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Impact of Strategic Behaviors of the Electricity Consumers on Power System Reliability

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Sustainable Interdependent Networks II

Abstract

Over the past few decades, electricity markets have created competitive environments for the participation of different players. Electricity consumers (as end users in power systems) can behave strategically based on their purposes in the markets. Their behaviors induce more uncertainty into the power grid, due to their dynamic load demands. Hence, a power system operator faces more difficulties in maintaining an acceptable level of reliability and security in the system. On the other hand, the strategic behaviors of electricity consumers can be as a double-edged sword in the power grid. There is a group of consumers who are flexible and so can be interrupted at critical time periods and pursue their economic targets in the electricity markets. However, the second group is concerned with electricity demand being provided to them with the desired reliability level. Hence, the decisions of this group of electrical consumers are in conflict with their corresponding demand response programs. According to the above statement, this chapter aims at investigating the impact of strategic behavior of the electrical consumers on power system reliability. In this way, different agents of electricity markets are defined in this chapter which their behavior can impact the market-clearing problem. Energy and reserve are assumed as electricity commodities in this chapter. Thus, a two-stage, day-ahead and real-time, stochastic unit commitment problem is solved to clear energy and reserve simultaneously considering the uncertainty of wind power generations and conventional generation units which impacts the reliability of sustainable power systems.

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References

  1. Amini, M. H., et al, (2013). Load management using multi-agent systems in smart distribution network. IEEE Power and Energy Society General Meeting.

    Google Scholar 

  2. Boroojeni, K. G., et al. (2017). Reliability in Smart Grids. In Smart Grids: Security and privacy issues (pp. 19–29). Cham: Springer.

    Chapter  Google Scholar 

  3. Ahmadi-Khatir, A., Conejo, A. J., & Cherkaoui, R. (2013). Multi-area unit scheduling and reserve allocation under wind power uncertainty. IEEE Transactions on power systems, 29(4), 1701–1710.

    Article  Google Scholar 

  4. Morales, J., Conejo, A., & Perez-Ruiz, J. (2009). Economic valuation of reserves in power systems with high penetration of wind power. IEEE Transactions on Power Systems, 24(2), 900–910.

    Article  Google Scholar 

  5. Mohammadi, A., et al. (2018). Diagonal quadratic approximation for decentralized collaborative TSO+DSO optimal power flow. IEEE Transactions on Smart Grid, 1. https://doi.org/10.1109/TSG.2018.279603.

  6. Shafie-khah, M., & Catalao, J. P. S. (2015). A stochastic multi-layer agent-based model to study electricity market participants behavior. IEEE Transactions on Power Systems, 30(2), 867–881.

    Article  Google Scholar 

  7. Wang, F., Xu, H., Xu, T., Li, K., Shafie-Khah, M., & Catalao, J. P. (2017). The values of market-based demand response on improving power system reliability under extreme circumstances. Applied Energy, 193, 220–231.

    Article  Google Scholar 

  8. Mishra, C., Singh, S. P., & Rokadia, J. (2015). Optimal power flow in the presence of wind power using modified cuckoo search. IET Generation, Transmission & Distribution, 9(7), 615–626.

    Article  Google Scholar 

  9. Chen, H., Zhang, R., Li, G., Bai, L., & Li, F. (2016). Economic dispatch of wind integrated power systems with energy storage considering composite operating costs. IET Generation, Transmission & Distribution, 10(5), 1294–1303.

    Article  Google Scholar 

  10. Silva, A. L., Sales, W., Manso, L., & Billinton, R. (2010). Long-term probabilistic evaluation of operating reserve requirements with renewable sources. IEEE Transactions on Power Systems, 25, 1.

    Article  Google Scholar 

  11. Mastos, M., Lopes, J., Rosa, M., Ferreira, R., Silva, A., Sales, W., Resend, L., Manso, L., Cabral, P., Ferreira, M., Martins, N., Artiaz, C., Soto, F., & Lopez, R. (2009). Probabilistic evaluation of reserve requirements of generation systems with renewable power sources: The Portuguese and Spanish cases. Electrical Power and Energy Systems, 31, 562–569.

    Article  Google Scholar 

  12. Leite da Silva, A. M., Rosa, M. A., Sales, W. S., & Matos, M. (2011). Long term evaluation of operating reserve with high penetration of renewable energy sources. IEEE Conference, Power and Energy Society General Meeting.

    Google Scholar 

  13. Sarwat, A. I., et al. (2016). Weather-based interruption prediction in the smart grid utilizing chronological data. Journal of Modern Power Systems and Clean Energy, 4(2), 308–315.

    Article  Google Scholar 

  14. Shokri Gazafroudi, A., Afshar, K., & Bigdeli, N. (2015). Assessing the operating reserves and costs with considering customer choice and wind power uncertainty in pool-based power market. International Journal of Electrical Power & Energy Systems, 67, 202–215.

    Article  Google Scholar 

  15. Ortega-Vazquez, M., & Kirschen, D. (2010). Assessing the impact of wind power generation on operating costs. IEEE Transactions on Smart Grid, 1, 3.

    Article  Google Scholar 

  16. Ortega-Vazquez, M., & Kirschen, D. (2007). Optimizing the spinning reserve requirements using a cost/benefit analysis. IEEE Transaction on Power Systems, 22, 1.

    Article  Google Scholar 

  17. Reddy, S. S., Bijwe, P. R., & Abhyankar, A. R. (2015). Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Systems Journal, 9, 1.

    Article  Google Scholar 

  18. Lyon, J. D., Hedman, K. W., & Zhang, M. (2014). Reserve requirements to efficiently manage intra-zonal congestion. IEEE Transactions on Power Systems, 29, 1.

    Article  Google Scholar 

  19. Wang, F., & Hedman, K. W. (2015). Dynamic reserve zones for day-ahead unit commitment with renewable resources. IEEE Transactions on Power Systems, 30, 2.

    Article  Google Scholar 

  20. Wang, Z., Bian, Q., Xin, H., & Gan, D. (2016). A distributionally robust co-ordinated reserve scheduling model considering CVaR-based wind power reserve requirements. IEEE Transactions on Sustainable Energy, 7, 2.

    Article  Google Scholar 

  21. Beuchat, P. N., Warrington, J., Summers, T. H., & Morari, M. (2016). Performance bounds for look-ahead power system dispatch using generalized multistage policies. IEEE Transactions on Power Systems, 31, 1.

    Article  Google Scholar 

  22. Pandžić, H., Dvorkin, Y., Qiu, T., Wang, Y., & Kirschen, D. S. (2016). Toward cost-efficient and reliable unit commitment under uncertainty. IEEE Transactions on Power Systems, 31, 2.

    Article  Google Scholar 

  23. Yousefi Ramandi, M., Afshar, K., Shokri Gazafroudi, A., & Bigdeli, N. (2016). Reliability and economic valuation of demand side management programming in wind integrated power systems. International Journal of Electrical Power & Energy Systems, 78, 258–268.

    Article  Google Scholar 

  24. Amini, M. H., Boroojeni, K. G., Iyengar, S. S., Pardalos, P. M., Blaabjerg, F., & Madni, A. M. (2018). Sustainable interdependent networks: From theory to application. Cham: Springer.

    Book  Google Scholar 

  25. Najafi, S., et al. (2018). Decentralized control of DR using a multi-agent method. In Sustainable interdependent networks (pp. 233–249). Cham: Springer.

    Chapter  Google Scholar 

  26. Buldyrev, S. V., et al. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291), 1025.

    Article  Google Scholar 

  27. Amini, M. H., et al. (2018). A panorama of future interdependent networks: From intelligent infrastructures to smart cities (pp. 1–10). Cham: Sustainable Interdependent Networks. Springer.

    Google Scholar 

  28. Najafi, M., Ehsan, M., Fotuhi-Firuzabad, M., Akhavein, A., & Afshar, K. (2010). Optimal reserve capacity allocation with consideration of customer reliability requirements. Energy, 35, 3883–3890.

    Article  Google Scholar 

  29. Ahmadi-Khatir, A., Fotuhi-Firuzabad, M., & Goel, L. (2009). Customer choice of reliability in spinning reserve procurement and cost allocation using well-being analysis. Electric Power Systems Research, 79, 1431–1440.

    Article  Google Scholar 

  30. Shokri Gazafroudi, A., Shafie-khah, M., Abedi, M., Hosseinian, S. H., Dehkordi, G. H. R., Goel, L., Karimyan, P., Corchado, J. M., & Catalão, J. P. S. (July 2017). A novel stochastic reserve cost allocation approach of electricity market agents in the restructured power systems. Electric Power Systems Research, 152(C), 223–236.

    Article  Google Scholar 

  31. Conejo, A. J., Carrion, M., & Morales, J. M. (2010). Decision making under uncertainty in electricity markets. In International series in operations research & management science. New York: Springer.

    Google Scholar 

  32. GAMS Release 2.50. (1999). A user’s guide. GAMS Development Corporation. Retrieved September 20, 2017, from http://www.gams.com

  33. The MathWorks. (2017). MATLAB. Retrieved September 20, 2017, from http://www.mathworks.com

  34. Shokri Gazafroudi, A., Prieto-Castrillo, F., Pinto, T., & Corchado, J. M. (2017). Organization-based multi-agent system of local electricity market: Bottom-up approach. 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS).

    Google Scholar 

  35. Shokri Gazafroudi, A., Pinto, T., Prieto-Castrillo, F., Prieto, J., Corchado, J. M., Jozi, A., Vale, Z., & Venayagamoorthy, G. K. (2017). Organization-based multi-agent structure of the smart home electricity system. IEEE Congress on Evolutionary Computation (CEC).

    Google Scholar 

  36. Shokri Gazafroudi, A., De Paz, J. F., Prieto-Castrillo, F., Villarrubia, G., Talari, S., Shafie-khah, M., & Catalão, J. P. S. A review of multi-agent based energy management systems. 8th International Symposium on Ambient Intelligence (ISAmI).

    Google Scholar 

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Acknowledgments

Amin Shokri Gazafroudi and Juan Manuel Corchado acknowledge the support by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient and Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref. 641794. Amin Shokri Gazafroudi acknowledges the support by the Ministry of Education of the Junta de Castilla y León and the European Social Fund through a grant from predoctoral recruitment of research personnel associated with the research project “Arquitectura multiagente para la gestión eficaz de redes de energía a través del uso de técnicas de intelligencia artificial” of the University of Salamanca. M.Shafie-khah and J.P.S. Catalão acknowledge the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 - POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/ 50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013, and 02/SAICT/2017 - POCI-01-0145-FEDER-029803.

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Correspondence to Miadreza Shafie-khah .

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Appendix 1: Nomenclature

Appendix 1: Nomenclature

Indices and Numbers

n

Index of system buses, from 1 to N B.

i

Index of conventional generating units, from 1 to N G.

j

Index of loads, from 1 to N L.

t

Index of time periods, from 1 to N T.

m

Index of energy blocks offered by conventional generating units, from 1 to N Oit.

ω

Index of wind power, electrical load, and power grid scenarios, from 1 to Ω.

Continuous Variables

\( {C}_{it}^{\mathrm{SU}}\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Scheduled start-up cost ($).

P S it

Power output of units in the DAM (MW).

\( {p}_{itm}^G \vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}}\)

Power output from the m-th block of energy offered by the unit in DAM (MW).

L S jt

Power consumed of load in DAM (MW).

R U it

Up-spinning reserve in DAM (MW).

R D it

Down-spinning reserve in DAM (MW).

R NS it

Non-spinning reserve in DAM (MW).

R U jt

Up-spinning reserve from demand side in DAM (MW).

R D jt

Down-spinning reserve from demand side in DAM (MW).

\( {P}_t^{S,\mathrm{WP}}\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Wind power in DAM (MW).

C A itω

Start-up cost due to change in commitment status of units in DAM and RTM ($).

P G itω

Power output of unit in RTM (MW).

L C jtω

Electrical consumed in RTM (MW).

r U itω

Up-spinning reserve in RTM (MW).

r D itω

Down-spinning reserve in RTM (MW).

r NS itω

Non-spinning reserve in RTM (MW).

r U jtω

Up-spinning reserve from demand side in RTM (MW).

r U jtω

Down-spinning reserve from demand side in RTM (MW).

\( {r}_{itm\omega}^G\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Reserve deployed from the m-th block of energy offered in RTM (MW).

\( {L}_{jt\omega}^{\mathrm{shed}}\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Load shedding (MW).

S

Wind power generation spillage (MW).

f ( n,  r)

Power flow through line (n, r) (MW).

P loss ( n,  r)

Power loss in line (n, r) (MW).

δ tωn

Voltage angle at node.

Binary Variables

u it

Commitment status of units in DAM.

v itω

Commitment status of units in RTM.

Random Variables

P WP

Wind power generation in RTM (MW).

Constants

d t

Duration of time period (h).

\( {\lambda}_{it}^{\mathrm{SU}}\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Start-up offer cost of unit ($).

\( {\lambda}_{itm}^G\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Marginal cost of the m-th block of energy offered ($/MWh).

\( {\lambda}_{jt}^L\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Utility of electrical load ($/MWh).

λ WP t

Marginal cost of the energy offer submitted by the wind producer ($/MWh).

VOLLjt

Value of loss load for load ($/MWh).

V S t

Wind spillage cost ($/MWh).

π ω

Probability of scenarios.

\( {\overline{P}}_{\mathrm{i}}\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Maximum capacity of units (MW).

\( {\underline{P}}_i \vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}}\)

Minimum power output of generation units (MW).

B ( n,  r)

Absolute value of the imaginary part of the admittance of line (n, r) (p.u.).

\( {\overline{f}}_{\left(n,r\right)}\vphantom{\frac{1^{1^{1}}}{1_{1_{1}}}} \)

Maximum capacity of line (n, r) (MW).

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Gazafroudi, A.S., Shafie-khah, M., Fitiwi, D.Z., Santos, S.F., Corchado, J.M., Catalão, J.P.S. (2019). Impact of Strategic Behaviors of the Electricity Consumers on Power System Reliability. In: Amini, M., Boroojeni, K., Iyengar, S., Pardalos, P., Blaabjerg, F., Madni, A. (eds) Sustainable Interdependent Networks II. Studies in Systems, Decision and Control, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-319-98923-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-98923-5_11

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