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access icon openaccess Optimal agency contract for incentive and control under moral hazard in dynamic electric power networks

The authors propose an optimal contract mechanism under moral hazard in discrete-time dynamic electric power networks. As the utility (system operator) cannot adjust the control input of the agents (end-users) directly in real time out of respect for individual decision–making, the agents’ control input maximising their own profit does not always maximise social welfare. To avoid the issue, the authors introduce an aggregator as intermediary between the utility and the agents. The aggregator pays compensation for defective ancillary services, which are caused by random disturbance and the agents’ voluntary control. To reduce the compensation risk, the authors first present an optimal incentive/control contract problem for the aggregator's compensation. The problem is usually regarded as a principal-agent problem under moral hazard in contract theory. However, it is generally difficult to solve a contract problem with dynamics expressed as discrete-time simultaneous Bellman equations and a hierarchical control structure as a Stackelberg game. The authors next show that the problem can be solved by regarding it as a linear-exponential-quadratic-Gaussian dynamic game and employing a numerical optimisation technique. Due to the ex-ante appropriate payment contract, the agents select control inputs preferable for the aggregator. The effectiveness of the proposed contract mechanism is finally demonstrated through simulation.

References

    1. 1)
      • 22. Berger, A.W., Schweppe, F.C.: ‘Real time pricing to assist in load frequency control’, IEEE Trans. Power Syst., 1989, 4, (3), pp. 920926.
    2. 2)
      • 1. GridWise Architecture Council: ‘Gridwise transactive energy framework: version 1.0’, Pacific Northwest National Laboratory, PNNL-22946, 2015.
    3. 3)
      • 12. Holmstrom, B.: ‘Moral hazard in teams’, Bell J. Econ., 1982, 13, (2), pp. 324340.
    4. 4)
      • 8. Wasa, Y., Sakata, K., Hirata, K., et al: ‘Differential game-based load frequency control for power networks and its integration with electricity market mechanisms’. Proc. 1st IEEE Conf. on Control Technology and Applications, Mauna Lani, 2017, pp. 10441049.
    5. 5)
      • 14. Li, Z., Chen, L., Nan, G.: ‘Small-scale renewable energy source trading: a contract theory approach’, IEEE Trans. Ind. Inf., 2018, 14, (4), pp. 14911500.
    6. 6)
      • 27. Kennedy, J.: ‘Swarm intelligence’, in Zoyama, A.Y. (Ed.): ‘Handbook of nature-inspired and innovative computing’ (Springer, New York City, 2006), pp. 187219.
    7. 7)
      • 9. Murao, T., Hirata, K., Okajima, Y., et al: ‘Real-time pricing for LQG power networks with independent types: a dynamic mechanism design approach’, Eur. J. Control, 2018, 39, pp. 95105.
    8. 8)
      • 20. Yang, I., Callaway, D.S., Tomlin, C.J.: ‘Indirect load control for electricity market risk management via risk-limiting dynamic contracts’. Proc. 2015 American Control Conf., 2015, pp. 30253031.
    9. 9)
      • 24. Fan, C.H., Speyer, J.L., Jaensch, C.R.: ‘Centralized and decentralized solutions of the linear-exponential-Gaussian problem’, IEEE Trans. Autom. Control, 1994, 39, (10), pp. 19862003.
    10. 10)
      • 7. Uchida, K., Hirata, K., Wasa, Y.: ‘Incentivizing market and control for ancillary services in dynamic power grids’, in Stoustrup, J., Annaswamy, A.M., Chakrabortty, A., et al (Eds.): ‘Smart grid control’ (Springer, Cham, 2018), pp. 4758.
    11. 11)
      • 10. Tanaka, T., Cheng, A.Z.W., Langbort, C.: ‘A dynamic pivot mechanism with application to real time pricing in power systems’. Proc. 2012 American Control Conf., Montreal. Canada, 2012, pp. 37053711.
    12. 12)
      • 11. Anand, V., Gupta, V.: ‘Markov Pricing Equilibrium in a prosumer-aggregator dynamic game’. Proc. 2016 American Control Conf., Boston, USA, 2016, pp. 41204125.
    13. 13)
      • 25. Klompstra, M.B.: ‘Nash equilibria in risk-sensitive dynamic games’, IEEE Trans. Autom. Control, 2000, 45, (7), pp. 13971401.
    14. 14)
      • 3. Tesfatsion, L.: ‘Electric power markets in transition: agent-based modeling tools for transactive energy support’, in Hommes, C., LeBaron, B. (Eds.): ‘Handbook of computational economics’, vol. 4, (Elsevier, London, 2018), pp. 715766.
    15. 15)
      • 4. Ela, E., Gevorgian, V., Tuohy, A., et al: ‘Market designs for the primary frequency response ancillary service—part I: motivation and design’, IEEE Trans. Power Syst., 2014, 29, (1), pp. 421431.
    16. 16)
      • 23. Jacobson, D.H.: ‘Optimal stochastic linear systems with exponential performance criteria and their relation to deterministic differential games’, IEEE Trans. Autom. Control, 1973, AC-18, (2), pp. 124131.
    17. 17)
      • 13. Zhang, K., Mao, Y., Leng, S., et al: ‘Incentive-driven energy trading in the smart grid’, IEEE Access, 2016, 4, pp. 12431257.
    18. 18)
      • 17. Holmstrom, B., Milgrom, P.: ‘Aggregation and linearity in the provision of intertemporal incentives’, Econometrica, J. Econ. Soc., 1987, 55, (2), pp. 303328.
    19. 19)
      • 16. Munoz, F.D., van der Weijde, A.H., Hobbs, B.F., et al: ‘Does risk aversion affect transmission and generation planning? a western North America case study’, Energy Econ., 2017, 64, pp. 213225.
    20. 20)
      • 6. van der Veen, R.A., Hakvoort, R.A.: ‘The electricity balancing market: exploring the design challenge’, Util. Policy, 2016, 43, pp. 186194.
    21. 21)
      • 21. Rajagopal, R., Bitar, E., Varaiya, P., et al: ‘Risk-limiting dispatch for integrating renewable power’, Int. J. Electr. Power Energy Syst., 2013, 44, (1), pp. 615628.
    22. 22)
      • 2. Bejestani, A.K., Annaswamy, A., Samad, T.: ‘A hierarchical transactive control architecture for renewables integration in smart grids: analytical modeling and stability’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 20542065.
    23. 23)
      • 26. Basar, T., Olsder, G.J.: ‘Dynamic noncooperative game theory’ (SIAM, Philadelphia, 1999).
    24. 24)
      • 19. Itoh, H.: ‘Cooperation in hierarchical organizations: an incentive perspective’, J. Law Econ. Organ., 1992, 8, (2), pp. 321345.
    25. 25)
      • 18. Schättler, H., Sung, J.: ‘On optimal sharing rules in discrete-and continuous-time principal-agent problems with exponential utility’, J. Econ. Dyn. Control, 1997, 21, (2–3), pp. 551574.
    26. 26)
      • 15. Wasa, Y., Hirata, K., Uchida, K.: ‘A dynamic contract mechanism for risk-sharing management on interdependent electric power and gas supply networks’. Proc. 11th Asian Control Conf., Gold Coast, Australia, 2017, pp. 12221227.
    27. 27)
      • 5. Samad, T., Annaswamy, A.M.: ‘Controls for smart grids: architectures and applications’, Proc. IEEE, 2017, 105, (11), pp. 22442261.
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