Abstract
New energy power systems with high-permeability photovoltaic and wind power are high-dimensional dynamic large-scale systems with nonlinear, uncertain and complex operating characteristics. The uncertainty of new energies creates challenges in detailed analyses of operating conditions and the efficient planning of distribution networks. Probabilistic power flows (PPFs) are effective tools for uncertainty analyses of distribution networks, and they can be applied in stochastic programming, risk assessment and other fields. We propose different forms of PPFs, which are origin moments rather than means and variances, based on point estimation. We design a stochastic programming model suitable for new energy planning in practice, and the PPF results can be used to improve energy stochastic programming methods by considering the principle of maximum entropy (POME) and quadratic fourth-order moment (QFM) estimation. The origin moments of PPFs are transformed into central moments as inputs of QFM based on probability theory. QFM can efficiently estimate the constraint probability levels of stochastic optimal planning models, and the proposed method is verified based on an IEEE 33-node distribution network.
Highlights
In the context of smart grids and low-carbon power, the broad application of intermittent renewable distributed generation (DG) has led to uncertainty in distribution network planning (Injeti and Thunuguntla, 2020)
We propose a Probabilistic power flows (PPFs)-based stochastic programming model for new energies in distribution networks as follows: fobj numPV, pPV, numWP, pWP min E ploss, (1)
The most important contribution of this paper is the novel PPF results, which include the mean loss and the probability of voltage qualification. These results provide the basis for the stochastic planning of distribution networks because traditional PPF results cannot be used directly in stochastic programming
Summary
In the context of smart grids and low-carbon power, the broad application of intermittent renewable distributed generation (DG) has led to uncertainty in distribution network planning (Injeti and Thunuguntla, 2020). The impact of new energy uncertainty on the operation optimization of distribution networks cannot be ignored, and high requirements for planning and design have been proposed. Zhang et al presented a scenario method for reactive power optimization, and the uncertainty of DG was obtained from distribution functions (Zhang et al, 2016). A statistical model for uncertainty planning of distributed renewable energy sources is proposed based on statistical machine learning in Stochastic Programming Model Section. When new renewable energy capacity is added to the grid, the uncertainty of the distribution network will increase, and the operation scenario become increasingly complex, creating challenges related to the efficient planning and use of new energies. We propose a PPF-based stochastic programming model for new energies in distribution networks as follows: fobj numPV, pPV, numWP, pWP min E ploss ,. The power generated by new energy must be a function of the planned capacity, as shown below (Rohani and Nour, 2014)
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