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As a profit-seeking market participant, load serving entities (LSEs) have been trying to apply demand response (DR) programs to induce the demand elasticity to further their profit. However, due to the different preference of DRs, it is difficult for LSEs to generate the optimal strategic bidding strategy in the ISO/RTO's market. Therefore, this paper proposed a bi-level optimization model considering DR bidding to maximize the total profit of LSEs: conceptually, the consumers participate DR through setting their bidding prices to LSEs with respect to their own preference and LSEs should determine the optimal reward value of DR as well as the amount of demanded electricity; technically, an original method has been implemented to solve the bi-level optimization model. The closed form of shadow price function with respect to the total load demand is derived to reduce the complexity. Hence, the proposed model is converted to a mixed integer second order cone programming and able to achieve the global optimality. It needs to be note that the closed form of shadow price introduced in this paper can also be applied to other bi-level programming models. Moreover, case studies have been performed to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The emerging electricity market behaves more like an oligopoly than a perfectly competitive market. Each supplier and consumer will have profits of varying extents influenced by differences in the degree of imperfection of knowledge of their rivals. This paper discusses the optimal bidding strategies of the Load Serving Entities (LSEs) using Differential Evolution (DE) algorithm. It is assumed that each LSE bids a linear demand function and chooses the coefficient in the linear demand function to maximize their benefits, subject to expectations of the bidding of the rivals. Bidding behaviors of the rivals are described using a normal Probability Distribution Function (PDF), and the problem of building optimal bidding strategies for LSE is formulated as stochastic optimization problem. The proposed algorithm is tested with three LSEs-three large consumers and three generating companies. The results obtained are compared with those obtained by other optimization techniques. Transmission line flow limits are incorporated and contingency analysis is also carried out for a nine bus system, and the system security and stability are verified. [ABSTRACT FROM AUTHOR]