Abstract
Distributed wind power (DWP) needs to be consumed locally under a 110 kV network without reverse power flow in China. To maximize the use of DWP, this paper proposes a novel method for capacity planning of DWP with participation of the energy storage system (ESS) in multiple scenarios by means of a variable-structure copula and optimization theory. First, wind power and local load are predicted at the planning stage by an autoregressive moving average (ARMA) model, then, variable-structure copula models are established based on different time segment strategies to depict the correlation of DWP and load, and the joint typical scenarios of DWP and load are generated by clustering, and a capacity planning model of DWP is proposed considering investment and operation cost, and environmental benefit and line loss cost under typical scenario conditions. Moreover, a collaborative capacity planning model for DWP and ESS is prospectively proposed. Based on the modified IEEE-33 bus system, the results of the case study show that the DWP capacity result is more reasonable after considering the correlation of wind and load by using a variable-structure copula. With consideration of the collaborative planning of DWP and load, the consumption of DWP is further improved, the annual cost of the system is more economical, and the quality of voltage is effectively improved. The study results validate the proposed method and provide effective reference for the planning strategy of DWP.
Highlights
There are generally two typical integration forms of wind power into power systems: centralized and distributed
I=1 where Yt is the value of Distributed wind power (DWP) or load at point t of series; εt and εt−i are the prediction error term at t and i time points ahead of t, respectively; α is the correlation coefficient, which reflects the dependence of the prediction error at different segments; Yt−i is the value with i time points ahead of t; β is the correlation coefficient; p is the order of autoregressive process; and q is the order of moving average process
After estimating the marginal distribution function of DWP and load, respectively, this paper estimates the parameters based on maximum likelihood estimation (MLE), and the evaluation indices can subsequently be calculated
Summary
There are generally two typical integration forms of wind power into power systems: centralized and distributed. A joint optimization in [17] was proposed to plan the capacity and location of ESS, and distributed generating units in a stand-alone micro-grid were presented These studies mainly implement collaborative planning from the perspective of economics and pricing-based demand response [20], providing a good reference for this paper. A variable-structure copula model is employed to describe the joint density of DWP and local load This method can well capture the nonlinear, asymmetry and tail correlation characteristics among variables, it can analyze the marginal distribution of each random variable individually, and can illustrate the varied correlated structure between variables
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