Adaptive Distributionally Robust Optimization for Electricity and Electrified Transportation Planning

Hajebrahimi, Ali; Kamwa, Innocent; Delage, Erick; Abdelaziz, Morad

doi:10.1109/tsg.2020.2987009

Cited by 31 publications

(8 citation statements)

References 41 publications

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“…By separating the nominal ξ-independent terms from the ξ-dependent terms, (14) decomposes to the following two equations:…”

Section: Reformulation Of Power Balance and Objective Functionmentioning

confidence: 99%

“…Ambiguity sets based on moment information, e.g., mean and covariance, provide superior tractability properties [8]. Therefore, the moment-based DRO approach has been used more often in the existing literature of capacity expansion planning [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…An extensive literature review about uncertainty modeling in expansion planning problems including DRO is available in [10] and [17]. The existing works on expansion planning problems, e.g., [10]- [14] and [18], neglect structured distributions in ambiguity sets. Additionally, the violation magnitude risk of operational limits caused by shortterm uncertainties is not considered in the existing literature on distributionally robust expansion planning models [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

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Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

Pourahmadi

Kazempour

2021

IEEE Trans. Power Syst.

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As more renewables are integrated into the power system, capacity expansion planners need more advanced longterm decision-making tools to properly model short-term stochastic production uncertainty and to explore its effects on expansion decisions. We develop a distributionally robust generation expansion planning model, accounting for a family of potential probability distributions of wind forecast error uncertainty. Aiming to include more realistic distributions, we construct more informed moment-based ambiguity sets by adding structural information of unimodality. We include operational-stage unit commitment constraints and model the risk of operational limit violations in two distinct forms: chance and conditional value-at-risk (CVaR) constraints. In both forms, the resulting expansion planning model is a mixed-integer second-order cone program. Using a thorough out-of-sample numerical analysis, we conclude: (i) the distributionally robust chance-constrained generation expansion planning model exhibits a better out-ofsample performance only if sufficiently accurate information about the first-and the second-order moments as well as the mode location of potential distributions is available; (ii) conversely, if such accurate information is unavailable, the distributionally robust CVaR-constrained generation expansion planning model outperforms; (iii) these two models have a similar performance when unimodality information is excluded.

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“…By separating the nominal ξ-independent terms from the ξ-dependent terms, (14) decomposes to the following two equations:…”

Section: Reformulation Of Power Balance and Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

Pourahmadi

Kazempour

2021

IEEE Trans. Power Syst.

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“…Fog-Enabled Smart Cities (FESC) is a technique that permits computing resources to be pre-processed through constrained latency with minimum energy consumption. It was revealed that energy consumption efficiency and estimations in a wide range of computing areas, for instance, in energy-based models using smart high-tech [18], [19], ensuring the energy-efficient quality of service [20]. The node density impact on energy consumption was demonstrated explicitly [21], enactment assessment of metaheuristics in resource-awareness like the energy in specifics simultaneous programming issues (i.e., energy-aware real-time scheduling problems) in different networks [22]; the projected model is depicted in Fig.…”

Section: Edge Computingmentioning

confidence: 99%

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Shafik

Matinkhah

Sanda

et al. 2021

JOIV : Int. J. Inform. Visualization

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In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.

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“…For instance, the submitted optimal solution from optimal power flow in transmission may not be an optimal solution from the viewpoint of the distribution side and vice versa. In the context of power system planning, most of the previous studies [100], have investigated the impact of technology penetration such as the electric vehicles on power system planning without considering the distribution grid. An optimal T&D load flow is able to address this challenge.…”

Section: Contributionmentioning

confidence: 99%

Transmission and distribution co‐simulation: a review and propositions

Mohseni‐Bonab

Hajebrahimi

Kamwa

et al. 2020

IET Generation, Transmission & Distribution

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Adaptive Distributionally Robust Optimization for Electricity and Electrified Transportation Planning

Cited by 31 publications

References 41 publications

Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

Distributionally Robust Generation Expansion Planning With Unimodality and Risk Constraints

Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Transmission and distribution co‐simulation: a review and propositions

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