Valuation of measurement data for low voltage network expansion planning

Nijhuis, Michiel; Gibescu, Madeleine; Cobben, Jfg Sjef

doi:10.1016/j.epsr.2017.05.017

Cited by 11 publications

(11 citation statements)

References 16 publications

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“…The advanced metering infrastructures (AMI) are installed at the customer's premises to update the power consumption to the distribution system operators. Also, the distribution system operators communicate with the parking lot controller through a wide area network [30]. The FLIA is based on the services of fuzzy logic inference mechanism which comprehensively solve the complicated temporal varying inputs from the smart grid and inputs from the EVs and determine an accurate charging and/or discharging decision for each of the connected EVs.…”

Section: B Proposed System Model Of Fliamentioning

confidence: 99%

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Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

2019

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Smart grid is expected to support electric vehicles parking lots with the existing power line infrastructure. In order to support all electric-vehicles (EVs) users to complete their charging needs before leaving the parking lot, the power grid requires that the charging demands of EVs should be within the allowable power limit to avoid the grid overloading. This paper proposes a fuzzy logic inference based algorithm (FLIA) to manage the available power efficiently for EVs in the parking lot. The problem is mathematically formulated and solved by the credibility of the fuzzy inference mechanism to control charging and discharging of the EVs. The key idea is to introduce the fuzzy inference mechanism that evaluates several uncertain input parameters from the electric grid and from EVs to obtain an adequately accurate charging or discharging decision for each of the connected EVs. The proposed scheme is applied to a parking lot with different parking capacities and compared with the conventional-based systems. The simulation results demonstrated the feasibility and effectiveness of the proposed algorithm when dealing with the available power management and satisfying the EV user's requirements. INDEX TERMS Electric vehicles, fast charging stations, fuzzy logic inference, parking lot, power management.

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Section: B Proposed System Model Of Fliamentioning

confidence: 99%

“…The total power load of the low-voltage distribution system can be obtained by summing-up the baseload and the PL load at any time t, as expressed by Eq. (30).…”

Section: Problem Formulation and The Proposed Algorithmmentioning

confidence: 99%

Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

2019

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“…To overcome this limitations, pseudo-measurements are used [8], [2]. The key disadvantage of pseudo-measurements is the potential error propagation from the pseudo-measurement to the output of the DSSE, error which increases the level of uncertainty of the results [9].…”

Section: Challenges and Motivationmentioning

confidence: 99%

“…As a result, high granularity demand power data will come at a cost to UK DNOs. An example method to meet this requirement is to aggregate the demand power data for >1 nearby households on the LV circuit [2]. However, to ensure its applicable for DSSE, aggregation should be done for those on the same phase.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning

Mokhtar

Robu

Flynn

et al. 2019

2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive 'fit-and-forget' approach to LV network management will be inefficient to ensure its effective operations. A more adaptive approach is required that includes the prediction of risk and capacity of the circuits. Many of the proposed methods require full observability of the networks, motivating the installations of smart meters and advance metering infrastructure in many countries. However, the expectation of 'perfect data' is unrealistic in operational reality. Smart meter (SM) roll-out can have its issues, which may resulted in low-likelihood of full SM coverage for all LV networks. This, together with privacy requirements that limit the availability of high granularity demand power data have resulted in the low uptake of many of the presented methods. To address this issue, Deep Learning Neural Network is proposed to predict the voltage distribution with partial SM coverage. The results show that SM measurements from key locations are sufficient for effective prediction of voltage distribution.

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“…On the other hand, the level of supplied voltage is also of great relevance to the distribution company, and not only because of consumer complaints. It is well known that voltage control allows utilities to improve the operation efficiency of their networks by minimizing power losses [14], taking advantage of conservation voltage reduction [15], delaying new investments [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

Accurate Assessment of Decoupled OLTC Transformers to Optimize the Operation of Low-Voltage Networks

2019

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Voltage control in active distribution networks must adapt to the unbalanced nature of most of these systems, and this requirement becomes even more apparent at low voltage levels. The use of transformers with on-load tap changers is gaining popularity, and those that allow different tap positions for each of the three phases of the transformer are the most promising. This work tackles the exact approach to the voltage optimization problem of active low-voltage networks when transformers with on-load tap changers are available. A very rigorous approach to the electrical model of all the involved components is used, and common approaches proposed in the literature are avoided. The main aim of the paper is twofold: to demonstrate the importance of being very rigorous in the electrical modeling of all the components to operate in a secure and effective way and to show the greater effectiveness of the decoupled on-load tap changer over the usual on-load tap changer in the voltage regulation problem. A low-voltage benchmark network under different load and distributed generation scenarios is tested with the proposed exact optimal solution to demonstrate its feasibility.

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Valuation of measurement data for low voltage network expansion planning

Cited by 11 publications

References 16 publications

Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning

Accurate Assessment of Decoupled OLTC Transformers to Optimize the Operation of Low-Voltage Networks

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