Distributed Software Infrastructure for Evaluating the Integration of

Photovoltaic Systems in Urban Districts

Lorenzo Bottaccioli

, Edoardo Patti

, Michelangelo Grosso

, Gaetano Rascon

, Angelo Marotta

Salvatore Rinaudo

, Andrea Acquaviva

and Enrico Macii

DAUIN, Politecnico di Torino, Torino, Italy

ST-Polito s.c.a.r.l., Torino, Italy

STMicroelectronics s.r.l., Catania, Italy

Keywords:

Photovoltaic, GIS, Distributed Software Infrastructure, Urban Planning, Spatio-temporal Analysis, Renewable

Energy Planning.

Abstract:

Nowadays, the adoption of renewable energy sources distributed across the city is crucial for planning and de-

veloping the future Smart City. An accurate simulation and modelling of energy sources, such as Photovoltaic

Panels (PV), is necessary to evaluate both economical and environmental beneﬁts. With the growth of renew-

able sources in the city simulations of energy production became crucial for the DSO for evaluating retroﬁts

or for network balancing events. In this paper, we present a software infrastructure for simulating the solar

radiation and estimating the energy production of a district. The infrastructure simulates the PV production

and evaluates the integration of such systems considering real electricity consumption data. In its core, the

proposed solution models the behaviours of PV systems taking into account the digital surface of rooftops and

sub-hourly meteorological data (e.g. solar radiation and temperature) to compute real-sky conditions. Then,

such information is used to feed a model of the hardware components of PV systems to gain more accurate

estimations of energy production in the district in real-sky conditions.

1 INTRODUCTION

Nowadays, we are moving forward to more smart and

sustainable cities that aim at reducing greenhouse gas

emissions. A Smart City approach fosters a smart

energy use also taking advantage from an increasing

renewable energy sources deployment. In this con-

text, Information and Communication Technologies

(ICTs) play a crucial role in both planning and moni-

toring of distributed energy sources. The crucial roles

of ICTs and the emerging Internet-of-Things (IoT)

are highlighted by the spread diffusion of heteroge-

neous and pervasive sensors in our houses, district and

cities. IoT devices and sensors allow to collect large

amounts of energy related data capable of describing

the consumption behaviours of the citizens. Electric-

ity consumption data can be used in simulation pro-

cesses for evaluating: i) energy management actions;

This work was partially supported by the EU projects

DIMMER and FLEXMETER, and by the Italian project

”Ediﬁci a Zero Consumo Energetico in Distretti Urbani In-

telligenti”.

ii) management of electricity distribution networks;

iii) integration of renewable sources in the city.

In this work, we present a methodology for de-

veloping a distributed software infrastructure to fos-

ter the usage of solar energy. Our solution exploits

a Microservices approach for integrating heteroge-

neous sensors, services and simulation tools for evalu-

ating the integration of Photovoltaic (PV) energy sys-

tems in urban districts. If a large amount of ﬂuctu-

ating energy sources are planned to be installed, an

integration analysis has to be performed considering

network constraints. Such analysis can be achieved

correlating simulated PV systems energy production

with electricity consumption data coming from IoT

devices. In its core, our solution includes hardware

models of PV system components to give more accu-

rate estimations of energy production. The proposed

methodology takes advantages of weather informa-

tion to simulate sub-hourly real-sky solar radiation of

rooftops and to analyse the hardware component per-

formance of the PV system.

The rest of the paper is organized as follows. In

Bottaccioli, L., Patti, E., Grosso, M., Rasconà, G., Marotta, A., Rinaudo, S., Acquaviva, A. and Macii, E.

Distributed Software Infrastructure for Evaluating the Integration of Photovoltaic Systems in Urban Districts.

In Proceedings of the 5th International Conference on Smar t Cities and Green ICT Systems (SMARTGREENS 2016), pages 357-362

ISBN: 978-989-758-184-7

357

Section 2 the actual state of art of methodologies and

services for PV energy simulation are presented. Sec-

tion 3 analyse the motivation that prompt us to embark

on this research. Section 4 presents the speciﬁcations

and the methodology to develop our solution. Finally,

Section 5 provides the concluding remarks.

2 STATE OF THE ART

Geographic Information Systems (GIS) tools have

been applied for solar energy applications in urban

context as reported by (Freitas et al., 2015). One

of the major limitation of actual GIS tools consists

on neglecting time domain, as reported by (Camargo

et al., 2015). Both (Camargo et al., 2015; Freitas

et al., 2015) highlight the fact that an accurate solar ra-

diation estimation is needed and a detailed modelling

of the components of the PV system is required.

GIS tools have been exploited also for web-

based applications for fostering photovoltaic poten-

tial and system integration platforms (Suri et al.,

2008; Mapdwell Solar System, ; de Sousa et al.,

2012; De Amicis et al., 2012). Such platforms do

not provide time-dependent simulation about energy

production and PV system performances. Time de-

pendent simulation of PV production, with an accu-

rate evaluation of components performance, is crucial

for: ii) planning deployment activities; ii) business

plan evaluation; iii) monitoring of existing plants and

smart energy use. The main limitation of such ser-

vices are summarized in the following and reported in

Figure 1: i) they provide yearly data in real-sky con-

ditions; ii) they do not provide hourly or sub-hourly

data in real-sky conditions; iii) they do not take into

account PV system hardware components to model

their performance and behaviour. In addition, such

services do not correlate electricity consumption data

with PV production simulation, which is relevant to

evaluate PV systems integration.

Figure 1: Confront with available services.

With respect to state of the art solutions our

methodology provides real-time energy production

data of existing and of feasible PV systems in the dis-

trict. In the simulation process, our solution takes into

account also the operation efﬁciency of each com-

ponent of the PV systems. Taking advantage of the

correlation of PV energy production simulation with

electricity consumption data our solution is able to

evaluate the energetic impact of the integration of PV

systems in the district.

3 MOTIVATION AND EXPECTED

OUTCOMES

This research aims at developing a software infras-

tructure that exploits GIS tools, weather and electric-

ity consumption data for evaluating PV systems inte-

gration in urban context. Our infrastructure aims at

providing sub-hourly information of real-sky incident

radiation on rooftops. It is devoted to both planning

and monitoring phases of PV systems, spanning all

scales starting from single building up to block, dis-

trict and city. Such infrastructure can be exploited in

order to produce forecast simulation of PV systems

energy production for smart energy use. This solu-

tion is intended to satisfy the needs of different end-

users such as: i) Single citizen can evaluate the eco-

nomic and environmental savings achievable with the

installation of a PV system; ii) Energy aggregators

and Energy Communities can use the simulations to

schedule consumption of their clients for maximiz-

ing self-consumption and minimizing energy bills. In

particular Energy Communities can exploit such in-

frastructure to perform feasibility studies as proposed

in our previous research (Bottaccioli et al., 2015);

iii) PV system engineers can simulate the behaviour

of converters with the application of realistic condi-

tions. This simulation helps in dimensioning, vali-

dating and optimizing each system before and after

installation; iv) Distribution system operators (DSO)

can take advantage of the proposed solution for net-

work balancing and for planning retroﬁts and/or ex-

tensions of the existing distribution grid; v) Energy

and City planners can exploit the infrastructure for

evaluating the impacts of large PV systems installa-

tions or for monitoring the performance of existing

ones.

4 PV SYSTEM INTEGRATION

New methodologies and procedures for a high de-

tailed simulations, in both spatial and temporal do-

mains, are recently emerging in the research ﬁled of

PV potential estimation (Camargo et al., 2015; Jaku-

biec and Reinhart, 2013; Luka et al., 2014). These

SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems

358

Figure 2: Distributed Software Infrastructure.

works have opened the way to spatio-temporal anal-

ysis in the assessment of PV potential. In particu-

lar, (Camargo et al., 2015) highlights the necessity

to integrate simulated PV production with electricity

consumption data for a correct PV integration to avoid

network congestions. The limitations of the state of

art solution in PV energy simulation are: i) they run as

Desktop applications and simulated results can not be

provided easily to external users and/or other software

components; ii) they do not integrate weather data;

iii) they do not take advantage of hardware compo-

nents models for evaluating the efﬁciency of a PV sys-

tem; iv) they do not correlate PV simulated data with

real electricity consumption data. Hence, the main

objective of our research are: i) the development of a

distributed software infrastructure for PV energy sim-

ulation; ii) the integration of the meteorological data

coming from the third-party services; iii) the adop-

tion of hardware components models of PV systems

in order to evaluate the overall efﬁciency; iv) the cor-

relation between PV production and electricity con-

sumption data in order to evaluate the integration of

PV systems in the district considering network con-

straints.

Figure 2 shows the three layers of our distributed

software infrastructure. The bottom layer is the data

source integration layer that is in charge of collecting

the required information from the following heteroge-

neous data sources: i) Digital Surface Model(DSM),

which is a raster image that represents terrain el-

evation considering the presence of manufactures;

ii) Linke Turbidity coefﬁcients that express the at-

tenuation of solar radiation related to air pollution;

iii) Weather data of third party services for collecting

solar radiation, temperature and wind speed; iv) Elec-

tricity consumption data that are used for evaluating

the integration of PV systems.

The middle layer is the core of our methodol-

ogy. It consists of simulation and integration ser-

vices that are summarized in the following: i) GIS-DB

stores clear-sky and suitable surface maps; ii) Suitable

area identiﬁes suitable surface for PV modules on

rooftops; iii) Clear-Sky simulation; iv) Real-Sky sim-

ulation; v) Solar decomposition provides diffuse and

direct components of solar radiation (as described in

Section 4.2); vi) PV energy estimation module consid-

ers hardware components models for evaluating the

performance behaviours of the PV system; vii) PV

integration module takes into account network con-

straints for evaluating the integration of PV systems

into the grid. Furthermore, it implements algorithm

for load shifting and demand side management. The

PV integration module performs also economic eval-

uation of feasible PV energy systems.

The upper layer is devoted to user applications,

such as Web-Map interface and Dashboards. Both of

them can provide information about performed simu-

lation across the city with different level of details.

4.1 Software Infrastructure for PV

Simulation

In this section, we present the methodology we are

exploiting to develop our distributed simulation in-

frastructure for PV system planning and monitoring

in a Smart City context. The simulation of energy

production is performed taking into account the ef-

ﬁciency behaviours of each hardware component of

the PV system. The GIS-Server module, showed in

Figure 3, is the core of our solution. We selected

the open-source software GRASS-GIS that provides,

through r.sun, functionalities for computing clear-sky

solar radiation. This tool has been proved to provide

an accurate simulation of solar radiation in urban con-

texts (Ronzino et al., ; Freitas et al., 2015).

The inputs required by the clear-sky simulation

process are: i) the Digital Surface Models (DSM) and

ii) monthly Linke turbidity coefﬁcients. In addition,

the GIS-Server stores in its database (GIS-DB) pre-

and post-processed data: i) DSM; ii) generated clear-

sky hourly radiation maps and iii) suitable rooftops

surface maps.

The proposed distributed infrastructure takes ad-

vantage of a Microservices approach in order to in-

tegrate different software and models in a form of

interoperable services. It exploits the Web Process-

ing Services (WPS), Web Feature Service (WFS) and

Web mapping Services (WMS) that are the standards

deﬁned by the Open Geospatial Consortium (OGC).

OGC speciﬁes a service interface for publishing and

performing geospatial process over the web. WPS are

used for ﬁle upload and for executing the simulation

process. They provide the rules for standardizing in-

puts and outputs of the process. WFS are used for

querying and retrieving features about the elements of

Distributed Software Infrastructure for Evaluating the Integration of Photovoltaic Systems in Urban Districts

359

Figure 3: PV Simulation Infrastructure Time Diagram.

a polygon-map. WMS are used for the visualization

of the produced map in the Web-Map interface.

Figure 3 shows the interactions between the soft-

ware actors of our distributed infrastructure for sim-

ulating PV systems energy production and hardware

components behaviours. The computation starts when

the User uploads the DSM through a WPS to GIS-

Server.The upload of the ﬁle automatically executes

the clear-sky simulation module by specifying the in-

puts directly in request. Based on the DSM, given a

date and time this process produces the related set of

clear-sky radiation maps. The maps are saved in the

GIS-DB and the User is notiﬁed. Then the User can

execute the task for identifying the suitable areas for

PV systems installation. This is computed by the Suit-

able Surface module that takes the DSM, slopes and

orientation as inputs. The resulting maps are stored

again in the GIS-DB. Alternatively the user can up-

load in the GIS-DB his own suitable surface maps.

Finally, the User invokes the PV Simulation mod-

ule for estimating the energy production of a PV sys-

tem exploiting also meteorological conditions. PV

Simulation module needs the already stored infor-

mation about suitable surface and clear-sky radiation

maps. If new parameters for identifying suitable sur-

face are given, such maps are re-calculated by Suit-

able Surface. Meteorological conditions are needed

as well and they are provided by the nearest weather

station though third-party web-services (e.g. (Weather

Underground, )). In particular, solar radiation data

are useful for estimating real-sky conditions by deﬁn-

ing clear-sky indexes for both direct and diffuse ra-

diation. Wind speed and air temperature are needed

for evaluating detailed performance of each PV sys-

tem hardware component (e.g. PV modules, Inverter

and Maximum Power Point Tracker). Optionally, if

direct and diffuse solar radiation components are not

provided by third party services, the solar radiation

decomposition modules is used by the simulation pro-

cess. More information about weather data integra-

tion are reported in Section 4.2. The PV Simulation

modules query the PV array data sheet in order to col-

lect information on the PV array characteristic needed

by the hardware components models.

In a nutshell, the described software infrastruc-

ture is able to simulate sub-hourly real-sky solar ra-

diation of rooftops in a given city district area. Then

it provides an estimation of PV systems energy pro-

duction, also analysing its hardware components per-

formances. Section 4.3 presents the model for energy

conversion efﬁciency of PV hardware components,

that is the core of the PV Simulation module.

4.2 Weather Data Integration

In the last years the availability of weather station data

present in our cities is strongly increased. This is due

to the lower price of weather stations and the appear-

ance of open web-services for data management and

publication, such as (Weather Underground, ). In or-

der to simulate real-sky solar radiation on a pitched

surface, information on direct and diffuse radiation

is needed. The majority of present weather stations

do not provide direct and diffuse radiation measure-

ment, because accurate and expensive sensors would

be required. In order to overcome such limitation, in

our solution we have developed a module for extract-

ing direct and diffuse radiation from global horizon-

tal radiation. The module exploits solar radiation de-

composition techniques present in the literature such

as (Boland et al., 2013; Orgill and Hollands, 1977;

Erbs et al., 1982). Those experimental techniques use

SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems

360

information only on global radiation to predict the di-

rect and diffuse components. The user can specify

which technique is the most suitable for the area of

interest. This because decomposition models do not

perform with the same accuracy all over the world

due to their experimental nature. Indeed, depending

from latitude, longitude and environmental condition

the accuracy of the decomposition model can change.

This module gives to our infrastructure a big ﬂex-

ibility of employment due to the possibility of inte-

grating common and available weather station data.

4.3 Energy Conversion Efﬁciency

Modelling

Photovoltaic cells produce direct current (DC) en-

ergy at only a fraction of a volt. The utility wiring

or grid and appliances within the home typically use

alternating current (AC) power with voltages greater

than 100V. To convert power from DC to AC, an in-

verter must be integrated into the PV system. Typ-

ically, another DC-DC converter is added to step up

the low voltage DC produced by photovoltaic cells to

the substantially higher DC voltage necessary at the

input of the inverter. ”Smart” converters embed Max-

imum Power Point Tracker (MPPT) logic that adapt

the power transfer to the changing working conditions

in real time.

The traditional grid-tied architecture of photo-

voltaic systems concentrates all the electronics in the

central inverter. This is the centralized approach. To

gain in terms of global system energy production, re-

liability, safety, communication and monitoring, the

trend today is to move towards the distributed ap-

proach where the electronics is partially or fully dis-

tributed close to each panel (microinverter). In this

way, the power transfer related to non-uniform shad-

ing conditions can be maximized using local MPPT.

Solar radiation and temperature have a huge inﬂu-

ence on the characteristics and performance of each

photovoltaic module, so modelling is mandatory and

very useful to quantify how these environmental fac-

tors inﬂuence the performance of the system. The

availability of models of the converter chain compo-

nents is very important in system sizing, cost analy-

sis, and monitoring. For a power electronics engineer

working with renewable energies, it is imperative to

have an accurate model as it can support testing and

development of optimal solutions. At the same time,

the models need to be fed with realistic input condi-

tions taking into account solar radiation, panel loca-

tion and inclination and weather data.

The authors in (Marotta et al., 2011) describe a

behavioural steady-state averaged model for the so-

lar boost converter SPV1020 by STMicroelectron-

ics, equipped with logic running a Perturb&Observe

MPPT algorithm. The same methodology is being

adapted here for modelling and evaluating the efﬁ-

ciency of the various converters used in the district

depending on their actual components and on the spe-

ciﬁc environmental conditions. It must be noted that

the system engineers are more concerned with long-

term behaviour than in the transients: this is the rea-

son for which a steady-state behavioural model is

employed. The rate of change of ambient temper-

ature and insulation usually varies over minutes or

hours, so slow-changing stimuli are handled. In ad-

dition, the simulation with a switching model would

be extremely slow due to the prohibitively small time

step needed. The behavioural steady-state averaged

model is implemented to handle series and parallel

connected PV panels.

4.4 Electricity Consumption Data

Integration

With the increase of Smartness in our cities, a large

amount of IoT sensors and actuators have appeared.

During this process energy monitoring and manage-

ment are receiving great attention and many sensors

are recording energy-related data and ﬂuxes. Thanks

to smart-meters, more accurate information about

user energy proﬁles is available. Furthermore, smart-

plugs (Ganu et al., 2012) or Non intrusive load mon-

itoring algorithm (Zoha et al., 2012) provide detailed

information about load consumption of each appli-

ance. Such detailed information can be used by En-

ergy aggregators or Energy managers to schedule the

consumption of each user with respect to best prices

or renewable production. At the same time consump-

tion data can be use to evaluate the integration of

renewable sources considering self-consumption and

network constrains.

Thanks to a web-services based approach, our in-

frastructure allows the interaction among our solu-

tion and other distributed software architectures or

services devoted to the integration of real-time data

coming from the Smart-Grid (Patti et al., 2015; Patti

et al., ). Hence, our solution is able to easily in-

tegrate data provided by smart-meters and/or smart-

plugs. The integration of electricity consumption data

gives us the possibility to evaluate with more detail

the level of self-consumption of each user. Such de-

tailed analysis is crucial to evaluate economics in-

dex such as Return on Investment, Rate of Return or

Pay Back Time. Additionally, information about con-

sumption and production proﬁles can be used from

Energy aggregators or Energy Communities to sched-

Distributed Software Infrastructure for Evaluating the Integration of Photovoltaic Systems in Urban Districts

361

ule the consumption proﬁles of their users for maxi-

mizing the self-consumption and the economics sav-

ings. DSOs can use consumption proﬁle information

for network balancing, active network management or

for ancillary services. Finally, electricity consump-

tion data are used to evaluate the integration of PV

system in the selected area considering also the distri-

bution network constraints.

In many areas, smart-meters are still not deployed

and data regarding users consumption load proﬁles

are not available. To give ﬂexibility to our methodol-

ogy, we have integrated a load proﬁle simulation mod-

ule that is able to estimate load proﬁle with a good

accuracy as reported in our previous work (Bottacci-

oli et al., 2015). The load proﬁle simulation module

is able to simulate the consumption proﬁles for differ-

ent users. For residential users, the module requires

information regarding the size of the houses in square

meters and the number of inhabitants for each house-

hold. For industrial and commercial customers, nor-

malized standard load proﬁles are used. Those stan-

dard proﬁles are rescaled with respect to total yearly

or monthly electricity consumption.

5 CONCLUSION

In this paper, we presented a methodology for the de-

velopment of a distributed software infrastructure for

simulating PV system behaviours and evaluating their

integration in a Smart City context. Combining re-

alistic radiation modelling framework and electricity

consumption data, our infrastructure can offer to users

detailed information of PV energy production in real-

sky conditions. With such detailed results, different

users can take the optimal decisions in deﬁning the

structure and the architecture of a solar plant in an ur-

ban context, spanning all scales starting from single

building up to block, district and city.

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