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10. Internationale Energiewirtschaftstagung an der TU Wien IEWT 2017

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Legal framework and economic feasibility of neighborhood energy storage systems

Fabian Scheller(1)1, Mario Götz(2), Balthasar Burgenmeister(3), Stephan Seim(1),

Rosa Haberland(1), David Georg Reichelt(4), Hendrik Kondziella(2), Stefan

Kühne(5), Thomas Bruckner(1,2)

(1)Institute for Infrastructure and Resources Management (IIRM), University Leipzig (2)Fraunhofer Center for International Management and Knowledge Economy (IMW), Leipzig

(3)Industrielle Werke Basel (IWB), Basel (4)Institute for Applied Informatics (InfAI), Leipzig

(5) University Computing Center, University Leipzig

Abstract:

Battery technologies for electricity storage applications have been available on the market for

several years. Although they are often regarded as decisive for the integration of renewables,

batteries still lack business models for their widespread implementation at the distribution

grid level. According to different publicly funded projects, electricity storage systems on a

neighborhood scale might represent a sustainable and economic solution. In this sense, this

research paper aims to identify regulatory challenges as well as to assess economic

opportunities of neighborhood energy storage systems (NES-systems). The future of the

legal framework and regulations for battery storages remains uncertain. As of now, the legal

definition of a NES-system is still deficient and comprises additional financial burdens which

reflect neither their current role nor their potential in the future energy system. While the

NES-system reveals poor economic conditions for today the economic situation could

improve significantly over the years, especially by taking into account the requested legal

conditions as well as the current cost trends. Nonetheless, an economic success requires the

inclusion of the social, the technical and the environmental advantages of NES-systems.

Keywords: neighborhood energy storage system, municipal energy system optimization,

business models for renewable energy integration, legal and economic analysis

1. Innovative energy storage concepts

Battery technologies for electricity storage applications have been available on the market for

several years. Although they are often regarded as decisive for the integration of renewables,

batteries still lack business models for their widespread implementation at the distribution

grid level [1]. According to different publicly funded projects, electricity storage systems, on a

neighborhood scale, might represent a sustainable and economic solution. Such

neighborhood energy storage systems (NES-systems; also designated as Quartierspeicher)

are defined as “’facilities which are able to receive energy and then release it again […] in the

1 Jungautor, Universität Leipzig, Grimmaische Straße 12, 04109 Leipzig, [email protected]


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form of electricity […] at a later time’ [2]. Additionally, community electricity storages provide

services based on balancing strategies for an association of prosumers, renewable energy

producers and loads that are connected to the same distribution grid. At least one of the

following operation strategies has to be implemented: maximizing self-consumption for all

participants, increasing shareholder’s profits in electricity markets, or optimizing community

welfare. Optionally the operation strategy should be grid supportive and increase the grid’s

hosting capacity for decentral renewable generation.” [3].

Despite significant efforts of utilities to promote technological innovations like these, major

barriers still prevail: uncertainties in unpredictable policy frameworks, lacking profitability of

projects, insufficient budgets for innovation, and the possibility of expensive mistakes, to

name but a few [4] [5]. In view of those uncertainties, this research paper aims to assess

economic opportunities of NES-systems systems. Due to the diverse uncertainties, this

research follows an interdisciplinary research approach: first, a deep-reaching qualitative

examination of legal, social and technical opportunities and challenges from a societal

perspective; second, a scenario-based quantitative evaluation of costs and earnings from

actors' perspective. While the first part of the research question is answered by reviewing

and synthesizing literature, the second part of the research is carried out by applying

scenario-based modelling and mixed integer optimization. Although this applied research

focuses on the conditions of the German energy system, general statements are derived

where possible.

2. Neighborhood energy storage systems

NES-systems might represent a sustainable and economic business model. For a

comprehensive understanding, this chapter outlines the qualitative analysis of the business

model in order to lay the foundation for the subsequent model based analysis (chapter 3 and

chapter 4). Thereby, the structural design of NES-systems and the legal framework in which

they are embedded, as well as the economic, ecologic and social implications are discussed.

Next to that, this chapter finishes with an overview of current market applications in

Germany.

2.1 Model structure

An exemplary NES-system model structure is illustrated in figure 1. Similar to electrical

storage systems on the individual household level, the power bank stores excess energy

generated by decentralized energy-systems (DE-systems; e.g. photovoltaic systems (PV-

systems) or combined heat and power systems (CHP-systems)) of individual prosumers in a

central electrical storage to bridge the time gap of renewable electricity supply and energy

demand. Every participant is connected to the public grid in spatial proximity. The prosumer

can increase the account by charging the storage and decrease the account by self-

consumption at a later time. In this context, the main strategy of this investigation is to

maximize self-consumption for the individual participants.

Moreover, similar to a financial account, the NES-system operator can further benefit by

utilizing the account balance in terms of spot market trading or by offering balancing system

services in dependence of the respective account balances. This might increase the


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profitability of the storage technology as well as grid reliance and goes also in line with the

one of the formulated operation strategy.

NES-systemCust2

Custn

Cust1

Cust3

Bi-directional

flow of energy

P1P2

PnP3

Spotmarket and

balancing marketoptimization

potential

Energy

consumption andgeneration

Cust 1 Cust 2 Cust n

Balance [kWh] [kWh] [kWh]

Balance [€] [€] [€]

Energy

accountingsystem

NES-system

operation andoptimization

distributiongrid

Figure 1: Urban quarter sharing a neighborhood storage system (cust = customer)

2.2 Legal framework

A legal definition of NES-systems does not exist. Moreover, the legal definition of electrical

storage systems in general is deficient [2]. In accordance with the Federal Supreme Court

(BGH), electrical storage systems are legally treated as final consumers in Germany, since

electrical energy is converted into chemical energy and back. This classification has been

criticized by the Bundesverband Energiespeicher (BVES) for being neither convincing from a

scientific perspective nor reasonable from an energy policy standpoint [6].

Final consumers currently are subject to full fees and levies, which causes severe

consequences for “NES” business models. The operation of NES-systems comprises two

steps: the intermediate storage of electrical energy (Prosumer -> Grid -> Battery) and the

feed-in of stored electrical energy (Battery -> Grid -> Prosumer). Both steps involve the grid

and thus might be associated with grid fees and levies. While the German regulatory

framework for the storage of energy provides some exemptions from fees, it is fragmented

and the applicability for the (individual) NES case remains partly vague. The relevant

legislation for an NES in Germany is outlined below.

The EEG (Renewable Energy Sources Act) regulates the development of renewable energy

sources in Germany. The newest revision of the EEG 2017 still does not provide any

clarification for the legal status of electrical storage systems. However, article § 61k

terminates the double burden of the EEG surcharge, which originally incurred both when the

battery is charged and discharged. According to the BVES [7], this also holds true for mixed

use storage systems, i.e. where the stored electricity is partly transferred to the grid and


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partly self-consumed by producers. Still, the EEG surcharge has to be paid for all electricity

that is taken out of storage, with no exemption for self-consumption.

The EnWG (Energiewirtschaftsgesetz) includes the regulation concerning the grid fees and

further fees, namely the offshore liability levy, the concession fee, the cogeneration levy, the

§ 19 StromNEV levy and the levy for deferrable loads. § 118 EnWG exempts newly installed

electrical storage systems from the grid fees for 20 years. With respect to the other fees,

there exist different perspectives. According to the Bundesnetzagentur, an exemption from

the grid fees through § 118 does not include the exemption from all other fees, which incur

regardless [8]. The BVES on the other hand refers to § 9 Abs. 7 KWKG (Kraft-Wärme-

Kopplungsgesetz) to argue, that the cogeneration levy is considered an annex to the grid fee

and can thus only incur when grid fees incur. The remaining fees should be treated

accordingly [9].

The electricity tax is regulated in StromStG and incurs every time electricity is consumed.

Pumped-storage power plants are explicitly exempted from this, batteries or other types of

energy storage systems however are not mentioned. Furthermore, § 9 StromStG provides an

exemption for the case of spatial proximity between the generation and the storage of

electricity. However, as there is no clear definition of spatial proximity provided the

applicability of the concept for a given NES remains uncertain [3].

Table 1: Composition of electricity costs for different NES-system scenarios

Cost components

Grid-usage (reference) [Ct/kWh]

Household storage [Ct/kWh]

NES-system (favourable) [Ct/kWh]

NES-system (unfavour.) [Ct/kWh]

NES-system (requested) [Ct/kWh]

EEG surcharge

6,170 0 6,170 6,170 0

Grid fees 6,760 0 6,760 6,760 6,76 x 0,2=

1,352

Cogeneration levy

0,254 0 0,254 0,254 x 2=

0,508 0,254

Offshore liability levy

-0,051 0 -0,051 -0,051 x 2=

-0,102 -0,051

Concession fee 1,660 0 1,660 1,66 x 2=

3,32 1,660

§19StromNEV levy

0,237 0 0,237 0,237 x 2=

0,474 0,237

Levy for deferrable loads

0,006 0 0,006 0,006 x 2=

0,012 0,006

Electricity tax 2,050 0 2,050 2,05 x 2=

4,100 0

Generation costs

7,120 -* -* -* -*

VAT 4,600 0* 3,246** 4,036** 0,657**

Total costs 28,806 0 20,330 25,280 4,120

* Generation costs depend on the decentralized energy system; ** Value-added-tax of 19% imposed

without generation costs

Table 1 shows the total costs of electricity and their composition for five cases: electricity

from the grid; PV electricity self-consumption with a household storage; electricity from a

NES for the case that the current legislation is interpreted favourably and unfavourably; and

for electricity from an NES for the case that legislation is adapted according to the requests

of the BVES. The BVES argues, that self-consumption of NES stored PV electricity should

be exempt from both EEG surcharge and electricity tax, in order not to put the community


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solution worse off than the private solution, i.e. the household storage. Modern measuring

technology allows us to determine in which cases self-consumption occurs. Furthermore, the

exemption from the electricity tax should be possible even though the prosumer and the NES

operator are not the same person. Additionally, the BVES claims NES are eligible for

reduced grid fees when they are operated in a way that relieves the distribution net in

question. Indeed, § 19 Abs. 2 S. 1 StromNEV (Stromnetzentgeltverordnung) regulates grid

fee reductions for so-called atypical consumers, the maximum reduction being 80% [9].

From the comparison of the electricity costs shown in table 1 and figure 2 it becomes

apparent, that incurring fees and levies can make the business model NES prohibitively

expensive. Summing up, the existent regulatory framework for NES-systems is fragmented

and not consistent, and possibly distorts competition, both between electricity storage

systems and other flexibility options, and within the field of different storage solutions [10].

Hence, the regulatory framework is considered the most crucial influencing factor for the

future development of NES solutions [11].

Figure 2: Cost components for NES-system scenarios

In contrast to utilization of the public grid, the establishment of a local microgrid could also be

feasible. All grid-dependent fees could be avoided for electricity stored in the power bank.

However, due to specific legal requirements and significant extra costs, this solution is not

fostered here.

2.3 Optimal utilization

The optimal capacity of an NES that aims to maximize the self-consumption rates (SC) of PV

electricity for households is smaller than the aggregated capacities of all individual household

storage systems it replaces. This is because load profiles vary between households. A

reduction of capacity translates into lower specific investment costs and lower material use.

So far, there exist limited insights about the optimal dimensioning of an NES and the capacity

reduction that can be achieved.


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A study by German network solbat showed a capacity reduction potential of 25% for LI-

batteries and of 15% for Vanadium-Redox-Flow batteries, which have a lower efficiency. The

starting point of the study design was the assumption that the self-consumption rate of the

home storage case should be achieved with the NES-system (which was about 72% [12]).

Furthermore, it could be shown that when the load profile of a household changes (a young

couple turns into a family, a family turns into a retired couple, …), it leads to a non-optimal

dimensioning of the individual storage solution and the SC might drop during the lifetime of

the storage. In the NES, individual changes of load profiles balance each other out, and the

SC rate remained fairly stable in the changed scenario.

In practice, these considerations are sometimes neglected altogether. For example, the

NES-system pilot in Epplas by IBC solar has been dimensioned for a power input of 70 kW

and a capacity of 330 kWh to store peaks above a certain threshold, based on the

assumption that all participating PV systems simultaneously feed in with maximum power,

whilst no consumption takes place [13].

While the direct comparison of household storage systems and NES-systems is of interest, it

should be highlighted that an NES-system is not limited in its function to the maximization of

PV SC rates in households. The optimal dimensioning of an NES-system turns out to be

more complex when it includes more and diverse user groups (commercial customers,

households without own PV systems), when a complementary mixture of PV- and CHP-

systems, which possesses further beneficial characteristics for the system, is applied [9] or

when participation in the reserve control or spot market is pursued. This is reflected in the

wide range of current market applications for NES-systems (cf. table 2 in chapter 2.7).

Consequently, the optimal dimensioning of the capacity and power of a NES-system has

been rated the most pressing research issue by participants of the 1st international

Community Electricity Workshop at the HTW Berlin in February 2016 [11].

2.4 Economic foundation

Assessing the economic foundation of NES-systems, the electricity costs arising from the

legal framework compared to the reference cases of grid electricity and household storage

systems (sf. chapter 2.2), additional revenue streams that can be achieved and costs that

can be avoided with an NES need to be considered.

With respect to the legal framework, it has already been demonstrated that the incurring fees

and levies have substantial influence on the NES business model. For the case that the

current legislation is interpreted unfavourably, NES is clearly not competitive (cf. table 1).

The favourable NES case requires a more differentiated look. While the same fees occur as

for grid electricity, which would leave a margin in the amount of the generation costs, the

feed-in remuneration for renewable energy needs to be taken into account as well. For PV

systems below 10 kWp it is currently 12,31 Ct/kWh. Consequently, the prosumer would be

better off feeding the excess electricity into the grid for the remuneration and buying grid

electricity for consumption at a later point than using the NES option. The NES (requested)

case is currently not applicable but demonstrates how the economic performance of NES

could change in case the legal framework is altered in the future.


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In view of the technology costs (investment costs, O&M and replacement costs), NES

provide economic advantages over household storages due to scale effects. This is

especially true when household storages are virtually connected to participate in the control

reserve market, which is associated with higher expenditures for the necessary control

systems. Currently, household PV battery systems amount to average net system prices

(storage system including battery, without assembly) of approximately 2,300 €/kWh for

lithium-based systems and 1,300 €/kWh for lead-based systems with a significant trend to

decreasing prices, especially for lithium-based systems [14]. The costs of NES are estimated

to be significantly lower with approximately 800 €/kWh [15]. Experiences from the

“Strombank” model project show costs of 15 ct/kWh to cycle electricity in the NES [9]. Or in

other words, if the NES-system is scaled large enough (larger than 18 residential storages

according to the experiences of the Strombank model), one can even assume half price per

kWh compared to residential storage systems [16].

Next to avoided costs, additional revenue streams can increase the profitability of the NES-

system for the operator as well as for the prosumer. The stored energy but also the free

capacity can be used for trading on the spot market or to offer system services depending on

the state of charge. A survey with participants of the “Strombank” model project showed, that

a majority has no desire to actively control the marketing of their excess electricity, but rather

wants the operator to achieve moderate profits for them [17]. The attractive prices in the

primary balancing energy market in particular, however, might decrease over time for

multiple large scale battery operators offering these services [18].

In summary, the current economic viability of NES-systems is questionable. However, this

might change in the near future. In addition to the the influence of the regulatory framework,

trends that need to be observed include decreasing feed-in tariffs, decreasing technology

costs and high grid electricity prices. Furthermore, additional system benefits provided by

NES become more important with the progress of the energy transition although their

remuneration might decrease.

2.5 Ecologic expediency

Ecological advantages of an NES-system over household storage systems arise throughout

their life cycle. The materials for different battery technologies are associated with a

substantial cumulative energy demand (CED) and global warming potential (GWP). The

reduction potential of the overall capacity results in a lower material use and thus decreased

environmental impacts in the cradle-to-gate stage. A comparative life cycle assessment of

four stationary battery technologies found that the impacts attributed to losses in the use

phase of batteries are even higher [19]. NES solutions offer an increased harmonization

between supply and demand of electricity, thereby reducing these use phase losses as well

as their associated impacts.

2.6 Social anchorage

There are a number of social benefits potentially arising from the deployment of an NES. For

prosumers, the neighbourhood storage has a distinct potential to enhance the social

interaction in the community, establishing a regional identity of participants. While household

storages only benefit owners of PV systems and are normally applied to increase their self-


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consumption rate, NES schemes provide the opportunity to both integrate other consumers

and pursue further goals, for example participation in the reserve control market. Thereby,

they increase participation in the energy transition, which also fosters a higher level of

acceptance [3]. Furthermore, in contrast to household storages, the investment costs for the

NES are initially carried by the operator and not the prosumer. In this context, current

investigations show that around 75% of respondents clearly prefer the NES-system to

residential storage systems [16].

Beyond the individual perspective, the community as a whole can benefit by improving its

image in terms of environmental protection and innovativeness. This can be enhanced by

linking further innovative business models to the NES, for example eMobility loading stations.

Moreover, the energy management as well as the operation and maintenance of the asset

can contribute to regional value creation.

2.7 Market application

Table 2 briefly outlines particular projects with respect to NES-systems in Germany. In this

context, most of the projects still rely on public funds.

Table 2: Overview of selected NES-system projects in Germany

Provider Project Description

KACO new

energy

Model project

“Weinsberg”

- 150 kWh storage capacity (Li-ion)

- 23 households

- 145 kW PV and 11 kWel CHP

- meeting 97% of electrical energy demand

- heat pump 35 kWel, 1 central + 18 small thermal

storages

- start of operation: Nov. 2013

MVV Energie Model project

“Strombank”


- max. power input: 100 kW

- 14 households, 4 commercial customers

- PV and CHP

- start of operation: Dec. 2014

IBC Solar Model project

“Epplas”

- 330 kWh storage capacity (Li-ion) (660 kWh in

final phase)


- 13 households

- 287 kW PV

- start of operation: Apr. 2015

TUM /

Kraftwerke Haag

GmbH

Model project

EEBatt / TUM Energy

Neighbor



- 20 households

- 300 kW PV

- start of operation: Oct. 2015

ENTEGA AG Model project

“Solarsiedlung”


- max. power input: 250 kW (final phase)

- 24 households (82 in final phase)

- PV

- start of operation: Sep. 2016


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3. Model based analysis

To assess the present and the future economic potential of NES-systems, this research

paper deploys a scenario based analysis at neighborhood scale, considering different market

actors, electrical loads, storage systems and photovoltaic components as well as different

system relations and strategic initiatives. The evaluation period has been set to twenty years

(2015-2035). Potential future states of the energy system are investigated by applying a

green scenario (strong increase of renewables) and a fossil scenario (slow transition towards

renewables). The model parameters of the scenarios are based mainly on the following

developments: spot market prices and balancing power prices as well as the respective

consumer retail prices, EEG levies, feed-in tariffs, electrical load profiles, technology

specifications and environmental data. In this context, three optimization models have been

applied to receive answers with respect to different levels of aggregation as well as to

different levels of time horizon.

3.1 Optimization system

Major objective of this research paper is to assess present and future system behavior

regarding optimal NES-system design while optimizing the customer costs and the utility

profits. To determine optimal energy and finance flows according to the objectives, the

energy model IRPsim (Integrated Resource Planning and Simulation) is applied [20]. The

bottom-up techno-economic model with a municipal scope, which is implemented in

GAMS/CPLEX and embedded in a client-server-architecture, allows for solving mixed-integer

problems in a quarter-hourly resolution for perennial periods. In the context of this research,

the rolling optimization horizon comprises the quarter-hours of two days. While the

optimization results of the first day are stored, the results of the second day are neglected

but the second day displays the starting point of the new optimization horizon and so on.

Central purpose is the evaluation of innovative business models in a dynamic market

environment from different actors’ perspectives. In this context, the modular structure allows

the configuration and optimization of various energy systems in general and of NES-systems

in particular. The specific modules are described by actors, technologies, energy flows,

power measurements, dependencies, tariffs as well as the market and the environment.

The objective function of the optimization maximizes the total profit of individual actors’

variable financial flows, while the operational planning of system components is optimized by

a dispatch model. In the framework of this paper, the model works with an actor related two-

step optimization systematic. The model firstly optimizes from an aggregated customer

perspective, determining the residual energy demand and excess energy supply with all

components the customers have regulative access to. With respect to the first optimization

step (customer optimization), especially the tariff scheme as well as the variable costs of

decentral energy systems are decisive. In the subsequent step, the model optimizes all other

energy and financial flows from the utilities’ perspective, considering all residual energy

demand and supply. A regional energy deficit might be balanced by storage systems,

generation plants activities and by spot market trading. Excess energy is sold to the spot

market or stored to storage systems. Next to that, operating reserve like spinning reserve

and non-spinning reserve can be pooled and offered at the operating reserve market. With

respect to the second optimization step (organization optimization), especially the market


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prices as well as the variable costs of the central and decentral energy systems are decisive.

This two-step optimization process results in a multi-perspective optimization and attempts to

reflect realistic market conditions. From a conceptual point of view, this idea of a multi-level-

entity oriented optimization approach (which is similar to the two-step approach selected

here) has been already presented by authors in earlier studies [21] [22]. The approach also

goes in line with the applied operation strategy of an NES-system in this research:

self-consumption is maximized by electrical load coverage with respect to NES-

system participants and their tariff scheme,

charging and discharging behavior of NES-system participants is transferred to and is

considered by the NES-system operator

storage utilization is maximized by spot market trading and balancing system service

offerings with respect to NES-system operator.

The optimization model IRPsim determines the net present value (NPV) of payment series of

individual customer groups as well as business divisions, by the sum of variable and fixed

cash flows [20].

MICOES-Reserve LICOES-EuropeMICOES-Europe

Markets

Utility

Application

Control

Power: PR/SR/MR

Spot Market:

Day-ahead

Investments

& Capacity Remuneration

IRPsim

Sales GridTradeGeneration Services

Electricity Heat Gas

Power-

to-HeatDSM/DR

NES-

system

Direct

marketing…

Business Models

Time Horizonshort term long term

Ag

gre

gati

on

le

ve

l

…

Figure 3: Applied optimization model structure

However, a meaningful assessment with IRPsim is only possible with the help of robust input

data sets. In terms of business model assessment, especially the uncertain market

developments represent a crucial aspect. Required spot market and reserve market prices

have projected by applying the optimization model MICOES-Europe (Mixed Integer Cost

Optimization of Energy Systems) and the optimization model MICOES-Reserve, respectively.

While the fundamental optimization model MICOES-Europe uses marginal costs to

determine future spot market prices of selected European countries [23], the fundamental

optimization model MICOES-Reserve uses opportunity costs to calculate future control

power prices [24]. In this context, only the application and the combination of the three


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optimization models allows a fundamental analysis of the economic potential of business

models in general as well as of NES-systems in particular. An overview of the described

optimization model landscape is given in Figure 3.

3.2 Scenario foundation

Based on content of the World Energy Outlook of 2013 [25], two future scenarios are defined

for the model-based scenario analysis: a green (G) scenario and a fossil (F) scenario, which

relate back to the ‘450 scenario’ and the ‘current policy scenario’ of the above publication.

The fossil scenario, thus, depicts a baseline picture of how the global energy system would

evolve, if current trends in energy demand and supply remain unabated. The green scenario,

on the other hand, represents a global energy market scenario, which allows for significant

chance for successful climate change abatement with regard to staying below the global

warming target of a maximum global mean temperature change of two 2 °C (compared to the

pre-industrial level). It thereby depicts a much more transformative scenario with regard to a

de-carbonization of the energy system.

These two market scenarios set some of the boundary conditions for the NES-system

assessment, particularly defining global fuel and CO2-prices and in turn, affecting overall

energy prices. The quarter-hourly data sets are based on German spot market and spinning

and non-spinning power price projections [26]. Additional to the scenario dependent price

projections regarding 2025 and 2035, the initial year of 2015 uses historical data as a starting

point. An overview of selected market price characteristics of one of the fossil scenario is

given below. While figure 3 depicts an average week with respect to the spot market prices,

table 3 outlines symmetrical structure (SY), block bid length (BL) and the mean value (MV)

with respect to the positive (pos) and negative (neg) primary (PRL), secondary (SLR) and

tertiary (MRL) control power prices. The scenarios are, furthermore, completed by different

retail prices for end-consumers, technological learning factors as well as projected feed-in

tariff levels. In addition, various different electrical load profiles of residential buildings in

Germany with a temporal resolution of fifteen minutes have been used. With respect to the

calendar dependency and the consistency, respective data of all years have been matched

to the calendar days of 2010. The different scenarios as well as the different years show an

interest rate of 4 % and a value added tax of 19 %.


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0

10

20

30

40

50

60

70

80

90€/M

Wh

2015

2025

2035

Figure 3: Average week of the spot market prices for 2015, 2025 and 2035 of the fossil scenario [26]

Next to that, the customer base includes ten residential prosumers. The prosumers

demonstrate slightly differing electrical load profiles with respective valleys and peaks. The

load profiles are based on the temporal highly resolved residential profile set of HTW Berlin

[27] and have been integrated from one-minute time steps to fifteen-minute time steps. The

average energy consumption of a prosumer per year has been fixed to 3.800 kWh. Next to

that, every single prosumer is equipped with a similar PY-system and connected to the NES-

system. Every single household can consume or store energy as qualitatively described in

chapter 2.

Table 3: Spot- and reserve market price characteristics of the fossil scenario [26]

2015 2025 2035

SY

[-]

BL

[h]

MV

[€/MW p.h.]

SY

[-]

BL

[h]

MV

[€/MW p.h.]

SY

[-]

BL

[h]

MV

[€/MW p.h.]

PRL

pos YES 24 21,7703 NO 4 2,1256 NO 4 0,3059

PRL

neg YES 24 21,7703 NO 4 0,1184 NO 4 0,2014

SRL

pos NO 4 2,9274 NO 1 4,5984 NO 1 1,0639

SRL

neg NO 4 1,3316 NO 1 0,0141 NO 1 3,0E-05

TER

pos NO 4 0,5746 NO 1 0,1408 NO 1 0,4511

TER

neg NO 4 1,7865 NO 1 0,0107 NO 1 1,1E-06

With respect to the tariff scheme, a subdivision into flat and variable tariffs has been applied.

The grid electricity flat tariff in the 2015 scenario is given by historical data. For every time

step in 2015, the customer pays 28.81 Ct/kWh proportionally to sales side, grid side and

political side. To derive a variable tariff for 2015, the cost component ‘sales’ was


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deconstructed into the spot market price and the utility margin. Given the average spot

market price in 2015 of 3.16 Ct/kWh, the remaining 3.96 Ct/kWh of the ‘sales’ cost

component is assumed to be the utility margin. The variable tariff thus consists of the spot

market price of this point in time and the fixed margin of 3.96 Ct/kWh. The flat and variable

tariff for the projected green and fossil scenarios has been determined in the same way.

While the ‘grid’ and ‘political’ components have been kept constant, the ‘sales’ component

has been derived with the projected spot market prices. This is largely consistent with BMWi

[28] and in the range of projections by Öko-Institut [29].

The feed-in remuneration is fixed for 20 years, while the time of initial operation determines

the respective level of feed-in remuneration. A growing customer base results in new

customers starting their initial operation at different years with respective feed-in tariffs. In

this context, it is assumed that every year the same amount of customers adopts the NES-

system to reflect a customer growth dynamic. This leads to different feed-in tariffs (FIT) with

respect to the individual NES-system participants. Since this research concentrates on

average costs and profits, this will be harmonized among the whole customer group by

taking the weighted average of respective year-dependent feed-in tariff and year-dependent

customers starting initial PV-system operation. The values of projected PV-based feed-in

remuneration in 2035 are extracted from Agora Energiewende [30], while the values for the

year 2025 were linearly interpolated.

The assumptions about the technological characteristics of the NES-systems as well as of

the PV-systems are outlined in table 4. All values are given with respect to one single

participant. The assumptions are based mainly on the results of [31].

Table 4: Technological and economical characteristics of selected NES- and PV-systems

PV-system NES-system System size 22 m² Storage capacity 4 kWh

Module capacity. 6.4 m²/kWp Max power charge 2,5 kW

Module efficiency 18 % Max power discharge 2,5 kW

Degradation 0.1 % p.a. Charge efficiency 95 %

Investment cost 1300 €/kWp Discharge efficiency 95 %

Installation cost 15 % (of invest.) Self-disch. efficiency. 0,1 %

Operat. & Maint. cost 1.7 % (of invest.) Degradation 0.1 % p.a.

Lifetime 25 a. Investment cost 445 €/kWh

Installation share 25 % (of invest.)

Operat. & Maint. share 1 % (of invest.)

Lifetime 15 a.

3.3 Optimization scenarios

Table 4 outlines the assessed optimization case studies of this research paper with respect

to the storage operation strategy and the market tariff framework. Listings with #GC

represent cases with grid-coverage only, listings with #PV represent cases with only PV-

systems and listings with #NES represent grid-coverage and NES-application cases as

described above. In this context, the first mentioned cases ensure a comparison of the NES-

system with the common business of municipal energy providers and thus, serve as

reference scenarios. Moreover, as depicted before, all cases have been optimized on the

data basis of 2015, 2025 and 2035.


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Table 5: Optimization scenario planning

#Case

NES-system operation strategy Market tariff structures framework Self-consumpt. maximizat.

NES-spot market trading

NES-balancing services

Market dev. Scenario

Grid-coverage tariff

NES-system tariff

#GC1 n.a. n.a. n.a. Green Flat n.a. #GC2 n.a. n.a. n.a. Green Variable n.a. #GC3 n.a. n.a. n.a. Fossil Flat n.a. #GC4 n.a. n.a. n.a. Fossil Variable n.a. #PV1 Yes n.a. n.a. Green Flat n.a. #PV2 Yes n.a. n.a. Green Variable n.a. #PV3 Yes n.a. n.a. Fossil Flat n.a. #PV4 Yes n.a. n.a. Fossil Variable n.a. #NES1 Yes Yes No Green Flat Req./ Fav. #NES2 Yes Yes No Green Variable Req./ Fav. #NES3 Yes Yes No Fossil Flat Req./ Fav. #NES4 Yes Yes No Fossil Variable Req./ Fav. #NES5 Yes Yes Yes Fossil Flat Req./ Fav.

4. Case evaluation and business implications

To quantitatively evaluate the economic potential of the business model NES, this chapter

presents the optimization results of the outlined cases first (cf. table 5). Subsequently, the

results are analyzed with respect to different factors of uncertainties.

4.1 Result overview

An overview of results of the optimized cases is given in table 6. While the results of NES-

system participants’ are given on the left-hand side, the right-hand side shows the results

from the NES-system operators’ perspective. The year 2015 marks the base year. Thus, the

optimized values of the year 2025 and 2035 are discounted to the respective year. In this

context, the optimized values are always broken down to one average customer. Selected

characteristics of the analysis and the table 6 are (negative = expenses, positive = profits):

self-consumption rate (SC) as the proportion of electricity which is directly consumed

or stored in a battery relative to the total electricity output of the PV-system;

yearly total expenses (TE): grid energy costs (generation cost, margin and all fees,

levies) as well as self-generated energy costs (PV-system costs, feed-in-tariff);

favourable (FC) and requested (RC) fees and levies: additional costs in terms of NES-

system usage according to the legal framework (not incl. in TE);

grid revenues (GR): economic accounting of the sales department of the provider

without any grid- or political-related earnings (excl. system cost and earnings)

NES-system cost (ES) as well as additional spot (ST) and balancing services (BS):

business model related costs and earnings with respect to the technology as well as

spot market and balancing market optimization.


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Table 6: Optimization scenario results

#Case Year NES-system participant NES-system operator

SC [%]

TE [€/year]

FC [€/year]

RC [€/year]

GR [€/year]

ES [€/year]

ST [€/year]

BS [€/year]

#GC1

2015 n.a. -1094,79 n.a. n.a. 140,13 n.a. n.a. n.a.

2025 n.a. -814,21 n.a. n.a. 94,79 n.a. n.a. n.a.

2035 n.a. -541,83 n.a. n.a. 61,31 n.a. n.a. n.a.

#GC2

2015 n.a. -1106,96 n.a. n.a. 150,36 n.a. n.a. n.a.

2025 n.a. -822,28 n.a. n.a. 101,58 n.a. n.a. n.a.

2035 n.a. -550,54 n.a. n.a. 68,62 n.a. n.a. n.a.

#GC3

2015 n.a. -1094,79 n.a. n.a. 140,13 n.a. n.a. n.a.

2025 n.a. -806,99 n.a. n.a. 91,89 n.a. n.a. n.a.

2035 n.a. -561,71 n.a. n.a. 61,48 n.a. n.a. n.a.

#GC4

2015 n.a. -1106,96 n.a. n.a. 150,36 n.a. n.a. n.a.

2025 n.a. -818,52 n.a. n.a. 101,58 n.a. n.a. n.a.

2035 n.a. -570,68 n.a. n.a. 68,62 n.a. n.a. n.a.

#PV1

2015 35 830,87 n.a. n.a. 91,61 n.a. n.a. n.a.

2025 35 -633,13 n.a. n.a. 60,97 n.a. n.a. n.a.

2035 35 -438,49 n.a. n.a. 36,21 n.a. n.a. n.a.

#PV2

2015 35 -840,79 n.a. n.a. 99,95 n.a. n.a. n.a.

2025 35 -641,09 n.a. n.a. 67,66 n.a. n.a. n.a.

2035 35 -449,96 n.a. n.a. 45,76 n.a. n.a. n.a.

#PV3

2015 35 -830,87 n.a. n.a. 91,61 n.a. n.a. n.a.

2025 35 -619,31 n.a. n.a. 59,04 n.a. n.a. n.a.

2035 35 438,45 n.a. n.a. 38,79 n.a. n.a. n.a.

#PV4

2015 35 -840,79 n.a. n.a. 99,95 n.a. n.a. n.a.

2025 35 -629,53 n.a. n.a. 67,66 n.a. n.a. n.a.

2035 35 -446,74 n.a. n.a. 45,76 n.a. n.a. n.a.

#NES1

2015 59 -735,31 -236,41 -47,91 62,51 -193,39 23,98 n.a.

2025 59 -543,86 -158,02 -32,08 41,75 -130,64 12,93 n.a.

2035 59 -372,21 -106,84 -21,65 24,19 -88,26 16,84 n.a.

#NES2

2015 59 -740,44 -236,29 -47,88 67,11 -193,39 23,69 n.a.

2025 59 -547,51 -158,46 -32,11 45,63 -130,64 12,48 n.a.

2035 59 -379,59 -106,99 21,68 30,99 -88,26 16,04 n.a.

#NES3

2015 59 -735,31 -236,41 -47,91 62,51 -193,39 23,98 n.a.

2025 59 -535,62 -158,02 -32,08 39,82 -130,64 10,28 n.a.

2035 59 -374,87 -106,84 -21,65 25,57 -88,26 8,45 n.a.

#NES4

2015 59 -740,44 -236,29 -47,88 67,11 -193,39 23,69 n.a.

2025 59 -542,28 -158,02 -32,08 45,63 -130,64 10,09 n.a.

2035 59 -381,01 -106,84 -21,65 30,92 -88,26 8,36 n.a.

#NES5

2015 59 -735,31 -236,41 -47,91 62,51 -193,39 8,01 100,7

2025 59 -535,62 -158,02 -32,08 39,82 -130,64 2,82 61,01

2035 59 -374,87 -106,84 -21,65 25,57 -88,26 5,76 9,02

The first cases show a quite well-known picture, a dramatic drop in profits for the energy

provider as soon as the customers adopt PV-systems (#GC1-4 vs. #PV1-4). The customer,

however, improves his position since the PV-system costs are below the savings and feed-in

tariffs. After the introduction of the NES-system, the provider as well as the participants are in

a worse position than before (#PV1-4 vs. #NES1-5). The NES-system costs even exceed the

grid related profits. However, the participant benefits compared to the grid related scenarios

(#GC1-4 vs. #NES1-5) and also compared to the PV-cases by applying the requested legal

framework (#PV1-4 vs. NES1-5). Next to that, an optimal operation with respect to the spot

market and the balancing market enables a coverage of the investment costs of the NES-

system nearly up to fifty percent in individual years (#NES5). Thus, a corresponding


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allocation of the costs between the operator and the participants as well as an optimal

operation might lead to a sustainable and economic business model of local municipalities.

Nonetheless, an economic success is only possible by taking into account the social,

technical and environmental advantages. Selected issues are presented below.

4.2 Market development

Two future scenarios have been implemented: a green scenario and a fossil scenario. In

terms of all of the optimized cases, the effect of the different scenarios is almost negligible.

While the green scenario is slightly more expensive compared to the fossil scenario in year

2025, the fossil scenario is slightly more expensive than the green scenario in year 2035

(#NES1 vs. #NES3). This can be attributed to the spot market price projections. In this

context, one assumes that in the medium-term the average spot prices of a fossil scenario

are lower than the average spot prices of a green scenario. In the long-term it is exactly the

other way round. Worth mentioning is, that the customer is even in a slightly better position

although the feed-in remuneration is lower in the green scenario compared to the fossil

scenario. Next to that, a green scenario might demonstrate a steeper fall of battery storage

system prices. Since the investment costs account for a large share of the economic

feasibility of the business model the market development might play a more decisive role

than initially recognizable.

4.3 Pricing policy

As can be seen in #GC1 vs. #GC2, given the same average annual electricity consumption,

a variable tariff scheme always leads to slightly higher annual household costs. This is not

surprising, since the variable tariff directly passes on spot market price fluctuations to the

end-consumer. With their conventional demand pattern, the end-consumers in all scenarios

have their peak demand in times with higher prices (due to an overall peak demand). Since

they cannot adapt their consumption pattern in this setting, they face higher costs in this

research. An equivalent effect can be observed for the annual utility profit.

The NES-model in this research is not really affected. The participants are only allowed to

store renewable energy into the NES-system (#NES1-5). A completely different picture might

arise if the participants are also allowed to charge grid energy to the NES-system.

4.4 Trading revenues

Additional revenue streams can increase the profitability of the NES-system for the operator

as well as for the participant. Especially spot market trading as well as balancing market

services demonstrate a promising approach. The question is to what extent the extra income

can cover the investment costs. In this context, this research takes two possibilities into

account: spot market trading only as well as joint spot and balancing market optimization. At

a first glance, the results reveal a sobering result. The trading benefits of spot market trading

only cover around 10% of the investment costs today (#NES1). However, they increase to

18% in 2035. Moreover, by joint spot market and balancing market optimization a much

higher share of up to 50% is reachable (#NES5). Since it is assumable that the control power

prices are higher in the green scenario, the market revenues could even be greater.


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4.5 Legal provisions

The legal framework also plays an important role with respect to the economic feasibility.

Compared with the reference #PV cases, a single customer would even benefit of a NES-

system by applying the requested legal framework. While the system participant has total

costs of 830,87 € to cover his demand in case #PV1 in the year 2015, he only has total costs

of 782,51 € (735,31 € + 47,91 €) in case #NES1 in the year 2015. In this context, the

participant could bear a certain amount of investment costs without having any serious

disadvantages. In this context, in case #NES5 nearly all of the investment cost could be

covered by market revenues of the provider and savings of the customer. On the same time,

by applying the favourable framework the customer is in a slightly poorer position compared

to the #PV cases. Thus, a legal rethink with respect to batteries in general and NES-systems

in particular demonstrate a crucial aspect for the economic feasibility.

4.6 Capacity utilization

Due to varying load profiles, the optimal capacity of an NES can be smaller than the

aggregated capacities of all individual household storage systems. This reduction might

translate into lower specific investment costs and lower material use. To get first insights into

the proper dimensioning of the NES-system, it is helpful to identify the amount of time steps

in which the state of charge (SOC) of the storage system is higher than a certain percentage.

For this purpose, the optimization results of #NES5 have been selected for in-depth analysis.

Figure 4 displays the sorted annual SOC duration curve of the NES-system and of the

individual residential storage systems. In this context, the sorted annual SOC duration curve

illustrates the variation of the SOC of the batteries in a downward form such that the greatest

SOC is plotted in the left and the smallest one in the right.

It is visible that the SOC duration curve for the NES-system is steeper at the beginning and

more flat at the end than the SOC duration curves of the individual residential storage

systems. In concrete terms, while the large system only possesses a SOC equal or greater

than 90% (around 400 quarter-hourly time steps) less than one weak of the year the smaller

systems possess a SOC of equal or greater than 90% around two weeks of the year. Thus, a

potential reduction of the NES-system does not have to result in an equivalent utilization

restriction. Nonetheless, individual participants would be placed in a slightly less favourable

position regarding the self-consumption rate in the case of a reduction than before. On the

same time, also a moderate storage reduction could have an impact on potential trading

profits. Thus, to identify an optimal economic solution a more detailed analysis is necessary.


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SO

C [%

]

time steps [1/4h]

NES-system Battery-1 Battery-2

Battery-3 Battery-4 Battery-5

Battery-6 Battery-7 Battery-8

Battery-9 Battery-10

Figure 4: SOC duration curve of the NES-system as well as of the individual residential systems

The self-consumption rate represents the proportion of electricity which is directly consumed

or stored in a battery relative to the total electricity generated. Hence, the amount of hours

above a certain SOC is only one indicator but does not allow drawing conclusions regarding

the respective losses of self-consumable energy in terms of a storage capacity reduction.

Only a high frequency of changes between very high and very low states of charge leads to a

respective reduction of the self-consumption rate2. Thus, to assess the average degradation

of the self-consumption rate more information about the charging and discharging behavior

of the participants is necessary. Figure 5 depicts a characteristic curve of the self-

consumption quotient depending on the storage capacity reduction quotient. The self-

consumption quotient represents the ratio of the self-consumable energy in terms of the

reduced storage capacity and the self-consumable energy in terms of the initial storage

capacity. The storage reduction quotient states the share of the storage capacity of the initial

storage capacity.

If the NES-system remains the initial size (100% of the initial storage capacity) the self-

consumable energy stays the same (100% of the self-consumable energy) both for the

individual as well as for the overall system. The other way round, if the storage capacity is

reduced to zero (0% of the initial storage capacity) the self-consumable energy of the

residential systems (Battery-1 and Battery-2) decreases to zero. In terms of the NES-system,

however, the self-consumable energy only drops to 11,45%. This is justified by the fact that

the stored energy of some participants can partly be used directly by other participants

without having to be stored at all (harmonization effect due to slightly varying demand

curves). Moreover, the relative SOC of the NES-system varies with a lower frequency and a

smaller amplitude compared to the SOC of the individual storage systems since they do not

2 The self-consumption rate of the PV-system is not considered in the following since it stays

constant.


Seite 19 von 23

vary synchronously. These two aspects lead to the fact, that the self-consumption as a

percentage falls considerably more sharply in terms of a capacity reduction of the individual

storage system compared with the overall system. Thus, in contrast to individual systems a

moderate NES-system reduction has a far slighter effect on the customer benefit regarding

self-consumption. In concrete terms, a reduction of 20% of the storage capacity results in

only a restriction of the enabled self-consumable energy of the NES-system (-5,74 %) half

the rate of individual storage systems (-12,08 %). Thus, if a NES-system operator wants to

implement an energy tariff regarding the discharging of the system it is necessary to identify

the optimal economic solution between the revenue regarding customer related discharging

and the savings regarding the system related investment costs.

Figure 5: Characteristic curve of the storage reduction quotient and the self-consumption quotient

4.7 Business implications

The results of the optimization cases (#GC1-4 vs. #PV1-4) demonstrate the necessity of new

business models for energy utilities. Regional and municipal utilities are predestined to take

an active role in shaping the energy transition, due to their decentralization, their customer

relation and interrelation in local politics and administration. In terms of green market

development NES-systems display one economic feasible opportunity, if they are optimally

designed and operated (#NES5). Based on the municipal conditions and in cooperation with

the neighborhood, a NES-system operator needs to find appropriate configurations (#NES1-

5) to following key issues to develop a successful business model:

Operation strategy: self-consumption vs. system-welfare,

Demand behavior: grid tariff vs. feed-in tariff,

Storage sizing: investment cost vs. trading profits,


Seite 20 von 23

Capacity reduction: investment savings vs. customer payments,

Legal dependency: public grid vs. private grid,

Customer expenses: variable expenses vs. fix expenses.

5. Research summary and future work

The future of the legal framework and regulations for NES-systems remain uncertain. As of

now, the legal definition of electrical storages is still deficient and comprises additional

financial burdens which reflect neither their current role nor their potential in the future energy

system. Furthermore, the electricity tax exemption in the spatial context of generating and

storing electricity remains uncertain, as there is no clear definition of spatial proximity in § 9

StromStG. Nevertheless, taking into account the requested legal framework (as well as the

current trends (decreasing technology costs, decreasing feed-in tariffs, higher grid electricity

prices), the business model of NES-systems might become more and more economically

viable. Next to that, it possesses different beneficial opportunities in comparison to residential

storage systems and comprises additional revenues on the basis of an optimal utilization.

However, as discussed different possibilities might also have a drawback with respect to the

consumer or the operator.

While this work gives a first insight of the optimal NES-system operation in general, future

work needs to evaluate different cases in more detail especially with respect to the balancing

market. Furthermore, future work should focus on the right sizing of the NES-system in

particular. On the one hand, the effect of higher or lower system capacities as well as the

nominal charging and discharging power needs to be investigated. On the other hand, the

reduction opportunities need to be analyzed with respect to investment savings but also with

respect to trading losses. The point of intersection between optimal marketing strategy and

investment costs must be determined. Doing this, for robust results, the future research

should include multi-energy carrier (e.g. system fit in terms of a heat pump roll-out) and a

more diverse consumer structure (e.g. system fit within a neighborhood of commercial and

residential consumers).


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