Modelling locational price spreads in competitive electricity markets; applications for transmission rights valuation and replication

The restructuring of the electric utilities industry has forced industry participants to rethink their approach to a number of decision processes. To manage risk and plan investment in generation assets, as well as to examine the efficient expansion of the current transmission grid, one needs to have a clear understanding of the interaction between the grid properties and the behaviour of the regional power markets. In this paper we discuss a fundamental modelling approach which extracts the stochastic properties of electricity prices by modelling the impact of physical and economic drivers affecting the production, delivery, and consumption of electricity. If the fundamental inputs are directly observable, we can use historical data to calibrate the model parameters. In the case of electricity, this simple and abundant set of training data can make a crucial difference. ; We present the bid‐based stochastic model (BSM) and look into its application to valuing of financial derivatives, especially options based on the locational spread in electricity price between two markets. The advantage of the bid‐based model is that one is able to link the capacity of the transmission line, in megawatts, directly to the correlation between electricity prices at the end nodes. This leads us to a valuation method for a locational spread option, the financial equivalent of a physical transmission right. The model represents an improvement over standard spread option formulation in that it accounts for the effect of the nonlinear flows in the transmission network on the correlation and distribution of locational prices. We also address the question of whether financial transmission rights can be replicated with a dynamic portfolio of forward contracts at the end nodes. ; This poses the possibility of model‐based arbitrage between existing forward markets and the emerging transmission rights markets. Furthermore, it allows users to simulate the effect of transmission outages or expansion. For example, a for‐profit transmission provider who is contemplating addition of a new transmission line between two markets needs to know whether he will be able to recover the fixed cost of investing in the line by selling transmission rights to market participants. By calibrating the bid‐based model according to current price levels and adding the capacity of the new transmission line, the transmission owner can simulate future cash flows and estimate the profitability of the investment.