Stochastic programming of vehicle to building interactions with uncertainty in PEVs driving for a medium office building

Cardoso, Gonçalo; Städler, Michael; Bozchalui, Mohammad Chehreghani; Sharma, Ratnesh; Marnay, Chris; Barbosa‐Póvoa, Ana Paula; Ferrão, Paulo

doi:10.1109/iecon.2013.6700407

Cited by 9 publications

(5 citation statements)

References 14 publications

(20 reference statements)

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“…DER-CAM has been continuously improved to incorporate new technologies and features, as reported in several peer-reviewed publications [25,26]. Recently, it has also been updated to incorporate cold storage [27], stochastic EV modeling [28], as well as stochastic reliability issues [29]. It can also be applied to EV fleet scheduling [30].…”

Section: Der-cammentioning

confidence: 99%

Modeling of thermal storage systems in MILP distributed energy resource models

et al. 2015

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-Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO 2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

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Section: Der-cammentioning

confidence: 99%

Modeling of thermal storage systems in MILP distributed energy resource models

et al. 2015

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“…The EVs are charged with 487 MWh annually, supplying 166 MWh to the building, considering a building electricity load of 189.5 MWh. Cardoso et al [20] proposed an aggregator model for an EV fleet that can handle investment and operation problems of distributed energy resources. The authors presented a case study in which the energy demands of a mediumsized office building in San Francisco are fulfilled by several energy resources, including heat resources, electricity resources and EVs.…”

Section: Related Workmentioning

confidence: 99%

Techno-Economic Potential of V2B in a Neighborhood, Considering Tariff Models and Battery Cycle Limits

Pohlmann

Klinck

2023

Energies

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To limit climate change, decarbonization of the transportation sector is necessary. The change from conventional combustion vehicles to vehicles with electric drives is already taking place. In the long term, it can be assumed that a large proportion of passenger cars will be battery–electric. On the one hand, this conversion will result in higher energy and power requirements for the electricity network; on the other hand, it also offers the potential for vehicles to provide energy for various systems in the future. Battery–electric vehicles can be used to shift grid purchases, optimize the operation of other components and increase the self-consumption rate of photovoltaic systems. An LP model for the optimal energy management of the neighborhood consisting of buildings with electricity and heat demand, a PV system, a BEV fleet, a heat pump and thermal storage was formulated. The potential of the BEV fleet to provide energy via V2B in the neighborhood was investigated, considering electricity tariff models and individual charging/discharging efficiencies of vehicles and stochastic mobility profiles. The vehicle fleet provides between 4.8kWh−1sqm−1a (flat-fee) and 25.3kWh−1sqm−1a (dynamic tariff) per year, corresponding to 6.7, 9.5% and 35.7% of the annual energy demand of the neighborhood. All tariff models lead to optimization of self-consumption in summer. Dynamic pricing also leads to arbitrage during winter, and a power price tariff avoids peaks in grid draw. Due to individual charging efficiencies, the power supplied by the fleet is distributed unevenly among the vehicles, and setting limits for additional equivalent full cycles distributes the energy more evenly across the fleet. The limits affect the V2B potential, especially below the limits of 20 yearly cycles for flat and power tariffs and below 80 cycles for a dynamic tariff.

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“…Authors claim that the presented strategy can optimally plan EV charging schedules with consideration of online information and uncertainties. Cardoso et al [9] investigated optimal sizing and scheduling of distributed energy resource (DER) capacity at a given site, considering the potential effect of electric vehicles and uncertainty in driving schedules. A novel EV fleet aggregator model was introduced and stochastic formulation was added to DER-CAM, a widely used deterministic model in DER sizing and scheduling problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal recharging framework and simulation for electric vehicle fleet

Usman

Knapen

Yasar

et al. 2020

Future Generation Computer Systems

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Electrical vehicles are considered sustainable transport alternatives as compared to conventional combustion engine vehicles due to their lower energy consumption and less pollutants production. The increased penetration of electric vehicles in the market depends upon technology to overcome the driving range barrier. That can be achieved by significantly planning the use of available charging stations. In this paper, a planning simulation model is presented which evaluates the feasibility of electric vehicles driving range when recharging is considered at home, at work or at quick charging stations in Flanders, Belgium. The proposed procedure plans a charging strategy for each electric vehicle so that entire scheduled tours of the individual can be executed successfully. The simulation starts by activating an agent for each electric vehicle that takes the daily schedule of the driver, registered charging requests at each charging station, and devises a charging strategy that may or may not require a detour to a charging station. Detouring to a charging station causes the time loss that result in the utility drop due to decreased participation in the planned activities. The charging station that leads to the minimum detour travel time, waiting time and recharging time is selected to minimize the time loss. The simulation uses a realistic travel demand predicted by an activity-based model. The results show the percentage of the population that can drive electric vehicle with charging only at home and/or work location; it also shows those who need a stop for recharging at a charging station. The results indicate the use of all charging stations over the day and also the waiting time as function of charging points at each charging station.

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Stochastic programming of vehicle to building interactions with uncertainty in PEVs driving for a medium office building

Cited by 9 publications

References 14 publications

Modeling of thermal storage systems in MILP distributed energy resource models

Modeling of thermal storage systems in MILP distributed energy resource models

Techno-Economic Potential of V2B in a Neighborhood, Considering Tariff Models and Battery Cycle Limits

Optimal recharging framework and simulation for electric vehicle fleet

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