Abstract
With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.
Highlights
Wind power industry has been rapidly developed
A stochastic multi-objective optimal reactive power dispatch model is studied concerning about load and wind power generation uncertainties, including real power losses and operation cost of wind farms [2]
A multi-objective optimization algorithm based on the non-dominated sorting differential evolution is used to solve the economic environmental dispatch stochastic optimization model of power system connected with large scale wind farms [5]
Summary
The demand of electricity is influenced by variable factors, such as weather, economy, laws, policies, electrical load conditions, and so on These factors make electric dispatch became a task. A stochastic multi-objective optimal reactive power dispatch model is studied concerning about load and wind power generation uncertainties, including real power losses and operation cost of wind farms [2]. The day-ahead multi-objective optimal dispatching model containing thermal power, hydro power, wind-power and pumped storage units is given to minimize the total costs and CO2 emission under multiple constraints. A multi-objective optimization algorithm based on the non-dominated sorting differential evolution is used to solve the economic environmental dispatch stochastic optimization model of power system connected with large scale wind farms [5]. The experimental results verify the effectiveness of the proposed method, and have some certain practical significance for power system optimal scheduling
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