Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Abstract

We present a distributionally robust optimization (DRO) approach for the transmission expansion planning problem, considering both long-and short-term uncertainties on the system demand and renewable generation. On the longterm level, as it is customary in industry applications, the deep uncertainties arising from social and economic transformations, political and environmental issues, and technology disruptions are addressed by long-term scenarios devised by experts. The system planner is then allowed to consider exogenous long-term scenarios containing partial information about the random parameters, namely, the average and the support set. For each constructed long-term scenario, a conditional ambiguity set is used to model the incomplete knowledge about the probability distribution of the uncertain parameters in the short-term. Consequently, the mathematical problem is formulated as a DRO model with multiple conditional ambiguity sets. The resulting infinite-dimensional problem is recast as an exact, although very large, finite-deterministic mixedinteger linear programming problem. To circumvent scalability issues, we propose a new enhanced-column-and-constraintgeneration (ECCG) decomposition approach with an additional Dantzig-Wolfe procedure. In comparison to existing methods, ECCG leads to a better representation of the recourse function and, consequently, tighter bounds. Numerical experiments based on the benchmark IEEE 118-bus system are reported to corroborate the effectiveness of the method.

Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm

“…In [22], demand and renewable generation uncertainties were included in TEP problem, disregarding uncertainties in electricity generation of conventional power plants such as fuel price uncertainty. Whereas, thermal electricity generation still consists of a notable part of electrical power generation.…”

Optimal Hybrid Energy Storage System Planning of Community Multi-Energy System Based on Two-Stage Stochastic Programming

“…These uncertainties affect both the planning and operation of the MES, and hence they need to be well addressed in the planning stage to ensure the performance of the MES. The commonly used methods for dealing with the uncertainties in the planning problem include stochastic programming, robust optimization [23], etc. In [24], a chance-constrained stochastic programming model is proposed for the integrated planning of power generation, gas network, and gas storage.…”

Essays on Two-Stage Robust Models for Power Systems: Modeling Contributions and Applications of the Column-and-Constraint-Generation Algorithm

“…Numerical experiments based on the benchmark IEEE 118-bus system are reported to corroborate the effectiveness of the method. The contents of this chapter are based on the paper accepted for the IEEE Transactions on Power Systems [74].…”

“…The contents of this chapter are based on the paper accepted for the IEEE Transactions on Power Systems [74].…”

“…For the sake of reproducibility, detailed input data used in the experiments of both Sections 5.6.1 and 5.6.2 can be downloaded from [148]. All tests were run using Gurobi 7.0.2 under JuMP (Julia 0.5) on a Xeon E5-2680 processor at 2.5 GHz and 128 GB of RAM.…”