Abstract
In this paper, we suggest a procedure based on capacity charges for managing transmission constraints in electricity networks. The system operator states nodal capacity charges for transmission prior to market clearing. Market clearing brings forth a single market price for electricity. For optimal capacity charges the market equilibrium coincides with that of optimal nodal pricing. Capacity charges are based on technical distribution factors and estimates of the shadow prices of network constraints. Estimates can be based on market information from similar congestion situations, and then capacity charges can be brought near the optimal values through an iterative process.
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Notes
- 1.
The “DC” approximation is the customary approximation used in the literature when dealing with the management of transmission constraints. Under these assumptions, and with well-behaved cost and benefit functions, the optimal dispatch problem is convex. For the specifics of the “DC” approximation, see for instance Wu and Varaiya (1995), Chao and Peck (1996), or Wu et al. (1996). In the “DC” approximation both losses and reactive power are left out.
- 2.
See Dolan and Aldous (1993).
- 3.
In general AC systems the load factors depend on the distribution of loads over the network. Our method applies also for general AC systems, however, requiring recalculations of the load factors according to the load.
- 4.
Refer to Wu et al. (1996) for the characteristics of optimal nodal prices.
- 5.
The number of independent loops are \(m - n + 1 = 8\,\mbox{ \textendash }\,6 + 1 = 3\).
- 6.
In reality, this may not be so, as the grid lines may be operated with different capacities depending on the direction of the flow over the interconnection. This is especially so if links are aggregated individual lines.
References
Bjørndal, M., & Jörnsten, K. (2001). Zonal pricing in a deregulated electricity market. The Energy Journal, 22, 51–73.
Bjørndal, M., Jörnsten, K., & Rud, L. (2000). A price adjustment process for managing congestion in the Norwegian scheduled power market. Conference Proceedings, IAEE Norge Conference, August 2000.
Chao, H. -P., & Peck, S. (1996). A market mechanism for electric power transmission. Journal of Regulatory Economics, 10, 25–59.
Dolan, A., & Aldous, J. (1993). Networks and algorithms: an introductory approach. Wiley.
Everett, H., III (1963). Generalized lagrange multiplier method for solving problems of optimum allocation of resources. Operations Research, 11, 399–417.
Glavitch, H., & Alvarado, F. (1997). Management of multiple congested conditions in unbundled operation of a power system. IEEE Transactions on Power Systems, 13, 374–380.
Hogan, W. W. (1992). Contract networks for electric power transmission. Journal of Regulatory Economics, 4, 211–242.
Minoux, M. (1986). Mathematical programming. theory and algorithms. Wiley.
Schweppe, F. C., Caramanis, M. C., Tabors, R. D., & Bohn, R. E. (1988). Spot pricing of electricity. Kluwer.
Stoft, S. (1998). Congestion pricing with fewer prices than zones. The Electricity Journal, (May), 23–31.
Wu, F., & Varaiya, P. (1995). Coordinated multilateral trades for electric power networks: theory and implementation. Department of Electrical Engineering and Computer Sciences, University of California.
Wu, F., Varaiya, P., Spiller, P., & Oren, S. (1996). Folk theorems on transmission access: proofs and counterexamples. Journal of Regulatory Economics, 10, 5–23.
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Bjørndal, M., Jörnsten, K., Rud, L. (2010). Capacity Charges: A Price Adjustment Process for Managing Congestion in Electricity Transmission Networks. In: Bjørndal, E., Bjørndal, M., Pardalos, P., Rönnqvist, M. (eds) Energy, Natural Resources and Environmental Economics. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12067-1_16
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DOI: https://doi.org/10.1007/978-3-642-12067-1_16
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