Optimal generation dispatch with renewable energy embedded using multiple objectives

Optimal Operation of Wind-thermal generation using differential evolution

“…(5) Where N w represents the total wind power plants in the system; P Gjt is the output of active power for the j th wind power plant at time t; P Dt is the load at time t.…”

“…Quantum genetic algorithm is adopted in this paper, the calculation of which is to borrow fully the concept and theory of Quantum computing [4]. The transformation of the multi-objective dispatch problem into Optimal Operation of Wind-thermal generation using differential evolution www.iosrjournals.org 2 | Page a single-objective one to compromise different objectives is presented in [5] using, the optimal generation dispatch (OGD) model. Then the OGD is solved by a particle swarm optimization algorithm.…”

Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimization and Cuckoo Search Algorithm

“…The sensitivity analysis gives idea about multiple places where the DGs can be placed [3], while curtailment and clipping of the wind turbine is adopted in order to avoid the wind turbine output to be used for a certain fraction of the output. Generator dispatch problems are solved with multi objective in [5]. Placement of wind and solar alone with its mathematical static model is implemented by kayal [6].…”

Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

“…In literature wind uncertainty has been represented by using fuzzy models [3][4][5], by using probability distribution functions [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], by assuming a fixed penetration percent [22] or by employing forecast wind speed-power curve [23][24][25][26][27][28] available from day-ahead wind speed forecasting. The beta distribution [6] and Weibull probability distribution [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] are used for computing wind power from wind speed. The complexity of stochastic wind power modeling is avoided in [29] by modeling wind variation as an aggregate wind power to be assumed constant over the validity interval in wind-thermal dispatch.…”

“…Therefore sufficient up/ down reserve capacity is necessary to maintain the demandsupply balance under continuously changing wind and load profiles. In a large number of papers, the cost of over estimation is added in the cost equation as spinning reserve compensation cost and under estimation of wind energy is represented as wind curtailment penalty to include the effect of wind power prediction errors [6,8,9,15,[18][19][20][21][22]25]. A stochastic model for wind power forecast error is proposed using a probabilistic approach to compute the cost of over/under estimation [30].…”