A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid

Quddus, Abdul; Shahvari, Omid; Marufuzzaman, Mohammad; Usher, John M.; Jaradat, Raed

doi:10.1016/j.apenergy.2018.08.018

Cited by 91 publications

(39 citation statements)

References 44 publications

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“…A typical day is chosen to support the details of the research, the period of MPC based scheduling is 24 h and ∆ t is 1 h. The office arrival time of EVs owners is represented by a normal distribution with mean value 9:00 a.m. and standard deviation 1 h; and the office departure time follows a normal distribution with mean value 6:00 p.m. and standard deviation 1 h. The scenarios of the available number of EVs at each time are generated by scenarios generation and reduction technique. It is assumed that the thermal power demand is completely dependent on the CHP units without additional equipment [41,42]. The capacity of each EV is assumed to be 48 kWh, and the total number of available EVs is 100 in this case.…”

Section: Case 1: Mes In Office Buildingsmentioning

confidence: 99%

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

2019

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Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach to coordinated scheduling of MES considering CHPs, uncertain EVs and battery degradation based on model predictive control (MPC), aimed at achieving the most economic energy scheduling. After exploiting the pattern of the drivers’ commuting behavior, the stochastic characteristics of available charging/discharging electric power of aggregated EVs in office or residential buildings are analyzed and represented by the scenarios with the help of scenario generation and reduction techniques. At each step of MPC optimization, the solution of a finite-horizon optimal control is achieved in which a suitable number of available EVs scenarios is considered, while the economic objective and operational constraints are included. The simulation results obtained are encouraging and indicate both the feasibility and the effectiveness of the proposed approach.

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Section: Case 1: Mes In Office Buildingsmentioning

confidence: 99%

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

2019

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“…The reduced rate of the cooling and heating loads caused by changes to the design of building envelopes was analyzed and the results were used to estimate approximate reductions in GHG emissions. Similarly, energy savings in buildings with energy efficiency systems [50][51][52][53] (i.e., microgrid with renewable energy exchange, efficient power sharing within energy districts, robust energy scheduling, energy storage for communities, renewable energy technologies), energy sharing technologies [54][55][56], and zero-energy building technology [57] cannot be analyzed by the method proposed in this study. In other words, energy consumption with respect to energy efficiency and energy sharing, including HVAC system and plant equipment cannot be analyzed.…”

Section: Limitations and Future Workmentioning

confidence: 99%

An Analysis of the Effectiveness of Greenhouse Gas Reduction Policy for Office Building Design in South Korea

et al. 2019

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This paper examines the effectiveness of South Korea’s policy for reducing greenhouse gas (GHG) emissions in office buildings and evaluates if national targets can be met. A sample of office buildings was categorized into two groups—conventional (Group A) and new (Group B)—based on when their construction was approved. Furthermore, data regarding the three design elements of the building envelope, that is building form, window systems, and U-values were collected. By statistically processing data of each element, reference building models were developed and a case study was conducted for each building. Design changes that were incorporated, keeping in mind the GHG reduction policy, showed 13.1% of saving energy in case 8 (reference building of Group B) than case 1 (reference building of Group A). The savings in case 8 were more than the average GHG reduction rate (12.8%) compared to business as usual (BAU). However, case 4 (a conventional (Group A) building form with new (Group B) window systems and U-values) achieved the greatest savings in building loads. The policy to enhance insulation in new buildings to reduce GHG emissions in the building sector has prompted changes in building forms and window systems and has reduced emissions by 10%, that is 3% more than the expected value. Thus, new innovations in building envelope design could achieve an average 12.8% reduction in emissions in buildings.

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“…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. From a CSO perspective, the first issues to solve are grid connection requirements and demand charges, rather than grid services such as frequency regulation, which can be implemented in a later stage.…”

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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A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid

Cited by 91 publications

References 44 publications

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

An Analysis of the Effectiveness of Greenhouse Gas Reduction Policy for Office Building Design in South Korea

Local Energy Storage and Stochastic Modeling for Ultrafast Charging Stations

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