Decision letter for "Optimal GENCO's bidding strategy in a power exchange facilitating combined power and emission trading using Intelligent Programmed Genetic Algorithm"

2014 ◽  
Vol 521 ◽  
pp. 476-479 ◽  
Author(s):  
Guo Zhong Liu

The impacts of Emission trading on building the optimal bidding strategy for a generation company participating in a day-ahead electricity market is investigated. The CO2 emission price in an emissions trading market is evaluated by using an optimization approach similar to the well-developed probabilistic production simulation method. Then upon the assumption that the probability distribution functions of rivals bidding are known, a stochastic optimization model for building the risk-constrained optimal bidding strategy for the generation company in the framework of the chance-constrained programming is presented. Finally, a numerical example is served for demonstrating the feasibility of the developed model and method, and the optimal bidding results are compared for the two situations with and without the CO2 emissions trading.


Author(s):  
Arvind Kumar Jain ◽  
S.C. Srivastava

In an electricity market, suppliers are more concerned with maximizing their profit and minimizing the financial risk, which can be achieved through strategic bidding. In this paper, Equal Incremental Cost Criteria (EICC) has been used for developing the optimal bidding strategy. The rival's bidding behavior has been formulated using a stochastic optimization model. Genetic Algorithm (GA), along with ac sensitivity factors, has been used to decide the optimal bidding strategy including congestion management to maximize the profit of the suppliers, considering single sided as well as double sided bidding. Both pure as well as probabilistic strategies have been simulated. Results with Sequential Quadratic Programming (SQP), a classical optimization method, and dc sensitivity factors have also been obtained to compare and establish the effectiveness of proposed method. Value at Risk (VaR) has been calculated as a measure of financial risk.


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