A Two-Stage Chance Constrained Volt/Var Control Scheme for Active Distribution Networks With Nodal Power Uncertainties

Nazir, Firdous Ul; Pal, Bikash C.; Jabr, Rabih A.

doi:10.1109/tpwrs.2018.2859759

Cited by 87 publications

(25 citation statements)

References 30 publications

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“…In the bilinear constraint (15) for DPVs, there is a product of discrete and continuous variables. Then the auxiliary variable P PV;Z n;t is utilised to convert the bilinear product in the nonlinear constraint [22], given by…”

Section: ) Big-m Methods For Dpv Bilinear Constraintmentioning

confidence: 99%

“…In the bilinear constraint () for DPVs, there is a product of discrete and continuous variables. Then the auxiliary variable

P_{n, t}^{PV,Z}

is utilised to convert the bilinear product in the non‐linear constraint [22], given by

P_{n, t}^{P V, Z} = I_{n}^{PV} P_{n, t}^{P V, \max}, \forall t \in T, \forall n \in {scriptN}^{DPV}

P_{n, t}^{P V} + P_{n, t}^{C U} = P_{n, t}^{P V, Z}, \forall t \in T, \forall n \in {scriptN}^{DPV}

- M \cdot I_{n}^{P V} \leq P_{n, t}^{P V, Z} \leq M \cdot I_{n}^{P V}, \forall t \in T, \forall n \in {scriptN}^{DPV}

\begin{array}{l} P_{n, t}^{P V, \max} - M (1 - I_{n}^{normalP normalV}) \leq P_{n, t}^{P V, Z} \leq P_{n, t}^{P V, \max} + M (1 - I_{n}^{normalP normalV}), \\ \forall t ... \end{array}

…”

Section: Formulation Of the Hc Assessment Model In The Iehsmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of distributed photovoltaic hosting capacity in integrated electricity and heat systems considering uncertainty

Zhao

Wan

et al. 2021

IET Energy Syst Integration

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Distributed photovoltaics (DPVs) have been widely integrated into power systems due to their abundance, renewability and low cost, while the stochastic nature of DPVs imposes significant influence on the hosting capacity (HC) of DPVs. Here, the integrated electricity and heat system (IEHS) is explored to increase the deployment of DPVs in comparison to the power system by exploiting the interaction between electric and thermal energy. An MILP-based HC assessment model for DPVs of the IEHS is proposed to efficiently promote the penetration level of DPV generation in distribution networks considering the uncertainty of DPV generation. To reduce the computational burden for HC assessment, the linearisation method combined with the incremental formulations of the big-M method is developed to simplify the complex non-linear model of energy devices and networks in IEHS. A scenario-based uncertainty modelling approach is applied to characterise DPV uncertainty, which improves the accuracy of HC assessment for DPVs. Comprehensive case studies according to a 33-bus electric network and a 6-node heat network validate the superiority of the proposed assessment model.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: ) Big-m Methods For Dpv Bilinear Constraintmentioning

confidence: 99%

“…In the bilinear constraint () for DPVs, there is a product of discrete and continuous variables. Then the auxiliary variable

P_{n, t}^{PV,Z}

is utilised to convert the bilinear product in the non‐linear constraint [22], given by

P_{n, t}^{P V, Z} = I_{n}^{PV} P_{n, t}^{P V, \max}, \forall t \in T, \forall n \in {scriptN}^{DPV}

P_{n, t}^{P V} + P_{n, t}^{C U} = P_{n, t}^{P V, Z}, \forall t \in T, \forall n \in {scriptN}^{DPV}

- M \cdot I_{n}^{P V} \leq P_{n, t}^{P V, Z} \leq M \cdot I_{n}^{P V}, \forall t \in T, \forall n \in {scriptN}^{DPV}

\begin{array}{l} P_{n, t}^{P V, \max} - M (1 - I_{n}^{normalP normalV}) \leq P_{n, t}^{P V, Z} \leq P_{n, t}^{P V, \max} + M (1 - I_{n}^{normalP normalV}), \\ \forall t ... \end{array}

…”

Section: Formulation Of the Hc Assessment Model In The Iehsmentioning

confidence: 99%

Assessment of distributed photovoltaic hosting capacity in integrated electricity and heat systems considering uncertainty

Zhao

Wan

et al. 2021

IET Energy Syst Integration

View full text Add to dashboard Cite

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“…OLT-VAR optimization (VVO) is one primary function in the distribution management system to maintain voltage in an acceptable range by optimally coordinating equipment, e.g., capacitor banks, on-load tap changers (OLTC) and voltage regulators [1][2][3][4][5][6]. Paper [2] proposes a deterministic VVO as mixed-integer quadratic programming to control voltage and VAR devices for day-ahead operation.…”

Section: Introductionmentioning

confidence: 99%

“…The penetration of renewable energy has dramatically increased over the past decade. However, due to its variable and intermittent nature, it poses operational and security challenges to VVO by affecting normal operations of OLTCs and capacitor banks [6][7][8]. Existing literature has considered renewable power uncertainties in designing economic and reliable periodical equipment scheduling plans.…”

Section: Introductionmentioning

confidence: 99%

“…Existing literature has considered renewable power uncertainties in designing economic and reliable periodical equipment scheduling plans. A two-stage V 3 chance-constrained VVO is employed to handle the uncertainties of distributed generation and load demand [6]. Paper [7] proposes a chance-constrained optimization to model the randomness of renewable energy and minimize feeder power losses while avoiding voltage violations, solved by a gradient descent based algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Reactive Power Optimization in Integrated Electricity and Gas Systems

Zhao

Xiang

et al. 2021

IEEE Systems Journal

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Volt/VAR optimization (VVO) is one important operation in distribution systems to maintain acceptable voltage profiles. However, the high penetration of renewable generation poses severe challenges to VVO, leading to voltage deviation and fluctuation. This is further complicated by the growing coupling between electricity and natural gas systems. To resolve unacceptable voltage deviation under energy system interdependency, this paper proposes a co-optimization of VVO for an integrated electricity and gas system (IEGS) with uncertain renewable generation. A two-stage data-driven distributionally robust optimization (DRO) is developed to model the coordinated optimization problem, which determines two-stage VVO and operation schemes with dispatch and corrective adjustment through active power regulation and reactive power support in both dayahead and real-time stage. A semidefinite programming is reformulated to ensure the tractability and the proposed problem is solved by a constraint generation framework. Simulation studies are conducted on a 33-bus-6-node IEGS. Case studies demonstrate that the interdependency between electricity and gas systems reduces siginificant operation cost and voltage rise. It thus can benefit integrated system operators with a powerful operation tool to manage systems with fewer costs but integrate more renewable energy while maintaining high supply quality.

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Optimal coordination between PV smart inverters and different demand response programs for voltage regulation in distribution system

Gupta

Ghose

Chatterjee

2020

Int Trans Electr Energ Syst

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Summary With the increase in the number of renewable energy resources (RES) in the distribution network, the distribution system operators are facing several operational challenges to maintain network power quality and reliability. The stochastic nature of wind speed and solar irradiance may cause voltage sag and swell during high and light load conditions, respectively. Again, owing to low X/R ratio in the active distribution network, the voltage becomes more susceptible to change with the change in net real power injections in the bus, unlike high voltage networks. This article proposes an optimal framework for voltage regulation in active distribution system using the available flexibility of load reduction based on demand response programs, and reactive power injection capability of a smart inverter (SI) of the PV system, where the coordination between the two is considered. However, for adequate grid voltage support, the selection of buses for DR programs is based on voltage sensitivity analysis. This article proposes a simple and effective technique to determine the sensitivity of a DR bus on the voltage profile of the network. The decision for the optimal location for DR resource, DR magnitude, and SI reactive power is finalized using Nondominated Sorting Genetic Algorithm II (NSGA‐II) in a bid to minimize the DR procurement cost, SI ageing cost and network loss. The work also incorporated DR modeling and probabilistic model for RES generation with the consideration of uncertainty involved in both the cases. The modified IEEE 33‐bus distribution system is chosen for authenticating the suggested technique.

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A Two-Stage Chance Constrained Volt/Var Control Scheme for Active Distribution Networks With Nodal Power Uncertainties

Cited by 87 publications

References 30 publications

Assessment of distributed photovoltaic hosting capacity in integrated electricity and heat systems considering uncertainty

Assessment of distributed photovoltaic hosting capacity in integrated electricity and heat systems considering uncertainty

Reactive Power Optimization in Integrated Electricity and Gas Systems

Optimal coordination between PV smart inverters and different demand response programs for voltage regulation in distribution system

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