Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule

Thomas, Dimitrios; Deblecker, Olivier; Ioakimidis, Christos S.

doi:10.1016/j.apenergy.2017.07.035

Cited by 298 publications

(126 citation statements)

References 42 publications

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“…Meanwhile, Dimitrios et al [39] proposed an energy management system for the smart building connected to a power system considering the uncertainties of PV generation and the operation schedule of electric vehicles. D. van der Meer et al [40] described his energy management system that predicts PV generations for charging electric vehicles by detailing the PV generation system.…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Micro Grid Optimization Theory

Jung

Yoon

2020

Mathematics

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A Micro Grid is an aggregate of many small-scale distributed energy resources (DERs); loads and can be operated independently or together with the existing power grid as a local power grid. The operator of such a grid takes charge of the energy supply and consumption of these resources and loads available in the grid. Meanwhile, the system operator of the grid considers the entire Micro Grid system to be a single load or a generator and assigns the responsibility of its internal management to the operator. The power production from a passive production resource is largely influenced by external environmental factors such as weather conditions, rather than operating conditions. Thus, this study conducted simulations for the cases where four kinds of conditional expressions had not been applied at all or one of them had been applied to compare and evaluate the effectiveness of each expression. As a result, the conditional equations were found to be effective when attempting to optimize the Micro Grids efficiently.

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Section: Related Researchmentioning

confidence: 99%

An Efficient Micro Grid Optimization Theory

Jung

Yoon

2020

Mathematics

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“…Constraint (40) ensures null values for the intervals when the EV battery cannot be used. Constraint (41) calculates the fuel cost for each trip s. Constraint (42) gives the total cost of the fuel consumption. Constraint (43) is the binary and nonnegative restrictions.…”

Section: Plug-in Hybrid Ev Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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Summary Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.

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“…However, they are not considered as appropriate for quick stops at ultra-fast charging stations along transit axes. Indeed, a key condition to implement such schemes is the idle time a vehicle remains attached to a plug, which makes sense at office buildings [9] or at home. A typical example was proposed by Quddus et al [10] with a sector coupling scheme between vehicles, charging stations, and commercial buildings, with hourly operational decisions for energy flows, but it does not address the minute-scale power peak issues of ultra-fast charging stations.…”

Section: Introductionmentioning

confidence: 99%

Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations

2019

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Multi-stall fast charging stations are often thought to require megawatt-range grid connections. The power consumption profile of such stations results in high cost penalties due to monthly power peaks and expensive linkage fees. A local energy storage system (ESS) can be used to address peak power demands. However, no appropriate sizing method is available to match specific constraints, such as the contracted power available from the grid and the projected recharging demand. A stochastic distribution of charging events was used in this paper to model power demand profiles at the station, with a one minute resolution. Based on 100 simulated months, we propose an optimum number of charging points, and we developed an algorithm to return the required local ESS capacity as a function of the available grid connection. The role of ESSs in the range of 100 kWh to 1 MWh was studied for all stations with up to 2000 charging events per week. The relevance of ESS implementation was assessed along three parameters: the number of charging points, the available grid connection, and the ESS capacity. This work opens new possibilities for multi-stall charging station deployment in locations with limited access to the medium voltage grid, and provides sizing guidelines for effective ESSs implementation. In addition, it helps build business cases for charging station operators in regions with high demand charges.

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Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule

Cited by 298 publications

References 42 publications

An Efficient Micro Grid Optimization Theory

An Efficient Micro Grid Optimization Theory

A realistic energy optimization model for smart‐home appliances

Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations

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