Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

Myers, Kurt S.; Bak‐Jensen, Birgitte; Paudyal, Sumit

doi:10.3390/en10010037

Cited by 21 publications

(13 citation statements)

References 37 publications

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“…Besides, an optimization method was used in the design of a bottom-fixed OWC (Oscillating Water Column), which focused on the importance of time scales [31]. Bhattarai et al [32] presented a multi-timescale control strategy for deploying electric vehicles (EV). In electricity storage, a combined assessment methodology was proposed to compare the stationary electricity storage technologies of different time and system scales [33].…”

Section: Motivationmentioning

confidence: 99%

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

et al. 2019

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With the rapid growth of Smart Grid, electricity load analysis has become the simplest and most effective way to divide user groups and understand user behavior. This paper proposes an AUD-MTS (Abnormal User Detection approach based on power load multi-step clustering with Multiple Time Scales). Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. Secondly, time scale conversion is performed so that the analysis subject can be transformed from load pattern to user behavior. Finally, a two-step clustering in long-term is adopted to divide users from both coarse-grained and fine-grained dimensions so as to detect abnormal users referring to customized OutlierIndex. Experiments are conducted using annual 24-point power load data of American users in all states. The accuracy of clustering methods in AUD-MTS reaches 87.5% referring to the 16 commercial building types defined by the U.S. Department of Energy, which outperforms other common clustering algorithms on AMI (Advanced Metering Infrastructure). After that, the OutlierIndex score of AUD-MTS can be increased by 0.16 compared with other outlier detection algorithms, which shows that the proposed method can detect abnormal users precisely and efficiently. Furthermore, we summarized possible causes including federal holidays, climate zones and summertime that may lead to abnormal behavior changes and discussed countermeasures respectively, which accounts for 82.3% of anomalies. The rest may be potential electricity stealing users, which requires further investigation.

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Section: Motivationmentioning

confidence: 99%

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

et al. 2019

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“…PEVs can be considered as loads, generating sources, energy storage or small portable power plants [22,23]. These can be achieved through opportunistic charging in surface parking garages in workplaces and municipal parking decks [24].…”

Section: Introductionmentioning

confidence: 99%

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

et al. 2017

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Abstract:The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.

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“…Ahmed et al [14] the home energy management scheduling controller of the residential DR strategy is proposed to predict the optimal ON/OFF status for home appliances which can significantly reduce the peak-hour energy consumption. Bhattarai et al [15] presents a multi-timescale control strategy to deploy electric vehicle demand flexibility to solve grid unbalancing and congestions. Shad et al [16] presents a methodology for estimating and predicting the state of individual domestic electric water heaters (DEWHs) from models of their thermodynamics and water consumption.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

et al. 2017

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Demand response (DR) is counted as an effective method when there is a large-capacity power shortage in the power system, which may lead to peak loads or a rapid ramp. This paper proposes a bi-level coordinated optimization strategy by quantitating the DR potential (DRP) of smart appliances to descend the steep ramp and balance the power energy. Based on dynamic characteristics of the smart appliances, the mathematic models of online DRP are presented. In the upper layer, a multi-agent coordinated distribution method is proposed to allocate the demand limit to each agent from the dispatching center considering the online DRP. In the lower layer, an optimal smart appliances-controlling strategy is presented to guarantee the total household power consumption of each agent below its demand limit considering the consumers' comfort and response times. Simulation results indicate the feasibility of the proposed strategy.

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Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

Cited by 21 publications

References 37 publications

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

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