Data-Driven Stochastic Transmission Expansion Planning

Bagheri, Ali; Wang, Jianhui; Zhao, Chaoyue

doi:10.1109/tpwrs.2016.2635098

Cited by 74 publications

(31 citation statements)

References 32 publications

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“…Regarding the application of DRO to TEP (DRO-TEP) problem, to the best of the authors' knowledge only a few works have been published so far. While a data-driven ambiguity set was applied in [27] to account for the estimation uncertainty of empirical probability distributions, the network security was addressed in [28]. Both previously reported works used as solution methodology the traditional columnand-constraint-generation (CCG) technique, developed in [29] to tackle optimization problems originated from ARO-based models.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

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

Velloso

Pozo

Street

2020

IEEE Trans. Power Syst.

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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.

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Section: A Motivation and Literature Reviewmentioning

confidence: 99%

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

Velloso

Pozo

Street

2020

IEEE Trans. Power Syst.

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“…It maximizes the average performance of the power system under all scenarios, weighted by their probabilities of occurrence. As such, the optimal stochastic transmission plan finds a good balance between the (positive or negative) impact and the likelihood of all scenarios [11][12][13][14][15][16]. However, this approach also faces a dilemma.…”

Section: Introductionmentioning

confidence: 99%

Scenario Selection for Iterative Stochastic Transmission Expansion Planning

2020

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Reliable transmission expansion planning is critical to power systems’ development. To make reliable and sustainable transmission expansion plans, numerous sources of uncertainty including demand, generation capacity, and fuel cost must be taken into consideration in both spatial and temporal dimensions. This paper presents a new approach to selecting a small number of high-quality scenarios for transmission expansion. The Kantorovich distance of social welfare distributions was used to assess the quality of the selected scenarios. A case study was conducted on a power system model that represents the U.S. Eastern and Western Interconnections, and ten high-quality scenarios out of a total of one million were selected for two transmission plans. Results suggested that scenarios selected using the proposed algorithm were able to provide a much more accurate estimation of the value of transmission plans than other scenario selection algorithms in the literature.

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“…These uncertainties were ignored in [17]. Data‐driven optimisation approaches are efficient for modelling uncertainties in TEP [22]. In a data‐driven optimisation approach, by learning from historical data, an unknown probability distribution is considered for an uncertain parameter, and confidence set with a certain confidence level is constructed.…”

Section: Introductionmentioning

confidence: 99%