Abstract
Wind power prediction research shows that it is difficult to accurately and effectively estimate the probability distribution (PD) of wind power. When only partial information of the wind power probability distribution function is available, an optimal available transfer capability (ATC) assessment strategy considering the uncertainty on the wind power probability distribution is proposed in this paper. As wind power probability distribution is not accurately given, the proposed strategy can efficiently maximize ATC with the security operation constraints satisfied under any wind power PD function case in the uncertainty set. A distributional robust chance constrained (DRCC) model is developed to describe an optimal ATC assessment problem. To achieve tractability of the DRCC model, the dual optimization, S-lemma and Schur complement are adopted to eliminate the uncertain wind power vector in the DRCC model. According to the characteristics of the problem, the linear matrix inequality (LMI)-based particle swarm optimization (PSO) algorithm is used to solve the DRCC model which contains first and second-order moment information of the wind power. The modified IEEE 30-bus system simulation results show the feasibility and effectiveness of the proposed ATC assessment strategy.
Highlights
Wind power energy has become one of the fastest growing renewable energies in recent years.the intermittency, randomness and unpredictability of wind power increase the power volatility of the transmission system [1]
The infimum is introduced in Equations (8) and (9) in the distributional robust chance constrained model to express that for any possible probability distribution function, branch power and node voltages should satisfy the constraints, which is shown in Equations (10)
To compare the distributional robust chance constrained Available Transfer Capability (ATC) (DRCC-ATC) model and the traditional chance constrained ATC (TCC-ATC) model, we set the expectation of wind power probability distribution to 2.0 MW, and the covariance of the wind power probability distribution to 0.4 MW2
Summary
Wind power energy has become one of the fastest growing renewable energies in recent years. For describing the wind power uncertainty, the statistical probability method [12,13,14,15] and scene analysis method [16] are adopted Both of these methods assume that the probability distribution function of wind speed or wind power is determined. The linear matrix inequality (LMI)-based particle swarm optimization (PSO) algorithm is used to solve the problem according to its characteristics This optimization strategy can efficiently maximize the ATC of the transmission system with consideration to all the possible wind power probability distributions with the security operation constraints satisfied. For optimal ATC assessment for wind integrated transmission systems considering the uncertainty on the wind power probability distribution chance constrained programming is employed to avoid the operational scheme being limited by small probability events and satisfy the security constraints of the transmission system. Where Pa → b denotes all tie-line power output from area a to area b
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