Two-Stage Optimal Scheduling of Electric Vehicle Charging Based on Transactive Control

Liu, Zhaoxi; Wu, Qiuwei; Ma, Kang; Shahidehpour, Mohammad; Xue, Yusheng; Huang, Shaojun

doi:10.1109/tsg.2018.2815593

Cited by 102 publications

(50 citation statements)

References 36 publications

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“…Previous literature has addressed the coordinated dispatch of a fleet of distributed devices in comparable problem frameworks. Prior approaches can be broadly clustered into three categories: optimal control, mean-field control [4]- [6] and transactive energy [7]- [10]. Our approach lies in the first category and we directly optimise power flows between storage and the grid via explicit feedback policies.…”

Section: A Backgroundmentioning

confidence: 99%

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

Evans

Tindemans

Angeli

2020

IEEE Trans. Smart Grid

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We consider the problem of dispatching a fleet of heterogeneous energy storage units to provide grid support. Under the restriction that recharging is not possible during the time frame of interest, we develop an aggregate measure of fleet flexibility with an intuitive graphical interpretation. This analytical expression summarises the full set of demand traces that the fleet can satisfy, and can be used for immediate and straightforward determination of the feasibility of any service request. This representation therefore facilitates a wide range of capability assessments, such as flexibility comparisons between fleets or the determination of a fleet's ability to deliver ancillary services. Examples are shown of applications to fleet flexibility comparisons, signal feasibility assessment and the optimisation of ancillary service provision. NOMENCLATURE nTotal number of devices D i ith device N Set of all devices e i (t)Energy of the ith device at time t u i (t)Power output of the ith device at time t u(t) Vector of power outputs at time t, across all devices p i Maximum power rating of the ith devicē pVector of maximum power ratings, across all devices P Diagonal matrix of maximum power ratings, across all devices UpProduct set of power constraints, with vector of maximum powersp x i (t), z i (t) Time-to-go of the ith device at time t x(t), z(t) Vector of time-to-go values at time t, across all devices X State-space X(t) ). This work was supported by EPSRC studentship 1688672. P r (t)Reference at time t P r [t0,t)

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Section: A Backgroundmentioning

confidence: 99%

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

Evans

Tindemans

Angeli

2020

IEEE Trans. Smart Grid

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“…We employ a battery model similar to the one proposed in [23] to represent the aggregate EV chargers flexibility. However, given the mid-term nature of the service and timeevolving nature of the EV scenarios, uncertainty in our model cannot be readily handled in the same way as proposed in [23]- [26] for a DA scheduling problem. We propose an alternative way to deal with uncertainty in our work, as will be explained later in Section IV.…”

Section: A Ev Aggregate Modelmentioning

confidence: 99%

A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators?

Ziras

Kazempour

Kara

et al. 2020

IEEE Trans. Smart Grid

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A large number of mechanisms are proposed to manage potential problems in distribution networks caused by the participation of distributed energy resources (DERs) in the wholesale markets. In this paper, we first introduce a practical and straightforward mechanism, based on capacity limits, which avoids conflicts between the transmission system operator and the distribution system operators (DSOs). Using a large number of real electric vehicle (EV) commercial charging stations we then show how an EV aggregator can forecast the opportunity cost incurred by offering a mid-term capacity limit service to the DSO. This cost is computed based on the estimated profit that the aggregator could gain in the day-ahead and real-time markets. The proposed methodology guarantees robustness against evolving EV uncertainty, both in terms of service delivery and driving requirements. It also allows the use of a variety of timeseries forecasting methods without forecasting electricity prices and EV scenarios. The results of our empirical analysis show the exponential increase of opportunity cost and the considerable increase of the prediction intervals as the capacity limit decreases. The produced offering curves can be used as an indication of the underutilization cost of DERs caused by the DSO's limitations.

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“…In Reference [24], a day-ahead transactive market framework is proposed for DER scheduling to reduce local supply costs. TCs are used for BEV charging in References [25][26][27]. A TC based on model predictive control (MPC) is used for real-time scheduling of BEVs in Reference [25], where the MPC is used to clear a day-ahead transactive market.…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

et al. 2019

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With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers’ operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers’ savings.

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Two-Stage Optimal Scheduling of Electric Vehicle Charging Based on Transactive Control

Cited by 102 publications

References 36 publications

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

A Graphical Measure of Aggregate Flexibility for Energy-Constrained Distributed Resources

A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators?

Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

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