An Investigation Process for Hybrid Energy Grid Optimization

Tae-Gil Noh

, Daniel Schwabeneder

, S

ebastien Nicolas

, Maja Schwarz

, Anett Sch

ulke

and Hans Auer

NEC, Laboratories Europe, Heidelberg, Germany

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

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

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-

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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

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

)

and prices p = (p

, p

)

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 =

∑

y=1

(1 + r)

−y

∑

t=1

p(y, 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

, the output by q

out

and

the matrix, describing the coefﬁcient of performance,

COP

. Furthermore, q describes the energy pur-

chased from a supply company and the investment

cost of the heat pump are given by I

. Then the

optimization problem of this customer can be written

as:

min

∑

y=1

(1 + r)

−y

∑

t=1

p(y, t)

· q(y, t) − I

, (2)

s.t. q(y, t) + q

out

(y, t) = d(y, t) + q

(y, t), (3)

out

(y, t) = COP

· q

(y, t), (4)

q(y, t), q

out

(y, t), q

(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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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

was used as power grid simulation, Dymola

for district heat simulation, and FMI++

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

http://www.digsilent.de/

http://www.3ds.com/products-services/catia/products/

dymola

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

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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