An enhanced peak shaving method for short term hydrothermal scheduling

Optimal gamma based fixed head hydrothermal scheduling using genetic algorithm

“…These evolutionary approaches such as genetic algorithms (Chang & Chen, 1998;Kumar & Naresh, 2007;Orero & Irving, 1998;Wong & Wong, 1996;Wu, Ho, & Wang, 2000), simulated annealing (Basu, 2005), evolutionary strategy (Lakshmnarasimman & Subramanian, 2008;Sinha, Chakrabarti, & Chattopadhyay, 2003), particle swarm optimisation (Mandal, Basu, & Chakraborty, 2008;Yu, Yuan, & Wang, 2007) and peak shaving (Simopoulos, Kavatza, & Vournas, 2007) involve large number of problem variables, which not only depend on the number of generating plants but also the number of intervals considered in the planning horizon and thus are highly ineffective. Therefore, a genetic algorithm (GA) based efficient approach that involves minimum number of GA variables, which are independent of the number of intervals in the scheduling period, is developed for fixed head HTS in this paper and the results are presented.…”

Optimal self-scheduling of a dominant power company in electricity markets

“…In other words, the Producer withholds capacity either by holding certain intermediate load units off-line (hours [3][4][5][6][7][8][9] or by enforcing them to operate below their nominal power output, so that he raises the energy market clearing price to the price cap (150 €/MWh) (see Fig. 4).…”

“…Constraints (7) denote that the blocks of the price quota curve of product ' accepted by the ISO in every hour are upper bounded positive values.…”

“…hours [1][2][3][4][5][6][7][8] only. In these hours, base-load and intermediate-load units (low-cost and medium-cost units, respectively) increase their output so as to maintain the constant difference (=2400 MW) between the net producer's generation and the hourly system load, as described in the previous paragraph (see Fig.…”

“…Many different optimization methods have been proposed in the literature for the solution of the above problems, such as dynamic programming [1], Lagrangian relaxation [2,3], network flow programming [4], mixed integer programming [5] and metaheuristic techniques [6,7]. An extensive review of the literature for solving hydrothermal coordination problems can be found in [8].…”