An Intelligent Computational Algorithm for Optimal Self Scheduling of GENCOs to Improve The Profit in a Day-ahead Energy and Reserve Market - Senthilvadivu, A; Gayathri, K; Asokan, K (International Journal of Computer Sciences and Engineering, 2018)
This paper presents an effective methodology for self scheduling of thermal generators to improve the profit of generation companies (GENCOs) in a day-ahead joint energy and reserve market. A recently projected Exchange Market Algorithm (EMA) is proposed to solve self scheduling problem. EMA is a powerful tool and having two dominant absorbing operators to pulling the solutions toward optimality and two smart searching operators for extract optimum point in optimization problem. Therefore, the proposed approach provides capability to determine global optimal solution for self scheduling problem. The problem modelled in the form of bi-objective optimization framework to simultaneously maximize the profit of GENCOs and reduce emission quantity taking into account reserve power generation.. The thermal generators emit the greenhouse gases into the atmosphere, which is answerable for change of climate and global warming in our environment. Sufficient spinning reserve is one of the major factors for reliable operation and profit maximization of power suppliers. So the problem is carefully coined with a view to maximize the profit of GENCOs by considering reserve power generation and added in the objective function. Also generated reserve power is sold in the reserve market. Numerical example with IEEE 39 bus (10 units with 24 hour) test system is considered to evaluate the performance of the proposed EMA. From the simulation results, it is found that the EMA based approach is able to afford the better solutions in terms of fuel cost, revenue, profit and emission with lesser computational effort.