etelligence final report

a new

approach

New

energy sources

require

Renewable

energies

Intelligent

energy use

Cuxhaven

Electricity

marketplace

Cutting-edge

communication

technology

Dr. Michael Agsten, Fraunhofer Application Center System Technology AST

Dr. Dierk Bauknecht, Öko-Institut e. V.

Andreas Becker, EWE AG

Dr. Werner Brinker, Chairman of the Board of Management, EWE AG

Ralf Conrads, EWE AG

Volker Diebels, EWE AG

Dr. Thomas Erge, Fraunhofer Institute for Solar Energy Systems ISE

Stefan Feuerhahn, Fraunhofer Institute for Solar Energy Systems ISE

Christoph Heinemann, Öko-Institut e. V.

Dr. Jörg Hermsmeier, EWE AG

Raphael Hollinger, Fraunhofer Institute for Solar Energy Systems ISE

Thomas Klose, energy & meteo systems GmbH

Dr. Matthias Koch, Öko-Institut e. V.

Dr. Christoph Mayer, OFFIS e. V.

Günter Pistoor, EWE AG

Christine Rosinger, OFFIS e. V.

Hannes Rüttinger, Fraunhofer Application Center System Technology AST

Dr. Tanja Schmedes, EWE AG

Contents

Foreword by Mr Brinker ... 4

Foreword by Mr Hermsmeier ... 6

eTelligence: using energy where it’s available ... 8

Household customers: field test ... 12

Virtual power plant ... 16

Schedule-based plant and building operations management ...20

eTelligence marketplace ...24

Smart grids ...30

Benefits of eTelligence flexibility for the German power plant fleet 34 ICT development, standards and information security ...38

Summary ...42

The consortium ...46

a new

Dear Readers,

“I’d put my money on the sun and solar energy. What a source of power! I hope we don’t have to wait until oil and coal run out before we tackle that.”

Thomas Alva Edison, the inventor of the incandescent light bulb and provider of the above quote, surely would have been delighted to hear about eTelligence. Edison was already confronting the finite nature of resources back in 1931. And it is this same topic that we are involved with via the eTelligence research project: how can energy produced from the sun, wind and biomass be recon-ciled with commercial and private consumption in order to reduce the amount of fossil fuels used in electricity production?

In the four years of our project, we have found several answers to this question. Energy from volatile renewable sources can be “stored” in the form of thermal energy in refrigerated warehouses, for example. Private households are prepared to use energy when the wind is blowing or the sun is shining, i.e. when there is a lot of power from renewable energies in the grid. The regional marketplace enables the sale of energy even from smaller plants, pointing the way towards future profitable use. Fully developing the eTelligence scenario required the completion of a number of steps. Measuring technology systems were installed at around 100 substations around Cuxhaven. A total of 650 private homes received electronic electricity meters so that they could work out their individual consumption. Energy providers and users were thus integrated within a virtual power plant.

Dr. Werner Brinker

Chief Executive Officer, EWE AG

of eTelligence – both within and outside of the project. Partners from German science and industry have contributed to the success of the project in the model region around Cuxhaven. At some points, up to 100 people were researching 11 different project areas related to the energy supply of the future.

Thomas Edison’s groundbreaking inventions, of which the incandescent light bulb is the best known, had a significant influence on the development of tech-nology and society. Similarly groundbreaking innovations will emerge in the next few years with regard to the restructuring of our energy supply. The eTelligence E-Energy project provides us with important tools for the future by intelligently linking the fields of telecommunication, IT and energy.

Dear Readers,

E-Energy projects had already recommended approaches for a gradual switch-over to renewables as well as a more decentralised energy system long before the politicians started talking about an “energy revolution”. In line with the motto “Nothing ventured, nothing gained”, innovative names in the energy in-dustry implemented a vision of the energy system of the future in a few model regions.

Germany’s aim to obtain 50% of its energy from renewables by the year 2030 was already a reality in Cuxhaven back in 2008, where the sense of urgency to look for new solutions is already evident. The core message was: “The energy supply of the future is intrinsically linked with the convergence of energy and communication networks, and intelligent solutions regarding information tech-nologies are key to ensuring the secure and sustainable supply of renewable energies.”

EWE has set the course for the future with its eTelligence lighthouse project in Cuxhaven by linking large and small-scale consumers and producers via modern information and communication technologies (ICT) within an intelligent system. The model region showed how the regional balance between generation and consumption can contribute to security of supply and how ICT, in combination with existing structures of the energy industry, can enable the optimisation of supply. As a result of the project, the foundations have been laid for the energy supply system of the future. By depicting an efficient system that integrates decentralised energy generation intelligently into the energy system, eTelligence has broken new ground in terms of the energy supply of the future.

The fundamental elements of the project – such as the regional marketplace, the virtual power plant and the intelligent distribution network – show the po-tential of intelligent ideas and technologies. One instant result of these efforts is the “intelligent load manager” function designed by EWE, which creates an economically viable ideal scenario using power plants’ inherent flexibility. Plant operators can benefit economically from the flexibility of their systems while making a major contribution to the integration of renewable energies. The grid operator can make best use of the facilities available and avoid expensive grid expansions and complex energy storage systems.

Since the start of the project, eTelligence has generated a great deal of publicity. In a range of events for citizens, business leaders and politicians, we have high-lighted the complex content of the research and introduced ourselves to inter-ested parties and participants.

Dr. Jörg Hermsmeier

Department Director of Research, EWE AG

Another high point was the successful integration of two Cuxhaven refrigerat-ed warehouses into the eTelligence programme. The association that resultrefrigerat-ed – that wind energy can be stored in the form of cold – took the eTelligence concept far beyond the limits of the city and state. As a major German tourist destination, Cuxhaven offers an exceptional platform for the high-profile presentation of the eTelligence project and its results. Technologies that are developed and implemented in the north-west of Germany can be presented here to visitors from Germany and abroad.

The technical challenges posed by the energy revolution and the sustainable restructuring of the energy supply system are the driving forces for EWE AG’s research and development work. With eTelligence, we have established an early market-based response to the essential challenges posed by the integration of renewables and the flexibilisation of the energy supply on a distribution net-work level.

This publication is the culmination of a successful project, which was able to provide answers to a range of pressing questions thanks to the incredible dedi-cation of all those involved in the consortium. At the same time, its influential role has highlighted essential approaches to finding the solutions to facilitate the energy revolution and has set the course for future research and develop-ment projects.

eTelligence: using energy when it’s available

Without electricity, modern society would come to a standstill. Our energy requirements are growing along with our demands – which in turn increases our requirements of a secure supply structure. We want electricity to be reliably available and for a low price. At the same time, it should also be sustainable and generated in a way that is deemed socially acceptable.

Although it has served us well for decades, our current energy supply system is increasingly meeting its limits. While electricity has, until now, been generated centrally in major power stations and distributed to the consumer depending on their consumption, the major fluctuations in the supply of energy from wind and PV systems is leading to major changes and moving towards the decentralisation of the electricity supply. To ensure failure-free grid operation, the combined total output of all power-generating systems should only equal the amount that con-sumers actually need. The increased integration of renewable energies requires the increased use of forecasting and control elements in the grids because wind and PV systems are purely dependent on the weather and are not governed by peaks in demand from industrial and private users. To balance out the supply and demand, new approaches must be found in order to store energy in the most cost-effective way possible and to adapt consumption to the availability of renewable energies.

The key to realising a smart grid is the effective data transfer between the areas of generation, transport, storage, distribution and consumption. The energy

supply must move away from consumption-oriented power stations towards generation-oriented consumer behaviour. The customer

should use electricity when it becomes available and benefit from tariff incentives when wind and PV systems are able to produce energy. The linking element between the

forecasts from volatile generation and flexible con-sumers is a communication network that can

pro-vide information at any time about the current flow and the current load and can also link the

many users and generators involved in the energy system – from the major power plants

to the smallest power systems.

eTelligence:

using

energy

The energy grid of the future will have to deal with these challenges, with the focus on the necessary expansion of renewables, the integration of decentral-ised power-generating systems, the active integration of end consumers and the future integration of energy storage units within the existing grid infrastructure. Despite the seemingly large number of difficulties involved with the future operation of the grid, they all have one thing in common: in order to ensure an efficient and stable energy supply, an intelligent network of information and communication technology (ICT) is absolutely essential.

With eTelligence, we have shown that the vision of a holistic energy system is already a reality. To create this system, we combined large and small regional consumers and producers using cutting-edge ICT by developing a virtual power plant and by expanding and starting up a digital energy marketplace to create an intelligent system. The combination of ICT and existing energy structures enables the far-reaching optimisation of the supply situation. By doing so, generation and consumption can be adapted to the local characteristics of the electricity grid and these characteristics can be used to their best advantage. Products developed for small energy systems were tested on the energy market-place and new products with a regional relevance were traded there. The inte-gration of various actors was completed via a consistent information and com- munication system for both the business process level and the automation level. Environmental aspects such as security of supply and efficiency targets played a major role in this. Efficiency research and the scalability of the model region of Cuxhaven to other regions, potentially even the whole of Germany, were also of particular importance. A further aspect was the project’s publicity, which has promoted the acceptance of climate policy measures within the population. The project has created the basis for a future-proof energy supply system. The expan-sion of renewable energies and the integration of decentralised power-generat-ing systems will require further grid expansion but the revolutionary aspect here is to link the networks with ICT in order to ensure that they can continue to be operated reliably, economically and securely. A smart grid fulfils all the require-ments that we may have of our future energy supply system because it is not just electricity that is essential for modern society but communication tech- nology, too.

9

a new

eTelligence topics

• Energy efficiency: by making electricity more visible as a limited resource through the use of dynamic tariffs and transparent feedback systems, eTelligence shows how individual households can help to increase energy efficiency.

• Supply security: eTelligence shows how the regional balance between generation and consumption can contribute to increased supply security. In this way, even small regional wind farms or refrigerated warehouses can support the security of the entire system.

• Expansion of renewable energies: eTelligence shows how renewables and

de-centralised cogeneration plants can be best integrated into the existing energy system.

• Market transparency and competition: eTelligence shows how even small

pow-er-generating systems can be run economically and integrated into the existing market mechanisms – including in conformity with government support via the German Renewable Energy Act [EEG] or the German Cogeneration Protection Act [KWKG].

• Scenario analysis: eTelligence examines how flexibility can be increased across Germany in a range of different future scenarios with regard to the integration of renewables to reduce costs and reduce CO2 emissions.

• Policy recommendations: eTelligence has entered into a dialogue with people from the world of science and politics to examine the existing political and legal frameworks, highlighting any potential hindrances or areas for improvement and suggesting potential approaches for future policy formulation.

Households

The savings potential and load shift potential in private energy use were investigat-ed in 650 households. The most important instruments for this were

customer-specific tariff incentives and the use of intelligent metering technology (smart meters), as well as the depiction of electricity consumption in real time. On average, electricity consumption was reduced by 11% with the real-time visualisation, which corresponds to lower costs and

lower CO2 emissions.

Power-generating systems

Cogeneration plants and major consumers were equipped with measurement and control

technolo-gy in order to enable optimised operations and to facilitate their participation in the energy market.

The direct marketing of renewables plays a major role in this regard.

eTelligence:

using

energy

how or extra time to participate in the electricity market and they were still able to achieve excellent prices when generation and consumption by the plants followed the requirements of the overall energy system. EWE TRADING GmbH also offered a link with the wholesale market to ensure sufficient liquidity on the marketplace. Some of the market participants included EWE NETZ GmbH, various cogeneration plants and a virtual power plant consisting of a PV system, a wind farm and two refrigerated warehouses.

Active distribution network

In order to understand the network behaviour of the model region, grid measure-ment data (active and reactive power, voltages, currents, frequency) was collected from 100 substations in the distribution network, saved and exported on request. The data was recorded for the participation of the network in the regional market-place. The installation of sensor and feedback technologies is essential in order to overcome infrastructural challenges within electricity grids. If the successful inte-gration of renewable energies is to continue, we will require technologies that are able to make the grid transparent and thus controllable. An intelligent approach helps to avoid the random expansion of the electricity grid, helps to support the integration of renewable energies and offers the location a major economic advan-tage, namely the stability of the energy system and supply security.

Consortium

As part of the E-Energy technology competition, six different model projects across Germany have been awarded funding since 2008 in a cross-ministry part-nership between the German Federal Ministry of Economics and Technology (BMWi) and the German Federal Ministry for the Environment, Nature Conserva-tion and Nuclear Safety (BMU). Energy services provider EWE AG heads the eTelli-gence consortium, which links partners from the world of scientific research and industry. The project is supported by the scientific institutes OFFIS e. V. and the Fraunhofer-Gesellschaft (ISE and AST), as well as by BTC Business Technology Consulting AG, energy & meteo systems GmbH and Öko-Institut e. V. The subcon-tractors Swiss Post Solution GmbH, co2online gGmbH, the

Bundestechnologiezen-trum für Elektro- und Informationstechnik e. V., Quaere Novum Enterprises and the Institute for Energy Law at the University of Jena complete the consortium.

Metering systems make a good start Metering systems are a key component of eTelligence. The field test trialled meters from four different manufacturers: Landis + Gyr, QNE, ITF Fröschl and EasyMeter. The meters either have an integrated or separate communication module with which the data can be transmitted to the energy data gateway via a data link. For eTelligence household customers, a standard Internet router based on the AVM Fritz!Box 7270 was used as the energy data gateway. The router, which we came to call the “multibox” due to its many functions, is equipped with additional software via which it is possible to process meter data. After receiving the data, the multibox transmits it to the server system via the Internet on a secure channel that is not accessible to third parties.

All three channels at a glance

The meter data from the servers is prepared differently for three feedback systems: application (app) on the Apple iPod touch, “Mein Energieportal” [My Energy Portal] on www.eTelligence.de and monthly consumer statements. The eTelligence app on the iPod touch displays consumption, costs and CO2 emis-sions over the last seven days, for example. It can also display the exact load in the household at any given time. “Mein Energieportal” enables users to access the information over a longer period of time. Customers can view their data from any point since they started using the service to the present day and can analyse the data in various temporal resolutions. The key data also includes the con-sumption, costs and CO2 emissions values. The third feedback system is the monthly consumer statement, which the customer receives as a print-out. The statement also features various energy-saving tips and the latest information from the project. The data is only published on “Mein Energieportal” once it has been posted to the customer so that there is a clear distinction between the online and offline systems. All feedback systems offer customers the option of comparing themselves against other households of a similar size within the project.

Changing behaviour saves money

The calculation of costs and the cost statement in the feedback systems are based on tariffs specially developed for eTelligence, which intend to prompt customers to make savings and facilitate load shifts. The eTelligence Event-Tarif [event tariff] and the eTelligence Mengen-Tarif [quantity tariff] were both tested. The prices and tariff zones were defined using simulations, surveys and analyses on the basis of anonymous load profiles from earlier projects.

Household customers:

The prices in the tariff zones have been created such that customers only occa-sionally incur slightly higher costs if they do not change their consumption behav-iour. In order to achieve savings and the relevant payout at the end of the field test, however, it is necessary for the customer to change his/her behaviour. In order to create an incentive, extreme price ranges were defined in contrast to conventional high and low tariffs (HT/LT). The eTelligence tariffs both included a basic price that was 2€ higher than the EWE tariff. This aimed to show the customer that the new measurement technology results in higher costs even if the amount only compen-sates the actual additional charges to a minor degree.

Weekends Mon.–Fri. Midnight 11.67 ct/kWh 11.67 ct/kWh 11.67 ct/kWh 8 a.m. 8 p.m. Midnight 39.79 ct/kWh 0 kWh 1st Day 31 st 80% of the average monthly consumption The eTelligence Event-Tarif:

The eTelligence Mengen-Tarif:

20.00 ct/kWh 36.70 ct/kWh Weekends Mon.–Fri. Midnight 11.67 ct/kWh 11.67 ct/kWh 11.67 ct/kWh 8 a.m. 8 p.m. Midnight 39.79 ct/kWh 0 kWh 1st Day 31 st 80% of the average monthly consumption The eTelligence Event-Tarif:

The eTelligence Mengen-Tarif:

20.00 ct/kWh 36.70 ct/kWh

Figure 1

The eTelligence Event-Tarif:

In addition, bonus events or malus events can also be applied in the period between 8 a.m. and 8 p.m. Bonus events occur when there is a lot of energy available, for example, when there are high winds that enable wind farms to generate high amounts of energy. Malus events occur when electricity con-sumption is unusually high and there is little energy available. The price range in these events can vary from € 0.00/kWh to € 0.80/kWh.

The eTelligence Mengen-Tarif:

The consumption thresholds are calculated based on the customer’s individual past consumption.

New

a new

approach

Energy sources

An average of 11% savings and some interesting load shifts

The final analysis showed that customers on the eTelligence Mengen-Tarif used on average 11% less electricity than in the same period of the previ-ous year. Those with the Event-Tarif used an average of 12% less electricity in the peak period between 8 a.m. and 8 p.m. during the field test. There were no significant changes in consumption behaviour in the “economy” period. More interesting still than the savings potential in the eTelligence Event-Tarif was the load shift pattern in the tariff events. In some events, there was evidence of unexpected behaviour: in malus events with extremely high prices, there was a shift of around 20% in the time period of the event. In bonus events with free electricity, there was an increase in consumption of up to 30% within the period of the event. This means that the incentive to use more electricity when the prices are low is significantly more effective than the incentive to refrain from using electricity when prices are high. If all tariff events are viewed as a whole, more electricity was used in total over the entire period of events than would have been the case had there been no events.

00 :00:00 0.000 0.050 0.100 0.150 0.200 0.250 0.300 01 :00:00 02 :00:00 03 :00:00 04 :00:00 05 :00:00 06 :00:00 0 7 :00:00 08 :00:00 09 :00:00 10 :00:00 11 :00:00 12 :00:00 13 :00:00 14 :00:00 15 :00:00 16 :00:00 17 :00:00 18 :00:00 19 :00:00 20 :00:00 21 :00:00 22 :00:00 23 :00:00 Local time 6.85 kWh 6.85 kWh 6.53 kWh 3.80 kWh 2.62 kWh 2.24 kWh 2.38 kWh 2.14 kWh 2.13 kWh

Average consumption per 15 minutes in kWh

Comparison of event types (weekend)

TE40 – 25/02/2012 (bonus) NTE (no event) – 17/03/2012 TE50 – 06/05/2012 (malus)

Figure 2 Changes in load on days with bonus tariff events (Source: EnCT 2011)

Household customers:

Great excitement, right to the end of the project

The evaluation of the load profile data for the entire test period was expected with great anticipation. We were aware from other projects that a habituation effect starts to kick in after a certain period of time and that the motivation to take part in field tests begins to decrease. This was not the case, however, for eTelligence: the energy-saving effect of the eTelligence Mengen-Tarif was in fact strengthened over the entire test period. The savings and load-shifting potential on the eTelligence Event-Tarif also remained almost constant. The use of the portal and iPod touch feedback systems decreased over time during the test. However, this did not have any significant impact on consumption patterns.

Contact Ralf Conrads EWE AG Tirpitzstrasse 39, 26122 Oldenburg Germany [email protected]

As part of a field test, eTelligence developed a virtual power plant, which it then operated continuously for one year. The virtual power plant can manage both generation and consump-tion systems online via a common control room. The main generating systems included wind farms, photovoltaics and biogas systems, while the consumers were refrigerated warehouses. The aim is to coordinate the operations of decentralised systems and to participate in the energy market just as reliably with this pool of systems as is possible with a conventional power station.

Forecasting and resource scheduling

The heart of the virtual power plant is resource scheduling. This is done in several steps that are based on various trading periods. In a first step, a day-ahead plan was created in order to create a basic schedule for the power systems, taking into account the current market prices.

Within the project, the virtual power plant is linked to the eTelligence market-place, which publishes the schedules as orders. Only when the orders are accepted is the schedule for the following day established.

Several forecasting systems are required in order to create efficient schedules. Naturally, these schedules differ for the different types of power system. In order to predict the expected wind and solar capacity, we drew on the experience of energy & meteo systems, with the existing methods optimised for application to individual power systems or groups of energy parks. For other systems, new forecasting models were developed in order to determine the gas evolution in a biogas plant and the energy requirements of refrigerated warehouses. Mistakes in forecasting can necessitate the adaptation of schedules within the day if supply network deviations are to be avoided. If more wind energy is produced than was expected at the time of trading, for example, controllable producers can be instructed to reduce the amount they generate, or consumers can be instructed to increase their consumption.

Virtual

Controlling power systems

The schedules and any adjustments to be made during the day are transmitted to the systems within the virtual power plant and are then implemented. To do this, the virtual power plant links the local control boxes to the existing control systems. Using the schedule information, the control boxes generate control commands and transmit them to the control system. This takes into account detailed system characteristics, such as start-up and shutdown times of the various individual units, which enables the efficient operation of the individual units. The resource scheduling system, on the other hand, only receives the information that is rele-vant for the creation of a schedule.

Figure 3 How the virtual power plant works

The above graphic shows how the virtual power plant works when in operation. At the point of the first event , a refrigerated warehouse (grey) is shut down. This is a planned change to the schedule. At the point of the second event , the refrigerated warehouse is started up again in order to collect wind energy (blue) that has been fed in unsched-uled. During the third event , the power input is reduced again because the amount of wind energy fed in is close to its ideal level. All three interventions were able to compen-sate for short-notice deviations from the schedule (incidence shown in the yellow section on the lower part of the graphic).

1 2 3

1

2 3

Lower costs

thanks to optimised schedules

By optimising schedules, the virtual power plant was able to generate additional revenue from the purchase and sale of energy. This entailed power-generating plants being used primarily at times when the market price for electricity is high, and controllable consumption systems being used during cheaper periods. This optimisation enabled the refrigerated warehouses in the virtu-al power plant to reduce their electricity supply costs on the wholesvirtu-ale market by 6% to 8% during the field test. These savings follow a clear pattern throughout the year: while savings are above average in winter, the summer months have a lower savings potential. This is due to both the higher volatility of electricity prices in the winter months and the higher flexibility of refrigerated warehouses during periods where the outside temperature is very low.

Virtual

Reduction in the number of forecasting errors with regard to renewables

The joint operation of fluctuating producers such as wind energy and photovoltaics via controllable systems such as refrigerated warehouses enables us to react to forecasting errors within the virtual power plant and thus minimise supply network deviations. Within the eTelligence field test, adherence to the schedule by the virtual power plant was improved by about 15% as compared with adherence to schedules by renewable energies. To compensate for deviations from the schedule, plants have to purchase balancing energy. The following graphic depicts the decrease in the amount of balancing energy required from August to October.

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 01/08/11 00:00:00 01/09/11 00:00:00 01/10/11 00:00:00 01/11/11 00:00:00 Time kWh

Balancing energy (virtual power plant) Balancing energy (renewable)

Figure 4 Reduction in the amount of balancing energy from August to October

Contact Thomas Klose

energy & meteo systems GmbH

Marie-Curie-Strasse 1, 26129 Oldenburg Germany [email protected]

New

a new

approach

Energy sources

Cogeneration plants in the smart grid In the intelligently managed electricity grid of the future, which will increasingly be supplied with renewable energies, small- and medium-output decentralised cogeneration units (50–500 kW) in particular offer major potential in terms of adapting to the current supply conditions in the grid and still be able to provide heat.

The project’s focus was on the development of a suitable sales solution and the technical implementation in real pilot plants linked to the eTelligence marketplace. The efficiency of the technical solutions and the accessible power flexibility potential were proven during the field test phase.

Figure 5 eTelligence pilot plants

based

plant and

building

operations management

ahoi!-spa

Cogeneration unit with 460 kWel/720 kWth

Natural gas

Cuxhaven

wastewater treatment plant

Cogeneration unit with 1,052 kWel/1,200 kWth

Natural gas/sewage gas

“SenerTec Dachs”

in the EWE administration building

Cogeneration unit with 5.5 kWel/14.5 kWth

Integration through communication

The basis for any intelligent energy management solution has to be good communi-cation links between all individual components, as well as the optimisation of the whole system according to specific criteria while taking technical constraints into account. The first step was to develop the eTelligence gateway – a straightforward but high-performance computer system with standardised interfaces with the heat/power supply systems as well as with the eTelligence marketplace. In addition to the actual energy management, the gateway is also used for other functions such as for forecasting heat/power loads, generating and advertising operation timetables on the eTelligence marketplace and the monitoring and management of plant operations. Modular software programming and the use of open software protocols enable the gateway to be adapted flexibly for future applications in the event of changes in operating conditions.

In this respect, a future-proof standardised communication strategy was exception-ally important. In addition to the implementation of the IEC 61970 (CIM) norm for communication with the marketplace, the IEC 61850 standard will also link intelli-gent decentralised producers with the smart grid in the future. An open version of the standard (“openIEC 61850”) was developed and is available online for free download.

Communication between the virtual power plant and the eTelligence gateway

eTelligence gateway IEC 61850 server Load forecast Timetable Virtualpower plant IEC 61850 client Thermal load forecast Implementation of timetable Optimisation Price forecast

Figure 6 Concept for the integration of a cogeneration plant into a virtual

The field test – testing solutions in practice The hardware and software solutions were installed at three real plants in Cuxhaven: the ahoi! spa, the wastewater treatment plant be-longing to Entwässerungsgesellschaft Cuxhaven [Cuxhaven Sewage Company] and a small cogenera-tion plant in the EWE administracogenera-tion building. The main objective for the optimisation of operations was to achieve the highest electricity production possible at times where there was a high demand on the eTelligence marketplace (represented by a high electricity price). The plants could be operated either individu-ally or within the virtual power plant. This resulted in a particular challenge with regard to the necessity for precise forecasts for thermal energy consump-tion patterns, since these are decisive with regard to marketable quantities of electricity. The ahoi! spa plant, which participated in the eTelligence marketplace throughout the whole of 2011, both 230 kW cogeneration units sold a total of about 2,598 MWh of electricity via the market agent on the eTelligence market-place gateway. 4,043 MWh of heat were also produced at the same time. Optimised operations – higher revenue

The results of the field test analysis revealed that selling electricity on the eTelligence marketplace when the market price is high leads to higher revenues than purely heat-led operation. However, there was limited management flexibil-ity as a result of the high loads, primarily in winter, and a lack of thermal storage options. Achieving a high degree of reliability in the electricity supply is of

major importance because system malfunctions and errors in fore-casting can quickly have a damag-ing effect on economic results. The ahoi! spa in particular showed that in-depth technical knowledge of the heat/power system as a whole is a prerequisite for the reli-able optimisation of operations. Figure 7 shows the profit margin for a heat-led operation subject to the German Cogeneration Protec-tion Act [KWKG] with the real pilot operation selling electricity at ex-change prices. The basis for com-0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % Field test I EEX exchange price I

Heat-led I usual price I Pr of it in % of ideal oper ations

Figure 7 Field test operations vs. heat-led operations subject to the German Cogeneration Protection Act [KWKG]

based

plant and

building

parison was the idealised optimised operations at exchange prices with no outages or forecast errors.

Flexibility – the key to success

Further tests revealed that higher time-shift potential in production can significantly increase financial revenue, for example via the use of thermal storage. In addition, high technical flexibility also enables plants to provide additional services to the electricity grid, such as the provision of reserve energy or the supply of reactive power to maintain voltage.

Cogeneration unit in the EWE administration building in Cuxhaven (output 5.5 kW).

Contact

Raphael Hollinger

Fraunhofer-Institut für Solare Energiesysteme ISE Heidenhofstrasse 2, 79110 Freiburg

Germany

eTelligence

marketplace

Aims of the eTelligence marketplace As part of the eTelligence project, a marketplace was developed where energy products from vari-ous participants could be traded. The main objec-tive is to meet the needs of smaller, decentralised actors, thus enabling them to participate directly in a transparent marketplace. To this end, a market platform and market agents were developed and thoroughly tested to enable participation in the market platform. The development of the marketplace in particular aimed to reduce the effects of existing barriers to market participation for smaller plants and to enable the trade of innovative products (such as inactive power). As such, tests were conducted to find out to what extent such a marketplace could be reconciled with today’s (regulatory) requirements.

Field test

In order to test the functioning of the marketplace with real participants with regard to regional energy products, eTelligence conducted a one-year field test. The energy market, which operates under real conditions, served to gauge the economic possibilities of such a marketplace under the most realistic conditions possible.

Structure

Hourly and quarter-hourly contracts for active power were traded on the eTelli-gence marketplace. Trading occurred on the day before the actual fulfilment, i.e. on a day-ahead basis. By using what is known as a market maker, it was possible to increase the liquidity of the marketplace with its limited number of participants. The market maker sold surplus electricity and bought up balancing energy for the eTelligence marketplace on the wholesale energy market. Based on forecasted EEX spot market prices, it created bids for active power on a daily basis. The eTelligence market platform served as a way of managing transactions. The participants set up orders that generally consisted of the name of a product, a pe-riod of time and a price, as well as details of whether it was to be bought or sold. It is of central importance to the eTelligence concept that market participation

25

functions automatically. To this end, market participants are linked online with the market platform via market agents. The use of product description language that complies with standard norms helps to ensure the exchange of information. The virtual power station integrated into the eTelligence marketplace consists of a wind farm, a PV system and two refrigerated warehouses. The overall output was 1,190 kW, of which around 510 kW was controllable. In order to ensure the optimised participation of the cogeneration plants in the eTelligence field test, they were equipped with an energy gateway that was specially designed for the project.

Results

The market situation can be divided into the amounts of energy traded and the amounts of energy physically generated. Figure 9 shows the link between gener-ation (the ahoi! spa cogenergener-ation plant, the SenerTec Dachs cogenergener-ation plant and the virtual power station) and trade. The minor deviations between the amounts traded and generated were balanced out by calculating surplus/short-fall on the marketplace with the participants. As a result of its size and continu-ous operation, the cogeneration plant at the ahoi! spa accounted for the largest market share, with 70.7%. The significantly smaller SenerTec Dachs cogenera-tion plant, with installed capacity of 5.5 kWel, accounted for a market volume of 0.6%. The virtual power plant used the volatile generation of the wind farm and stabilised the load variations via load shifts from the refrigerated warehouses. The trade volume of the virtual power station represented a market share of 28.7%.

Figure 8 Participants in the eTelligence marketplace ahoi! spa PV system Cuxhaven refrigerated warehouses Refrigerated warehouse at Lentzkai Wind farm in Tossens ahoi! spa cogeneration plant Virtual power plant

The field test confirmed the business potential of an integrative marketplace for cogeneration plants. Despite the high risk on the individual level (especially with regard to forecasting and price risk), we were still able to increase profit as compared with the heat-led operation of cogenera-tion plants. Simulacogenera-tions showed that the availability of thermal storage can significantly increase commercial potential.

Thanks to the optimised resource scheduling of the virtual power plant, energy costs were between 6% and 8% lower for the participating refrigerated warehouses. The savings showed a seasonal trend and were more pronounced in winter and lower in summer. This is firstly due to higher price volatility in win-ter, which corresponds to higher revenues per deferred kilowatt-hour. Secondly, it is due to the fact that energy requirements and the flexibility of refrigerated warehouses are higher when outside temperatures are low. In addition to mak-ing savmak-ings in the purchase of energy, automatic control processes also enabled schedule deviations by renewables to be reduced by up to one third.

Figure 9 Trading activity on the eTelligence marketplace RW = refrigerated warehouse, PV = photovoltaics plant, WF = wind farm

Purchase 2,067.21 MWhrs in 3,714 hrs Purchase 2,067.21 MWhrs in 3,714 hrs Sales 88.21 MWhrsin 3,714 hrs Sales 88.21 MWhrs in 3,714 hrs Sales 1,462.3 MWhrs in 3,510 hrs Sales 11.78 MWhrs in 1,812 hrs eTelligence marketplace European energy exchange

Sales 593.13 MWhrs

in 3,714 hrs Purchase 88.21 MWhrsin 3,714 hrs

Market maker

Cogeneration plant (producer)

5.5 kWel

Virtual power plant (prosumer)

RW (260 kWel) RW (250 kWel)

PV (80 kWinst) Wf (600 kWinst)

Cogeneration plant (producer)

460 kWel (2*230 kWel)

eTelligence

The simulated market

The participation of a distribution network operator was implemented as part of the market simulations along with the corresponding trade in inactive power products. To supplement the field test, the simulated market also enabled intra-day trading for some selected scenarios in addition to intra-day-ahead trading. Further-more, there was the option of integrating a larger number of simulated market participants and thus increasing internal liquidity.

As a result of regulatory restrictions, it was not possible to enable distribution network operators to participate on the real market – i.e. the market using actual money. With just a few controllable, available plants in the distribution network, the distribution network operator would have also had too few retail partners available. The natural monopoly would have also made things difficult for the pricing and economic equilibrium, but this could be balanced out with a larger number of plants. Furthermore, as a result of the low number of market partici-pants, the option to use intra-day trading was not fully exploited in the real eTelli-gence market. In order to fully exploit this potential, a market simulation was conducted. In addition to highlighting the advantages of such a set-up, this also aimed to show possible limitations and hindrances. Only by doing so can the planned concepts for products and services be optimised and adapted to actual requirements.

To create the simulation, copies were made of the eTelligence market platform and market agents, however the participating physical plants and their manage-ment mechanisms were replaced by software simulations. The infrastructure, which consisted of the market platform, market agents and communications pathways, was also used for the simulation – which thus took the form of a decentralised simulation within the real eTelligence ICT infrastructure.

Finally, this platform enabled other concepts to be tested and trialled by other market actors, such as the virtual power plant or plant operators, without them having to take any of the associated financial risks.

New

a new

approach

Energy sources

Results of the simulated market

As a result of the high number and diversity of plants, the liquidity and trading frequency in-creased as compared with the real market. Figure 10 shows this pattern, using the traded amounts of energy over a four-week period to illustrate it. In total, the overall output registered on the market was 9.67 MW, provided by:

• 8 wind farms

• 19 PV systems

• 53 cogeneration units

• 3 refrigerated warehouses

• 50 electric vehicles (E3)

The virtual power plant and various cogeneration units were the major pro-ducers on the eTelligence marketplace. The market maker had a high market share because it summarised the day-ahead energy quantities on the market-place. The only recipients of inactive power were the participating distribution network operators.

Figure 10 Trading activity in a scenario involving intra-day and day-ahead trade

Purchase 2 MWh Purchase 8 MWh Purchase 35 MWh Purchase 1 MWh Purchase 24 MWh Purchase 1.678 MWh Purchase 206 MWh Purchase 93 MVArh Sales 70 MWh Sales 18 MWh Sales 284 MWh Sales 5.67 MVArh Sales 493 MWh Sales 1.02 MVArh Sales 1,098 MWh Sales 86.12 MVArh eTelligence Marktplatz

Virtual power plant 7,914.6 kWinst Market maker Distribution network GridSurfer E3 SenerTec Dachs cogeneration unit 264 kWinst ahoi! spa cogeneration unit 460 kWinst Wastewater treatment plant cogeneration unit 1,052 kWinst Sales 13 MWh

eTelligence

marketplace

The individual simulation phases showed that trading inactive power schedules on a local energy marketplace, particularly with day-ahead trade, only has a par-tially positive influence on the grid. This is primarily due to the insufficient ability to forecast the grid’s demand for inactive power effectively. Even taking into ac-count exogenous factors, the forecasting quality is only sufficiently high at very few points on the grid. For intra-day trading, which has significantly shorter fore-casting times and thus requires a higher level of forefore-casting quality, determining schedules by the hour presents a possibility for supporting grid management. The field test also showed, however, that trading grid services offers plant opera-tors an attractive opportunity to use their plants for the good of the grid as a whole and to achieve additional revenue. However, it was also shown that pre-dictability poses a problem for them. When the grid enters critical levels, it is particularly important that it can reliably fulfil its orders. As a result, longer-term contracts, such as the option of drawing on select plants in critical situations, are seen as the more sustainable solution.

Intra-day trading proved an inter-esting option for trading active power, too. For smaller producers in particular, it is only possible to forecast production schedules with relatively large deviations. Intra-day trading enables these plants to balance out short-term changes to the schedule, thus minimising the requirements for balancing energy.

Contact

Dr. Michael Stadler

BTC Business Technology Consulting AG Escherweg 5, 26121 Oldenburg

Germany

Smart

grids

The eTelligence smart grids focus on the medi-um- and low-voltage grid, which increasingly has the task of receiving and redirecting electricity supplied by decentralised power-generating systems in addition to supplying end consumers.

Decentralised generation initially directly affects the power flow within the grid. In the past, these flows were set up unidi-rectionally, from the higher to the lower voltage levels. Today, they tend to be bidirectional, which means that the electricity flows from lower to higher voltage levels at particular times. In addition, decentralised generation also causes an increase in voltage at the point of connection to the grid, which can lead to problems in maintaining voltage limits for the end con-sumer at a maximum of ±10% of the nominal voltage. This primarily occurs in heterogeneous grids that feature mains lines dominated by input and current load on a substation busbar. The eTelligence research project investigated ap-proaches that enable the optimised operation of a distribution network by sup-plying grid services such as inactive power via decentralised power-generating systems.

A further effect that must be overcome in the future is the limited mains protec-tion as a result of decentralised generaprotec-tion. Mains protecprotec-tion serves to shut down faults selectively and safely. If this does not function properly, it may lead to the failure or erroneous activation of protective devices, which is detrimental to the reliability of the energy supply. The concepts and approaches developed aim to link the utilities within the distribution network intelligently, resulting in the optimised management of the smart grid system as a whole.

In order to meet the above challenges head-on, it was first necessary to make a complete analysis of the situation. This included the modelling and simulation of the medium-voltage distribution network in order to identify potential bottle-necks and critical system statuses. On the basis of the findings, network hubs (such as switchboards and substations) were selected to be equipped with high-definition grid measurement technology. The resultant data enabled us to create load flow forecasts. Load forecasts and simulation calculations formed the basis for the subsequent optimisation measures. The aim was to minimise grid losses by influencing the supply of inactive power to the decentralised

power-generat-ing systems and to homogenise the voltage range. In order to create the optimi-sation schedules, enquiries were made in a non-discriminatory way within the local energy and energy services market. When addressing the topic of mains protection, we switched to the Bremervörde distribution network because it had a far higher penetration of decentralised power-generating systems. First of all, we examined the mains protection behaviour using current parameters with different supply situations. We then developed a concept and validated it using simulations that enabled the adaptation of protection parameters for distance relay protection devices. In addition to technical challenges, the grid also has to overcome various economic hurdles. From the customer’s point of view, the grid charges are the most important of these. The project analysed the existing approaches and developed alternatives that took into account the grid’s load situation.

Phases during the project

The analysis of the eTelligence smart grids was done in three phases. The first 30 months focussed on the data collection, analysis of available data and the design and development. Another important point in phase one was the prepa-ration of the field test, which was implemented in phase two, which last about 12 months. This resulted in important findings that highlighted the need for a partial revision of the concepts. The transition to phase three, which involved the evaluation of the field test, was equally fluid. This also resulted in feedback for the field test. In a final stage, the need for further research and development in future projects was highlighted as a result of the analyses.

Results

The participation of the grid operator in the simulated market showed that the trading of grid services offers attractive opportunities for plant operators to gain additional revenue (see Benefits of eTelligence flexibility for the German power plant fleet, p 34 ff.). However, grid operators are also subject to risk by partici-pating in the energy market. In the January scenario, for example, 19,072 intra-day and intra-day-ahead orders were published on the eTelligence regional market, but only 1,457 were concluded. This corresponds to a rate of 7.6%, with intra-day trading doing much worse at 1.8% than intra-day-ahead trading (8.1%). As a result, we can conclude that the shorter notice the availability on the products, the lower the degree of fulfilment. These uncertainties represent a major risk in terms of ensuring the reliable operation of the system. In addition to the products realised within the field test, long-term contracts seem to be more feasible, such as the

option to access select plants in the event of critical situations. In order to achieve adaptive mains protection, an approach was selected that enabled the cyclical and dynamic calculation of the current grid topology via the use of a mobile agent. A search path process is used to calculate zones and zone reaches by analysing the specifications for the design of a distance relay protection device. This approach was able to achieve an adaptation of protection device parameters in simulations and ensured the original levels of protection. The approach for calculating grid charges acted as a further incentive for grid-compatible behaviour. This enables the subscriber to purchase a specific “band-width”, rather like in telecoms contracts. This bandwidth determines the maximum output that can be used at the point of connection (see table at top right). This ap-proach is advantageous in that it, like the design of the grid infrastructure, is based on the maximum output and not the amount of energy used. A suitable procedure for dealing with cases where this level is exceeded still has to be established, i.e. should the system be disconnected, or should a price increase be applied? This moves away from the quantity-based approach towards a more capacity-based approach.

Smart

grids

The future of smart grids

The eTelligence project has highlighted lots of issues and aspects with regard to the optimum operation of a distribution network. However, it has also high-lighted the fact that further research is necessary, particularly in the field of improving and accelerating optimisation algorithms. Only by doing so can the operations management system be decentralised alongside the decentralisation of the energy supply. With the number of regulating and control units – i.e. the decentralised power-generating systems but also the controllable loads – set to increase in the future, centralised systems and their operators will increasingly be pushed to the limit.

Furthermore, the main focus of eTelligence was on the electricity distribution network. The interaction with other network structures and infrastructures was not considered in detail. Hybrid systems, including the combination of electricity, gas and heat networks, as well as the integration of electromobility, will set new challenges and require the development of relevant solutions and concepts. Finally, the conditions for distribution network operators wanting to participate in the market remain undefined. It is now down to the legislature and industry associations to create the necessary legal and regulatory framework.

Figure 11 Sample flat-rate bands and grid charges for low-voltage customers

band

F1

F2

F3

12 kW

16 kW

20 kW

€

11.10

€

14.86

€

18.70

range

per customer

per month

Contact

Dr. Michael Agsten

Fraunhofer-Anwendungszentrum Systemtechnik AST Am Vogelherd 50, 98693 Ilmenau

Germany

The PowerFlex power plant model, which was developed by the Öko-Institut as part of the eTelligence project, aims to find out how the flexibility offered by eTelligence can affect CO2 emissions and prime costs for generating electricity. This not only applied to the model region of Cuxhaven but also the general application of flexibil-ity in the context of the German power plant fleet. To sup-plement the question of how distribution network operators stand to benefit from smart grids (see Smart grids, p 30 ff.), we also investigated the effects of integrating decentralised power-gener-ating systems and flexible consumers into the electricity market.

In this context, flexibility signifies the deferral of energy consumption and the adaptation of power plant capacity to the varying inputs of renewable energies. The more the amount of energy generated via fluctuating means increases, the more flexibility the system requires.

The decentralised flexibility offered by smart grids changes the way we use conventional power plants and enables us to use more electricity from renewa-ble sources, which would otherwise have to be capped. These effects have

had a positive influence on the way we see smart grids and both aspects were investigated within the eTelligence project. In order to assess the flexibility of eTelligence, four future sce-narios were developed each for the years 2020 and 2030. In order to fully analyse the smart grids, it would not have been sufficient to evaluate the results of the E-Energy field test under today’s conditions. On the con-trary, flexibility will play an in-creasingly significant role with the share of renewables set to increase in the future. This is why a scenario analysis was essential for the overall evaluation.

eTelligence

flexibility

for the German

power plant fleet

The scenarios are not intended to describe possible developments of the electricity sector in the future or to serve as targets, rather to investigate the bandwidth of potential set-ups where the degree of flexibility varies. The scenarios can be divided into two main types: on the one hand, with regard to the proportion of renewables in the electricity system. Determining the development of demand for electricity and the boom in renewables helps to work out how much flexibility will be required in the future. On the other hand, the scenarios varied with regard to how the alter-native flexibility options develop with regard to controllable thermal power plants and centralised pumped-storage power plants. These options are occasionally in opposition to one another, as are flexible demand and controllable decentralised producers.

The model calculations cover the Germany-wide potential of all the flexible plants investigated in the eTelligence project. The calculations show that the flexibility val-ues decrease significantly from scenario one to scenario four. Flexibility is particu-larly useful when the proportion of renewable energy increases significantly and other flexibility options only develop slowly.

Figure 12 Definition of model scenarios in 2030

Scenario 1 Scenario 2 Scenario 3 Scenario 4

Inflexible with max. renewables 40% 45% 15% 79% Proportion of renewables 63% 47% 34% 11.5 GW Pumped storage 16.5 GW 23.5 GW 11.5 GW 30% 55% 15% 30% 55% 15% 40% 45% 15%

Power plant fleet

Coal Natural gas Biomass Flexible with renewables Highly flexible with renewables Inflexible with conven-tional power

New

a new

approach

Energy sources

However, in addition to the composition of the power plant fleet (flexible gas power plants vs. inflexible coal-fired power plants), the size of the power plant fleet also plays a decisive role: if in-vestments in power plants cease despite ongoing increases in demand, then flexible consumers will contribute to using the remaining generation capacity more efficiently by capping peak loads and compensating for low loads.

While the amount of fluctuating renewable energy produced can be almost fully exploited in 2020 in all scenarios, by 2030 15% (scenario one) or 7% (scenario two) of the wind and PV power available would have to be limited because the amount produced occasionally exceeds the demand. This results in a change in the effects of smart flexibility between 2020 and 2030. By 2020, it primarily leads to the evening out of the load profile to be covered by conventional power plants, so that these can be operated in a more efficient way. When applied to the whole of Germany, start-up and shutdown losses with regard to costs and CO2 emissions could be reduced by around 20% (scenarios one and four) for the

scenario of a coal-based power plant fleet, and by almost 10% (scenarios two and three) for the scenario of a natural gas-based power plant fleet.

Furthermore, electricity generation is shifting towards lower-cost base-load power plants, which leads to a higher amount of electricity being generated by lignite-fired power plants, particularly in scenario four. The resultant increase in CO2

emissions would, however, be overcompensated for by another effect: smart grid flexibility partially replaces the use of pumped-storage power plants, thus reducing storage losses in the electricity system thanks to the higher efficiency levels.

eTelligence

flexibility

for the German

power plant fleet

This means that less electricity would have to be produced overall, which would lead to low savings in terms of electricity generation costs and CO2 emissions.

In 2030, flexibility will increasingly be used to better exploit fluctuations in the amount of energy from renewables and thus replace electricity generated by conventional power plants. The electricity price thus not only correlates with de-mand but also with the amount of energy fed in from renewables. Decentralised power-generating systems and flexible consumers can use this as a guide and supplement the feed-in profile with PV systems, for example.

Overall, it can be summarised that flexibility can make a relevant contribution to balancing out generation and consumption, especially in tapping commercial po-tential. However, additional options will also be required in order to compensate for the fluctuations in supply from renewable energies. In addition to the benefits for the overall system, the local advantage of flexibility is primarily significant for grid operators.

Contact

Dr. Dierk Bauknecht Öko-Institut e. V.

Merzhauser Strasse 173, 79100 Freiburg Germany [email protected]

New

a new

approach

Energy sources

and

Information

security

standards

ICT development,

The eTelligence ICT infrastructure is the nerve centre of the project. It provides the interfaces for collecting data from the field level (from power-generating systems and consumers, sub-stations and smart meters). It also transmits data, saves and processes it, preparing it for visualisation. Some of the visualisation systems, such as the field test portal or components for generating a monthly customer consumption statement, are also part of the ICT infrastructure themselves. When creating such an infrastructure, particular con-sideration must be given to the issues of information security and com-pliance with industry standards with regard to the transmission of data. The former is absolutely essential, including for data protection. The latter enables the straightforward adaptation and expandability of an IT system to fulfil new requirements – by exchanging elements or expanding it with additional stand-ards-based components.

Aims and methods within the project

The myriad requirements of the ICT infrastructure were systematically highlight-ed, including by using what are known as use cases. This resulted in a rough ar-chitecture that was refined and then widened to accommodate the development and integration of individual IT modules. To develop a reference architecture, a process model was also developed that integrates the security concepts into the architecture but also aids the identification of vulnerabilities and drives standardi-sation. This process model is shown in figure 13 and takes into account the security-by-design principle, which integrates security concepts right from the start of the design process.

39

The architecture design is the starting point. The integration of the security con-cepts then occurs on a step-by-step, or loop-by-loop, basis. This process model is based on the general basic protection guidelines from the German Federal Office for Information Security.

Managing large amounts of data

High data throughput and high flexibility were shaped via data stream-oriented data processing: each data processing system can simply “subscribe” to the nec-essary data from various sources and then receives this data. Alternatively, the system can have direct access to the data stream, which is bundled within an enterprise service bus. A particular challenge in the realisation of this kind of IT structure was the processing of enormous amounts of data while simultaneously ensuring its availability. After all, the data for over 650 households involved in the field test was priced every quarter of an hour. This data was then depicted in the feedback system for online access, while data was recorded every five min-utes from the 100 participating substations and a further 250 grid measurement sensors. The availability of the IT in the field test was 98.5%, which means that errors in data processing only occurred on five days in a field test that covered a period of one year – an exceptional achievement for a system that is merely a prototype!

Identification of gaps

in standardisation of protection goalsEvaluation and risk analysis

Selection of patterns and standards for security design

Creation of threat scenarios

of security concepts

Standardisation

Integration into the architecture design

Realisation of the architecture design

System hardening via penetration testing Update

or development of a new standard

Figure 13

Process model for securing a reference architecture

Standards are decisive

International standards play a major role in data communication within the eTelligence ICT infra-structure. The advantage here is in the cross-vendor communication between the individual components of the system and their interchangeability, regardless of vendor. Before components (such as new plants) are integrated into the eTelligence infrastructure, they are checked for conformity with standards by the testing machines developed as part of the project that ensure that communication conforms to standards. Figure 14 illustrates the communication process within a test and the design of the testing machine for an IEC 61850 system.

For the communication of metering and plant measurement data within the ICT infrastructure, the CIM data model was used (IEC 61968, meter data reading). Likewise, the CIM data model was used for the transmission of orders and order information between the eTelligence market platform and market agents to ensure the automatic participation of plants in the market. The IEC 61850 standard was used for the communication between the central ICT infrastructure and grid meas-urement sensors, as well as for the communication between plants and higher-level plant management for market participation and schedule management. A major topic in the standardisation process was also the identification and classification of security norms and standards for the field of energy. The extension of the most widely ranging norms was documented in the project and introduced to the relevant standardisation committees.

Figure 14 Communication during a test and design of the testing machine

Web application Human user Automated user IEC 61850 system 1.1: Commands CIM tests Web

service IEC 61850tests Control

1.1.1: Responses 1: Initiate test job

1.1.1.1: Test results, including logs Testing machine

and

Information

security

standards

ICT development,

41

More security for smart metering

Guaranteeing data protection and information security for smart metering is a major part of the eTelligence concepts. Figure 15 above shows the ICT infra-structure for smart meters and feedback systems within the eTelligence project. An index of procedures was created for data protection, which was subject to technical and organisational measures to ensure data protection within the field test. These were agreed with the State of Lower Saxony’s data protection officials.

Another area of focus was represented by the digital, remote-readout electricity meters for both consumers and producers integrated into the communication network. Due to the fact that energy policy and energy law are starting to push through the mandatory introduction of such metering systems, binding security requirements are necessary for the benefit of all market participants due to the resultant hazard potential. As such, there are high requirements of the security architecture for smart metering systems, which producers and users of these systems must fulfil. To do this, we drew on expertise from the accompanying research into E-Energy, the specialist group for law, as well as experience and knowledge gained through the “protection profile” work process established by the German Federal Office for Information Security. Work is continuing on a relevant protection profile, which is due to be published by the Federal Office for Information Security this year.

Contact Dr. Christoph Mayer OFFIS e. V. Escherweg 2, 26121 Oldenburg Germany [email protected] Dr. Michael Stadler

BTC Business Technology Consulting AG Escherweg 5, 26121 Oldenburg Germany

[email protected]

Figure 15 eTelligence ICT infrastructure for smart meters and feedback systems

Internet (secure connection)

Meter data network (virtual private network) iPod touch

feedback system

Field test household

WLAN

Smart meter

Firewall

Metering and cost database

Thank you, Cuxhaven! eTelligence has shown what the future is capable of This much is clear: our energy supply will continue to develop in the future, away from a centralised system dominated by major power plants towards a more decentralised, renewables-based system. This process has to be supported by innovative solutions. Over a period of four years, the eTelligence consortium has worked on these solutions and demonstrated their feasibility in Cuxhaven. The following results are of particular note:

• Electricity consumption, costs and CO2 emissions were made transparent for all participants in the eTelligence field test.

• With the aid of the innovative Mengen-Tarif, households were able to save an average of €100 during the 12-month test phase.

• In the time-variable Event-Tarif, it was possible to achieve load transfers of up to 30% and electricity was primarily used when sufficient energy from renew-ables was available.

• As major consumers, the refrigerated warehouses in the region were able to save more than 6% on their usual electricity costs.

• Innovative technologies such as digital electricity meters, the eTelligence app and a special online portal for analysing consumption were all proven in prac-tical scenarios.

• The standards that were developed and implemented as a result of eTelligence, including the common information model (CIM) and the IEC 61850 standard, enabled the straightforward integration of various decentralised power-gener-ating systems, the implementation of new market processes and the

processing of large amounts of data.

• The scenario analysis shows how the benefits of eTelligence’s flexibility are set to increase significantly for the electricity system of the future.

With the E-Energy programme, the German Federal Ministry of Economics and Technology, and the German Federal Ministry for the Environment, Nature Conservation and