Optimal Charging of Electric Vehicles for Load Shaping: A Dual-Splitting Framework With Explicit Convergence Bounds

Floch, Caroline Le; Belletti, Francois; Moura, Scott J.

doi:10.1109/tte.2016.2531025

Cited by 64 publications

(28 citation statements)

References 32 publications

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“…In this paper, we study the EV battery degradation from performing net load shaping, which aims to mitigate the fluctuation of net load. A distributed optimal charging algorithm [28] has been built into V2G-Sim. The objective (12) is to minimize the variance of net load.…”

Section: Net Load Shapingmentioning

confidence: 99%

Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services

Wang

Coignard

Zeng

et al. 2016

Journal of Power Sources

217

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The risk of accelerated electric vehicle battery degradation is commonly cited as a concern inhibiting the implementation of vehicle-to-grid (V2G) technology. However, little quantitative evidence exists in prior literature to refute or substantiate these concerns for different grid services that vehicles may offer. In this paper, a methodology is proposed to quantify electric vehicle (EV) battery degradation from driving only vs. driving and several vehicle-grid services, based on a semi-empirical lithium-ion battery capacity fade model. A detailed EV battery pack thermal model and EV powertrain model are utilized to capture the time-varying battery temperature and working parameters including current, internal resistance and state-of-charge (SOC), while an EV is driving and offering various grid services. We use the proposed method to simulate the battery degradation impacts from multiple vehicle-grid services including peak load shaving, frequency regulation and net load shaping. The degradation impact of these grid services is compared against baseline cases for driving and uncontrolled charging only, for several different cases of vehicle itineraries, driving distances, and climate conditions. Ten-year degradation simulation results show that frequency regulation and peak load shaving at power rates typical for vehicle charging and discharging will not significantly accelerate battery degradation in comparison to the degradation incurred from driving and calendar aging.

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Section: Net Load Shapingmentioning

confidence: 99%

Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services

Wang

Coignard

Zeng

et al. 2016

Journal of Power Sources

217

View full text Add to dashboard Cite

show abstract

“…CAISO deployed demand response to change the duck curve shape to ensure grid reliability [13]. Floch et al proposed a distributed charging algorithm for plug-in electric vehicles to flatten the duck curve [14]. Sanandaji et al utilized thermostatically controlled loads such as air conditioners and refrigeration units to provide fast regulation reserve service during the sunset [15].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China

et al. 2019

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The concepts of probabilistic duck curve and probabilistic ramp curve are proposed.• The probabilistic duck curve is modeled considering dependencies among PV and loads. A B S T R A C TThe high penetration of photovoltaic (PV) is reshaping the electricity net-load curve and has a significant impact on power system operation and planning. The concept of duck curve is widely used to describe the timing imbalance between peak demand and PV generation. The traditional duck curve is deterministic and only shows a single extreme or typical scenario during a day. Thus, it cannot capture both the probability of that scenario and the uncertainty of PV generation and loads. These weaknesses limit the application of the duck curve on power system planning under high PV penetration. To address this issue, the novel concepts of probabilistic duck curve (PDC) and probabilistic ramp curve (PRC) are proposed to accurately model the uncertainty and variability of electricity net load and ramp under high PV penetration. An efficient method is presented for modeling PDC and PRC using kernel density estimation, copula function, and dependent discrete convolution. Several indices are designed to quantify the characteristics of the PDC and PRC. For the application, we demonstrate how the PDC and PRC will benefit flexible resource planning. Finally, an empirical study on the Qinghai provincial power system of China validates the effectiveness of the presented method. The results of PDC and PRC intuitively illustrate that the ramp demand and the valley of net load face considerable uncertainty under high PV penetration. The results of flexible resource planning indicate that retrofitting coal-fired units has remarkable performance on enhancing the power system flexibility in Qinghai. In average, reducing the minimal output of coal-fired units by 1 MW will increase PV accommodation by over 4 MWh each day.

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“…Decentralized algorithms have been explored for coordinating electric vehicles [18], [19], smart inverters [20], and for fleets of diverse DERs [21], [22], [23].…”

Section: A Der Controlmentioning

confidence: 99%

Blockchains for decentralized optimization of energy resources in microgrid networks

Munsing

Mather

Moura

2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

Self Cite

281

188

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Abstract-We present an architecture for peer-to-peer energy markets which can guarantee that operational constraints are respected and payments are fairly rendered, without relying on a centralized utility or microgrid aggregator. We demonstrate how to address trust, security, and transparency issues by using blockchains and smart contracts, two emerging technologies which can facilitate decentralized coordination between nontrusting agents. While blockchains are receiving considerable interest as a platform for distributed computation and data management, this is the first work to examine their use to facilitate distributed optimization and control. Using the Alternating Direction Method of Multipliers (ADMM), we pose a decentralized optimal power flow (OPF) model for scheduling a mix of batteries, shapable loads, and deferrable loads on an electricity distribution network. The DERs perform local optimization steps, and a smart contract on the blockchain serves as the ADMM coordinator, allowing the validity and optimality of the solution to be verified. The optimal schedule is securely stored on the blockchain, and payments can be automatically, securely, and trustlessly rendered without requiring a microgrid operator.

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Optimal Charging of Electric Vehicles for Load Shaping: A Dual-Splitting Framework With Explicit Convergence Bounds

Cited by 64 publications

References 32 publications

Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services

Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services

Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China

Blockchains for decentralized optimization of energy resources in microgrid networks

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