Integrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability

Sharma, Satish; Jain, Prerna

doi:10.1002/2050-7038.12160

Cited by 24 publications

(9 citation statements)

References 47 publications

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“…Grid integration of EVs: Load management systems of EVs [14,[42][43][44][45][46][47] are proposed to ensure the stability and increase the efficiency of the power grid under the dynamic charging behaviors of EVs. [43] presents a reputation-based system to allocate power to EVs in the smart grid, which considers the power demand and deadlines of EVs.…”

Section: Related Workmentioning

confidence: 99%

Poet

Yuan

Zhao

Lin

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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Electric vehicle (EV) fleets, e.g., electric taxis, buses, and trucks, have been increasingly implemented in cities. Compared with internalcombustion vehicles, the operation of EV fleets requires resilient charging infrastructures. Notably, the charging of EV fleets can bring significant reliability and efficiency challenges to the operation of the underlying power system. Based on the real data collected from New York City (NYC), our analysis shows that an increased power load ramping can result from various charging behaviors of electric taxis (e-taxis). Additionally, the increase of the peak load can further overload the local power distribution network.To address these problems, we exploit the possibility of utilizing e-taxi fleets' mobility to improve the reliability and reduce the cost of the power system, while maintaining the taxi service quality. Specifically, we design POET, a POwer-system-aware E-Taxi coordination algorithm, and evaluate the solution with comprehensive datasets for taxis and power distribution systems from NYC. These data include (i) more than 13,000 taxis with more than ten million taxi trips per month, (ii) a city local power distribution network with 38 local area substations and nearly 45 GWh overall power consumption per day, and (iii) deployed EV charging stations. The extensive data-driven evaluation demonstrates that, compared with the existing e-taxi charging solution that focuses on optimizations of taxi service quality, our solution decreases the power load ramping of local regions by 22.3% and reduces the daily peak charging load by 44.2% while achieving almost the same taxi revenue. CCS CONCEPTS• Hardware → Power and energy; • Computing methodologies → Modeling and simulation.

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Section: Related Workmentioning

confidence: 99%

Poet

Yuan

Zhao

Lin

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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“…The upper limit of EV owners' charging cost (CB) could be determined from unregulated baseline charge scheduling case. 26 The charging cost of an i th EV must be less than this CB as expressed by (18). Constraint (19) is the congestion management constraint preventing overloads of distribution transformer due to connected baseload, aggregated charging load and regulation down capacities at each timeslot.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Monte Carlo simulation (MCS) approach is an effective stochastic optimisation approach, which gives precise result for a large number of scenarios. 25,26 Nevertheless, analysing the aforementioned set leads to the intractable problem along with huge computing times. Considering this, an effective scenario reduction algorithm is necessitated, which can retain the pattern of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Risk‐averse integrated demand response and dynamic G2V charge scheduling of an electric vehicle aggregator to support grid stability

Sharma

Jain

2021

Int Trans Electr Energ Syst

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Electric vehicle aggregator (EVA) trades in energy and ancillary service markets for profit maximisation through grid-to-vehicle (G2V) charge scheduling of EVs. It can provide regulation services to system operator (SO) through coordinated and distributed charging of EVs. However, EV owner aim at minimum charging cost. Price-based demand response (PBDR) integrated charge scheduling can motivate EVs by off peak charging for reduced cost along with system load levelling. Real-time (RT) PBDR, adopted in literature, has low acceptance rate by EVs for being too dynamic to respond and being infeasible for few charge cycles in a day. Time of use (TOU) PBDR, being less dynamic, is a natural price signal for EVs and shields them from RTP volatility as well.Additionally, EV charging cost constrained EVA's charge scheduling is a realistic formulation and is subjected to multiple uncertainties like EVs' mobility behaviour and market prices. Considering this, risk-averse charge scheduling of EVA with TOU PBDR and EV charging cost constraint is proposed. EVs' mobility behaviour and market prices' uncertainties are modeled through stochastic programming. Agglomerative hierarchical clustering is proposed to develop TOU price for EVA from RTP. Conditional value at risk (CVaR) is used as risk measure. Results of a case study of EVA with 1000 EVs illustrate a List of symbols and abbreviations: N s , set of the scenarios with index s; T, set of the timeslots with index t (h); N, set of the electric vehicles with index i; P max i =P min i , upper/lower charging rate limit (kW) for EV i; BC i , the battery capacity of i th EV (kWh); Ef i , charging efficiency of EV i; TDC, the deliverable capacity of distribution transformer (MW); M t , markup pricing

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“…In the next step, the electricity price should be modeled which depends upon demand level and its changes can be modeled by multiplying two parameters, the basic electricity price (  ) and the price level factor [29,46].…”

Section: Figure 3 the Test Systemmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV's charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/ discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.

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Integrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability

Cited by 24 publications

References 47 publications

Poet

Poet

Risk‐averse integrated demand response and dynamic G2V charge scheduling of an electric vehicle aggregator to support grid stability

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

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