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Modelling urban energy requirements using open source data and models
Applied Energy 231 (2018) 1100–1108
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Modelling urban energy requirements using open source data and models ⁎
T
Alaa Alhamwi , Wided Medjroubi, Thomas Vogt, Carsten Agert DLR Institute of Networked Energy Systems, Grid and System Modelling Group, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
H I GH L IG H T S
approach for simulating spatial-temporal urban electricity requirements. • Systematic open source GIS-based tool for modelling local demand and supply. • AThevalidated successfully reproduced the electricity profiles compared to real datasets. • Highmodel of renewables can reduce the required balancing energy from storage. • High shares • autarky level can be achieved at a specific mix of renewables for the case study.
A R T I C LE I N FO
A B S T R A C T
Keywords: Urban energy systems Energy autarky Urban energy requirements OpenStreetMap FlexiGIS
The transition towards sustainable and self-sufficient cities requires extensive knowledge of the electricity requirements for diverse consumers that interact in a temporal and geospatial platform. Energy models contribute to the strategic planning of future urban energy systems by providing relevant insights and scientific recommendations. In this context, energy policy advice have to entail transparency of the tools and models used as well as their respective input datasets which are generally neither open nor publicly available. The aim of this contribution is, to model urban energy requirements, namely local electricity consumption and on-site renewable power generation, using solely open source data and models. A systematic approach for a bottom-up simulation of urban electricity supply and demand down to the building unit level is developed here. The methodology combines spatial parameters of urban geometries and settlements and links them to real-world data using Geographic Information Systems. The developed model is showcased and validated for the city of Oldenburg (Germany). Quarter-hourly time series of electricity demand and supply are extracted for each existing urban unit such as buildings and streets. A regression analysis has been performed to validate the model outputs against measured data. Accordingly, up to 94% of the variance of the simulated urban electricity demand and supply are predictable from the measured datasets. The resulting demand and supply profiles have been used to investigate storage needs in urban areas. Furthermore, the model presented here offers a detailed load representation of different urban consumers’ segmentation. It determines also the configurations of on-site urban renewable energy mixes at which the share of electricity from grid and/or storage are at its minimum. Preliminary results show that, high levels of urban energy autarky could be achieved for a specific combination of local renewable energy sources.
1. Introduction Significant challenges are facing energy infrastructure in cities due to worldwide increasing urbanisation and the ever-growing demand on energy services [1,2]. In Germany, two thirds of the population is living in cities, thus consuming more than 40% of the total primary energy only in the building sector which accounts to more than 75% of the total greenhouse gas emissions [3,4]. Nevertheless, urban settlements and the buildings sector represent a remarkable chance to effectuate the
⁎
transition towards cleaner and more sustainable cities [1,5]. The German Government has set strategies to reduce the total primary energy consumption by 80% until 2050 in order to ensure that the buildings sector is nearly climatically neutral and its carbon emissions are reduced to its minimum [4,2]. Modelling Urban Energy Systems (UES) has received much attention and research in recent years due to its role in planning and facilitating the decarbonisation of the electric grid systems [1,6,7]. UES enables setting scientific guidelines for a realistic rehabilitation of urban energy
Corresponding author. E-mail address: [email protected] (A. Alhamwi).
https://doi.org/10.1016/j.apenergy.2018.09.164 Received 8 December 2017; Received in revised form 13 September 2018; Accepted 19 September 2018 0306-2619/ © 2018 Published by Elsevier Ltd.
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proposed model is then validated against measured data. The models proposed here aims at leveraging the integration of open source data and models of different urban geometric and non-geometric data such as weather parameters in a GIS modelling environment. Further, this study models both urban requirements, consumption and generation in time and space. The geospatial dimension is presented in a previous contribution as a component of the so-called FlexiGIS platform [12], developed by the authors of this paper. This work complements the spatial modelling in [12] by adding the temporal one. The main aim of this contribution is to allow for a quantitative analysis of different possible future scenarios of decentralised storage at urban levels. In addition, it supports drawing concrete recommendations to promote the understanding of UES and the integration of Renewable Energy Sources (RESs) and flexibilisation technologies in the context of future smart cities. The work conducted here is structured as follows: the input data collection and the model for simulating electricity consumption and renewable power generation in urban settings are introduced in Section 2. While in Section 3 the authors apply and validate the developed methodology against real data for the city of Oldenburg, in Germany. Section 4 discusses the implications of the proposed model for urban energy systems. Finally, Section 5 summarises the key findings of this contribution and outlines future work.
infrastructures through the integration of renewable power generation and flexibilisation options into the distribution networks [8–10]. In this context, using Geographic Information Systems (GIS) tools and techniques facilitates a transparent, realistic and muli-layered representation of UES [11] contributing to better energy model’s accessibility and reproducibility [12]. The modelling of the urban energy infrastructure requires a suitable characterisation of different urban energy requirements for both energy consumption and generation. In order to adequately detect the patterns of such requirements that interact and vary over space and time, urban energy properties should be adequately incorporated in a spatial-temporal framework including both static and dynamic input datasets. The majority of developed urban energy models focus either on energy demand like in [13–15] or energy supply such as [16–19]. However, some previous contributions simulated both supply and demand but not explicitly focusing mainly on one sector. For example, the authors in [20] modelled the energy consumption using energy supply datasets to investigate the impact of demand side management and the integration of renewable energies in cities. In addition, most of the recent contributions in this field modelled the electricity consumption only for an individual building prototype such as residential buildings. Moreover, those models focus mainly either on one dimension (time or space), or simulate merely one of the energy requirements (demand or supply). As examples, the authors cite the models developed in [21,15,22] which estimated the electric demand in households. Other contributions focused on non-residential consumers such as service buildings [23–25] or industrial buildings [26]. However, some modelling efforts integrated two sectors types such as in [27,14,13] where the authors estimated the electric load profiles (electricity consumption profiles) for both residential and commercial consumers. One common denominators of all cited contributions is that the input data used as well as the models are not freely accessible [28–30]. Some datasets might be publicly available but the owners of such data (such as Google Maps) apply, for commercial purposes, a restrictive copyright and terms of use making them not openly accessible [12]. This hinders any evaluation or validation of the models outcomes and also does not provide a quality measure for the input data used. Some of the electricity consumption forecasting-based methods use top-down approaches and depend on comprehensive years of historical demand datasets in order to produce fairly accurate load profiles [31,32]. These extensive historical datasets might be incomplete and rarely publicly accessible [33]. Furthermore, bad data included in such big datasets affect the precision of the generated load profiles [34]. Although many developed load forecasting-based models can be applied to different case studies like in [35,36], some other black-box utility forecasting methods are purely data-driven, where the required datasets are restricted to copyrights and linked to a specific geospatial settings making the developed models not transferable to other cities. Furthermore, these methods usually generate load curves for coarseresolutions such as national scales or entire cities and do not provide insights into geospatial distribution on lower levels like districts, streets and or buildings within urban settings [13]. On the other hand, the proposed GIS-based method simulates bottom-up electricity consumption for a wide suite of prototypical urban geometric using publicly available data resources. It links explicitly the generated disaggregated loads to real geospatial database down to the finest urban granularity using GIS techniques. Moreover, it helps allocating spatially and temporally local demand and on-site supply from renewable power generation required for the placement and determination of flexibility technologies like storage. The developed model can be readily extended to other urban settings. The present contribution seeks to cover the shortcomings of previous contributions in this area. For this purpose, a model is developed for a bottom-up simulation of urban electricity consumption and generation, referred to here as urban energy requirements, for different urban object prototypes, that is based on publicly available data. The
2. Modelling urban energy requirements In order to systematically estimate the urban energy demand along with the micro-generation from non-dispatchable RESs of an urban area, the model developed here incorporates both spatial and temporal datasets to generate high-resolution time series of energy requirements of UES including electricity generation, consumption and storage profiles. The model developed in this contribution is mainly a data-driven simulation model. It is based on primary inputs of urban geometrics (or units) that includes buildings and roads geometrical details (such as area, location, etc.), electricity generation from RES and electricity demand profiles, see Fig. 1 for an illustration of its different components. The model is implemented in the open source programming language Python1 and makes use of SQL (Structured Query Language) databases. In the following, the collection and processing of required static and dynamic input datasets as well as the methodology used by the authors to develop the model are introduced in details. 2.1. Input data collection and processing Currently, most of the available urban energy models are neither open nor transparent in terms of their input data or their model assumptions [28]. As a contribution to the open source energy modelling community, this work uses only open source data and models. In the following subsections, the data extraction and processing are introduced. 2.1.1. Spatial input datasets Spatial simulation of energy requirements in cities requires access to raw input geodata of urban features and urban energy infrastructures. In this study, two main types of urban geo-datasets are used. First datasets include buildings, landuse, roads and squares. The second type is urban power data that consists of on-site installed power capacities and substations. The renewable capacities data was obtained using the open source database EnergyMap.info,2 while the required geometric details of urban energy infrastructure in this contribution are extracted from 1 2
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Python Software Foundation. EnergyMap.info website.
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Fig. 1. Overview of the bottom-up urban energy simulation model. The model uses open source input data. The middle segment in the figure represents the required spatial information which is imported from the first component of the FlexiGIS platform [12]. The rectangular boxes represent the input and output information while models, validation and processes involved are represented in curved boxes.
the open source geo-referenced database OpenStreetMap3 using open source extraction tools.4 As shown in Fig. 1, the required geodata and urban features such as buildings location, area, rooftop area, rooftop type and buildings usage are provided by using FlexiGIS; a platform developed by the authors (refer to [12] for detailed and extensive information about acquiring and pre-processing urban geodata including the scripts used and the resulting datasets). The spatial data input from OSM datasets have been extracted for the time-stamp of 14 September 2017 and represent the status of the OSM database at this date. A comprehensive description of the required datasets extraction and processing is available in [12]. An assessment of OSM data quality and completeness for urban energy modelling is presented by the authors of this contribution in [37]. Using GIS, multiple layers of information are created from the extracted urban OSM datasets which provide the required geo-referenced input datasets for modelling the local electricity demand and on siterenewable power generation. Based on the intersection of both “building” and “landuse” layers, the urban settlements are classified based on their usage into six clusters: agricultural, educational, commercial, industrial, residential and urban roads and squares. Note that the last two clusters need to have street lighting during night hours, which has been taken into account in the modelling. The spatial dataset extracted and post-processed includes data related to the number of buildings of each cluster and its consumption prototype of the aforementioned sectors. The area of each building, roof top type and squares areas as well as roads details such as their length and width which are calculated using the open source GIS desktop (QGIS).5
SPLs are generic load profiles for the power consumption of typical customer groups below the annual consumption limit of 100.000 kWh/ year. Based on the measurements of representative groups of consumers, SLPs are constructed for different prototypes (e.g. households, commercial, industrial, street lights, etc.). Although generic, SLPs are considered as representative load profiles that can be applied to various consumer groups (e.g. commercial G0, household H0, agriculture L0), each with a similar patterns and acceptable behaviour of the respective real profiles [38]. The use of SLPs have been acknowledged in the scientific literature and used to simulate electric load profiles for different consumer groups like households and service sectors such as in [39,14]. Furthermore, they have been already used by grid operators to predict the temporal fluctuations of urban electricity consumption and to allocate the required investments for expansion of the distribution grid [14]. For each of the existing urban cluster presented in Table 1, a specific SLP is assigned based on the building characteristics. Table 1 shows the selected standard load profiles for the different urban categories modelled in this contribution. SLPs are available with a 15 min resolution for one year. In this work, the selected SLPs have been normalised for a consumption of 1000 kWh/year. As source for meteorological data from 2015, the authors used the Modern-Era Retrospective analysis for Research and Applications (MERRA) dataset [40]. The datasets include wind speeds and solar irradiation time series. These data are publicly available with a spatial resolution of 0.625° in latitude and 0.5° in longitude and a temporal resolution of 15 min. Wind data are provided at a 10 m height, such as wind speed data v(t) at 100 m height are extrapolated using the Hellmann exponential law [41] for locations in which urban wind turbines are already under operation. The time series of Photovoltaic’s (PV) performance ratio θ (t ) are provided by the energy meteorology research group at the University of Oldenburg [42]. In this study, OpenStreetMap is the main source of geo-referenced urban energy infrastructure datasets (building, landuse and road infrastructure) that is globally available. Although the completeness of the datasets differs from city to city [37,43], an acceptable level has
2.1.2. Temporal input datasets The time series of urban electricity consumption L(t) is modelled using publicly available Standardised Load Profiles (SLPs) developed by the German Association of Energy and Water Industries (BDEW) [4]. 3
OpenStreetMap website. Osmosis: OSM data processing tool. 5 QGIS-Desktop. 4
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Table 1 Assigned standardised load profiles for the selected urban clusters considered in the modelling of electricity consumption at the urban level. Urban category
Building type
SLP
Agricultural Commercial Educational Industrial Residential Roads
Stable, farm-auxiliary Supermarkets, retail Schools, universities Manufacture Households Streets, squares
L0 G0 G1 G3 H0 SB2
L (t|p) =
∑
∊p ∗NSLPj, p (t ) ∗Aj, p (1)
j, p
where L (t|p) is the time series of electricity demand as a function of time (t) and unit portfolio (p), ∊p represents the electricity usage intensity for each classified urban prototype, Aj, p is the area of every urban unit j (in square meters) for each urban sector p and NSLPj, p (t ) represents the normalised standard load profile of the corresponding consumer group. 2.2.2. Calculation of renewable power generation Time series of the electricity equivalent from solar resources S (t ) are generated using the following equation:
been observed in a quality assessment analysis conducted by the authors in a previous contribution [37]. In addition, the coverage of the OSM datasets is remarkably increasing since more volunteers have been engaged. However, comprehensive land recordings of existing urban properties like cadastre and/or statistics could be provided to complement the missing OSM datasets which have been publicly available by the respective authorities in some regions. Although SLPs are widely commonly available and have been already used by network operators, some alternative source like statistical datasets and demographic surveys could be provided by city administrations. For example, city census, weather data and statistical data of population density can be used alternatively to generate simplified load curves allocated to specific regional areas. On the other hand, if necessary data are lacking, different techniques can be applied for handling the missing values depending on the kind of problem and the data type. In case the lost data are temporal datasets (such as meteorological variables of solar irradiation), various mechanisms and predictive models like Regression Imputation techniques can be used to process the missing data problem as done in [44]. Nevertheless, if the lost required input data are not readily available (e.g. spatial features of urban objects) and no model-based method can be applied to handle the missing information, appropriate and realistic assumptions can be made. In general, assumptions inherent to energy modelling and are considered to be totally accepted in the scientific research. However, all simplifications and assumptions should not be buried in black boxes but made publicly open and transparent in order to reproduce and evaluate the generated outputs.
n, m
S (t|p) =
∑
GHIj, p (t ) ∗rAj, p ∗θ (t ) ∗η (2)
j, p
where S (t|p) is the temporal distribution of PV electricity equivalent, GHIj, p (t ) is the time series of solar irradiation for building j in sector p multiplied by its roof top area rAj,p. The roof shape of residential buildings is assumed to be a gable/tilted roof and the share of its usable area is considered as 57.8% [45]. The other urban objects are assumed to have flat roof tops using 26.7% of their overall accessible area [45]. θ (t ) represent the time series of performance ratio and η is the PV module efficiency. To estimate the power potential from the wind source, a reference wind power curve is used for a representative wind turbine that is already installed. A simplified model [47] is used to calculate the electricity equivalent from wind, as follows: 3
⎧ Pr v (3t ) , if vin ⩽ v (t ) ⩽ v (r ) ⎪ vr W (t ) = if vr ⩽ v (t ) ⩽ v (out ) ⎨ Pr , ⎪ 0, otherwise ⎩
(3)
where, the instantaneous electricity equivalent of wind resources is W (t ), v (t ) is the wind speed at time t , vr is the rated speed, vout is the cut-out speed, vin is the cut-in speed, and pr is the rated power. Required technical details are obtained from the manufacturer of the installed wind turbines. For the case study presented in this work, the wind turbine characteristics are provided by ENERCON6 for the E-101 wind turbines that are already in operation.
2.2. Model overview 3. Case study and validation After introducing the required input datasets of the proposed approach for the simulation and validation of electricity consumption and generation in cities, a model overview is presented here. In the model, the temporal distribution of urban electricity requirements is modelled and matched for each existing urban geometry using GIS. The required information of the buildings (including their type, roof type, location and area) are provided from the spatial section of the model (see Fig. 1). A detailed description of modelling consumption and generation using the collected and processed datasets is provided in the following.
In this contribution, Oldenburg was selected as a case study because of the availability of measured data of electricity consumption and generation generously provided by EWE Netz7. The available real datasets are not available on the city level but on district level. Therefore, the authors validated the developed model at district levels. The measured data are provided for electrical substations located in what is referred to as “transformers districts”. The authors assume that, the electricity measured is mainly consumed inside the geo-boarders of the considered transformers districts. The model described in the previous section is applied on two districts Osternburg and Oldenburg-West. Fig. 2 shows the extracted and filtered geodata for urban energy infrastructure for Osternburg, one of the transformers districts in Oldenburg, at which the measured demand data are available. It depicts the different categorised urban objects (buildings and roads) based on their portfolio and type of usage. The quarter-hourly time series of electricity consumption and generation for each existing urban feature extracted from the OSM datasets is generated. Total time series of electricity consumption for all urban blocks (buildings and streets) within the respective study area is then
2.2.1. Calculation of load profile The characterisation of different urban electricity consumption profiles is based mainly on the available building portfolio, consumption patterns and parameters such as building size, location and area. Since some building types are more electricity intensive than others, this work introduces an electricity usage indicator (∊) that expresses the building’s electricity consumption as a function of its type and area. ∊ is calculated by dividing the annual electricity consumption for an exemplary property to the average local area of this respective urban sector. The time series of electricity consumption L (t ) for every single urban object (building, street and square) are calculated with 15 min resolution using the following equation:
6 7
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ENERCON’s E-101. EWE Netz is the grid operator in north-western Germany.
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Fig. 2. Cartographic representation of urban energy infrastructure showcased for the Osternburg district in Oldenburg. Credits: OpenStreetMap contributors, QGIS Desktop.
Fig. 3. Time series of the modelled and real load profile in two transformers districts (left for Osternburg and right for Oldenburg-West) in 2015. The red line represents the measured datasets while the dotted black line the simulated one.
Fig. 4 illustrates both measured and simulated aggregated load profiles for 2015 for the winter and summer seasons as well as for interim days in Osternburg district. Fig. 4 shows that, for the aggregated day (top left figure) the morning peak of the simulated load occurs at 11:45 and at 11:30 for the measured time series. While, the second evening peak for the simulated data occurs at 18:45 and at 19:00 for the measured one. The observed time shift of the generated load profiles is relatively small and is the result of using standard load profiles. Better representative datasets inputs could improve the accuracy of the model. To fully validate the model results concerning the electricity consumption and generation, a linear approach is used. The authors use a linear regression model with a single explanatory variable to calculate a coefficient of determination R2. Based on the proportion of total variation of the simulated load profiles, the coefficient of determination provides a measure of how well the measured data are replicated by the model.
calculated as shown in Eq. (1). Fig. 3 shows the aggregated simulated load profiles against real measured data for both districts. Although the uniqueness of the investigated districts in terms of its urban fabric and units features, the model cope very well with different urban settings. In 2015, the measured electricity consumption provided by EWE Netz was 434.16 GWh. The total simulated aggregated load in Osternburg using the model developed by the authors is found to be 431.22 GWh. In Oldenburg-West the total modelled consumption is found to be 298.36 GWh while the measured was 297.66 GWh. Obviously and as shown in Fig. 3, the model cannot reproduce extreme load cases such as maintenance, technical problem in electrical substations and/or other grid faults that can lead to a higher consumption on other substations. The resulted load profiles are remarkably similar to the measured one and demonstrate the possibility and feasibility of using open source SLPs to generate realistic load profiles replicating the temporal fluctuations of electricity demand. 1104
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Fig. 4. Simulated electricity consumption (dotted line) against real measured data in red for an aggregated day and different seasons in Osternburg district, Oldenburg.
due to the stochastic behaviour and instantaneous fluctuations of wind speed that are not optimally represented in the provided meteorological data. In addition, the generated wind outputs is based mainly on the adopted power curve that may be deviated from actual operating conditions.
For the aggregated day in 2015 in Osternburg district, the coefficient of determination R2 has been found to be 0.92 which means that 92% of the variance of the simulated load profiles can be replicated by the variance of the measured data. Regarding different seasons, the values of R2 are: 0.94 for winter, 0.87 for summer and 0.91 for interim season. Using the same approach the Oldenburg-West, the proportion of total variation of simulated load profiles are as follows: 0.90 for the aggregated day, 0.94 for winter, 0.83 for summer and 0.89 for the interim day; respectively. These values indicate that the model successfully reproduced the electricity load profiles for different typical days and seasons when compared to the measured load data. The electricity equivalent of solar and wind sources is also calculated and validated against measured data. Fig. 5 shows the aggregated time series of simulated and measured solar PV power generation for one week in Oldenburg. For the purpose of the analysis, the modelled electricity consumption L (t ) , electricity equivalent from solar S (t ) and wind W (t ) are normalised to their averaged values. The value of R2 for the electricity equivalent is 0.98 for solar and 0.55 for wind. The low predictive accuracy of the model regarding wind power generation is
4. Configurations of renewable power generation for an optimal urban energy autarky Urban electricity consumption and generation represent bottleneck for the transition towards cleaner cities [46]. Modelling these energy requirements yields comprehensive information for a sustainable planning of energy systems at the city level. The aim of this section is, to determine the solar and wind combinations at which the shares of the electricity grid and/or storage capacity to match the energy demand are at their minimum. The following subsections explain in details the steps to determine the configurations of on-site non-dispatchable renewable power generation and showcased for the city of Oldenburg. The first step is to simulate the aggregated electrical load which is depicted in Fig. 6, where the different contributions are illustrated for an aggregated day in 2015. The model predicts that for 2015 a total annual electricity consumption of 1.8 TWh. For more details, the share
Fig. 5. Measured and modelled solar electricity generation datasets for an arbitrary week in 2015 at the city of Oldenburg. The dotted line represents the simulated solar power generation while the measured data are in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. Portfolio of the electricity consumption for different consumer’s categories showcased for an aggregated day in the city of Oldenburg. The day’s load peak occurs at noon and the night’s one at around 19:15. 1105
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Fig. 7. The segmentation of different urban consumers groups and their shares of electricity consumption showcased for the city of Oldenburg.
in reproducing urban electricity load profiles. In addition, it provides invaluable insights of the segmentation and contribution of electricity consumption for different urban consumers groups. Simulating the non-dispatchable renewable power generation can provide important information about the future power supply systems and energy autarky in cities. In the second step, the residual load is calculated as the difference between the modelled electricity consumption and power generation from the intermittent solar and wind sources at the urban level using Eq. (4) (see [47,48] for more details).
ξ (t|α, β ) =
L (t ) ⎛ W (t ) S (t ) ⎞ −⎜α +β ⎟ 〈L〉 ⎝ 〈W 〉 〈S〉 ⎠
(4)
where ξ is the residual load as a function of time and energy coefficients: α for wind and β for solar. L(t), W(t) and S(t) are calculated using the model introduced in Section 2. The time series of the average consumers’s load, average wind and solar electricity equivalents are: 〈L〉, 〈W 〉 and 〈S〉 respectively. In a third step, as the fluctuations of the urban residual load must be instantaneously matched either by dispatchable sources (e.g. backup capacity, grid, etc.) or by energy storage, these variations need to be estimated. The fluctuations λ of the residual load ξ are defined as follows:
λ ξ (α, β ) = ξ (t|α, β )−〈ξ 〉 =
L (t ) ⎛ W (t ) S (t ) ⎞ −⎜α +β ⎟−(ϑ(α , β )) 〈L〉 ⎝ 〈W 〉 〈S〉 ⎠
(5)
where ϑ(α, β ) = 1−(α + β ) is the ratio of electricity exchange within the distribution grid. In the following, the authors define the configurations of solar and wind mix for which the electricity taken from the grid and/or öbtained by storage capacities are minimal. For this purpose, a standard deviation of the residual load as a function of α and β is defined in Eq. (6). The cost function σξ can be interpreted as a measure of the minimum required balancing energy from the storage at specific combinations of wind and solar [48].
Fig. 8. (a) The required balancing power from storage at different combinations of energy coefficients: α for wind and β for solar. ϑ represents the grid share. (b) The energy autarky degree at different renewable energy configurations (α and β ) and required balancing energy from the storage.
σξ (α, β ) =
of electricity consumption for each building category is shown in Fig. 7. Note that, the share of residential buildings accounts for 78% which is equivalent to 60% of the total electricity consumption. The industrial consumers represent 1% and account for 23% of the electricity demand. On the other hand, about 3 GWh (0.1%) of the total demand is found to be used for streetlights although 18% of the urban objects are roads and squares. Due to the details available in the OSM datasets that include relevant information related to urban fabric (such as building type and area, etc.), the model presented here outperforms standard approaches
〈λ2 (t|α, β ) 〉
(6)
Fig. 8(a) shows the standard deviation σξ as a function of solar and wind coefficients. Note that, for 100% solar-only scenario the required balancing power is at its maximum due to the large imbalances of PV power outputs caused by the daily fluctuation in power generation. Reasonably, the higher the grid share is, the smaller the balancing energy is required from storage. The last setp is to define the urban energy autarky, which is here considered as the share of total consumed electricity from RESs to the 1106
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consumption and generation at local scales, a combination of different urban energy sources is vital to reduce the required balancing energy and in due course the system’s total costs. Moreover, high levels of electricity autarky can be achieved for specific configurations of the local non-dispatchable renewables power generation at minimum needs of energy from the storage. The resulting time series of urban electricity requirements are the main inputs of an ongoing work, that optimises the required local decentralised storage for the maximisation of renewable energy integration in urban energy systems.
total local electricity consumption, as follows:
ψ (α, β ) =
α ∫ W (t ) dt + β ∫ S (t ) dt
∫ L (t ) dt
(7)
where ψ (α, β ) is the energy autarky as a function of wind and solar coefficients. In order to increase the urban autarky degree, the on-site electricity generation from renewables have to be increased, which means that the total installed system’s capacity will increase and in due course the total system’s costs. In Fig. 8(b), it is clear that, for high values of urban energy autarky, the balancing energy required increases. From a meteorological perspective, the model predicts that a 100% self-sufficient city could be theoretically achieved at a combination of 80% wind and 20% solar with no need for back-up capacities or power from the grid while keeping the required storage size realistic. However, the installation of new renewable and storage capacities will likely increase sharply the total system costs making it economically unattractive. Hence, up to 30% intervention from the national grid can reduce balancing energy required by 30%, the required installed capacities and the total investment costs. A linear programming cost optimisation is performed by the author of this contribution [49] to explore the economic deployment of micro-generation and decentralised storage for the maximisation of self-sufficiency is successfully applied for the selected case study. Although reasonable results were obtained using the model presented here, it should be noted that the model uses assumptions and simplifications. These are necessary in any modelling process and the results have to be interpreted accordingly.
Acknowledgements The authors would like to acknowledge the EWE-Netz for providing the required validation data. The authors thank NASA for providing the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data and the Solar Energy Services for Professionals (SoDa) for the supply of solar and wind data. The first author gratefully acknowledges the financial support provided by the Heinrich Boell Foundation through a PhD scholarship. References [1] Yan J, Chen B, Wennersten R, Campana P, Yang J. Cleaner energy for transition of cleaner city. Appl Energy 2017;196:97–9. Available from: . [2] Kraas F, Leggewie C, Lemke P, Matthies E, Messner D, Nakicenovic N, et al. Humanity on the move: unlocking the transformative power of cities. In: Flagship Report 2016. WBGU - German Advisory Council on global change; 2016. p. 0–544. Available from: . [3] Schimschar S. Policy instruments: the case of Germany. Nearly zero energy building refurbishment. Springer; 2013. p. 15–60. Available from: . [4] For Economic Affairs FM, Energy. Energy efficiency strategy for buildings. In: Energy transition in the building sector. Federal Ministry for Economic Affairs and Energy (BMWi); 2015. Available from: . [5] Pichler PP, Zwickel T, Chavez A, Kretschmer T, Seddon J, Weisz H. Reducing urban greenhouse gas footprints. Sci Rep 2017;7(1) Available from: . [6] Pfenninger S, Hawkes A, Keirstead J. Energy systems modeling for twenty-first century energy challenges. Renew Sustain Energy Rev 2014;33:74–86. Available from: . [7] Morvaj B, Evins R, Carmeliet J. Decarbonizing the electricity grid: the impact on urban energy systems, distribution grids and district heating potential. Appl Energy 2017;191:125–40. Available from: . [8] Yan J, Zhai Y, Wijayatunga P, Mohamed AM, Campana PE. Renewable energy integration with mini/micro-grids. Appl Energy 2017;201:241–4. Available from: . [9] Keirstead J, Jennings M, Sivakumar A. A review of urban energy system models: approaches, challenges and opportunities. Renew Sustain Energy Rev 2012;16(6):3847–66. Available from: . [10] Campana PE, Quan SJ, Robbio FI, Lundblad A, Zhang Y, Ma T, et al. Spatial optimization of residential urban district - energy and water perspectives. Energy Procedia 2016;88:38–43. Available from: . [11] Girardin L, Marechal F, Dubuis M, Calame-Darbellay N, Favrat D. EnerGis: a geographical information based system for the evaluation of integrated energy conversion systems in urban areas. Energy 2010;35(2):830–40. Available from: . [12] Alhamwi A, Medjroubi W, Vogt T, Agert C. GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas. Appl Energy 2017;191:1–9. Available from: . [13] Heiple S, Sailor DJ. Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles. Energy Build 2008;40(8):1426–36. Available from: . [14] Zeyringer M, Andrews D, Schmid E, Schmidt J, Worrell E. Simulation of disaggregated load profiles and construction of a proxy-microgrid for modeling purposes. 2012 9th international conference on the European energy market (EEM). IEEE; 2012. p. 1–8. Available from: . [15] Upshaw CR, Rhodes JD, Webber ME. Modeling electric load and water consumption impacts from an integrated thermal energy and rainwater storage system for
5. Conclusions and outlook A systematic surge of the integration of urban micro-generation from fluctuating renewable energy sources in cities requires a comprehensive understanding of static and dynamic characteristics of urban energy requirements (consumption and generations). Many models have been developed to simulate energy demand and supply in cities but most of such models are neither freely available nor open. Such black-box models hinders any verification of the quality of input datasets, simulation outputs, comparison with other models and therefore impede public discussions. To the knowledge of the authors, the model developed in this contribution is the first model that is based on open source data and tools and considers different urban features. This work provides evidence that urban electricity consumption and generation can be reproduced using only open source data and models. The simulated local electricity load validated against measured electricity demand and resulted with a value of the determination coefficient up to 94%. The simulated on-site PV power generation is remarkably similar to the measured load and PV profiles. Prior work, done by the authors of this contribution, established the spatial dimension of a spatial platform to model urban energy systems using open source data and models. The validation of model’s results provides compelling evidence for modelling urban electricity consumption and generation using only open source data and models. For example, an appropriate employment of SLPs in urban energy models can help replicating the temporal distributions of local electricity consumption. However, few limitation are worth noting. Although urban energy systems can be effectively replicated using only open source data such as OpenStreetMap and Standard Load Profiles datasets, some improvements of the data quality can lend support to the model outcomes [50,37]. The authors refine the implications of modelling urban energy requirements and characterise the electricity consumption per building prototype. It has been found that, more than 90% of the total urban electricity consumption occur in residential, industrial and commercial buildings. Moreover, this work demonstrated that to balance the 1107
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Modelling urban energy requirements using open source data and models ⁎
T
Alaa Alhamwi , Wided Medjroubi, Thomas Vogt, Carsten Agert DLR Institute of Networked Energy Systems, Grid and System Modelling Group, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
H I GH L IG H T S
approach for simulating spatial-temporal urban electricity requirements. • Systematic open source GIS-based tool for modelling local demand and supply. • AThevalidated successfully reproduced the electricity profiles compared to real datasets. • Highmodel of renewables can reduce the required balancing energy from storage. • High shares • autarky level can be achieved at a specific mix of renewables for the case study.
A R T I C LE I N FO
A B S T R A C T
Keywords: Urban energy systems Energy autarky Urban energy requirements OpenStreetMap FlexiGIS
The transition towards sustainable and self-sufficient cities requires extensive knowledge of the electricity requirements for diverse consumers that interact in a temporal and geospatial platform. Energy models contribute to the strategic planning of future urban energy systems by providing relevant insights and scientific recommendations. In this context, energy policy advice have to entail transparency of the tools and models used as well as their respective input datasets which are generally neither open nor publicly available. The aim of this contribution is, to model urban energy requirements, namely local electricity consumption and on-site renewable power generation, using solely open source data and models. A systematic approach for a bottom-up simulation of urban electricity supply and demand down to the building unit level is developed here. The methodology combines spatial parameters of urban geometries and settlements and links them to real-world data using Geographic Information Systems. The developed model is showcased and validated for the city of Oldenburg (Germany). Quarter-hourly time series of electricity demand and supply are extracted for each existing urban unit such as buildings and streets. A regression analysis has been performed to validate the model outputs against measured data. Accordingly, up to 94% of the variance of the simulated urban electricity demand and supply are predictable from the measured datasets. The resulting demand and supply profiles have been used to investigate storage needs in urban areas. Furthermore, the model presented here offers a detailed load representation of different urban consumers’ segmentation. It determines also the configurations of on-site urban renewable energy mixes at which the share of electricity from grid and/or storage are at its minimum. Preliminary results show that, high levels of urban energy autarky could be achieved for a specific combination of local renewable energy sources.
1. Introduction Significant challenges are facing energy infrastructure in cities due to worldwide increasing urbanisation and the ever-growing demand on energy services [1,2]. In Germany, two thirds of the population is living in cities, thus consuming more than 40% of the total primary energy only in the building sector which accounts to more than 75% of the total greenhouse gas emissions [3,4]. Nevertheless, urban settlements and the buildings sector represent a remarkable chance to effectuate the
⁎
transition towards cleaner and more sustainable cities [1,5]. The German Government has set strategies to reduce the total primary energy consumption by 80% until 2050 in order to ensure that the buildings sector is nearly climatically neutral and its carbon emissions are reduced to its minimum [4,2]. Modelling Urban Energy Systems (UES) has received much attention and research in recent years due to its role in planning and facilitating the decarbonisation of the electric grid systems [1,6,7]. UES enables setting scientific guidelines for a realistic rehabilitation of urban energy
Corresponding author. E-mail address: [email protected] (A. Alhamwi).
https://doi.org/10.1016/j.apenergy.2018.09.164 Received 8 December 2017; Received in revised form 13 September 2018; Accepted 19 September 2018 0306-2619/ © 2018 Published by Elsevier Ltd.
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proposed model is then validated against measured data. The models proposed here aims at leveraging the integration of open source data and models of different urban geometric and non-geometric data such as weather parameters in a GIS modelling environment. Further, this study models both urban requirements, consumption and generation in time and space. The geospatial dimension is presented in a previous contribution as a component of the so-called FlexiGIS platform [12], developed by the authors of this paper. This work complements the spatial modelling in [12] by adding the temporal one. The main aim of this contribution is to allow for a quantitative analysis of different possible future scenarios of decentralised storage at urban levels. In addition, it supports drawing concrete recommendations to promote the understanding of UES and the integration of Renewable Energy Sources (RESs) and flexibilisation technologies in the context of future smart cities. The work conducted here is structured as follows: the input data collection and the model for simulating electricity consumption and renewable power generation in urban settings are introduced in Section 2. While in Section 3 the authors apply and validate the developed methodology against real data for the city of Oldenburg, in Germany. Section 4 discusses the implications of the proposed model for urban energy systems. Finally, Section 5 summarises the key findings of this contribution and outlines future work.
infrastructures through the integration of renewable power generation and flexibilisation options into the distribution networks [8–10]. In this context, using Geographic Information Systems (GIS) tools and techniques facilitates a transparent, realistic and muli-layered representation of UES [11] contributing to better energy model’s accessibility and reproducibility [12]. The modelling of the urban energy infrastructure requires a suitable characterisation of different urban energy requirements for both energy consumption and generation. In order to adequately detect the patterns of such requirements that interact and vary over space and time, urban energy properties should be adequately incorporated in a spatial-temporal framework including both static and dynamic input datasets. The majority of developed urban energy models focus either on energy demand like in [13–15] or energy supply such as [16–19]. However, some previous contributions simulated both supply and demand but not explicitly focusing mainly on one sector. For example, the authors in [20] modelled the energy consumption using energy supply datasets to investigate the impact of demand side management and the integration of renewable energies in cities. In addition, most of the recent contributions in this field modelled the electricity consumption only for an individual building prototype such as residential buildings. Moreover, those models focus mainly either on one dimension (time or space), or simulate merely one of the energy requirements (demand or supply). As examples, the authors cite the models developed in [21,15,22] which estimated the electric demand in households. Other contributions focused on non-residential consumers such as service buildings [23–25] or industrial buildings [26]. However, some modelling efforts integrated two sectors types such as in [27,14,13] where the authors estimated the electric load profiles (electricity consumption profiles) for both residential and commercial consumers. One common denominators of all cited contributions is that the input data used as well as the models are not freely accessible [28–30]. Some datasets might be publicly available but the owners of such data (such as Google Maps) apply, for commercial purposes, a restrictive copyright and terms of use making them not openly accessible [12]. This hinders any evaluation or validation of the models outcomes and also does not provide a quality measure for the input data used. Some of the electricity consumption forecasting-based methods use top-down approaches and depend on comprehensive years of historical demand datasets in order to produce fairly accurate load profiles [31,32]. These extensive historical datasets might be incomplete and rarely publicly accessible [33]. Furthermore, bad data included in such big datasets affect the precision of the generated load profiles [34]. Although many developed load forecasting-based models can be applied to different case studies like in [35,36], some other black-box utility forecasting methods are purely data-driven, where the required datasets are restricted to copyrights and linked to a specific geospatial settings making the developed models not transferable to other cities. Furthermore, these methods usually generate load curves for coarseresolutions such as national scales or entire cities and do not provide insights into geospatial distribution on lower levels like districts, streets and or buildings within urban settings [13]. On the other hand, the proposed GIS-based method simulates bottom-up electricity consumption for a wide suite of prototypical urban geometric using publicly available data resources. It links explicitly the generated disaggregated loads to real geospatial database down to the finest urban granularity using GIS techniques. Moreover, it helps allocating spatially and temporally local demand and on-site supply from renewable power generation required for the placement and determination of flexibility technologies like storage. The developed model can be readily extended to other urban settings. The present contribution seeks to cover the shortcomings of previous contributions in this area. For this purpose, a model is developed for a bottom-up simulation of urban electricity consumption and generation, referred to here as urban energy requirements, for different urban object prototypes, that is based on publicly available data. The
2. Modelling urban energy requirements In order to systematically estimate the urban energy demand along with the micro-generation from non-dispatchable RESs of an urban area, the model developed here incorporates both spatial and temporal datasets to generate high-resolution time series of energy requirements of UES including electricity generation, consumption and storage profiles. The model developed in this contribution is mainly a data-driven simulation model. It is based on primary inputs of urban geometrics (or units) that includes buildings and roads geometrical details (such as area, location, etc.), electricity generation from RES and electricity demand profiles, see Fig. 1 for an illustration of its different components. The model is implemented in the open source programming language Python1 and makes use of SQL (Structured Query Language) databases. In the following, the collection and processing of required static and dynamic input datasets as well as the methodology used by the authors to develop the model are introduced in details. 2.1. Input data collection and processing Currently, most of the available urban energy models are neither open nor transparent in terms of their input data or their model assumptions [28]. As a contribution to the open source energy modelling community, this work uses only open source data and models. In the following subsections, the data extraction and processing are introduced. 2.1.1. Spatial input datasets Spatial simulation of energy requirements in cities requires access to raw input geodata of urban features and urban energy infrastructures. In this study, two main types of urban geo-datasets are used. First datasets include buildings, landuse, roads and squares. The second type is urban power data that consists of on-site installed power capacities and substations. The renewable capacities data was obtained using the open source database EnergyMap.info,2 while the required geometric details of urban energy infrastructure in this contribution are extracted from 1 2
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Fig. 1. Overview of the bottom-up urban energy simulation model. The model uses open source input data. The middle segment in the figure represents the required spatial information which is imported from the first component of the FlexiGIS platform [12]. The rectangular boxes represent the input and output information while models, validation and processes involved are represented in curved boxes.
the open source geo-referenced database OpenStreetMap3 using open source extraction tools.4 As shown in Fig. 1, the required geodata and urban features such as buildings location, area, rooftop area, rooftop type and buildings usage are provided by using FlexiGIS; a platform developed by the authors (refer to [12] for detailed and extensive information about acquiring and pre-processing urban geodata including the scripts used and the resulting datasets). The spatial data input from OSM datasets have been extracted for the time-stamp of 14 September 2017 and represent the status of the OSM database at this date. A comprehensive description of the required datasets extraction and processing is available in [12]. An assessment of OSM data quality and completeness for urban energy modelling is presented by the authors of this contribution in [37]. Using GIS, multiple layers of information are created from the extracted urban OSM datasets which provide the required geo-referenced input datasets for modelling the local electricity demand and on siterenewable power generation. Based on the intersection of both “building” and “landuse” layers, the urban settlements are classified based on their usage into six clusters: agricultural, educational, commercial, industrial, residential and urban roads and squares. Note that the last two clusters need to have street lighting during night hours, which has been taken into account in the modelling. The spatial dataset extracted and post-processed includes data related to the number of buildings of each cluster and its consumption prototype of the aforementioned sectors. The area of each building, roof top type and squares areas as well as roads details such as their length and width which are calculated using the open source GIS desktop (QGIS).5
SPLs are generic load profiles for the power consumption of typical customer groups below the annual consumption limit of 100.000 kWh/ year. Based on the measurements of representative groups of consumers, SLPs are constructed for different prototypes (e.g. households, commercial, industrial, street lights, etc.). Although generic, SLPs are considered as representative load profiles that can be applied to various consumer groups (e.g. commercial G0, household H0, agriculture L0), each with a similar patterns and acceptable behaviour of the respective real profiles [38]. The use of SLPs have been acknowledged in the scientific literature and used to simulate electric load profiles for different consumer groups like households and service sectors such as in [39,14]. Furthermore, they have been already used by grid operators to predict the temporal fluctuations of urban electricity consumption and to allocate the required investments for expansion of the distribution grid [14]. For each of the existing urban cluster presented in Table 1, a specific SLP is assigned based on the building characteristics. Table 1 shows the selected standard load profiles for the different urban categories modelled in this contribution. SLPs are available with a 15 min resolution for one year. In this work, the selected SLPs have been normalised for a consumption of 1000 kWh/year. As source for meteorological data from 2015, the authors used the Modern-Era Retrospective analysis for Research and Applications (MERRA) dataset [40]. The datasets include wind speeds and solar irradiation time series. These data are publicly available with a spatial resolution of 0.625° in latitude and 0.5° in longitude and a temporal resolution of 15 min. Wind data are provided at a 10 m height, such as wind speed data v(t) at 100 m height are extrapolated using the Hellmann exponential law [41] for locations in which urban wind turbines are already under operation. The time series of Photovoltaic’s (PV) performance ratio θ (t ) are provided by the energy meteorology research group at the University of Oldenburg [42]. In this study, OpenStreetMap is the main source of geo-referenced urban energy infrastructure datasets (building, landuse and road infrastructure) that is globally available. Although the completeness of the datasets differs from city to city [37,43], an acceptable level has
2.1.2. Temporal input datasets The time series of urban electricity consumption L(t) is modelled using publicly available Standardised Load Profiles (SLPs) developed by the German Association of Energy and Water Industries (BDEW) [4]. 3
OpenStreetMap website. Osmosis: OSM data processing tool. 5 QGIS-Desktop. 4
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Table 1 Assigned standardised load profiles for the selected urban clusters considered in the modelling of electricity consumption at the urban level. Urban category
Building type
SLP
Agricultural Commercial Educational Industrial Residential Roads
Stable, farm-auxiliary Supermarkets, retail Schools, universities Manufacture Households Streets, squares
L0 G0 G1 G3 H0 SB2
L (t|p) =
∑
∊p ∗NSLPj, p (t ) ∗Aj, p (1)
j, p
where L (t|p) is the time series of electricity demand as a function of time (t) and unit portfolio (p), ∊p represents the electricity usage intensity for each classified urban prototype, Aj, p is the area of every urban unit j (in square meters) for each urban sector p and NSLPj, p (t ) represents the normalised standard load profile of the corresponding consumer group. 2.2.2. Calculation of renewable power generation Time series of the electricity equivalent from solar resources S (t ) are generated using the following equation:
been observed in a quality assessment analysis conducted by the authors in a previous contribution [37]. In addition, the coverage of the OSM datasets is remarkably increasing since more volunteers have been engaged. However, comprehensive land recordings of existing urban properties like cadastre and/or statistics could be provided to complement the missing OSM datasets which have been publicly available by the respective authorities in some regions. Although SLPs are widely commonly available and have been already used by network operators, some alternative source like statistical datasets and demographic surveys could be provided by city administrations. For example, city census, weather data and statistical data of population density can be used alternatively to generate simplified load curves allocated to specific regional areas. On the other hand, if necessary data are lacking, different techniques can be applied for handling the missing values depending on the kind of problem and the data type. In case the lost data are temporal datasets (such as meteorological variables of solar irradiation), various mechanisms and predictive models like Regression Imputation techniques can be used to process the missing data problem as done in [44]. Nevertheless, if the lost required input data are not readily available (e.g. spatial features of urban objects) and no model-based method can be applied to handle the missing information, appropriate and realistic assumptions can be made. In general, assumptions inherent to energy modelling and are considered to be totally accepted in the scientific research. However, all simplifications and assumptions should not be buried in black boxes but made publicly open and transparent in order to reproduce and evaluate the generated outputs.
n, m
S (t|p) =
∑
GHIj, p (t ) ∗rAj, p ∗θ (t ) ∗η (2)
j, p
where S (t|p) is the temporal distribution of PV electricity equivalent, GHIj, p (t ) is the time series of solar irradiation for building j in sector p multiplied by its roof top area rAj,p. The roof shape of residential buildings is assumed to be a gable/tilted roof and the share of its usable area is considered as 57.8% [45]. The other urban objects are assumed to have flat roof tops using 26.7% of their overall accessible area [45]. θ (t ) represent the time series of performance ratio and η is the PV module efficiency. To estimate the power potential from the wind source, a reference wind power curve is used for a representative wind turbine that is already installed. A simplified model [47] is used to calculate the electricity equivalent from wind, as follows: 3
⎧ Pr v (3t ) , if vin ⩽ v (t ) ⩽ v (r ) ⎪ vr W (t ) = if vr ⩽ v (t ) ⩽ v (out ) ⎨ Pr , ⎪ 0, otherwise ⎩
(3)
where, the instantaneous electricity equivalent of wind resources is W (t ), v (t ) is the wind speed at time t , vr is the rated speed, vout is the cut-out speed, vin is the cut-in speed, and pr is the rated power. Required technical details are obtained from the manufacturer of the installed wind turbines. For the case study presented in this work, the wind turbine characteristics are provided by ENERCON6 for the E-101 wind turbines that are already in operation.
2.2. Model overview 3. Case study and validation After introducing the required input datasets of the proposed approach for the simulation and validation of electricity consumption and generation in cities, a model overview is presented here. In the model, the temporal distribution of urban electricity requirements is modelled and matched for each existing urban geometry using GIS. The required information of the buildings (including their type, roof type, location and area) are provided from the spatial section of the model (see Fig. 1). A detailed description of modelling consumption and generation using the collected and processed datasets is provided in the following.
In this contribution, Oldenburg was selected as a case study because of the availability of measured data of electricity consumption and generation generously provided by EWE Netz7. The available real datasets are not available on the city level but on district level. Therefore, the authors validated the developed model at district levels. The measured data are provided for electrical substations located in what is referred to as “transformers districts”. The authors assume that, the electricity measured is mainly consumed inside the geo-boarders of the considered transformers districts. The model described in the previous section is applied on two districts Osternburg and Oldenburg-West. Fig. 2 shows the extracted and filtered geodata for urban energy infrastructure for Osternburg, one of the transformers districts in Oldenburg, at which the measured demand data are available. It depicts the different categorised urban objects (buildings and roads) based on their portfolio and type of usage. The quarter-hourly time series of electricity consumption and generation for each existing urban feature extracted from the OSM datasets is generated. Total time series of electricity consumption for all urban blocks (buildings and streets) within the respective study area is then
2.2.1. Calculation of load profile The characterisation of different urban electricity consumption profiles is based mainly on the available building portfolio, consumption patterns and parameters such as building size, location and area. Since some building types are more electricity intensive than others, this work introduces an electricity usage indicator (∊) that expresses the building’s electricity consumption as a function of its type and area. ∊ is calculated by dividing the annual electricity consumption for an exemplary property to the average local area of this respective urban sector. The time series of electricity consumption L (t ) for every single urban object (building, street and square) are calculated with 15 min resolution using the following equation:
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ENERCON’s E-101. EWE Netz is the grid operator in north-western Germany.
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Fig. 2. Cartographic representation of urban energy infrastructure showcased for the Osternburg district in Oldenburg. Credits: OpenStreetMap contributors, QGIS Desktop.
Fig. 3. Time series of the modelled and real load profile in two transformers districts (left for Osternburg and right for Oldenburg-West) in 2015. The red line represents the measured datasets while the dotted black line the simulated one.
Fig. 4 illustrates both measured and simulated aggregated load profiles for 2015 for the winter and summer seasons as well as for interim days in Osternburg district. Fig. 4 shows that, for the aggregated day (top left figure) the morning peak of the simulated load occurs at 11:45 and at 11:30 for the measured time series. While, the second evening peak for the simulated data occurs at 18:45 and at 19:00 for the measured one. The observed time shift of the generated load profiles is relatively small and is the result of using standard load profiles. Better representative datasets inputs could improve the accuracy of the model. To fully validate the model results concerning the electricity consumption and generation, a linear approach is used. The authors use a linear regression model with a single explanatory variable to calculate a coefficient of determination R2. Based on the proportion of total variation of the simulated load profiles, the coefficient of determination provides a measure of how well the measured data are replicated by the model.
calculated as shown in Eq. (1). Fig. 3 shows the aggregated simulated load profiles against real measured data for both districts. Although the uniqueness of the investigated districts in terms of its urban fabric and units features, the model cope very well with different urban settings. In 2015, the measured electricity consumption provided by EWE Netz was 434.16 GWh. The total simulated aggregated load in Osternburg using the model developed by the authors is found to be 431.22 GWh. In Oldenburg-West the total modelled consumption is found to be 298.36 GWh while the measured was 297.66 GWh. Obviously and as shown in Fig. 3, the model cannot reproduce extreme load cases such as maintenance, technical problem in electrical substations and/or other grid faults that can lead to a higher consumption on other substations. The resulted load profiles are remarkably similar to the measured one and demonstrate the possibility and feasibility of using open source SLPs to generate realistic load profiles replicating the temporal fluctuations of electricity demand. 1104
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Fig. 4. Simulated electricity consumption (dotted line) against real measured data in red for an aggregated day and different seasons in Osternburg district, Oldenburg.
due to the stochastic behaviour and instantaneous fluctuations of wind speed that are not optimally represented in the provided meteorological data. In addition, the generated wind outputs is based mainly on the adopted power curve that may be deviated from actual operating conditions.
For the aggregated day in 2015 in Osternburg district, the coefficient of determination R2 has been found to be 0.92 which means that 92% of the variance of the simulated load profiles can be replicated by the variance of the measured data. Regarding different seasons, the values of R2 are: 0.94 for winter, 0.87 for summer and 0.91 for interim season. Using the same approach the Oldenburg-West, the proportion of total variation of simulated load profiles are as follows: 0.90 for the aggregated day, 0.94 for winter, 0.83 for summer and 0.89 for the interim day; respectively. These values indicate that the model successfully reproduced the electricity load profiles for different typical days and seasons when compared to the measured load data. The electricity equivalent of solar and wind sources is also calculated and validated against measured data. Fig. 5 shows the aggregated time series of simulated and measured solar PV power generation for one week in Oldenburg. For the purpose of the analysis, the modelled electricity consumption L (t ) , electricity equivalent from solar S (t ) and wind W (t ) are normalised to their averaged values. The value of R2 for the electricity equivalent is 0.98 for solar and 0.55 for wind. The low predictive accuracy of the model regarding wind power generation is
4. Configurations of renewable power generation for an optimal urban energy autarky Urban electricity consumption and generation represent bottleneck for the transition towards cleaner cities [46]. Modelling these energy requirements yields comprehensive information for a sustainable planning of energy systems at the city level. The aim of this section is, to determine the solar and wind combinations at which the shares of the electricity grid and/or storage capacity to match the energy demand are at their minimum. The following subsections explain in details the steps to determine the configurations of on-site non-dispatchable renewable power generation and showcased for the city of Oldenburg. The first step is to simulate the aggregated electrical load which is depicted in Fig. 6, where the different contributions are illustrated for an aggregated day in 2015. The model predicts that for 2015 a total annual electricity consumption of 1.8 TWh. For more details, the share
Fig. 5. Measured and modelled solar electricity generation datasets for an arbitrary week in 2015 at the city of Oldenburg. The dotted line represents the simulated solar power generation while the measured data are in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. Portfolio of the electricity consumption for different consumer’s categories showcased for an aggregated day in the city of Oldenburg. The day’s load peak occurs at noon and the night’s one at around 19:15. 1105
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Fig. 7. The segmentation of different urban consumers groups and their shares of electricity consumption showcased for the city of Oldenburg.
in reproducing urban electricity load profiles. In addition, it provides invaluable insights of the segmentation and contribution of electricity consumption for different urban consumers groups. Simulating the non-dispatchable renewable power generation can provide important information about the future power supply systems and energy autarky in cities. In the second step, the residual load is calculated as the difference between the modelled electricity consumption and power generation from the intermittent solar and wind sources at the urban level using Eq. (4) (see [47,48] for more details).
ξ (t|α, β ) =
L (t ) ⎛ W (t ) S (t ) ⎞ −⎜α +β ⎟ 〈L〉 ⎝ 〈W 〉 〈S〉 ⎠
(4)
where ξ is the residual load as a function of time and energy coefficients: α for wind and β for solar. L(t), W(t) and S(t) are calculated using the model introduced in Section 2. The time series of the average consumers’s load, average wind and solar electricity equivalents are: 〈L〉, 〈W 〉 and 〈S〉 respectively. In a third step, as the fluctuations of the urban residual load must be instantaneously matched either by dispatchable sources (e.g. backup capacity, grid, etc.) or by energy storage, these variations need to be estimated. The fluctuations λ of the residual load ξ are defined as follows:
λ ξ (α, β ) = ξ (t|α, β )−〈ξ 〉 =
L (t ) ⎛ W (t ) S (t ) ⎞ −⎜α +β ⎟−(ϑ(α , β )) 〈L〉 ⎝ 〈W 〉 〈S〉 ⎠
(5)
where ϑ(α, β ) = 1−(α + β ) is the ratio of electricity exchange within the distribution grid. In the following, the authors define the configurations of solar and wind mix for which the electricity taken from the grid and/or öbtained by storage capacities are minimal. For this purpose, a standard deviation of the residual load as a function of α and β is defined in Eq. (6). The cost function σξ can be interpreted as a measure of the minimum required balancing energy from the storage at specific combinations of wind and solar [48].
Fig. 8. (a) The required balancing power from storage at different combinations of energy coefficients: α for wind and β for solar. ϑ represents the grid share. (b) The energy autarky degree at different renewable energy configurations (α and β ) and required balancing energy from the storage.
σξ (α, β ) =
of electricity consumption for each building category is shown in Fig. 7. Note that, the share of residential buildings accounts for 78% which is equivalent to 60% of the total electricity consumption. The industrial consumers represent 1% and account for 23% of the electricity demand. On the other hand, about 3 GWh (0.1%) of the total demand is found to be used for streetlights although 18% of the urban objects are roads and squares. Due to the details available in the OSM datasets that include relevant information related to urban fabric (such as building type and area, etc.), the model presented here outperforms standard approaches
〈λ2 (t|α, β ) 〉
(6)
Fig. 8(a) shows the standard deviation σξ as a function of solar and wind coefficients. Note that, for 100% solar-only scenario the required balancing power is at its maximum due to the large imbalances of PV power outputs caused by the daily fluctuation in power generation. Reasonably, the higher the grid share is, the smaller the balancing energy is required from storage. The last setp is to define the urban energy autarky, which is here considered as the share of total consumed electricity from RESs to the 1106
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consumption and generation at local scales, a combination of different urban energy sources is vital to reduce the required balancing energy and in due course the system’s total costs. Moreover, high levels of electricity autarky can be achieved for specific configurations of the local non-dispatchable renewables power generation at minimum needs of energy from the storage. The resulting time series of urban electricity requirements are the main inputs of an ongoing work, that optimises the required local decentralised storage for the maximisation of renewable energy integration in urban energy systems.
total local electricity consumption, as follows:
ψ (α, β ) =
α ∫ W (t ) dt + β ∫ S (t ) dt
∫ L (t ) dt
(7)
where ψ (α, β ) is the energy autarky as a function of wind and solar coefficients. In order to increase the urban autarky degree, the on-site electricity generation from renewables have to be increased, which means that the total installed system’s capacity will increase and in due course the total system’s costs. In Fig. 8(b), it is clear that, for high values of urban energy autarky, the balancing energy required increases. From a meteorological perspective, the model predicts that a 100% self-sufficient city could be theoretically achieved at a combination of 80% wind and 20% solar with no need for back-up capacities or power from the grid while keeping the required storage size realistic. However, the installation of new renewable and storage capacities will likely increase sharply the total system costs making it economically unattractive. Hence, up to 30% intervention from the national grid can reduce balancing energy required by 30%, the required installed capacities and the total investment costs. A linear programming cost optimisation is performed by the author of this contribution [49] to explore the economic deployment of micro-generation and decentralised storage for the maximisation of self-sufficiency is successfully applied for the selected case study. Although reasonable results were obtained using the model presented here, it should be noted that the model uses assumptions and simplifications. These are necessary in any modelling process and the results have to be interpreted accordingly.
Acknowledgements The authors would like to acknowledge the EWE-Netz for providing the required validation data. The authors thank NASA for providing the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data and the Solar Energy Services for Professionals (SoDa) for the supply of solar and wind data. The first author gratefully acknowledges the financial support provided by the Heinrich Boell Foundation through a PhD scholarship. References [1] Yan J, Chen B, Wennersten R, Campana P, Yang J. Cleaner energy for transition of cleaner city. Appl Energy 2017;196:97–9. Available from:
5. Conclusions and outlook A systematic surge of the integration of urban micro-generation from fluctuating renewable energy sources in cities requires a comprehensive understanding of static and dynamic characteristics of urban energy requirements (consumption and generations). Many models have been developed to simulate energy demand and supply in cities but most of such models are neither freely available nor open. Such black-box models hinders any verification of the quality of input datasets, simulation outputs, comparison with other models and therefore impede public discussions. To the knowledge of the authors, the model developed in this contribution is the first model that is based on open source data and tools and considers different urban features. This work provides evidence that urban electricity consumption and generation can be reproduced using only open source data and models. The simulated local electricity load validated against measured electricity demand and resulted with a value of the determination coefficient up to 94%. The simulated on-site PV power generation is remarkably similar to the measured load and PV profiles. Prior work, done by the authors of this contribution, established the spatial dimension of a spatial platform to model urban energy systems using open source data and models. The validation of model’s results provides compelling evidence for modelling urban electricity consumption and generation using only open source data and models. For example, an appropriate employment of SLPs in urban energy models can help replicating the temporal distributions of local electricity consumption. However, few limitation are worth noting. Although urban energy systems can be effectively replicated using only open source data such as OpenStreetMap and Standard Load Profiles datasets, some improvements of the data quality can lend support to the model outcomes [50,37]. The authors refine the implications of modelling urban energy requirements and characterise the electricity consumption per building prototype. It has been found that, more than 90% of the total urban electricity consumption occur in residential, industrial and commercial buildings. Moreover, this work demonstrated that to balance the 1107
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