Scenario-Based Optimal Bidding Strategies of GENCOs in the Incomplete Information Electricity Market Using a New Improved Prey—Predator Optimization Algorithm

PROJECT TITLE :

Scenario-Based Optimal Bidding Strategies of GENCOs in the Incomplete Information Electricity Market Using a New Improved Prey—Predator Optimization Algorithm

ABSTRACT:

So as to search out out the optimal bidding strategies (BSs) of generating companies (GENCOs) in an exceedingly competitive electricity market, it is necessary to resolve a bilevel optimization drawback. The primary level of the matter relates to the GENCOs to strategically bid, and the second level solves the freelance system operator's market clearing downside based on the maximization of social welfare. In order to model the incomplete info of participants within the market concerning price coefficients of opponents and their forecast errors, a state of affairs-based programming framework is presented. Additionally, a roulette wheel mechanism is employed for state of affairs-generation process so that the forecast errors of coefficients are considered as random variables with known probability distribution functions. Then, every GENCO solves the bilevel optimization drawback and maximizes its expected profit operate. These bilevel issues are nonconvex, and also the mathematical-based mostly optimization technique is unable to handle the problem and get the nearly world optima. So as to resolve this issue, a unique prey–predator optimization algorithm is instructed to solve the primary level of the bilevel downside and using the iterative methodology to search out out the supply operate equilibrium that is the optimal BSs of GENCOs. Applying to the IEEE fifty seven- and 118-bus test systems with incomplete information studies, the performance of the proposed approach is successfully approved.

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