mohammadi-Ivatloo B, Rabiee A, Soroudi A (2013) Nonconvex dynamic economic power dispatch problems solution using hybrid immune genetic algorithm. IEEE Syst J 7(4):777–785CrossRef

33.

Ding Y, Wang P, Goel L, Loh PC, Wu Q (2011) Long-term reserve expansion of power systems with high wind power penetration using universal generating function methods. IEEE Trans Power Syst 26(2):766–774CrossRef

34.

Atwa Y, El-Saadany E (2011) Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems. IET Renew Power Gener 5(1):79–88CrossRef

35.

Patel MR (2005) Wind and solar power systems: design, analysis, and operation. CRC, Boca Raton

36.

Stiebler M (2008) Wind energy systems for electric power generation. Springer, Heidelberg

37.

Windpro http://www.enercon.de/p/downloads/ENProduktuebersicht0710.pdf. Accessed Feb 2013

38.

Brooke AMA, Kendrick D, Roman R (1998) GAMS: a user’s guide. GAMS development corporation. Washington

39.

Mohammadi-ivatloo B, Rabiee A, Soroudi A, Ehsan M (2012) Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch. Energy 44(1):228–240CrossRef

40.

Pineda S, Conejo A (2010) Scenario reduction for risk-averse electricity trading. IET Gener Transm Distrib 4(6):694–705CrossRef

41.

Soroudi A, Ehsan M, Caire R, Hadjsaid N (2011) Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment. IET Gener Transm Distrib 5(9):961–972CrossRef

42.

Rabiee A, Soroudi A (2013) Optimal multi-area generation schedule considering renewable resources mix: a real-time approach. IET Gener Transm Distrib 7(9):1011–1026CrossRef

Titel: Energy Hub Management with Intermittent Wind Power
verfasst von: Alireza Soroudi
Behnam Mohammadi-Ivatloo
Abbas Rabiee
Verlag: Springer Singapore
Buch: Large Scale Renewable Power Generation
Print ISBN: 978-981-4585-29-3

Electronic ISBN: 978-981-4585-30-9

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-981-4585-30-9_16