Peak shaving and cost minimization using model predictive control for uni- and bi-directional charging of electric vehicles

Kriekinge, Gilles Van; Cauwer, Cedric De; Sapountzoglou, Nikolaos; Coosemans, Thierry; Messagie, Maarten

doi:10.1016/j.egyr.2021.11.207

Cited by 35 publications

(25 citation statements)

References 26 publications

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“…Equation ( 12), the proposed formulation is presented as a n Â h matrix with h = 14. For (12) we consider the following constraints:…”

Section: Proposed Optimization Methodologymentioning

confidence: 99%

“…• The sum of each row should be equal to the required time for the EV to be fully charged. This is expressed in Equation ( 14) and is applied for each row of the matrix in Equation (12).…”

Section: Proposed Optimization Methodologymentioning

confidence: 99%

“…Such methods consider the EVs' controlled operation as an optimization problem. Their goal is either (a) to optimize a unique objective function, for example, minimization of greenhouse gas (GHG) emissions, 10 voltage profile improvement 11 or peak demand minimization 12 or (b) to perform a multiobjective optimization under weighted joint formulation of individual objectives, 13 or under objectives' trade-off as in the Pareto Front analysis. 14 The implementation of intelligent charging methods is based on optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Predictive charging schedule models may suffer from lack of data, since they need information like historical arrival and departure data, or the drivers' transportation behavior, in order to be able to build the prediction model. 12 Aiming to simplify the approach, we propose in this work an hourly updated real-time charging model taking into account the updated information about the EVs' arrival and their SoC variations over time. Furthermore, the model examines the flexibility potential of the EVs by implementing and comparing both V2G and G2V schemes.…”

Section: Introductionmentioning

confidence: 99%

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Distribution network energy loss reduction under EV charging schedule

Bouhouras

Kothona

Gkaidatzis

et al. 2022

Intl J of Energy Research

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Summary This article deals with the problem of energy losses in distribution networks (DNs) under electric vehicle (EV) penetration. The problem of charging overlaps causing severe power losses and voltage drops is faced under appropriate EV charging. An optimized EV charging schedule is proposed under EV time‐of‐arrival consideration. The solution algorithm is based on a particle swarm optimization (PSO) variant, and both grid‐to‐vehicle (G2V) and vehicle‐to‐grid (V2G) schemes are included regarding the power interactions of the EVs with the grid. The goal is to optimally allocate the charging and/or discharging time periods of the EVs in order to minimize energy losses while delivering fully charges EVs to the owners. New departures after the first EVs' arrival are also handled. The results indicate that with the proposed scheduling, daily energy loss reduction could be reached up to 25% under an improved voltage profile that indicates more uniform loading for the DN.

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“…Equation ( 12), the proposed formulation is presented as a n Â h matrix with h = 14. For (12) we consider the following constraints:…”

Section: Proposed Optimization Methodologymentioning

confidence: 99%

Section: Proposed Optimization Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distribution network energy loss reduction under EV charging schedule

Bouhouras

Kothona

Gkaidatzis

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

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“…Integration of renewable energy sources (RES) to the grid addresses this problem. The authors proposed a method that minimizes the losses by peak load shaving considering coordinating EV charging/discharging [Kriekinge et al 2021]. An optimally coordinated charging pattern can also minimize this loss through a stochastic programming technique.…”

Section: Ps2ghrmentioning

confidence: 99%

Optimal Parameter Allocation in Renewable Energy Sources Integrated Fast Charging EV Station considering hGPS Algorithm

Ray

Babu

Khushwaha³

et al. 2022

JER is an international, peer-reviewed journal that publishes f

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A maiden attempt has been made to propose the detailed modelling of fast charging electrical vehicle (EV)stations connected to a hybrid grid-renewable energy source (RES like solar, mini-hydro, and wind) system considering EV demand characteristics and arrival time, departure time, state of charge and battery capacity. This helps achieve the maximum profit and reduce energy demand from the grid. Simulations are performed with a novel meta-heuristic algorithm named by hybrid genetic with pattern search (hGPS) algorithm for the first time. They are used for optimizing the charging station's system parameters, which maximize the net present value (NPV). The investigations are performed by the probabilistic distribution of the EV demand based on EV behaviours and is simulated with the sequential Monte-Carlo method by considering hourly intervals. The obtained economic considerations using hybrid genetic with pattern search (hGPS) algorithm are compared with Genetic Algorithm (GA), Pattern Search (PS) algorithm and are observed that hybrid genetic with pattern search (hGPS) maximizes the profit over others. It is also evident that with the proposed method, the power transferred capacity limit among system network and grid reduces the impact of the grid on the system network.

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