A Distributionally Robust Optimization Model for Real-Time Power Dispatch in Distribution Networks

Yang, Yue; Wu, Wenchuan

doi:10.1109/tsg.2018.2834564

Cited by 78 publications

(35 citation statements)

References 31 publications

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“…The mathematical model of proposed IGDT-based active/reactive power management is implemented in GAMS software and solved by BONMIN solver running on an Intel® Core™ i5-4210U CPU 1.70 GHz system with 6GB RAM. Taking risk-neutral strategy (α = 0), the optimal active/reactive power scheduling problem of SDN is solved with disregarding robust model Equations (33) to (36) and the risk-neutral case, ie, TC 0 , at peak hour obtained as $805.05 and $806.2 with and without DR program, respectively. Worth mentioning, these values are achieved based on the predicted values of uncertain variables.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…19,29 In the literature, to cope with the inherent uncertainties of distribution system operation and planning studies, different approaches have been employed. Stochastic programming, 30,31 probabilistic-based approaches, 32 robust optimization methodology, 33 fuzzy optimization approach, 34 and distributionally robust optimization (DRO) 35,36 have been proposed recently. These uncertainty modeling approaches can be generally categorized into stochastic or probabilistic and interval-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Samimi

Rezaei

2019

Int Trans Electr Energ Syst

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Summary Distributed energy resources (DERs) are main components for smart distribution networks (SDNs). Generally, energy and reactive power dispatch scheduling problems are managed separately in SDNs. In this paper, a robust simultaneous active/reactive scheduling framework is presented for SDNs. In order to handle the uncertainties of wind power generation, upstream grid prices, and load demand forecasting in a robust framework, information gap decision theory (IGDT) technique is proposed. The proposed robust model considers the minimization of the energy and reactive power cost, carbon taxes of CO2 emission of all distributed resources along with the cost of implementation of demand response program. Numerical results based on a 22‐bus distribution system validate the effectiveness of the proposed method from determining boundaries of uncertainties viewpoint. The obtained results verify that through the proposed IGDT‐based model, the distribution system operator (DSO) can effectively schedule the all DERs while considering the possibility of problem with a specific uncertainty budget.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Samimi

Rezaei

2019

Int Trans Electr Energ Syst

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“…This is an MISOCP, which can be solved by modern solvers, eg, Gurobi, 21 CPLEX, 32 and SCIP. 33 We properly construct the following assumption to analyze the property of (17).…”

Section: G He Model Formulationmentioning

confidence: 99%

“…17,18 Yang and Wu 17 presented a distributedly robust real-time power dispatch model in order to reduce the conservativeness of robust optimization solutions, which characterizes the uncertainty of distributed generation output with the information of first-order and second-order moments. 17,18 Yang and Wu 17 presented a distributedly robust real-time power dispatch model in order to reduce the conservativeness of robust optimization solutions, which characterizes the uncertainty of distributed generation output with the information of first-order and second-order moments.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several studies on applying robust optimization to network problems were presented. 17,18 Yang and Wu 17 presented a distributedly robust real-time power dispatch model in order to reduce the conservativeness of robust optimization solutions, which characterizes the uncertainty of distributed generation output with the information of first-order and second-order moments. Chiu et al 18 presented a robust min-max optimization method to find the positions of location-unaware sensors by minimizing the worst-case estimation error of the relative location estimation problem.…”

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confidence: 99%

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Approach to problem of minimizing network power consumption based on robust optimization

Das

Takahashi

Oki

et al. 2019

Int J Communication

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In this paper, a green and robust optimization model is introduced to minimize network power consumption, which allows fluctuations of traffic demands between source-destination pairs in the network. Our model is based on the green hose model, where the traffic is bounded by just total outgoing and incoming amount at each node. In addition to the green hose model, we use the ellipsoidal uncertainty set to allow a different type of fluctuations in traffic demands. Here, the total amount of squared errors in traffic demands is bounded by a constant which controls the total admissible fluctuations over the network.Applying the conic duality, we formulate our model in the form of mixed-integer second order cone programming (MISOCP) problem. Furthermore, we establish a relationship between our model and the green hose model with bound of link traffic (HLT) model, an developed version of the hose model known as the HLT.Numerical results demonstrate that each of the MISOCP problems can be solved to its optimality in a reasonable time by a general MISOCP solver, and that the proposed model has different tendency in performance with the green hose and green HLT models. KEYWORDSgreen computing, mixed-integer second-order cone programming, power consumption, robust optimization, second-order cone programming, traffic-demand matrix Int J Commun Syst. 2019;32:e3891.wileyonlinelibrary.com/journal/dac

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Introduction

Gandhi

2020

Reactive Power Support Using Photovoltaic Systems

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A Distributionally Robust Optimization Model for Real-Time Power Dispatch in Distribution Networks

Cited by 78 publications

References 31 publications

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Approach to problem of minimizing network power consumption based on robust optimization

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