Spatio-temporal Modeling for Renewable Distributed Energy

Generation Planning at the Municipal Scale

Luis Ramirez Camargo

Applied Energy Research group, Technologie Campus Freyung, Deggendorf Institute of Technology,

Grafenauer Straße 22, 94078, Freyung, Germany

Institute of Spatial Planning and Rural Development, University of Natural Resources and Life Sciences, Vienna, Austria

1 RESEARCH PROBLEM

An energy matrix based mainly on renewable sources

will significantly reduce the dependency on fossil

fuels and will contribute to reducing the amount of

concentrated in the atmosphere. In the case of

the European Union (EU), goals are set at 20% share

of the renewable energy sources (RES) in the gross

final energy consumption, and a reduction of 20% of

greenhouse gas emissions in comparison to the levels

of 1990 by 2020 (CEC, 2007). Furthermore, long

term aspirations include a decrease in greenhouse gas

emissions of at least 80%, which would imply a share

of RES of 75% and 97% by 2050 in the gross final

energy consumption and in electricity consumption

respectively (EC, 2011). These objectives can only be

achieved if there is a transformation from the current

centralized energy generation paradigm towards

distributed generation (Borbely and Kreider, 2001;

Lopes et al., 2007; Asmus, 2010).

Adopting distributed energy generation as a new

paradigm entails the challenge of finding technical

solutions that should ensure security of supply at

minimum economic and ecologic cost and be

acceptable for the local population (Wolsink, 2012).

One of these necessary technical developments is the

virtual power plant (VPP). In the European context, a

VPP refers to aggregating renewable-based energy

generation plants to supply certain desired demand in

a reliable way (Asmus, 2010).

Planning a VPP is, however, not a simple task. On

the one hand, the stochastic availability of RES such

as solar radiation and wind has to be compensated

with the spatial distribution of individual

technologies, the combination of different types of

RES and the strategic installation of backup and

storage systems. On the other hand, the on-site

production and the strong relation with use of space

implies that small administrative units such as

districts and municipalities play an active role in

shaping the energy system (Burgess et al., 2012;

Mendes et al., 2011).

Tools that properly consider the spatio-temporal

complexity of the problem are necessary to support

the planning process of VPPs. Geographic

information systems (GIS) have been largely used to

determinate the spatially explicit potential of RES

(Calvert et al., 2013; Angelis-Dimakis et al., 2011).

GIS-based procedures allow to stablish favorable

sites for RES exploitation by superposing several

technical, economic, environmental and/or regulatory

constrains (Biberacher et al., 2008a). Nevertheless,

most of the available GIS-based tools and procedures

for RES potential estimation neglect temporal

fluctuations of the resources. In the same way,

simulation and optimization tools conceived to deal

with the temporal variability of RES fail to consider

the RES spatial interdependencies and are not

appropriate for detailed modeling of entire

municipalities. A tool suitable for VPP planning at the

municipal scale is still absent.

2 OUTLINE OF OBJECTIVES

The purpose of this study is to develop a method that

combines GIS-based RES potential estimation

procedures with models for the simulation of energy

generation and consumption profiles in a high

temporal resolution. This new method should be able

to overcome the deficiencies of previous approaches

and serve for planning of VPPs at municipal scale

from a technical point of view. Furthermore, the

intention is to include a multicarrier approach in

which the VPP not only supplies enough electricity

but also enough energy for heating and water heating

for local energy demand. This consideration of

multiple energy carriers will contribute to conceive

highly efficient distributed energy generation

systems.

In order to achieve these objectives the following

questions have to be answered:

Ramirez Camargo L..

Spatio-temporal Modeling for Renewable Distributed Energy Generation Planning at the Municipal Scale.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

 Which established spatial models and methods for

the estimation of RES potential and determination

of the energy demand are suitable to be extended

with high temporal resolution models?

 How is it possible to couple spatial and temporal

models for RES generation potential and energy

demand for entire municipalities?

 Which procedures serve to consider the temporal

uncertainties, fluctuations and differences

between energy demand and generation? How can

they answer questions regarding RES technology

combination and sizing for the conception of

distributed generation systems, such as

multicarrier VPPs at the municipal scale?

3 STATE OF THE ART

A wide range of tools, methods and models have been

developed in the last decades to support national,

regional and local authorities and private individuals

in the planning and development of RES-based

projects. These decision and planning support

systems include GIS, simulation, optimization,

accounting and impact calculation tools, as well as

software packages that integrate several of these

approaches (Mondal and Denich, 2010; Manfren et

al., 2011; Mendes et al., 2011; Angelis-Dimakis et al.,

2011; Adhikari et al., 2012).

GIS have been largely used to determine the

potential and favorable areas for the deployment of

RES (Calvert et al., 2013). As described by

Biberacher et al., (2008a) the selection process can be

divided in three main steps. It usually begins with the

determination of the maximum theoretically available

amount of a certain resource (solar radiation, wind,

water, biomass and etc.). The next step is the

utilization of technical criteria as e.g. land cover type,

efficiencies and accessibility in order to reduce the

usable area to one that can be exploited under the

actual technical possibilities. The final step is the

inclusion of economical, ecological and sustainability

criteria, which can vary strongly depending on the

RES and the region under study. The resulting areas

and locations are normally used for two different

tasks. On the one hand, they serve as basis for coarse

calculations of total available energy from a certain

source on a yearly or seasonal basis. On the other

hand, they are the starting point for individual

detailed assessments, which lead to the definition of

the optimal dimensions for the energy generation

plants.

There is also a long tradition in using computer-

aided simulations and optimization algorithms for

sizing hybrid renewable-based energy generation

systems. Procedures can be traced back more than a

decade ago for example in Ai et al., (2003) and Yang

et al., (2003) and have been in permanent

development (see e.g. Yang et al., 2007, 2008, 2009).

Some of them, as e.g. HOMER (Lambert et al., 2006)

have become very popular and powerful planning

tools for hybrid systems

However, the combination of GIS-based

procedures and simulations in a high temporal

resolution for modeling RES-based systems is a field

with much less contributions. The examples that can

be found normally present a coarse temporal and/or a

low spatial resolution and are unsuitable for detailed

planning of VPPs at the municipal scale (see e.g.

Zeyringer et al., 2012; Biberacher et al., 2008b;

Mittlböck et al., 2006; Niemi et al., 2012).

4 METHODOLOGY

An extensive literature review on distributed energy

generation including methodological approaches and

models for the determination of RES potentials,

spatially explicit demand estimation and sizing of

renewable-based installations is the basis for

answering the proposed questions.

Promising methodological approaches for

modeling every individual RES and type of energy

demand are then compared and tested for usability

and compatibility. The selected spatial models for

RES potential and demand estimation will be

extended in its temporal component. Moreover, this

development is complemented with an algorithm that

allows to determine the location and required size of

every single plant of the distributed energy generation

system to fulfill the local demand. Finally, several

case studies with data of municipalities in Germany

and Austria are conducted in order to test and

calibrate the method and its individual components.

5 EXPECTED OUTCOME

The outcome of the project includes: (1) methods for

modeling individual renewable-based energy

generation technologies in high temporal and spatial

resolutions. (2) Methods for spatio-temporal

simulation of energy demand for electricity and for

space conditioning. (3) An algorithm for locating and

sizing individual plants in a VPP within a

municipality. (4) A software tool that allows to repro-

SMARTGREENS2015-DoctoralConsortium

Figure 1: Underlying concept of the method for sizing virtual power plants at the municipal scale.

duce the method in a wide range of municipalities (5)

Case studies of at least two municipalities (one in

Germany and one in Austria) (6) a proof of the gains

in efficiency that can be achieved when considering

multi-carrier technologies in distributed energy

generation systems. (7) A road-map for the

exploitation of RES and a system design of a VPP to

cover the local demand for the case study

municipalities.

The method to be developed consist of a series of

subsequent stages (see Figure 1). First, energy

generation potential from RES and the demand from

households, commerce and industry are

georeferenced. Second, Yearly potential production

and demand are disaggregated in a temporal

resolution of at least hours. The resulting time series

of supply and demand are the input data for a decision

tree algorithm. Third, this algorithm selects which

plants from the total potential should be build and

which size they should have in order to fulfil

efficiently most part of the local demand. Fourth, the

resulting load and difference with the demand are

used to determine the Biomass requirements and the

sizes of storage systems and combined heat and

power plants to cover the total local energy demand.

6 STAGE OF THE RESEARCH

After the literature review, the focus has been set to

Spatio-temporalModelingforRenewableDistributedEnergyGenerationPlanningattheMunicipalScale

model the most promising but also most variable

RES, solar radiation and wind. A review of available

methodologies and a comparison of proprietary and

open source GIS-based tools for estimating solar

radiation potentials, as well as a proposal for using

them for the estimation of solar radiation in a high

spatio-temporal resolution is presented by Ramirez

Camargo et al., (2014). A first attempt to test this

procedure and the relevance of distributing

photovoltaic plants through a region to reduce the

fleet-output variability and fulfil a certain demand is

available in Ramirez Camargo and Zink (2014).

Ramirez Camargo et al., (2015) introduce a GIS-

based methodology to generate time series for every

potential roof-top photovoltaic plant within a

municipality under consideration of real sky

conditions, temperature variation and detailed

technical parameters of photovoltaic plants.

Furthermore, these time series are evaluated against

the local demand of residential buildings using a

decision tree algorithm. This algorithm configures

photovoltaic plants sets that are the best match to the

demand when pursuing a certain photovoltaic

penetration level. Additionally, a methodology for

sizing storage systems for the photovoltaic solution

sets is introduced. A thorough evaluation of the

resulting system configurations is performed using a

pool of thirteen indicators. The resulting system

configurations are equivalent to designing a VPP that

relies entirely on photovoltaics and storage systems.

Figure 2 presents an example of the resulting energy

balance time series when using the methodology

presented by Ramirez Camargo et al., (2015) for

sizing a system to cover 50% of the yearly total

demand of 438 residential buildings with

photovoltaic systems. The solid line is the difference

between the energy produced by the selected set of

PV installations and the demand. The point-dash line

is the state of charge of an optimally sized storage

system and the horizontal dashed line is the optimal

storage energy capacity.

Modeling solar thermal systems in high spatial

and temporal resolutions is also possible since the

main required parameter, solar radiation, is modeled

already for the photovoltaic potential calculation.

The wind model is still in development. The aim

is to couple spatial models based on ecologic and

regulatory parameters, with reanalysis wind data and

technical parameters for typical wind farms.

Biomass is considered as the ideal fuel for heat-

driven combined heat and power plants. Since

biomass is storable and transportable, there is no need

to develop a high temporal definition model for

estimating its availability. Standard GIS-based

procedures are adopted for performing this task.

Concerning energy demand for heating and water

heating, the methodology available in Ramirez

Camargo (2012) has been actualized. This

methodology now works with solar radiation

information, calculated in the same way as for the

photovoltaic potential, and with building data not

only from Germany (as in the original version) but

also from Austria. The core of this methodology is the

resistance capacitance building model defined in the

the EN ISO 13790:2008, which is adapted to operate

with limited input data which in turn can be gained

whit a GIS-based procedure. The results of the

methodology are time series of energy requirements

for space conditioning and water heating for every

building within a municipality. The software

application has been re-written to avoid the use of

proprietary software.

The estimation of electricity demand of every

building relies in standard load profiles with 15

minutes temporal resolution, and the total electricity

consumption per building is determined based on

population and building use data.

Figure 2: Time series for the selected system configuration when pursuing a photovoltaic penetration rate of 50% of the yearly

total demand.

SMARTGREENS2015-DoctoralConsortium

Finally, the algorithm to configure the virtual

power plant is partially completed. The first step, in

which the combination of energy generation plants

from fluctuating RES with the best match to the local

demand is selected, is to be perform analogously to

the decision algorithm presented by Ramirez

Camargo et al., (2015). The match of the demand is

not only determined with the amount of properly

supplied energy, but also with the excess energy

supplied to the local system. Plants contributing with

a high amount of properly delivered energy and a low

load of excess energy will be preferred. The sizing of

storage systems can also be performed, but the

algorithm for dimensioning the biomass heat-driven

combined heat and power plants as well as the

integration with the previous algorithms is still work

in progress.

The software developed with every part of the

methodology exists in a prototype version that

combines bash, python and a series of free and open

source software for geospatial applications. The aim

is to deliver a software tool that runs entirely on

python and the free and open source software for

geospatial applications, which should be available for

a wide range of users.

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