An Investigation Process for Hybrid Energy Grid Optimization
Tae-Gil Noh
1
, Daniel Schwabeneder
2
, S
´
ebastien Nicolas
1
, Maja Schwarz
1
, Anett Sch
¨
ulke
1
and Hans Auer
2
1
NEC, Laboratories Europe, Heidelberg, Germany
2
Institute of Energy Systems and Electrical Drives, Vienna University of Technology, Vienna, Austria
Keywords:
Hybrid Energy Network, Economic Modeling, Cooperative Control Strategy.
Abstract:
A hybrid energy network is an energy system operated across different domains of energy grids, where en-
ergy can be transformed between energy carriers. It is regarded as one good solution for managing volatile
renewable energy sources in a better way. The paper introduces an investigation process that is designed to de-
velop and evaluate co-operative hybrid energy network control strategies. The proposed investigation process
consists of two-step holistic investigation with simulation-based and economic-model based analysis. The
process is designed to enable multi-aspect investigation on the range of flexibility provided by evolution of
hybrid energy grids.
1 INTRODUCTION
The electricity grid model is evolving from a hierar-
chic centralized architecture towards a decentralized
one. One of the main challenges in this course is
the lack of flexibility to integrate high penetrations
of volatile renewable energy sources in the existing
power grids. The challenge is about how a tempo-
rary energy surplus can be managed. It can be ei-
ther saved for the grid at low-generation times (e.g.
storage), or utilized more efficiently in a time- and
location-effective manner (e.g. local consumption, lo-
cal transformation). Hybrid energy networks can be
regarded as one of the opportunities to provide solu-
tions for managing this imbalance (Appelrath et al.,
2012) (Lehnhoff et al., 2013). A hybrid energy net-
work is an energy system operated across different
domains (such as gas, district heating, and electricity)
whereby energy can be transformed between energy
carriers: the energy can be consumed, stored, trans-
ported within a grid in its specific form or transformed
into other forms of energy between different grids for
different times and locations. The advantages of this
are including the increase in reliability, flexibility and
the synergy effect (Arnold, 2011).
The development of smart grids on each indepen-
dent energy grid has progressed with extensive re-
search for many years. One prominent next step for
the energy network evolution path will be the con-
nection and integration of different energy grids, and
realizing hybrid energy networks efficiently operated
through coupling points (e.g. Combined Heat and
Power (CHP) and power to gas plants) and cooper-
ative managements. The energy operators can take
advantage of the characteristics of each energy carrier
and exploit, for example, the possibility of transmit-
ting energy as electricity and storing energy as gas or
as warm water in accumulators.
Investigation of this hybrid evolution path is still
in its infancy. There has been some investigation of
individual components (Keirstead et al., 2012) (Derk-
sen et al., 2012), or possible control strategies (Arnold
et al., 2009), but they generally lack the full investi-
gation on the impact of hybridization in real-world
environment. A thorough investigation, which in-
cludes major stakeholders that are modeled from a
real-world city, will open more convincing and ex-
citing new possibilities for the energy network evolu-
tion.
The paper introduces an investigation process that
is designed to develop and evaluate co-operative, co-
existing hybrid grid control strategies. The process
starts with a set of hybridization setups observed from
two European cities. The setups represent hybridiza-
tion chances that are identified from the target sites. A
concrete hybridization scenario can be identified from
the setups, by adding specific technological and eco-
nomic goals of the stakeholders. Each identified sce-
nario is then implemented and investigated via two-
step investigation process based on co-simulation and
Noh, T-G., Schwabeneder, D., Nicolas, S., Schwarz, M., Schuelke, A. and Auer, H.
An Investigation Process for Hybr id Energy Grid Optimization.
In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2016), pages 263-270
ISBN: 978-989-758-184-7
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
263
economic model. The simulation investigation fo-
cuses on technological and operational impacts, while
the economic model examines social and economic
aspects in the long-term. The proposed process en-
ables researchers to investigate hybrid networks in de-
tail, with which one can provide concrete recommen-
dations for stakeholders in both technological (opera-
tional) and economic (strategic) aspects.
2 STATE OF THE ART
With the unbundling of the energy supply chain,
technological advancements of renewables, support
mechanisms to promote their installation, increasing
environmental awareness and more active participa-
tion of customers, the amount of distributed (small-
scale) generation plants and de-centralized feed-in of
electricity has considerably increased in recent years.
Thus, energy distribution system operators (DSO)
have to cope with bidirectional load flows in their net-
works and both, DSOs and energy supply companies,
have to deal with decreasing turnover. Furthermore,
fluctuating energy production from renewable energy
sources (RES) - from large-scale to household level -
is de-coupled from energy demand, which is causing
a lack of storage in the electricity network (Trebolle
et al., 2010).
In general, this issue can be partially tackled by
shedding renewables during hours of high produc-
tion, increasing transmission capacity of the electric-
ity network, installing additional capacities of energy
storages and/or increasing the flexibility of demand.
However, the challenge will probably not be solved
by one of these options alone and the storage and flex-
ibility potentials on the electricity domain are limited.
Considering other energy domains and networks
(gas, district heating) as well can significantly in-
crease the storage capacity and demand flexibility po-
tentials. Conversely, these domains can benefit from
a closer interaction with the electricity domain too.
Converting electric energy when production from re-
newables is high and electricity demand is low, e.g.,
can reduce the usage of fossil fuels for heat pro-
duction. Though these synergies are becoming in-
creasingly apparent, the full potential of cooperation
among different energy domains is not yet fully ex-
ploited. There are several reasons for this: For one
thing, different energy domains are, in fact, compet-
ing for the customers energy demand. Space heat-
ing, e.g., can be provided by district heating, or by a
gas or electricity network using e.g. heat pumps or
boilers. Thus, different market participants (DSOs,
supply companies) operating on different energy do-
mains are rather interested in maximizing their own
turnover and profit than in finding cooperative strate-
gies to increase total efficiency. Furthermore, there
are some structural and regulatory issues complicat-
ing a connection and cooperation between different
energy networks. It is possible, e.g., that using cheap
excess electricity from RES production for heating is
not economical compared to using other fuels due to
electricity network charges, even though an increased
electricity demand could support network operation.
The topic of hybrid energy grid is getting more
interests recently. Behavior of individual compo-
nents (Keirstead et al., 2012), optimizing local con-
trols (Bakken et al., 2006)(Arnold et al., 2009), or
infrastructural planning (Hinterberger and Kleimaier,
2013) have been investigated before. Compared to
previous work, the proposed process of this paper is
more holistic and aims to deliver the whole picture,
and focuses on the evolutionary path of the existing
energy networks. One special focus here is exploiting
the synergies among different energy domains and,
hence, increasing the flexibility of energy networks
and facilitating the integration of RES. The approach
emphasizes a multi-agent perspective by taking into
account the individual objectives of different market
participants and aiming to develop cooperative strate-
gies among competitors resulting in win-win situa-
tions. Moreover, this process aims to identify barriers
in todays regulations and go beyond current market
rules to examine possible future hybrid control strate-
gies and business models.
3 HYBRID-GRID
INVESTIGATION: SETUPS AND
REQUIREMENTS
3.1 Identifying Hybrid Setups from the
Two European Cities
The investigation process is first initialized by spot-
ting possible hybrid chances from two actual Euro-
pean sites. Our target sites are the city of Skellete
˚
a,
Sweden, and two districts of Ulm, Germany.
Skellefte
˚
a is a city in mid-northern Sweden, in a
subarctic climate region. Population of the city is over
32,000, and served by district heating (DH) grid with
around 4200 heating substations. For a typical year,
the DH grid provides about 343,000 MWh of heat to
the city. Base heat load is being served by a CHP,
which uses bio-mass fuel to generate heat and power.
Districts of Einsingen and Hittistetten are located in
the suburb of Ulm, Germany. They are small residen-
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
264
Table 1: Three Hybrid Setups from the Two Cities.
Name Involved Stakeholders Hybrid Means Target Site
Co-operative suppli-
ers
Energy providers, DSO Co-operative District &
Power grids
Skellefte
˚
a
Prosumer community Consumers with RES, and
DSO
Consumers with RES, and
DSO
Ulm
Interacting providers
& consumers
All of the above All of the above with higher
ICT connectivity
Both
tial districts with some commercial and public spaces,
and have registered population of 400 and 300 respec-
tively. Both are characterized by relatively high-level
of PV penetration: 21 panels and 233kWp in Einsin-
gen, 58 panels and 1.16MWp in Hittistetten. In addi-
tion to power grid, they have gas-grids that serve as
the most common means of heating.
With close support from the energy providers and
the DSOs in Skellefte
˚
a and Ulm, we have identified
three hybrid control setups, which form the starting
point of hybrid-investigations. Table 1 summarizes
the three setups. Co-operative suppliers is a hybrid
setup that focuses on the supplier side hybridization.
Here, the participants are energy providers and DSOs
of power and DH grids. The tools for hybridization
are devices that connect DH and electricity grids on
supplier side, such as CHP (as generation to both
grids), and e-boilers (convert electricity to heat grid).
In this setup, the synergy comes from operating both
power and heat supply in co-operation. On the con-
trast, the second setup Prosumer Community focuses
on consumer side. The participants of this setup are
consumers and DSO, where the consumers have RES.
The hybrid nature of this setup comes from the con-
sumers side, where each consumer is connected to
multiple grids with various devices. The hybrid syn-
ergy can be realized by co-operative control of various
house hold devices (such as domestic hot water, space
heating), in connection to their RES and surplus en-
ergy. The final setup, Interacting providers and con-
sumers, targets both Ulm and Skellefte
˚
a. This setup
aims at a more distant future situation on both sites,
such as introducing new grids or new business mod-
els. It assumes tighter level of ICT connections of
both sites, which will enable far higher level of in-
teractions between consumers, providers and the de-
vices, both in resolution and data amount.
Each hybrid setup represents a class of hybridiza-
tion potential applicable to typical European cities,
where it is assumed that the target sites are instances
of such a co-operative hybridization setup. The idea
is to identify and investigate hybrid scenarios that can
be repeated at other European cities. Note that each
setup only provides a general direction, which is still
without specific tools or goals. To form an actual in-
vestigation question, control goals and hybrid-means
should be added (Section 6).
3.2 Requirements for Hybrid-grid
Investigation
Before designing actual investigation process, the re-
quirements of the two target sites had been surveyed
first. It is not possible to list all of them due to space,
but the identified requirements can be summarized
into the following three groups.
Impact of Co-operative Control on Technological
Aspects. The investigation must enable us the ob-
servation of impacts of varying degrees of hybridiza-
tion and different co-operative controls. For exam-
ple, usual evaluation metrics on power grids and heat
grids, such as voltage quality of LV-grid or heat-losses
of DH-grid, should be measurable and comparable
with and without co-operative hybrid strategies.
Impact of Co-operative Control on Social-
economic Aspects. The investigation process
should be able to clarify and show social and eco-
nomic impacts of the co-operative control. This not
only includes basic cost analysis, but also has to pro-
cess more sophisticated issues such as guaranteeing
mutual benefits (Pareto-criteria), regulatory aspects,
and conditions for new business models.
Impact of Data on Co-operative Control. Syn-
ergy of co-operative control strategy depends a lot
on data. This includes prediction (price, demand)
data, meteorological data, and finally sensor data and
their supporting ICT infrastructures. Impact of vari-
ous data accuracy and resolution levels on the perfor-
mance of co-operative hybrid energy grid should be
explored.
The identified requirements affect the investiga-
tion in two folds: first, they provide basis for the eval-
uation metrics. Second, they directly and indirectly
affect the design of the investigation process.
An Investigation Process for Hybrid Energy Grid Optimization
265
4 ECONOMIC MODELING FOR
HYBRID GRIDS
Before novel cooperative control strategies can be im-
plemented in real life, their economic feasibility has
to be validated for each of the involved stakeholders.
This means that the long-term monetary benefits for
the actors participating in new control strategies need
to be verified. Thus, it is important to first get a ba-
sic understanding of the general structure of hybrid
energy retail markets.
4.1 Market Structure
The most important market participants in energy re-
tail markets are customers (pro-/consumers), supply
companies, and DSOs. Their major interactions, roles
and typical objectives are illustrated in Fig 1. Starting
from the right-hand side, the customers try to mini-
mize their cost for meeting their demand for energy
services, like e.g. lighting, heating, cooling etc. De-
pending on their available technology portfolio they
can satisfy parts of their demand with self-generation,
and the remaining residual load has to be purchased
from a supply company via distribution networks.
Supply companies retail energy in form of electric-
ity, gas or heat to their customers and try to maxi-
mize their profit. They can procure this energy ei-
ther by operating generation plants, by buying en-
ergy from wholesale markets or with long-term con-
tracts (e.g. long-term gas delivery contracts). Of
course they can also increase their profit by selling
energy on wholesale markets. DSOs are providing
the necessary infrastructure for energy delivery. They
are responsible for (re-)investments in their distribu-
tion networks and for the maintenance of the net-
work components. Network operation is character-
ized by economies of scale, subadditivity of cost and
sunk cost, which makes it a natural monopoly (Auer,
2011). Thus, DSOs have to be regulated by a pub-
lic authority in order to ensure economic efficiency,
security of supply and non-discriminatory third-party
access to the grids.
4.2 Methodology
In most cases, novel cooperative control strategies re-
quire clearly defined business models. Here it has to
be specified which market participants are involved
and how their role and responsibilities in these new
business models are allocated. It has to be described
which technology portfolio is considered in the busi-
ness model and which technologies are controlled by
the control strategy in which way. Furthermore, it is
Figure 1: Simplified Illustration of the Structure of Energy
Retail Market.
important to clearly define the ownership of the tech-
nology portfolio and to decide who operates the con-
troller. In addition, if the controller reacts on price
signals, the tariff design has to be specified.
Once the control strategy and the correspond-
ing business model are clearly defined, an economic
trade-off analysis has to be conducted. In order to ful-
fill the cooperative concept, a crucial condition here
is a Pareto-criterion. This requires that no market
participant has higher cost (or lower profit) with the
new business model than with the status quo. Thus,
the formal framework for economic modeling con-
sists of individual optimization problems for all in-
volved market participants.
4.3 Economic Models
Since different hybrid control strategies and busi-
ness models for different market participants are an-
alyzed, the economic models have to be capable of
considering various technology portfolios, several tar-
iff designs and multiple energy domains. To incor-
porate the hybrid point-of-view, all quantities q =
(q
E
, q
G
, q
H
)
T
and prices p = (p
E
, p
G
, p
H
)
T
are writ-
ten as three-dimensional vectors, with each compo-
nent representing one energy domain: electricity, gas
and heat. Depending on the research question, control
strategy and business model, the optimization prob-
lems have different time periods, time resolutions and
are either formulated as Linear Programmings (LPs)
or Mixed Integer Linear Programmings (MILPs), if
investment decisions are considered. In the follow-
ing, the considered time period in years is written as
N, the number of time steps per year is denoted by n
and r is the personal interest rate of each market par-
ticipant.
4.3.1 Customers
To minimize their cost for meeting their energy de-
mand, customers generally have several possibilities.
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
266
They can either procure all the energy from a supply
company via the energy distribution networks at a cer-
tain tariff or they can invest in different technologies
for energy self-generation, conversion and storing. If
the demand of a standard passive customer is denoted
by d, and the tariff by p, then the cost is given by
C =
N
y=1
(1 + r)
y
·
n
t=1
p(y, t)
T
· d(y, t) (1)
If a prosumer is considered, this cost function can
be gradually extended to an optimization model by
adding new terms and constraints, describing addi-
tional technologies. Consider, e.g., a customer with
a heat pump and let the energy input vector of the
heat pump be denoted by q
HP
in
, the output by q
HP
out
and
the matrix, describing the coefficient of performance,
COP
HP
. Furthermore, q describes the energy pur-
chased from a supply company and the investment
cost of the heat pump are given by I
HP
. Then the
optimization problem of this customer can be written
as:
min
N
y=1
(1 + r)
y
·
n
t=1
p(y, t)
T
· q(y, t) I
HP
, (2)
s.t. q(y, t) + q
HP
out
(y, t) = d(y, t) + q
HP
in
(y, t), (3)
q
HP
out
(y, t) = COP
HP
· q
HP
in
(y, t), (4)
q(y, t), q
HP
out
(y, t), q
HP
in
(y, t) 0. (5)
In a similar way other energy conversion technologies
as well as energy generation and storage systems can
be added to the model.
The customer tariff consists of three parts, namely,
(i) the energy tariff, which is paid to the supply com-
pany, (ii) network charges, which are paid to the dis-
tribution system operator, and (iii) fees and taxes.
Each of these parts, in general, can have several com-
ponents: (i) a one-time initial payment or connection
cost, (ii) an annual lump sum, (iii) a quantitative com-
ponent, which can be flat, time-of-use (TOU) or real-
time-pricing (RTP), and (iv) a peak-load-pricing com-
ponent; in order to incorporate these different tariff
types in the models, various objective terms and con-
straints have to be added, which will not be further
elaborated here.
4.3.2 Supply Companies
The supply company model has a very similar struc-
ture to the customer model. The objective is given
by the firms profit. The revenue is determined by the
quantities, retailed to the customers at a certain tariff
and possible sales on wholesale markets. The total
cost consists of the investment cost and operational
cost for generation plants, coupling technologies and
energy storage devices. Additionally, if the supply
company is buying energy from wholesale markets
or via long-term contracts, these expenses have to be
considered. If the purchased energy is transmitted via
a network and if it is used by a device at the sup-
ply companys site, e.g. a gas-fired CHP , network
charges have to be paid as well. Otherwise, the net-
work charges are applied to the customers, consuming
the energy.
Furthermore, supply companies have to predict
their generation and their customers demand and re-
port the respective schedules to a clearing and settle-
ment agency. If balancing energy is required to up-
hold smooth network operation, they have to pay a
share of the resulting cost ex post, based on their de-
viations from the announced schedule.
Though the models for customers and supply
companies presented here are strictly cost-optimizing,
they are adapted to match the operation mode of the
respective control strategy for business model evalua-
tion.
4.3.3 Distribution System Operators
It has already been mentioned that DSOs are reg-
ulated. Within the regulatory constraints, however,
they try to maximize their profit. Their revenue is
given by the network charges of their customers, also
including supply companies, and their cost consist of
investment cost and operational (maintenance) cost
for the network components. The DSO model is
formulated as a mixed-integer investment-planning
problem, where the annual decision options comprise
reinvesting, repairing or doing nothing per compo-
nent. The realized choices affect the expected failure
rate, the cost and the total asset value, which could be
subject to regulatory constraints.
Different regulatory frameworks provide incen-
tives for different investment and maintenance strate-
gies. Price- or revenue-cap regulations limit
the annual revenue and typically facilitate under-
investments. Cost-of-service or rate-of-return reg-
ulations limit the profit by a percentage of the in-
vestments and, thus, rather promote over-investments
(Auer, 2011).
5 CO-OPERATIVE CONTROL
MODEL AS OPTIMIZATION
OVER PLANNING HORIZON
The scope of hybrid grid control strategy includes ma-
jor elements of the participating grids. It not only
An Investigation Process for Hybrid Energy Grid Optimization
267
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Figure 2: Conceptual Data Flow of Control Model.
includes grid coupling devices such as CHP or heat-
pump, but also traditional elements such as fuel-based
boilers or fuel-based generators. The control also op-
timizes market side decisions (e.g. when to generate
electricity and sell for CHP), and provides signals for
consumer side (e.g. when to store PV surplus). For
the investigation, the control strategy is implemented
as one central module that can observe and signal all
participating elements.
This central module observes all relevant informa-
tion over the hybrid grid, and plans best co-operative,
co-beneficial decisions over the planning horizon. An
essential role of the control is to optimize multiple
grids together to gain benefits that were not realized
before in isolated grids.
Fig. 2 shows the conceptual data flow. The flow
starts with the data aggregator. It collects data over
various data sources such as sensors, market prices,
and demand prediction data. The aggregator provides
a coherent view over the grids. The data are then fed
into the control optimizer. The optimizer processes
the data to derive best control decisions. The opti-
mizer has two pre-set inputs. One input is pre-defined
models of the hybrid-grid elements and the grid struc-
ture (control setup), and the other is the target for op-
timization (control target). The two inputs formulate
constraints and objectives for mathematical optimiza-
tion, and present the control problem as an optimiza-
tion problem. To resolve this optimization problem,
the control module employs multiple solvers (mathe-
matical optimizers) to search and optimize. This in-
cludes various mathematical programming methods
(linear programming, quadratic programming) as well
as heuristic optimization methods (such as genetic al-
gorithm). Once the solvers resolve the problem, the
optimizer outputs the best control values for the plan-
ning horizon. The next step is dispatching of the con-
trol values. The dispatcher sends currently needed
control values to actuators, and stores planned values
to ease and reduce future problem spaces. The op-
timization process is repeated, as data from sensors
and prediction are updated. For the investigation, the
control is initially implemented to repeat planning for
each 15 minutes. The planning horizon is currently
fixed to 24 hours.
Control strategy modeling is similar to model-
ing of actors in economic model, in the sense that it
frames the problem with an optimization framework.
However, components in the control model are gen-
erally more detailed and complex (e.g. non-linear)
since the control deals with actual devices. Control’s
optimization behavior is thus heavily affected by tech-
nical constraints of grid elements, and the control’s
problem space is limited to short-term horizons.
6 HOLISTIC INVESTIGATION
ON THE IMPACT OF HYBRID
GRIDS AND CO-OPERATIVE
CONTROL STRATEGY
The investigation process as whole can be now ex-
plored, with the two introduced components of eco-
nomic and control models.
Defining Scenario. The investigation starts by de-
signing a specific hybrid evolution scenario. In this
paper, a scenario means a hybrid setup of Table 1 in-
stantiated into a concrete situation, bounded by spe-
cific control goals and hybrid devices. For exam-
ple, Co-operative suppliers setup of Skellefte
˚
a can
be turned into a scenario of “providing better peak-
boiler with hybrid strategy”, by adding electric boil-
ers in addition to existing CHP and oil peak boilers.
The control goal for the scenario can be: a) best cost
peak-heat supply by tapping into fluctuating electric-
ity price, and b) reduce total green-house gases. Fi-
nally, attaining the goals would require new control
strategies. This would form a concrete investigation
scenario that can be clearly evaluated and compared
with current state-of-the-art.
Scenario Implementation. Once a scenario is
given, the next step is Scenario Implementation. This
includes building the simulation models for the tar-
get site, implementing the control model that meets
the control goal, and the instantiation of the eco-
nomic model that can describe the major actors of
the scenario. If we follow the example of “better
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
268
peak-boiler” scenario, simulation models to be im-
plemented are district heat grid simulation and elec-
tricity grid simulation of the target site, where the
two simulations are to be run by a co-simulation
tool. A control model is implemented to control
the simulated components on the simulation; such as
CHP, added electric boilers, oil boilers, heat storage,
power grid switches and so on. The economic model
incorporates major contributors of the scenario, in-
cluding major devices (above mentioned CHP and
boiler behaviors, simplified), and stakeholders’ be-
havior (the expected long-term behavior of power and
heat providers).
Simulation-based Investigation. The next step is
Co-simulation-based investigation. This step is char-
acterized by many simulation runs for testing the
control strategies. Each competing control strategy
(including baseline) is being evaluated over the tar-
get grids in various relevant situations. Continuing
the example of peak-boiler scenario, a set of control
strategies will be evaluated across various demand
conditions (e.g. cold, not-so-cold winter), price con-
ditions (high/low price ratio of oil to electricity), var-
ious boiler sizes (adding single to multiple electricity
to heat devices), for many simulated winter months.
The investigation step systemically covers as many
combinations as possible to make fair comparisons.
Note that it is a co-simulation environment. In
a co-simulation, each simulation (e.g. heat grid and
power grid simulation) runs together and exchange
information at the same time, bounded by a co-
simulation tool. For our investigation, PowerFac-
tory
1
was used as power grid simulation, Dymola
2
for district heat simulation, and FMI++
3
as the co-
simulation driver. Detailed description of the co-
simulation environment is outside of the scope of this
paper. Interested readers are kindly asked to refer
to (Widl et al., 2015) for the environment we have
adopted.
Direct result of the simulation investigation is all
values that are observed and saved from a simulation
run. The observed values can be processed further
by technical evaluation measures, and can be com-
pared between different strategies and/or hybrid con-
figurations. This enables investigators to form con-
crete conclusions with supporting numbers and mea-
sures. In this paper, this conclusion out of simulation
investigation is called Operational recommendation.
It includes the modeled control strategy itself (the best
1
http://www.digsilent.de/
2
http://www.3ds.com/products-services/catia/products/
dymola
3
http://sourceforge.net/projects/fmipp/
control strategy among tested), and evaluated perfor-
mance differences and lessons learned from the sim-
ulation runs.
Economic-model-based Investigation. The last
step of the investigation is Economic-model-based in-
vestigation. The simulation-based model alone can-
not answer all important questions. Long-term effects
and indicators like the internal rate-of-return (IRR)
of investments need to be evaluated by an additional
economic model. This also includes competing in-
terests of stakeholders. The economic investigation
comprises an analysis of currently existing structural
barriers and the design of novel business models that
enable a distribution of benefits, where all stakehold-
ers can profit.
The economic model uses simulation results of the
previous step to calibrate various parameters within
the model. By doing this calibration, it can de-
scribe realistic long term effect of a hybrid grid strat-
egy (such as total energy saved by a specific con-
trol scheme, or the behavior of a specific hybrid ele-
ments in different conditions) without explicitly mod-
eling all technological details of the simulation. On
the other hand, the economic model explicitly mod-
els and provides all major economic values and their
stakeholders’ interest, which are absent in the simu-
lation. The values observable in economic model are
processed further by social and economic evaluation
measures, which can compare the proposed scenario
with baselines. This enables the investigators to draw
concrete conclusions for each proposed hybrid sce-
nario. This conclusion, supported by projected values
and measures of economic model, is called Strate-
gic Recommendation. This provides the best per-
ceived way of investments for the given scenario, and
their expected return, and possible (or needed) price-
scheme and business models.
7 OUTLOOK AND CONCLUSION
Currently there are two investigations on-going with
the proposed process. One scenario is about provid-
ing better peak heating for the DH grid of Skellefte
˚
a,
in a manner both economically and environmentally
beneficial (“better peak boiler” example of Section 6).
For the operational recommendation, it is expected to
show the optimal size and type of the added boilers,
the best co-operative control strategy over two grids,
and cost and green house gas footprints of compet-
ing control strategies. For the strategic recommenda-
tion, the economic model will provide 20-years view
An Investigation Process for Hybrid Energy Grid Optimization
269
of cost and investment analysis, with respect to vari-
ous possible future fuel / electricity price changes.
For Ulm site, the first investigation is about “stor-
ing surplus PV as heat in households”, where the
control goals are to maximize local PV consump-
tion, to minimize loads over critical grid elements,
and to maximize subscribers’ benefits on using sur-
plus power. Operational analysis will provide im-
pacts of various heating devices (e-boiler, heat pump)
and control strategies, with their impacts on storing
PV surplus power. Economic analysis will present
on what condition the strategies will make sense
(such as adoption of new feed-in tariff), and com-
parison to alternatives such as network reinforcement.
There are also plans for more advanced hybrid inves-
tigations: such as investigating ideal heat/power co-
supply in Skellefte
˚
a for the expected 20% more pop-
ulation in 2030, how to take benefit of hybridization
with demand side management, and investigation of
new business chances in Ulm site with a new hybrid
electricity-DH grid, etc.
The paper proposed two-step investigation pro-
cess that enables concrete and thorough checking of
hybrid energy grid scenarios. The competitive edge
of the proposed process comes from the approach’s
ability to check and sample details on both opera-
tional (short-term, technical) and strategic (long-term,
economic and social) aspects of the given setup. It
provides a set of holistic recommendations for stake-
holders of the investigated grids. It is our belief that
the holistic recommendations will provide valuable
insights for the possible future energy grid evolution.
ACKNOWLEDGMENT
We gratefully acknowledge funding and support by
the European Commission (project OrPHEuS (FP7
ICT-608930)). The sole responsibility for the content
of this publication lies with the authors. The Euro-
pean Commission is not responsible for any use that
may be made of the information contained therein.
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