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Procedia Engineering 205 (2017) 205–212
10th International Symposium on Heating, Ventilation and Air Conditioning, ISHVAC2017, 1922 October 2017, Jinan, China
GIS-based Dimensionless Assessment of Distributed Rooftop PV in Chinese Residential Communities Yibo Chena,b, Hongwei Tana,b,c,*, Simeng Lid, Xiaodong Songe bb
a a School of Mechanical Engineering, Tongji University, Shanghai 201804, China UNEP-Tongji Institute of Environment for Sustainable Development, Tongji University, Shanghai 200092, China cc Research Centre of Green Building and New Energy, Tongji University, Shanghai 200092, China ddCollege of Architecture and Urban Planning, Shandong Jianzhu Univeristy, Jinan 250101, China ee College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Abstract For the purpose of applicable regional assessment of the residential roof-mounted PV, a di-mensionless estimation based on the multi-criteria database is carried out in this paper. With Energy-Economic-Environment relevant factors incorporated in GIS, a dimensionless index was protocoled as the regional substitution rates of distributed rooftop PV (RSR-DRPV) for typical residential communities. This model reveals the potential quantitatively and spatially in forms of queryable interpolated GIS mappings, considering both supply and demand sides. Results indicated that the distribution of urban RSR-DRPV was in accord with that of solar resources. However, the rural RSR-DPVR tended to be equally distributed in China mainland, except for the eastern coastal regions with higher ranges. Policy implications were also given regarding quantitative magnitude and subsidy levels. This evaluation was conducted at community scale, and it can be taken as reference when establishing located subsidy levels for urban and rural areas respectively at planning stage. © 2017 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Ltd. committee of the 10th International Symposium on Heating, Ventilation and Air Peer-review under responsibility of Elsevier the scientific Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Conditioning. Air Conditioning. Keywords: distributed rooftop PV; substitution rate; GIS mapping; residential communities
* Corresponding author. Tel.: +86-13817229190. E-mail address:
[email protected] 1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Air Conditioning.
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Air Conditioning. 10.1016/j.proeng.2017.09.954
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1. Introduction On request of the U.S. - China Joint Announcement on Climate Change [1], China promises to increase the share of non-fossil fuels occupied in primary energy consumption to around 20% by 2030. Due to the advantages of short energy payback period (0.8 - 3 years) and steady per-formance, the distributed rooftop PV (DRPV) systems are intensively considered in both the newly-built and existed residential buildings, especially when considering the benefits of lowering down the peak power demand and avoiding the transmission procedures [2]. However, there are no applicable referenced indicators for planners to decide the potential of DRPV applied in a certain community [3]. Researches of DRPV potential focus more on the regional potential instead of a stand-alone PV generator [4]. For a DRPV, attentions are paid mostly on two aspects namely: 1) how to identify the application area; 2) what indicators can be used. Firstly, in terms of the area iden-tification, Horváth [5] classified buildings based on roof characteristics and other geometric factors on building typology. Ayompe et al. [6] assessed the energy generation potential of PV by satellite-derived solar radiation datasets. Secondly, when it comes to the assessment indicators, however, many researches were confined to aspects of power generation potential, radiation levels or fitting suitability, such as the indicators of stability degree, optimal period, peak sunshine hours and solar radiation. For example, Sun et al. [7] evaluated regional potential by an indicator of electricity generation, with the help of a solar radiation tool embedded in GIS. Castillo et al. [8] identified the potential mismatches between fund allocations and actual regional suitability, with solar radiation and other geographical factors. Moreover, due to the powerful abilities of spatial management and multi-criteria superposition, GIS (Geographic Information System) has been adopted as a crucial tool to illustrate the assessment results [9]. Thus, solar resource evaluation and adaptability can be achieved with high-resolution mappings [10]. It should be noticed that an indicator considering only the supply side, could not provide suf-ficient references for policy decision-makers and the planners of PV projects. This is because for a planned community, the planning indexes are usually defined as occupied proportions of energy gained from renewable energy and low-carbon technologies, in order to go with the regional sustainability regulations in LEED ND [11], BREEAM Communities and so on. In this paper, with Energy-Economic-Environment (3E) factors, the community-scale models were established for urban and rural communities, based on GIS platform. Nomenclature
ηPV(t) real-time conversion efficiency , %
real-time monitored real-time electric power generation, kWh area of panels, m2 ηmono, ηpoly , ηthin conversion efficiencies of monocrystalline silicon, polycrystalline silicon and thin film panels RQM modified annual radiation quantity, kWh RSR-DRPV regional substitution rates of distributed rooftop PV 3E Energy-Economic-Environment factors EPV(t) APV
2. Methods 2.1. Auxiliary experimental testing and modification In order to estimate the modified hourly radiation quantity (∑� ∑� ���� ), an experimental platform was performed to find out the dynamic relationship between the conversion efficiency and the solar radiation intensity (RI) of three kinds of PV panels namely: Monocrystalline silicon (MS), Polycrystalline silicon (PS) and Thin film (TF). The realtime data series were dealt with by Eq. (1), and formulas of conversion efficiency were fitted as Eq. (2) [12]. Thus the real electric power generation (EPG) can be obtained.
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η PV ( t ) =
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EPV ( t ) ×100% G ( t ) × APV
(1)
������������ � ������� �� � � � ��� ��� � � �������� � ������� � � ����
(2-1)
������������ � ������� �� � � � ��� ��� � � �������� � ������ � � ����
(2-2)
������������ � ������� �� � � � ��� � �� � � �������� � ������� � � ����
(2-3)
whereηPV(t) is the real-time conversion efficiency (%), EPV(t) is the monitored real-time EPG (kWh), G(t) is the real-time RI (W/m2), A�� is the area of panels (m2),ηmono, ηpoly and ηthin are the conversion efficiencies (%). On this basis, the diminished power contribution of RI below 200 W/m2 and the annual RQ can be calculated by Eq. (3) and Eq. (4) respectively.
Gi − M ,G ≤200 = Gi ,G ≤200 ×
ηMS ( Gi ,G ≤200 ) ηp
, G ≤ 200W / m2
RQM = RQM ,G ≤ 200 + RQG > 200 = i =0 Gi ,G ≤200 × 8759
ηMS ( Gi ,G ≤ 200 ) η MS ( G p )
(3)
+ i =0 Gi − M ,G > 200 8759
(4)
Where RQM is the modified annual RQ, kWh; RQM,G200 is the accumulated RQ of the hours when RI is higher than 200 W/m2, kWh. 2.2. Dimensionless modeling This model was established by considering the PV power generation (supply side) and consumption (demand side) in typical Chinese residential communities. Firstly, taking the MS panels as an example, the annual urban EPG intensity of DRPV (ESR,city,si, kWh/m2) could be calculated as: ESR,city,si=σR,city·SR·τ·EPsi,annu/Ncity, whereσR,city is the plot ratio of urban community; S� is the floor areas; N���� is the average number of floors; τ is the use ratio of roof area; EP������� is the annual EPG intensity (kWh/m2). Afterwards, this paper focuses on basic power consumption (BPC) and comprehensive power consumption (CPC) in urban communities, as well as the BPC in rural communities. For urban communities, the regional urban power consumption (ECR,city, kWh) can be estimated as: ECR,city,si= σ R,city ·SR · τ · ∑ ECR,city/Sr,city, where Sr,city is the average living area per capita gained from statistical yearbooks (m2/p); ∑ECR,city is the yearly electric power demand (EPD) per capita (kWh/p). The comprehensive constrained indicators of power consumption (CCIPC) in National Standard for Energy Consumption of Buildings were adopted as CPCs (Table 1). Table 1. Target systems of residential power demand in China. Climate regions
Severe cold region
Cold region
hot-summer and cold-winter region
hot-summer and warm-winter region
Mild region
CCIPC [kWh•a−1]
2200
2900
3100
2800
2200
With the supply and demand sides estimated above, an institution indicator called regional substitution rates of distributed rooftop PV (RSR-DRPV) is obtained as Eq. (5). This model is quantitatively and spatially dimensionless.
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Firstly, ESR,city,si and ECR,city,have the same units, while the roof area and plot ratio appear in both of them simultaneously. In other words, RSR-DRPV has no relationship with the roof area and plot ratio. Thus, this model can be regarded like a pixel, which can be located anywhere in a map from views of resolutions.
θ R ,city , si = ES R,city ,si / ECR ,city = τ ⋅ EPsi ,annu ⋅ sr ,city / ( N city ⋅ ECR ,city )
(5)
2.3. Overall dataflow outline and managements in GIS As demonstrated from No.1 to No.24 in Fig. 1, the labeled circles above describe input parameters, and the circles below are output parameters after a corresponding process. Distinguished by different border widths, there are three kinds of panels namely: basic panels, process panels and result panels. The basic panels compile meteorological data, technical related data, fictitious community parameters, as well as the basic and comprehensive EPD. Indicators of No.25, No.26 and No.27 produced by result panels represent respectively the amendatory effective radiant indicators, RSR-DRPV resulted from the basic EPD for urban/rural communities (basic urban/ rural RSR-DRPV), and the RSRDRPVs occupied in comprehensive EPD for urban communities (comprehensive urban/rural RSR-DRPV).
Fig. 1. Overview of demonstrated data flow for evaluation models
As described in Fig. 2, the process datasets of first grade indicators, such as the power generation (No.20 in Fig. 1), were obtained by resource endowment and effective supplying capacity. Therefore, the final evaluation was carried out by reconstruction and spatial superposition in GIS. Based on the above collected statistical and survey data, policy documents, fitting calculations and so on, a GIS database was managed in corresponding spatial attributive tables, covering the China mainland in caption of 270 weather stations.
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Fig. 2. Flow chart of data processing in 3E-GIS database
3. Results 3.1. RSR-DRPV for urban communities In this section, basic RSR-DRPV means that the RSR-DRPV is oriented to meet the demand of BPC. As demonstrated in Fig. 3(a), the basic urban RSR-DRPV for communities of average 6-storey buildings, varying between 19.7% and 53.3%, are divided into three partitions of < 30% zone, 30%-40% zone and > 40% zone. When adopting comprehensive constrained indicators, the comprehensive urban RSR-DRPV varies between 9.2% and 34.9%, see Fig. 3(b). These two maps suggest a similar spatial distribution. However, at the same time, the comprehensive RSR-DRPV is about 44.05% averagely reduced in comparison with the basic RSR-DRPV. This reduction can be attributed to the major proportion of air-conditioning power occupied in the CPC indicators.
Fig. 3. (a) Substitution rates of BPC of roof-mounted PV system in residential communities; (b) Substitution rates of CPC of roof-mounted PV system in residential communities.
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3.2. RSR-DRPV for rural communities In rural communities, the potential EPG largely exceeds the EPD, with RSR-DRPVs varying from 100% to 327% (Fig. 4). Besides, China mainland seems to have equally distributed substitution rates, except for the eastern coastal minority regions with higher values. Consequently, it is obvious that DRPV should be propelled in rural communities at full blast, especially in the rural areas along the eastern coastal line. In typical Chinese rural communities, the non-commercial energy resources occupy a large proportion. Given this premise, the comprehensive rural RSR-DRPV is not concluded in this paper.
Fig. 4. Substitution rates of rooftop PV system in rural residential community
3.3. Comparison of basic RSR-DRPV for urban and rural communities In Fig. 4, the distribution of rural basic RSR-DRPV seems to have little connection with that of the solar resources. For the sake of discussion, we take a deep look into the comparison of each weather station (Fig. 5).
Fig. 5. Comparison of Substitution rates of urban and rural residential communities
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Firstly, rural DRPV systems achieve higher substitution rates than those of the urban communities. In other words, the application of DRPV in rural communities is full of high potential. Secondly, the spatial distributions are quite different for urban and rural communities. This is because that the RSR-DRPV is decided not only by resources and technologies, but also the social factors implied in demand side, such as the average urban/rural living area per capita in different cities. Finally, even for the same location, substitution rates may be significantly different when applied in urban and rural communities respectively. 4. Discussion and policy implications This paper puts forward the modified solar assessment and the regional potential analysis of DRPV in residential communities throughout Chinese mainland, with the help of the GIS platform. There are following issues need to be discussed further. • The role of the results indicated in this paper. RSR-DPVR is defined to measure the potential at community scale. A corresponding value is easily acquired by clicking a specific location on the map in GIS. Firstly, this value can be treated as a potential reference when adjusting the local energy services and infrastructure by introducing PV. Afterwards, the substitution rates change along with the settings of a certain community, including average floors, and the assumed power consumption at planning stage. Finally, the planning schemes can be numerically compared with different settled scenarios. Currently, the integration of data and models in GIS is not perfect, further research need to be improved to provide a better service at regional energy planning. • Implication related to the numerical magnitude. In the 13th five-year plan (2016-2020), China has set up the goals of renewable energy application in fields of construction, and the proportion of renewable energy consumption in new demonstration buildings should account for more than 10% of the total EPD. However, the results in this paper indicate that only about 1/2 of China mainland can achieve this substitution goal, if the average number of floors changes from 6 to 12 in the comprehensive urban models. Consequently, when determining the location and PV application levels at planning stage, specific parameters of this planned community should be seriously taken into account. • Implication of the correlation between spatial distribution and subsidy level. For DRPV, there is a national subsidy (0.066 $/kWh) and other local subsidies, which vary from 0.0079 $/ kWh (Shandong province) to 0.0628 $/ kWh (individual buildings and schools in Shanghai), depending on the local economic development and governmental recognition. Therefore, it is recommended that future allocations take into account this RSR-DPVR indicator for optimized policy results, in order to avoid unnecessary or unreasonable local planning and subsidy policies. 5. Conclusions In order to make it easier for the decision-making of PV application in residential communities at planning stage, a regional platform was established in this paper to provide a quantitatively and spatially dimensionless indicator based on GIS. Spatial comparisons of distribution and quantitative magnitude between the urban and rural communities were conducted. 1) The much higher RSR-DPVR in rural communities in China mainland implies that the policies and subsidies need to be emphasized strongly in urban areas, especially in the eastern coastal areas. 2) The urban RSR-DPVR distribution is consistent with solar energy resources by and large, and is restricted to the designed indexes and energy consumption levels of communities. This proposed method attempts to identify potential contributions that the DRPV could make to the whole communities, and this dimensionless indicator can serve as a local reference when establishing subsidy levels for urban and rural areas separately. To better generalize the adoption of this model, researches regarding to mismatches between fund allocation and RSR-DPVR levels need to be explored in the future.
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