Mixed-Integer-Linear-Programming Model for the Charging Scheduling of Electric Vehicle Fleets

Bodenschatz, Nicki; Eider, Markus; Berl, Andreas

doi:10.1109/acit49673.2020.9208875

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The MILP optimization technique involves both integer and real-valued variables, along with linear objective functions and constraints (Lindholm et al 2020). MILP optimization algorithms can be applied to select a subset of charging Proceedings of the 4 th Asia Pacific Conference on Industrial Engineering and Operations Management Ho Chi Minh City, Vietnam, September 12-14, 2023 © IEOM Society International stations to be installed by minimizing the travel costs of electric vehicle drivers from the charging station to their destinations and penalties for unmet charging demands (Bodenschatz et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Location of Public Electric Vehicle Charging Station for Electric Motorcycles: A Case Study of Surakarta City

Septabiyya,

Sutopo

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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In a few years, electric vehicles will transition to vehicles that are massively used in transportation activities in order to reduce carbon footprint. The Indonesian government is committed to achieving the 'Net-Zero Emission' target by 2060. In its implementation, the existence of electric motor charging infrastructure such as charging stations is one of the critical factors in the electric vehicle adoption program. Consumers will feel anxious if their vehicle runs out of battery in the middle of their journey before reaching their destination. Therefore, this study focuses on determining the location of charging stations in Surakarta City, Central Java, Indonesia which is targeted to be an effective strategy in providing a large initiation impact on emission reduction in Central Java Province. This research takes into account the placement of locations that are suitable for electric motor users so that the utility of charging stations is optimal and able to serve electric motor users in Surakarta City with the minimum travel cost. The method used is it applies optimization model: a mixed integer linear programming (MILP) model to find the optimal locations for new charging stations that can maximize the coverage of the demand. This study uses user data, travel routes, and candidate charging station location points as data initiation for optimization models. The optimization is run to determine the most optimal solution for location of charging stations that consider electric motor users' travel costs so that the demands can be met well.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Location of Public Electric Vehicle Charging Station for Electric Motorcycles: A Case Study of Surakarta City

Septabiyya,

Sutopo

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…However, most of them do not forecast on household level. Proposed EMS controllers only aim at local ESS optimization [6,7] or cost-optimal EV charging [9] and, thus, do not exploit the flexibility potential on a global level. Solutions targeting flexibility provision typically do not address data privacy or uncertainty of flexibility delivery [4, 33-37, 40, 42].…”

Section: Introductionmentioning

confidence: 99%

Flexibility Disaggregation under Forecast Conditions

Danner

Seidemann

Lechl

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Stationary battery energy storage systems and electric vehicles become more and more popular at households with local photovoltaic generation. Besides improving self-consumption and autarchy, these batteries can provide flexibility to an external utility. Thereby, generation and demand uncertainty, as well as cost optimality, need to be considered when utilizing distributed flexibility.This paper discusses long short-term memory neural networks for photovoltaic generation forecast and persistence models for household load forecast with respect to their applicability in local energy management system optimization. Furthermore, a mixedinteger linear program is proposed to optimally utilize local flexible loads and storage systems. Its solution space yields the flexibility potential, which can be aggregated at flexibility pools. In order to disaggregate flexibility requests to a pool of distributed energy management systems, we propose a heuristic algorithm that can among others minimize the overall flexibility cost or maximize probability of flexibility delivery. The forecast models, the mixed integer linear program and the flexibility disaggregation are evaluated on realistic household photovoltaic and load profiles to demonstrate the full chain from local forecast to flexibility disaggregation under forecast conditions. Our experiments with flexibility disaggregation show that the probability to provide flexibility should not be neglected when it comes to distributed energy management optimization based on forecast models. CCS CONCEPTS• Hardware → Smart grid; • Computing methodologies → Uncertainty quantification; Neural networks.

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Challenges and Requirements of Electric Vehicle Fleet Charging at Company Parking Sites

Bodenschatz

Eider

Berl

2021

2021 11th International Conference on Advanced Computer Information Technologies (ACIT)

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Mixed-Integer-Linear-Programming Model for the Charging Scheduling of Electric Vehicle Fleets

Cited by 7 publications

References 13 publications

Optimizing Location of Public Electric Vehicle Charging Station for Electric Motorcycles: A Case Study of Surakarta City

Optimizing Location of Public Electric Vehicle Charging Station for Electric Motorcycles: A Case Study of Surakarta City

Flexibility Disaggregation under Forecast Conditions

Challenges and Requirements of Electric Vehicle Fleet Charging at Company Parking Sites

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