International Conference on Applied Energy 2019 Aug 12-15, 2019, Västerås, Sweden
Paper ID: 1089
TEMPORAL CITY-SCALE MATCHING OF SOLAR PHOTOVOLTAIC GENERATION AND ELECTRIC VEHICLE CHARGING
Ulrich Fretzen1, Mohammad Ansarin1, Tobias Brandt1
1 Department of Technology and Operations Management, Erasmus University, Rotterdam, Netherlands
ABSTRACT
A crucial determinant of the environmental contribution of EVs against global warming is the composition of the energy mix that fuels them. This study examines the potential of rooftop PV installations to satisfy the EV charging load at city-scale. The paper develops three base case approximations of uncoordinated EV charging loads and explores the benefit of a coordinated charging methodology that shifts EV charging loads depending on solar energy availability. Further, the impact of east/west-facing dual arrays on the temporal alignment is compared against scenarios with south-facing PV installations. PV reorientation is not found to have a significant potential for the alignment of PV electricity generation profiles with EV charging loads. Charging load shifting according to the developed hierarchical prioritization algorithm can considerably improve the temporal match without affecting the actual driving patterns of EV owners. Only in winter time, when the total energy balance is negative, PV generation and EV charging loads cannot be sufficiently coordinated.
Keywords: Electric vehicle charging, load shifting, smart charging, solar fraction, load fraction
NONMENCLATURE
Abbreviations
EV PV RES SF LF
Electric Vehicle Photovoltaic Renewable Energy Sources Solar Fraction Load Fraction
1. INTRODUCTION Climate change represents one of the greatest
challenges of our time. The severity of its consequences has led the EU to establish ambitious climate goals. Amongst others, these initiatives involve an increase in the share of renewable energy to 32% of overall consumption [1]. As the second largest contributor of greenhouse gasses, the transport sector will play a crucial role in the race against climate change. While large-scale EV adoption offers immense potential, the sources of electricity that fuel the fleet of EVs represent the crucial determinant for the environmental impact of the growing EV fleet [2]. In this respect, solar energy and specifically small-scale PV installations as part of a decentralized grid have been attributed the potential to alleviate the environmental burden in metropolitan areas [3]. Next to improvements in energy storage technology, effective means of aligning consumption patterns with energy generation are considered promising.
While EV charging loads and solar energy generation have been the focus of a large research body, studies on the matching of the two at city-scale have been scarce. Additionally, the granularity and accuracy of models has been criticized [4]. Furthermore, most papers only consider a single charging scenario or deploy smart charging with a focus on technical constraints instead of the immediate alignment of charging with PV energy generation. Lastly, although research uniformly attributes some benefit to PV powered EV charging, the extent to which solar energy can immediately be used to charge EVs in a distributed grid is unclear.
This paper sets out to fill this void in the existing research. Specifically, this study examines a smart
charging strategy that maximizes immediate solar energy utilization and compares this strategy with different uncoordinated charging scenarios across various PV and EV integration levels. Ultimately, this study identifies the potential of smart EV charging methodologies in matching the local solar energy generation.
The Dutch city of Rotterdam functions as the research setting. Considering the high EV adoption in the Netherlands, the low share of renewables and the rapidly increasing solar PV adoption, the city is a suitable study environment. Additionally, its geography, climatic conditions and size of ~600,000 inhabitants make it a relevant precedence case for a number of large European cities.
2. MATERIAL AND METHODS
2.1 Determining rooftop PV electricity generation
2.1.1 Identification of suitable rooftops for PV
Based on high resolution LiDAR data, the suitability of the city’s rooftops is determined under consideration of shadowing effects from proximate buildings, vegetation overhang and other obstructions on the roof. Ultimately, every roof is assigned a suitability level, as can be seen in Figure 1, allowing the exclusion of unfit rooftops and enabling the simulation of different integration levels. A total suitable area of 11.57𝑘𝑚# is identified. A distinction is made between tilted and flat rooftops.
Fig 1 PV suitability of selected rooftops in Rotterdam
2.1.2 PV characteristics
Fixed surface PV installations are assumed. Panels on tilted rooftops are assumed to have an angle of 35°. Due to space limitation and self-shading considerations south-facing PV installations on flat rooftops are assumed to have a tilt angle of only 10°. East/west-facing dual arrays are modeled with a 15° tilt.
2.1.3 Estimation of solar energy generation
Based on monthly average irradiance data obtained from [1], the per minute solar irradiance on the horizontal plane is determined in line with [5] following [6]. After conversion to effective irradiance on the generator plane [6], the per minute energy generation is estimated, assuming PV effectiveness levels of 20% in line with state-of-the-art technology. Azimuth levels from -90° to +90° are considered suitable in the context of this study. The local yearly irradiance in Rotterdam is found in Fig 2.
Fig 2 Annual solar irradiance on tilted surface
2.2 Identifying charging loads
2.2.1 Modeling parameters
The simulated EV fleet is assumed to consist of two EV types (small, large). The battery capacity and consumption of each individual EV is determined by truncated gaussian probability density functions to model inherent variability within the EV fleet [7]. Concerning charging speed, a power of 11kW is assumed.
2.2.2 Modeling of driving patterns
The EV travel patterns are simulated stochastically by means of a non-homogeneous Markov chain model, a method commonly applied in EV modeling [8]. The specific transition matrices are adopted from [8], who estimated travel patterns based on the Swedish travel survey, which allows the modeling of diurnal weekday/weekend patterns. It is argued that overall travel purposes and timings do not differ significantly between Sweden and the Netherlands. Similar to [9] three distinct parking states are assumed: “Home”, “Work”, “Other”. A visualization of the transition patterns of the entire EV fleet can be found in Figure 3.
The exact travel distances and durations are sampled from the Dutch mobility panel provided by the Dutch Ministry of Infrastructure and the Environment. In contrast to applying fixed time intervals [7], driving intervals are modeled dependent on the sampled driving distances and times, thus allowing for different travel speeds at minute resolution. Breaks during travel due to exhausted battery are considered and shift future travel patterns, which has been disregarded in previous studies [8,9].
Fig 3 Location distribution on a sample week day
2.2.3 Modeling of EV charging
Various EV charging strategies are simulated to reflect different uncoordinated charging approaches. An overview of charging scenarios is provided in Figure 4.
# Charging strategy
1 Charging whenever in every location
2 Charging at home after the last trip of the day
3 Charging whenever parked for > 120 minutes
4 Charging driven by PV energy availability Fig 4 Charging strategies
Charging rationales 1 and 2 are adopted from [7],
while strategy 3 represents an intermediate strategy, which reflects charging only during longer parking intervals. Ultimately, charging strategy 4 applies a hierarchical charging algorithm that prioritizes EVs according to their energy demands and charges them depending on the availability of PV energy.
2.3 Matching of PV and EV
2.3.1 Match indicators
The impact of charging load shift and the reorientation of solar panel in the east/west direction is measured along SF and LF at minute resolution in line with [9]. SF indicates the ability to source the charging load from solar energy, while LF depicts the self-consumption of the generated solar electricity. Further, the alterations that the charging load shifts are imposing on the driving schedules of EV owners is assessed.
2.3.2 Sensitivity analysis
A scenario analyses is conducted for different PV integration levels and EV penetration levels across sample weeks in three seasons. In this way various future scenarios are modeled. An overview of the simulated scenarios is provided in Figure 5.
EV penetration (% of total fleet)
5, 10, 15, 20, 30, 50, 70, 100
PV integration (% of suitable area)
5, 10, 15, 20, 25, 30, 40, 50
Seasons Winter, Spring, Summer Fig 5 Overview of modeled scenarios
3. THEORY A variety of previous studies have examined the
potential of controlled charging, as well as, the benefits of PV and EV technology. A comparatively marginal body of research has focused on the ability to actively match EV charging loads with locally generated solar energy based at city-scale [9]. Generally, PV systems have been found able to cover a large share of the EV charging load [9]. [11] substantiate the favorable relation between PV and EVs, concluding that the EV demand can be satisfied by PV electricity generation throughout the entire year. However, the temporal misalignment between the solar mid-day peak and the morning/evening EV load charging peak represents a serious impediment [9]. Next to energy storage, load shifting based on Demand Side Management is attributed some potential to align EV loads with PV generation [3]. The underlying assumption is that people are willing to shift their charging behavior based on some given incentive. Based on this rationale, smart charging methodologies have been developed. Smart charging at the individual scale involves the incentivization of EV owners with the intention to alter their charging habits, but can extend to active centralized
charging management at large scale [10]. Next to charging load shifting, panel reorientation in east/west direction has been attributed a beneficial impact on the alignment of electricity generation with the morning and evening peaks in the electricity demand.
4. RESULTS AND DISCUSSION
The results and discussion are illustrated by example of a scenario with 10% PV integration and 50% EV adoption.
4.1 Misalignment between EV and PV
In the case of opportunistic EV charging, the lack of coordination leads to considerable misalignment between EV charging loads and PV generation. As depicted in Figure 6, the EV morning and evening peaks are temporally disconnected from the PV generation. Even when high PV integration leads to a largely positive energy balance, the solar fraction remains far below 100% thus indicating limited self-sufficiency. These findings are in line with [9] who find that in most of their scenarios PV generation exceeds EV loads, but that the temporal match needs to be improved. When end-of-day charging is simulated the solar and load fractions are further diminished.
Fig 6 Uncoordinated charging strategy #1
4.2 Match improvement
4.2.1 Charging load shifting
Load shifting through coordinated charging is able to almost perfectly align EV loads with PV electricity generation as EV charging occurs depending on the availability of solar energy. As depicted in Figure 7, this methodology centers EV charging loads around noon, thereby mirroring the PV electricity generation. Only during the first weekday the EV charging load diverges considerably from the solar PV electricity generation because all cars start the simulation with full batteries and thus not all PV energy is necessary for EV charging during the first simulation day.
Fig 7 Coordinated charging strategy
As Figure 8 shows, the solar fractions for all EV/PV integration combinations are improved compared to the uncoordinated charging strategies. When the total energy balance is positive, the solar fraction converges towards 100%. Similarly, the load fraction is increased considerably by the coordinated charging strategy. In spring and summer, the proposed charging strategy proves sustainable. Particularly in summer, the EV battery charge levels are close to the total battery capacity at the end of the simulated week. Only in winter the simulation suggests a depletion of most EV batteries within 2 weeks as depicted in Figure 9. During spring and summer only 1.76% and 0.28% of the drivers are affected by the solar dependent charging methodology, meaning
that they have to stop for charging during a trip because their EV battery was not sufficiently charged at the beginning of the trip.
Figure 9 Average battery charge per season
4.3 Panel reorientation
Figure 10 shows the electricity generation curves for both panel installation types and the increase from the adoption of east/west facing panels as opposed to south-facing ones. The temporal distribution of the energy only differs marginally between the different orientations. The main change is the higher total energy output in the case of east/west installations. Due to limited self-shading, the inter-row spacing can be reduced. The
resulting increase in the ground coverage ratio, when compared to south-facing installations, leads to a higher total electricity output. As a consequence, the solar and load fractions increase marginally. However, no significant match improvement from installation of east/west-facing dual PV arrays can be established. Ultimately, reorientation of PV panels does not have a sizeable benefit for the alignment of PV electricity generation with EV charging loads.
Figure 10 Energy generation profiles for south- and east/west
facing PV installations
Fig 8 Solar fraction for Spring simulations of charging strategies 1, 2, 4
The results support the notion that on-site rooftop PV electricity generation can contribute considerably to the sustainable integration of electric vehicles. However, a temporal mismatch between uncoordinated charging and solar electricity generation exists and considerably reduces self-consumption and self-sufficiency. While a reorientation of PV panels can increase the total energy output per rooftop, it does not significantly improve the temporal match between PV electricity generation and EV charging loads. Charging load shifting proves to be a more effective match facilitator as it almost perfectly aligns charging loads with PV energy production without compromising the traveling patterns of electric vehicle drivers. Future research should focus on the technical feasibility of the proposed load shifts under the consideration of technical grid constraints, as well as, demand response mechanisms to actually incentivize people to change their behavior patterns or delegate charging responsibilities to distributors.
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