Rolling Multi-Period Optimization to Control Electric Vehicle Charging in Distribution Networks

O'Connell, Alison; Flynn, Damian; Keane, Andrew

doi:10.1109/tpwrs.2013.2279276

Cited by 107 publications

(34 citation statements)

References 22 publications

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“…To calculate the voltage values involved, the so-called network sensitivity matrix is employed by works such as [4,7,8] as mentioned in the previous section, assuming local linearity between the nodal voltage and the additional EV loads. The sensitivity matrix requires updating for every new operating point to ensure accuracy, and this makes the optimization inefficient.…”

Section: Implementation Of the Optimizationmentioning

confidence: 99%

“…The wholesale electricity price on the other hand is a popular choice for EV charging scheduling, either day-ahead price as in [5] or intra-day price as in [7], due to the fact that it directly reflects the supply-demand relationship in electricity market. The RTP signal is recognized to improve the performance of wholesale electricity market by mitigating market power and price volatility, [13].…”

Section: Real Time Price (Rtp) Signalmentioning

confidence: 99%

“…The UK day-ahead electricity price for a typical January weekday from N2EX, [15], which is an electricity exchange launched in 2010, is illustrated in Figure 1 together with the corresponding national demand curve. The price signal for scheduling EV charging, G2Vequi, can then be calculated using Equation (7). The high correlation observed in Figure 1 between the price and demand curve indicates that by responding to the RTP signals the EV charging load would be scheduled to periods of low load.…”

Section: Real Time Price (Rtp) Signalmentioning

confidence: 99%

“…A rolling optimization approach is proposed in [7], in which a moving window of length 12 hours achieves the local minimization of EV charging cost and then advances into the next period, sliding with a step of 30 minutes, until the simulation period of 24 hours is completed. Here the load flow is performed via an inverted Jacobian matrix, which relates the current change in each specific node to the voltage changes in all the nodes including the one under consideration.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Smart charging for electric vehicles to minimise charging cost

Wang

Infield²,

Gill³

2017

Proceedings of the Institution of Mechanical Engineers, Part A:

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This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system is available. Based on this information an aggregator with access to this data can be employed to minimize EV owner charging costs whilst maintaining acceptable distribution system voltages. In this study EV charging is assumed to take place only in the home. A single-phase LV distribution network is investigated where the local EV penetration level is assumed to be 100%. EV use patterns have been extracted from the UK Time of Use Survey data with 10-minute resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of EV charging. A simple and effective heuristic method is proposed to minimize the EV charging cost whilst satisfying the requirement of state of charge for the EV battery. A simulation in OpenDSS over a period of 24 hours has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimization results are compared with those obtained using dynamic optimal power flow.

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Section: Implementation Of the Optimizationmentioning

confidence: 99%

Section: Real Time Price (Rtp) Signalmentioning

confidence: 99%

Section: Real Time Price (Rtp) Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart charging for electric vehicles to minimise charging cost

Wang

Infield²,

Gill³

2017

Proceedings of the Institution of Mechanical Engineers, Part A:

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“…Both single phase [13] and 3-phase AC OPF [14] have been proposed to study various aspects of transmission and distribution networks [15], [16]. While single phase OPF is often sufficient for studies carried out on balanced networks, studying LV distribution feeders entails 3-phase unbalanced models.…”

Section: -Phase Optimal Power Flowmentioning

confidence: 99%

A study of operation strategy of small scale heat storage devices in residential distribution feeders

Bakhtvar

Andrade-Cabrera

Buttitta

et al. 2017

2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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Applications of optimization models for electricity distribution networks

Claeys

Vanin

Geth

2021

WIREs Energy & Environment

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Increased penetration of low‐carbon technologies, such as residential photovoltaic systems, electric vehicles, and batteries, can potentially cause voltage quality issues in distribution networks. Active distribution networks adopt control schemes where these assets are actively managed to prevent potential issues, increasing the network utilization. Mathematical optimization is a key technology in enabling such applications, either directly as the underlying solution, or for benchmarking effectiveness. As networks are operated closer to their engineering limits, models representing distribution network physics become increasingly important. This article reviews how distribution networks are modeled with varying degrees of detail in the context of optimization problems. It goes on to catalog the applications that use such models, and ends with an overview of toolchains to implement them, to enable the transition from the passive to active management of the distribution system. This article is categorized under: Energy Systems Analysis > Systems and Infrastructure

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Rolling Multi-Period Optimization to Control Electric Vehicle Charging in Distribution Networks

Cited by 107 publications

References 22 publications

Smart charging for electric vehicles to minimise charging cost

Smart charging for electric vehicles to minimise charging cost

A study of operation strategy of small scale heat storage devices in residential distribution feeders

Applications of optimization models for electricity distribution networks

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