GIS-Based Probabilistic Modeling of BEV Charging Load for Australia

Li, Mengyu; Lenzen, Manfred; Keck, Felix; McBain, Bonnie; Rey-Lescure, Olivier; Li, Bing; Jiang, Chaoyang

doi:10.1109/tsg.2018.2829917

Cited by 47 publications

(21 citation statements)

References 23 publications

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“…As well-known from the literature [68,69], GIS systems are widely used to store and to correlate information regarding several aspect of urban infrastructure, including the state of the pavements alongside the roads, the electrical grid and much more information. GIS can be used not only to visualize the data in thematic maps but, by using properly queries, it is possible to correlate spatially and temporally the data about "road accidents" with "luminosity data", especially if the luminosity data are periodically updated.…”

Section: The Proposed Solutionmentioning

confidence: 99%

Security Assessment of Urban Areas through a GIS-Based Analysis of Lighting Data Generated by IoT Sensors

et al. 2020

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The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities’ lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema.

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Section: The Proposed Solutionmentioning

confidence: 99%

Security Assessment of Urban Areas through a GIS-Based Analysis of Lighting Data Generated by IoT Sensors

et al. 2020

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“…We use daily trip chains [19][20][21] which are created to show the whole travel routes in one day with spatial and temporal information, shown in Figure 2 and Equation (2). A path among consecutive trip destinations in the trip chain is represented by ψ(q s , q s+1 ).…”

Section: Trip Chains and Travel Route Planningmentioning

confidence: 99%

Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network

2019

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Electric vehicles (EVs) can be regarded as a kind of demand response (DR) resource. Nevertheless, the EVs travel behavior is flexible and random, in addition, their willingness to participate in the DR event is uncertain, they are expected to be managed and utilized by the EV aggregator (EVA). In this perspective, this paper presents a composite methodology that take into account the dynamic road network (DRN) information and fuzzy user participation (FUP) for obtaining spatio-temporal projections of demand response potential from electric vehicles and the electric vehicle aggregator. A dynamic traffic network model taking over the traffic time-varying information is developed by graph theory. The trip chain based on housing travel survey is set up, where Dijkstra algorithm is employed to plan the optimal route of EVs, in order to find the travel distance and travel time of each trip of EVs. To demonstrate the uncertainties of the EVs travel pattern, simulation analysis is conducted using Monte Carlo method. Subsequently, we suggest a fuzzy logic-based approach to uncertainty analysis that starts with investigating EV users’ subjective ability to participate in DR event, and we develop the FUP response mechanism which is constructed by three factors including the remaining dwell time, remaining SOC, and incentive electricity pricing. The FUP is used to calculate the real-time participation level of a single EV. Finally, we take advantage of a simulation example with a coupled 25-node road network and 54-node power distribution system to demonstrate the effectiveness of the proposed method.

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“…The results indicated that the current RES could supply the charging load of EVs, however, not spatially. Li et al [14] compared a deterministic and a stochastic spatial models for EV charging in Australia. In the deterministic model, the travel survey trips were directly used to estimate the charging load.…”

Section: P Vmentioning

confidence: 99%

Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

Shepero

2018

Applied Energy

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In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots-where charging stations are installed-and the charging profiles of the potential users of these charging stations. In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types. A case study was made on the city of Uppsala, Sweden-a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio-temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used. In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one-minute-ramp in the charging load of 1.1 MW-if charging using 3.7 kW. Charging with higher powers will exacerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES). The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc.

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GIS-Based Probabilistic Modeling of BEV Charging Load for Australia

Cited by 47 publications

References 23 publications

Security Assessment of Urban Areas through a GIS-Based Analysis of Lighting Data Generated by IoT Sensors

Security Assessment of Urban Areas through a GIS-Based Analysis of Lighting Data Generated by IoT Sensors

Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network

Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

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