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Optimal Probabilistic Power Flow in ac-dc Hybrid Microgrid
2020 IEEE Students Conference on Engineering & Systems (SCES), 2020The dc based consumer loads and energy storage options have seen significant growth in past few years. It has led to consolidation of ac and dc microgrids into hybrid microgrids. The planning of real time optimal operation of hybrid microgrids requires probabilistic modelling of time varying loading conditions.
Himani Kala, Mahiraj Singh Rawat
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Probabilistic optimal power flow for power systems considering wind uncertainty and load correlation
Neurocomputing, 2015Abstract Considering wind uncertainty and load correlation, this paper is concerned with the probabilistic optimal power flow (POPF) calculation. The POPF model for wind-integration power system is firstly proposed. Two kinds of schemes in point estimate method, 2m and 2m+1 scheme, are then employed to solve the POPF.
Xue Li 0028, Jia Cao, Dajun Du
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An adaptive importance sampling method for probabilistic optimal power flow
2011 IEEE Power and Energy Society General Meeting, 2011A new probabilistic optimal power flow method is developed to manage electricity market price risk. The proposed method is an adaptive importance sampling method which based on the principle of importance sampling and reinforcement learning. The estimation result of conventional Monte Carlo simulation is taken as benchmark.
Jie Huang +3 more
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The Generalized Cross-Entropy Method in Probabilistic Optimal Power Flow
IEEE Transactions on Power Systems, 2018This paper presents a new powerful density estimator and the application of the generalized cross-entropy method. A framework for probability density function estimation of the probabilistic optimal power flow problem results is presented. Large-scale probabilistic problems have a lot of uncertain parameters, some of which are correlated.
Abolfazl Kazemdehdashti +2 more
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Data-Driven Probabilistic Optimal Power Flow With Nonparametric Bayesian Modeling and Inference
IEEE Transactions on Smart Grid, 2020In this paper, we propose a data-driven algorithm for probabilistic optimal power flow (POPF). In particular, we develop a nonparametric Bayesian framework based on the Dirichlet process mixture model (DPMM) and variational Bayesian inference (VBI) to establish a probabilistic model for capturing the uncertainties involved with wind generation and load
Weigao Sun +3 more
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A novel decoupling approach for solving probabilistic optimal power flow
2016 IEEE International Conference on Power System Technology (POWERCON), 2016The probabilistic optimal power flow (POPF) is usually difficult to solve for the non-linearity and concavity of the chance-constraints. A limit relaxation based method is proposed, which decouples the solution of POPF into the alternative iterations between a deterministic OPF and a probabilistic load flow (PLF).
Sidun Fang +4 more
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Analysis of Probabilistic Optimal Power Flow Taking Account of the Variation of Load Power
IEEE Transactions on Power Systems, 2008This paper presents a probabilistic optimal power flow (POPF) algorithm taking account of the variation of load power. In the algorithm, system load is taken as a random vector, which allows us to consider the uncertainties and correlations of load.
null Xue Li +2 more
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Downside Risk Constrained Probabilistic Optimal Power Flow With Wind Power Integrated
IEEE Transactions on Power Systems, 2016This letter proposes a concept of the downside risk (DR) for the study of probabilistic optimal power flow (POPF) with wind power integrated. It aims to obtain optimal dispatch solutions with the minimal expectation of generation cost and the minimal downside risk.
Y. Z. Li, Q. H. Wu
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Probabilistic security-constrained optimal power flow including the controllability of HVDC lines
IEEE PES ISGT Europe 2013, 2013In this paper we propose a security-constrained optimal power flow (SC-OPF) algorithm which incorporates the stochastic infeed of renewable sources, such as wind, as well as the controllability of HVDC lines. We extend our earlier work on SC-OPF problem and incorporate corrective control actions emanating form the controllability of the AGC lines. This
Maria Vrakopoulou +2 more
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Comparing three methods for solving probabilistic optimal power flow
Electric Power Systems Research, 2015Abstract This paper compares three methods for solving probabilistic optimal power flow (P-OPF) problem: Zhao's point estimate method (PEM), Quasi Monte Carlo simulation (QMCS) and Latin hypercube sampling (LHS). With Nataf transformation, P-OPF problem is formulated as a multiple integral over standard normal space.
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