Skip to main content

Advertisement

SpringerLink
Log in

Dynamic behavior of multi-carrier energy market in view of investment incentives

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

In this study, a dynamic two-level framework is proposed to model investment incentives in a multi-carrier energy market from a strategic company’s point of view. Capacity payment and firm contract are assumed as investment incentives to encourage the strategic producer to invest in generation units. In addition, financial incentives to invest in combined heat and power (CHP) include tax rebate and loans. Strategic company’s behavior is considered as a two-level model so that, in the first level, the objective function is to maximize the profit of the strategic producer by participating in an energy hub market. The strategic producer can invest in transmission lines, generation units, CHP, and gas furnace. In the second level, the aim is to maximize a multi-carrier energy social welfare encompassing heat, gas, and electric energy. In this model, units invested by rival companies are modeled using possible scenarios. Electric energy loads in this energy hub system are envisaged to be elastic, while heat loads are assumed to be inelastic to the market price. On the other hand, gas loads are indirectly elastic to the price. Besides, in the proposed framework, DC power flow and an exact gas flow model with the linearized Weymouth equation are used. The proposed model is implemented on two case studies including 6-bus system, and an energy hub system encompassing 24-bus IEEE RTS power system and 10-node natural gas system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

\({S/S}^{\prime }\) :

Index for scenario

\({y/y}^{\prime }\) :

Dynamic stage number (year)

t :

Demand block number

i / q /  / n :

Number of new/existing//generation units for strategic//rival company

c :

Demand number

m :

Number of candidate production units for investment

\({b/b}^{\prime }\) :

Power grid bus

\({l/l}^{\prime }\) :

Gas network bus

k :

CHP number

\(\text {TS}_{{yt}}\) :

Weighting coefficient of demand block t in year y

\(\text {SP}_{s}\) :

Likelihood of scenario s occurring in year y

BU:

SG’s investment funds

Y :

The cost of annual investment of the generation new unit (€/MW)

\(\text {OC}_{i}^{\text {Si}}/\text {OC}_{q}^{\text {Sq}}\) :

New/existing generation units’ operating costs i/q of strategic company i

\(\text {OC}_{n}^{\text {NS}}\) :

Generation units’ operating costs of rival company

\(\overline{C}_{{ytc}}\) :

Maximum consumption for demand c in block t and year y (MW)

\(B_{{tc}}^{d}\) :

The proposed price for demand c in block t and year y in scenario S

\(\text {CU}_{{yim}}\) :

Candidate capacity for investment m in the new unit i (MW)

\(\overline{P}_{q}^{\text {Sq}}/\overline{P}_{n}^{\text {NS}}\) :

Existing/generation units’s capacity q/n of strategic/rival company (MW)

\(S_{{bb^{\prime }}}\) :

Susceptance of transmission line \(b-{b}^{\prime }\) (PU)

\(\overline{\text {PF}}_{{bb'}}/\overline{\text {'PF}}_{{bb'}}\) :

Capacity of existing/new transmission line \(b-b^{\prime }\) (MW)

d :

Annual discount rate

FV/FP:

Volume/price of FC

CP:

Capacity payment rate (€/MWh)

\(\text {F.O.R}^{\text {Si}}/\text {F.O.R}^{\text {Sq}}\) :

Mandatory exit rate of new/available plants

\(\overline{P}^{C2E}_{k}\) :

Capacity of candidate CHP to be invested (MW)

\(\overline{PF}_{{'bb'}}\) :

Capacity of candidate transmission line to be invested (MW)

\({P}^{'G2F}_{l}\) :

Capacity of candidate gas furnace to be invested (MMBtu)

\(Y^{C2E}_{k}\) :

The annual cost resulted from investment of the new CHP (€/MW)

\(Y^{L}_{{'bb'}}\) :

The annual cost resulted from investment of the new transmission line (€/MW)

\( Y^{F}_{l}\) :

The annual cost resulted from investment of the new gas furnace (€/MW)

\(\eta ^{C2E}\ \eta ^{C2H}\) :

Electric energy and heat conversion efficiency produced by CHP

\(\underline{\pi }_{l} \ \overline{\pi }_{l}\) :

Minimum and maximum natural gas pressure at each gas bus (PSIG)

\(\underline{P}^{G}_{l}\ \overline{P}^{G}_{l}\) :

Minimum and maximum natural gas production (MMBtu)

\(\overline{F}^{G}\) :

Maximum natural gas flowed by pipelines (MMBtu)

\(K^{G}\) :

Dispatch factor between gas furnace and CHP

\(\eta ^\mathrm{pip}\) :

Pipeline constant (MMBtu/PSIG)

\(L^{H}_{ytl}\) :

Head demand (MMBtu)

\(R^{T}\) :

Tax rebate for investment in CHP

\(R^{L}\) :

Loan factor for investment in CHP

\(B^{H}_{ytl}/\overline{B}^{G}_{ytl}\) :

The proposed price for buying heat/gas (€/MMBtu)

\(\overline{P}_{yi}^{\text {Si}}\) :

Investment capacity of the new unit i of SG (MW)

\(P_{{ytis}}^{\text {Si}}/P_{{ytqs}}^{\text {Sq}}//P_{{ytns}}^{\text {NS}}\) :

New/existing//generation unit’s i / q//n production capacity by strategic//rival GENCO in year y in demand block t and scenario s

\(\overline{P}_{{yy'i}}^{\text {Siq}}/P_{{yy'tis}}^{\text {Siq}}\) :

Available capacity/produced power of the new unit i by the strategic company in year \(y^{\prime }\) in years after the construction year y (MW)

\(\text {OC}_{{yi}}^{\text {Siq}}/B_{{yy'tis}}^{\text {Siq}}\) :

Operating cost/proposed price of the new unit i by the strategic company in year \(y^{\prime }\) in years after the construction year y (MW)

\(\text {CU}_{{ytim}}^{b}\) :

For the candidate capacity h in investment year y, binary variable equals one, otherwise, it equals zero

\(B_{\text {ytis}}^{\text {Si}}/B_{\text {ytqs}}^{\text {Sq}}\) :

New/existing generation unit’s proposed price i / q by strategic company in the block y, demand block t, and scenario s (€/MWh)

\(B_{{ytns}}^{\text {NS}}\) :

Generation unit’s proposed price n by non-strategic company in the block y, demand block t, and scenario s (€/MWh)

\(C_{{ytcs}}\) :

Power consumption for demand c in year y, block demand t, and scenario s (MW)

\(A_{ytbs}\) :

The voltage angle of bus b in block demand t, and scenario s

\(U^{C}_{yk}/ U^{'C}_{yy'k}\) :

Binary variable for CHP investment

\(U^{L}_{ybb'}/ U^{'L}_{yy'bb'}\) :

Binary variable for transmission line investment

\(U^{F}_{yl}/ U'^{F}_{yy'l}\) :

Binary variable for gas furnaces investment

\(\widetilde{P}^{C2E}_{ytk}\ \widetilde{P}^{'C2E}_{yy'tk}\) :

Electricity produced by new CHP (MW)

\(\widetilde{P}^{C2H}_{ytk}\ \widetilde{P}^{'C2H}_{yy'tk}\) :

Heat produced by new CHP (MMBtu)

\(\widetilde{P}^{G2C}_{ytl)}\ \widetilde{P}^{G2C}_{yy'tl)}\) :

Gas required for supplying CHP (MMBtu)

\(P^{G2E}_{ytl}\) :

Gas required for supplying thermal generation units (MMBtu)

\(\pi _{ytl}\) :

Gas pressure at each gas bus (PSIG)

\(P^{G}_{ytl}\) :

Gas produced by utility (MMBtu)

\(F^{G}_{ytll'}\) :

Natural gas flowed by pipelines (MMBtu)

\(P^{G2F}_{ytl}\) :

Natural gas required for supplying existing gas furnace (MMBtu)

\(\widetilde{P}^{G2F}_{ytl}\ \widetilde{P}^{'G2F}_{yy'tl}\) :

Gas required for supplying new gas furnace (MMBtu)

\(\widetilde{B}^{C2H}_{ytk}/\widetilde{B}^{'C2H}_{yy'tk}\) :

The proposed price by CHP for selling heat (€/MMBtu)

\(\widetilde{B}^{F2H}_{ytl}/{\widetilde{B}^{'F2H}_{yy'tl}}\) :

The proposed price by gas furnace for selling heat (€/MMBtu)

\(B^{G}_{ytl}\) :

The proposed price by gas supplier for selling gas (€/MMBtu)

References

  1. Sumaili LR, Jean M, Vladimiro F, Carlos S, Marcelino S (2016) Mean shift densification of scarce data sets in short-term electric power load forecasting for special days. Electr Eng 99(3):881–98, 10

    Google Scholar 

  2. Mili L, Triantis K, Greer A (2018) Integrating community resilience in power system planning. Power Engineering: Advances and Challenges Part B: Electrical Power Publisher. CRC Press

  3. Vargas-Jaramillo JR, Montanez-Barrera JA, von Spakovsky MR, Mili L, Cano-Andrade S (2019) Effects of producer and transmission reliability on the sustainability assessment of power system networks. Energies 12:546

    Article  Google Scholar 

  4. Manshadi SD, Khodayar ME (2015) Resilient operation of multiple energy carrier microgrids. IEEE Trans Smart Grid 6(5):2283–2392

    Article  Google Scholar 

  5. Zhang X, Shahidehpour M, Alabdulwahab A, Abusorrah A (2015) Optimal expansion planning of energy hub with multiple energy infrastructures. IEEE Trans Smart Grid 6(5):2302–2311

    Article  Google Scholar 

  6. Geng J-B, Ji Q, Fan Y (2016) How regional natural gas markets have reacted to oil price shocks before and since the shale gas revolution: a multi-scale perspective. J Nat Gas Sci Eng 36:734–746

    Article  Google Scholar 

  7. Mazyan W, Ahmadi A, Ahmed H, Hoorfar M (2016) Market and technology assessment of natural gas processing: a review. J Nat Gas Sci Eng 30:487–514

    Article  Google Scholar 

  8. Abid M, Alimi M (2019) Stochastic convergence in us disaggregated gas consumption at the sector level. J Nat Gas Sci Eng 61:357–368

    Article  Google Scholar 

  9. Hedman B, Hampson A, Darrow K (2013) The opportunity for CHP in the United States. ICF International, Washington, DC

    Google Scholar 

  10. Nadel S, Economy E-E (2016) Electricity distribution & end use: challenges & opportunities. In: American Council for an energy-efficient economy

  11. Xu Y, Mili L (2018) Probabilistic power flow analysis based on the adaptive polynomial chaos-anova method. In: 2018 IEEE power and energy society general meeting (PESGM)

  12. Zangeneh A, Jadid S, Rahimi-Kian A (2010) Uncertainty based distributed generation expansion planning in electricity markets. Electr Eng 91(7):369–82, 03

    Article  Google Scholar 

  13. Xu Y, Huang C, Chen X, Mili L, Tong CH, Korkali M, Min L (2018) Response-surface-based Bayesian inference for power system dynamic parameter estimation. IEEE Trans Smart Grid 99:1

    Google Scholar 

  14. Xu Y, Mili L, Zhao J (2019) Probabilistic power flow calculation and variance analysis based on hierarchical adaptive polynomial chaos-ANOVA method. IEEE Trans Power Syst 34(5):3316–3325

    Article  Google Scholar 

  15. Kelarestaghi KB, Foruhandeh M, Heaslip K, Gerdes R (2019) Intelligent transportation system security: Impact-oriented risk assessment of in-vehicle networks. IEEE Intell Transp Syst Mag. https://doi.org/10.1109/MITS.2018.2889714

  16. Xu Y, Mili L, Sandu A, von Spakovsky MR, Zhao J (2018) Propagating uncertainty in power system dynamic simulations using polynomial chaos. IEEE Trans Power Syst 34:338–348

    Article  Google Scholar 

  17. Foruhandeh M, Gerdes R (2017) Analysis of authentication parameters in uplink scenario of heterogeneous networks. In: IEEE Trustcom/BigDataSE/ICESS, pp 833–838

  18. Mokgonyana L, Zhang J, Li H, Hu Y (2017) Optimal location and capacity planning for distributed generation with independent power production and self-generation. Appl Energy 188:140–50

    Article  Google Scholar 

  19. Prenc R, S̆krlec D, Durović MZ̆ (2015) The implementation of capital budgeting analysis for distributed generation allocation problems. Electr Eng 97(3):225–238

    Article  Google Scholar 

  20. Anbalagan P, Ramachandran V (2012) Service-oriented architectural model for generating system adequacy evaluation. Electr Eng 94(1):19–25, 03

    Article  Google Scholar 

  21. Spangardt G, Lucht M, Handschin E (2006) Applications for stochastic optimization in the power industry. Electr Eng (Archiv fur Elektrotechnik) 88(3):177–182, 03

    Article  Google Scholar 

  22. Sowa T, Vasconcelos M, Schnettler A, Metzger, Michael H, Alexander, Reischboek M, Köberle R (2016) Method for the operation planning of virtual power plants considering forecasting errors of distributed energy resources. Electr Eng 98(4):347–54, 12

    Article  Google Scholar 

  23. Jornada D, Leon VJ (2016) Robustness methodology to aid multiobjective decision making in the electricity generation capacity expansion problem to minimize cost and water withdrawal. Appl Energy 162:1089–108

    Article  Google Scholar 

  24. Valinejad J, Oladi Z, Barforoshi T, Parvania M (2017) Stochastic unit commitment in the presence of demand response program under uncertainties. IJE Trans B Appl 30(8):1134–1143

    Google Scholar 

  25. Valinejad J, Marzband M, Akorede MF, Barforoshi T, Jovanović M (2017) Generation expansion planning in electricity market considering uncertainty in load demand and presence of strategic gencos. Electr Power Syst Res 152:92–104

    Article  Google Scholar 

  26. Zeblah A, Hadjeri S, Chatelet E, Massim Y (2010) Efficient harmony search algorithm for multi-stages scheduling problem for power systems degradation. Electr Eng 92(3):87–97, 09

    Article  Google Scholar 

  27. Linders M, Krost G (2001) Advanced visualization for power system operation. Electr Eng 83(5–6):303–06, 11

    Article  Google Scholar 

  28. Doorman G, Botterud A (2008) Analysis of generation investment under different market designs. IEEE Trans Power Syst 23(3):859–67

    Article  Google Scholar 

  29. Murphy FH, Smeers Y (2010) On the impact of forward markets on investment in oligopolistic markets with reference to electricity. Operation 58(3):515–28

    Article  MathSciNet  MATH  Google Scholar 

  30. Garcia A, Stacchetti E (2011) Investment dynamics in electricity markets. Econ Theory 46(2):149–87, 02

    Article  MathSciNet  MATH  Google Scholar 

  31. Genc TS, Thille H (2011) Investment in electricity markets with asymmetric technologies. Energy Econ 33(3):379–87

    Article  Google Scholar 

  32. Grimm V, Zoettl G (2013) Investment incentives and electricity spot market competition. J Econ 22(4):832–51

    Google Scholar 

  33. Stoltmann A, Jaskólski M, Bucko P (2019) Optimization of combined heat and power (CHP) market allocation: the case of Poland. In: IOP conference series: earth and environmental science, vol 214, no 1, p 012036

    Article  Google Scholar 

  34. Deng L, Li Z, Sun H, Guo Q, Xu Y, Chen R, Wang J, Guo Y (2019) Generalized locational marginal pricing in a heat-and-electricity-integrated market. IEEE Trans Smart Grid. https://doi.org/10.1109/TSG.2019.2904522

    Article  Google Scholar 

  35. Zhang Q, Mclellan BC, Tezuka T, Ishihara KN (2013) An integrated model for long-term power generation planning toward future smart electricity systems. Appl Energy 112:1424–37

    Article  Google Scholar 

  36. Valinejad J, Barforoushi T (2015) Generation expansion planning in electricity markets: a novel framework based on dynamic stochastic MPEC. Int J Electr Power Energy Syst 70:108–17

    Article  Google Scholar 

  37. Zafirakis D, Chalvatzis KJ, Baiocchi G, Daskalakis G (2013) Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy. Appl Energy 105:138–54

    Article  Google Scholar 

  38. Murphy FH, Smeers Y (2005) Generation capacity expansion in imperfectly competitive restructured electricity markets. Oper Res 53(4):646–661, 08

    Article  MATH  Google Scholar 

  39. Fathtabar H, Barforoushib T, Shahabi M (2018) Dynamic long-term expansion planning of generation resources and electric transmission network in multi-carrier energy systems. Electr Power Energy Syst 102:97–109

    Article  Google Scholar 

  40. Gupta N, Khosravy M, Saurav K, Sethi IK, Marina N (2017) Value assessment method for expansion planning of generators and transmission networks: a non-iterative approach. Electr Eng 7:1–16

    Google Scholar 

  41. Capuder T, Zidar M, S̆krlec D (2012) Evolutionary algorithm with fuzzy numbers for planning active distribution network. Electr Eng 94(3):135–45, 09

    Article  Google Scholar 

  42. fard SPAA-T, Poure P, Saadate S (2011) Distribution system planning considering integration of distributed generation and load curtailment options in a competitive electricity market. Electr Eng (Archiv fur Elektrotechnik) 93:23–32, 03

    Article  Google Scholar 

  43. Orths A, Schmitt A, Styczynski ZA, Verstege J (2001) Multi-criteria optimization methods for planning and operation of electrical energy systems. Electr Eng 83(5–6):251–58, 11

    Article  Google Scholar 

  44. Hooshmand R A, Mohkami H (2011) New optimal placement of capacitors and dispersed generators using bacterial foraging oriented by particle swarm optimization algorithm in distribution systems. Electr Eng (Archiv fur Elektrotechnik) 93(1):43–53, 03

    Article  Google Scholar 

  45. Afkousi-Paqaleh M, Fard AA-T, Rashidinejad M (2010) Distributed generation placement for congestion management considering economic and financial issues. Electr Eng 92(6):193–201, 11

    Article  Google Scholar 

  46. Valinejad J, Marzband M, Akorede MF, Elliott ID, Godina R, Matias J, Pouresmaeil E (2018) Long-term decision on wind investment with considering different load ranges of power plant for sustainable electricity energy market. Sustainability 10:3811

    Article  Google Scholar 

  47. Valinejad J, Marzband M, Barforoushi T, Kyyrä J, Pouresmaeil E (2018) Dynamic stochastic EPEC model for competition of dominant producers in generation expansion planning. In: 5th international symposium on environment friendly energies and applications (EFEA) (2018)

  48. Birge JR, Louveaux F (2011) Introduction to stochastic programming. Springer, Berlin

    Book  MATH  Google Scholar 

  49. Foruhandeh M, Tadayon N, AÃ\(\acute{{\rm r}}\)ssa S (2016) Uplink modeling of K-tier heterogeneous networks: a queuing theory approach. IEEE Commun Lett 21:164–167

  50. Foruhandeh M, Aissa S (2015) Efficiency of DC combination of rectified waveforms in energy harvesting systems. IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), 4–7 Oct. 2015

  51. Conejo AJ, Morales MCJM (2010) Decision making under uncertainty in electricity markets. Springer, Berlin

    Book  MATH  Google Scholar 

  52. Andreoni AM, Garcia-Agreda A, Strada TJ, Saraiva JT (2007) Strategies for power systems expansion planning in a competitive electrical market. Electr Eng 89(5):433–41, 05

    Article  Google Scholar 

  53. Valinejad J, Barforoshi T, Marzband M, Pouresmaeil E, Godina R, Catalao JP (2018) Investment incentives in competitive electricity markets. Appl Sci 8(10):1978

    Article  Google Scholar 

  54. Vithayasrichareon P, MacGill IF (2014) Incorporating short-term operational plant constraints into assessments of future electricity generation portfolios. Appl Energy 128:144–55

    Article  Google Scholar 

  55. Marques AC, Fuinhas JA, Menegaki AN (2014) Interactions between electricity generation sources and economic activity in Greece: a VECM approach. Appl Energy 132:34–46

    Article  Google Scholar 

  56. Gabriel S, Conejo A, Fuller J, Hobbs B, Ruiz C (2012) Complementary modeling in energy markets. International Series in Operations Research & Management Science. Springer, New York

    MATH  Google Scholar 

  57. Siddiqui S, Gabriel S (2013) An sos1-based approach for solving MPECs with a natural gas market application. Netw Spat Econ 13(2):205–227

    Article  MathSciNet  MATH  Google Scholar 

  58. Grigg C, Wong P, Albrecht P, Allan R, Bhavaraju M et al (1999) The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE Trans Power Syst 14(3):1010–1020

    Article  Google Scholar 

  59. Valinejad J, Firouzifar S, Marzband M, AlâĂŘSumaiti AS (2018) Reconsidering insulation coordination and simulation under the effect of pollution due to climate change. Int Trans Electr Energy Syst 28:e2595

    Article  Google Scholar 

  60. Valinejad J, Marzband M, Busawona K, Kyyrä J, Pouresmaeil E (2018) Investigating wind generation investment indices in multi-stage planning. In: 5th International symposium on environment friendly energies and applications (EFEA)(2018)

Download references

Author information

Authors and Affiliations

  1. Bradley Department of Electrical and Computer Engineering, Virginia Tech, Northern Virginia Center, Falls Church, VA, 22043, USA

    Jaber Valinejad & Yijun Xu

  2. Department of Physics and Electrical Engineering, Faculty of Engineering and Environment, Northumbria University Newcastle, Newcastle upon Tyne, NE18ST, UK

    Mousa Marzband

  3. School of Electrical and Electronic Engineering, The University of Manchester, Ferranti Building, Manchester, M13 9PL, UK

    Hasan Uppal

  4. Advanced Power and Energy Center, Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE

    Ameena Saad Al-Sumaiti

  5. Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Taghi Barforoshi

Authors
  1. Jaber Valinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mousa Marzband
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yijun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hasan Uppal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ameena Saad Al-Sumaiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Taghi Barforoshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mousa Marzband.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valinejad, J., Marzband, M., Xu, Y. et al. Dynamic behavior of multi-carrier energy market in view of investment incentives. Electr Eng 101, 1033–1051 (2019). https://doi.org/10.1007/s00202-019-00834-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-019-00834-5

Keywords

Search

Navigation