Optimal Portfolio Selection of GenCo Under Congestion Risk in Multimarket Environment

Abstract

In a restructured electricity market derivative instruments help in reducing price risk. This paper proposes a portfolio optimization model considering uncertainty of electricity spot prices. Using this model, a thermal generation company (GenCo) holding contracts within and outside its jurisdiction can maximize its profit with limited risk exposure. The proposed model is formulated using mean variance portfolio theory, considering spot market, bilateral contracts and options with the possibility of managing congestion risk while trading between different locations. To handle uncertainties scenario generation and reduction techniques are used. The spot price inaccuracies are further represented as scenario tree. The producers risk preference is expressed by a utility function as the trade-off between expectation and variance of the return. For multiple scenarios the optimization problem is solved to obtain a stochastic solution to the optimal capacity allocation problem. The work addresses price variability of zonal transaction considering both physical and financial contracts to schedule the output of an opportunistic GenCo for maximizing its profit by creating hedging strategy through multiple scenarios. The results indicate that the proposed model is capable of improving the profit risk trade-off of the portfolios. GenCos profit seeking behavior during congestion relieving situation has also been demonstrated in the proposed Debasmita Panda