Metaheuristics for solving a real-world electric vehicle charging scheduling problem

García-Álvarez, Jorge; González, Miguel A.; Vela, Camino R.

doi:10.1016/j.asoc.2018.01.010

Cited by 47 publications

(27 citation statements)

References 50 publications

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“…The work proposed replicates the online scheduling of EVs in electricity networks carried out by EVA. 35,36 Only the past and current casual information (charging demand and departure deadlines of the EVs that arrive at or before the current time, the past and current base load from SO) is known by EVA. Thus, EVA schedules the EVs in each hour according to the availability of EVs.…”

Section: Mobility Behavior Considerations Of An Evmentioning

confidence: 99%

“…subject to constraints (26) to (36). Rev DCSP (i,s) and CC DCSP (i,s) can be solved independently or simultaneously.…”

Section: Dynamic Charging Scheduling Problemmentioning

confidence: 99%

“…subject to constraints (26) to (36). Dynamic charging scheduling with updating charging rate of each EV according to both prices (28) and load (32) constraints improves its performance.…”

Section: Dynamic Charging Scheduling Problemmentioning

confidence: 99%

“…This includes arrival and departure time, daily energy consumption in trips, EV battery capacity, charging efficiencies, upper limit of charging rate, and initial SOC. 36 However, this information is partially available in case of dynamic scheduling model since residual charging energy requirements from initial SOC, and departure times of the EVs that have arrived so far varies due to EV's mobility behavior. 37,38 Each EVs departure and arrival times vary according to their preferences.…”

Section: Introductionmentioning

confidence: 99%

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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

Sharma

Jain

2019

Int Trans Electr Energ Syst

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Summary Unregulated and simultaneous charge scheduling of large‐scale electric vehicles (EVs) may lead to grid instability and peak demand rise. EV aggregator (EVA) can adopt coordinated and distributed charging rates for EVs over the connection hours providing stability and regulation to system operator (SO). However, against flat rate, price‐based demand response (PBDR) integrated scheduling motivates EVs to charge in off peak hours and utilize their flexibility for load levelling and peak reduction. PBDR reduces EV charging cost as well. Real‐time price (RTP) demand response (DR), mostly adopted in literature, has low acceptance rate by EVs for being too dynamic to response and being infeasible for few charge cycles in a day. Time of use (TOU), being less dynamic, is natural price signal for EVs under PBDR but may reduce EVA's profit. Considering this, an integrated TOU‐PBDR model for EVA's grid‐to‐vehicle (G2V) charge scheduling is proposed for energy and regulation market. EV owner perspective for charging cost minimization is considered along with EVA profit maximization. TOU is designed from RTP using agglomerative hierarchical clustering (AHC) method. EVs mobility uncertainty characterization makes scheduling dynamic. Sensitivity analysis based on charging rate upper limit and number of EVs validates the method. Results show interesting trends regarding EV charging cost, EVA profit, and regulation provision to SO.

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Section: Mobility Behavior Considerations Of An Evmentioning

confidence: 99%

“…subject to constraints (26) to (36). Rev DCSP (i,s) and CC DCSP (i,s) can be solved independently or simultaneously.…”

Section: Dynamic Charging Scheduling Problemmentioning

confidence: 99%

“…subject to constraints (26) to (36). Dynamic charging scheduling with updating charging rate of each EV according to both prices (28) and load (32) constraints improves its performance.…”

Section: Dynamic Charging Scheduling Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Sharma

Jain

2019

Int Trans Electr Energ Syst

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“…As a more economical and attractive alternative, smart scheduling strategies can be employed to postpone the construction of power infrastructures to support EV charging demand, through which the goal of wise load regulation as well as economic benefits can be reached [8][9][10]. Nevertheless, owing to the differences in operational characteristics between EV and the power system, smart EV scheduling becomes a rather complex task that must rely on an efficient and robust charging algorithm [11]. In general, the operation of EVs should take into account, but not be limited to, a charging and/or discharging power rate limit, initial state of charge (SOC), customer travelling habits, final energy demand, and battery capacity.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

2019

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Electric vehicles (EVs) are now attracting increasing interest from both industries and countries as an environmentally friendly and energy efficient mode of travel. Therefore, the EV charging and/or discharging issue has become an important challenge and research topic in power systems in recent years. An advanced and economic EV charging process, however, should employ smart scheduling, which depends on effective and robust algorithms. To that end, a comprehensive intelligent scatter search (ISS) algorithm within the frame of a basic scatter search has been designed with both unidirectional and bidirectional charging considered. The ISS structure also supports both a flexible and constant charging power rate by respectively employing filter-SQP (sequential quadratic programming) and mixed-integer SQP as local solvers with module control. The detailed design of ISS is presented and the objectives of smoothing the daily load profile and minimizing the charging cost have been tested. Compared with methods based on GS (global search), GA (genetic algorithm), and PSO (particle swarm optimization), the outcome-verified ISS can produce attractive results with a significantly short computational time. Moreover, to handle a large scale EV charging scenario, a hybrid method comprised of a GA and ISS approach has been further developed. Simulation results also verified its prominent performance, plus superbly low computational time.

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Hybrid Heuristic and Metaheuristic for Solving Electric Vehicle Charging Scheduling Problem

Zaidi

Oulamara

Idoumghar

et al. 2021

Evolutionary Computation in Combinatorial Optimization

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Metaheuristics for solving a real-world electric vehicle charging scheduling problem

Cited by 47 publications

References 50 publications

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

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

Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

Hybrid Heuristic and Metaheuristic for Solving Electric Vehicle Charging Scheduling Problem

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