Transmission network expansion planning with stochastic multivariate load and wind modeling
File(s)
Accepted version
Author(s)
Sun, M
Konstantelos, I
Strbac, G
Type
Conference Paper
Abstract
The increasing penetration of intermittent energy sources along with the introduction of shiftable load elements renders transmission network expansion planning (TNEP) a challenging task. In particular, the ever-expanding spectrum of possible operating points necessitates the consideration of a very large number of scenarios within a cost-benefit framework, leading to computational issues. On the other hand, failure to adequately capture the behavior of stochastic parameters can lead to inefficient expansion plans. This paper proposes a novel TNEP framework that accommodates multiple sources of operational stochasticity. Inter-spatial dependencies between loads in various locations and intermittent generation units' output are captured by using a multivariate Gaussian copula. This statistical model forms the basis of a Monte Carlo analysis framework for exploring the uncertainty state-space. Benders decomposition is applied to efficiently split the investment and operation problems. The advantages of the proposed model are demonstrated through a case study on the IEEE 118-bus system. By evaluating the confidence interval of the optimality gap, the advantages of the proposed approach over conventional techniques are clearly demonstrated.
Date Issued
2016-12-05
Date Acceptance
2016-05-31
Citation
2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2016
Publisher
IEEE
Journal / Book Title
2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
PMAPS 2016
Subjects
Science & Technology
Technology
Energy & Fuels
Engineering, Electrical & Electronic
Engineering
Multivariate copulas
stochastic optimization
transmission network expansion planning
uncertainty analysis
wind power
UNCERTAINTY
SYSTEM
Publication Status
Published
Start Date
2016-10-16
Finish Date
2016-10-20
Coverage Spatial
Beijing