Multi-power Coordination Optimal Scheduling In Smart Grid

With the progress of science and technology and the growth of energy demand,the fossil energy,such as Petroleum,coal,n:atural gas,used by human beings is increasing day by day.These actions bring about a series of problems.It can make the environment be polluted and make the energy exhausted.So Finding new clean alternatives is becoming more important.Since 1990s,we have ushered in a prosperous new energy development era,of which the most rapid development is wind power and photovoltaic power generation.With the increasing penetration of wind power and photovoltaic power generation,the study of for new energy sources(NEG)has become more and more challenging.The rapid development of energy storage technology brings new hope for the efficient utilization of new energy.Large scale renewable energy connect to the grid brings about lot of trouble on the safe and stable operation of the existing power system,also put forward higher requirements on the grid and solar power consumption.In order to solve the above problems,it is very important to build a strong smart grid.At first,This paper introduces the concept of smart grid,and then describes the characteristics of new energy power,and power characteristics of thermal power,hydropower,energy storage,then summarizes the multi-objective optimization model with large-scale wind power,photovoltaic power and other new energy cooperation optimization,this paper proposes a collaborative optimization multi-objective genetic the particle swarm algorithm of multi-source complementary scenery storage fire based on hierarchical scheduling method(HCOS).Aiming at the maximum output,the minimum fluctuation of generalized load and the lowest cost of thermal power generation,the reduced dimension of complex optimization problem with nonlinear,non convex,high dimension and multiple constraints is achieved by hierarchical processing.Multi objective genetic algorithm to meet the scenery storage contribute the largest,generalized Pareto optimal minimum load fluctuation and meet the constraints of the solution by the upper,in response to the rapid and flexible storage space power to stabilize the intermittent energy,smooth generalized load fluctuation curve;to achieve lower power unit start stop and load distribution optimization based on Improved Particle Swarm Optimization algorithm.Simulation results show the effectiveness of the proposed algorithm.The main innovation of this paper is:(1)a cooperative optimization scheduling algorithm combining multi-objective genetic algorithm with particle swarm optimization(PSO)is proposed;(2)reduce the dimensionality of complex optimization problems with nonlinear,non convex,high dimensional and multi constraints,including discrete variables and continuous variables through hierarchical collaborative optimization scheduling;The simulation results show that the hierarchical cooperative optimization algorithm can guarantee the economy of the solar thermal power generation system under the premise of the safe and stable operation of power system.Able to overcome the randomness,volatility,and intermittency of large-scale,wind,solar,and other uncertain sources of energy.