Feasibility of CHP-plants with thermal stores in the German spot market

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Feasibility of CHP-plants with thermal stores in the German spot market

Giedre Streckiene a,*, Vytautas Martinaitis a, Anders N. Andersen b, Jonas Katz c

a Department of Heating and Ventilation, Vilnius Gediminas Technical University, Sauletekio Ave., LT-10223 Vilnius, Lithuaniab EMD International A/S, Niels Jernes Vej 10, 9220 Aalborg, Denmarkc DONG Energy A/S, Agern Allé 24-26, DK-2970 Hørsholm, Denmark

a r t i c l e i n f o

Article history:Received 23 September 2008Received in revised form 23 March 2009Accepted 28 March 2009Available online 25 April 2009

Keywords:CogenerationCHPFeasibility studySpot marketThermal store

a b s t r a c t

The European Energy Exchange (EEX) day ahead spot market for electricity in Germany shows significantvariations in prices between peak and off-peak hours. Being able to shift electricity production from off-peak hours to peak hours improves the profit from CHP-plant operation significantly. Installing a big ther-mal store at a CHP-plant makes it possible to shift production of electricity and heat to hours where elec-tricity prices are highest especially on days with low heat demand. Consequently, these conditions willhave to influence the design of new CHP-plants. In this paper, the optimal size of a CHP-plant with ther-mal store under German spot market conditions is analyzed. As an example the possibility to install smallsize CHP-plant instead of only boilers at a Stadtwerke delivering 30,000 MW h-heat for district heatingper year is examined using the software energyPRO. It is shown that, given the economic and technicalassumptions made, a CHP-plant of 4 MW-el with a thermal store participating in the spot market will bethe most feasible plant to build. A sensitivity analysis shows to which extent the optimal solution willvary by changing the key economic assumptions.

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1. Introduction

An increasing energy demand, depletion of fossil energy re-sources and the emission of green house gases provide incentivesto develop and fully utilize highly efficient energy technologies.Cogeneration (combined heat and power, CHP) is a well knownand highly efficient approach to produce electricity and heat in asingle thermodynamic process [1,2]. By cogenerating the electric-ity and heat, CHP-plants have the possibility to decrease fuel con-sumption by 20–30% as compared to decoupled production inconventional power plants and boilers [3,4]. This technology re-duces overall fossil fuel consumption and thus the energy is gener-ated in a more environmentally friendly way [1,5,6]. Promotion ofhigh-efficiency cogeneration based on a useful heat demand is alsorequired under the EU Directive 2004/8/EC [5].

Denmark is one of the countries in Europe that has been able todevelop a comparatively high share of CHP production, a signifi-cant part of it being decentralized plants with an electrical outputof less than 20 MW. This has been achieved mostly by grantingfeed-in tariffs. However, the biggest achievement is not merelythe high share of CHP production, but also that those plants havebeen incentivized to operate flexibly by the tariff structure whichhas been higher during the day than during the night time andeven higher during peak hours in the middle of the day. Plants

could be designed for flexible operation by installing significantthermal stores. This way decentralized CHP-plants in Denmarkhave been rewarded when matching their production better withthe electricity demand and are now well prepared for being an ac-tive participant in the power market. Having a thermal store alsomakes it possible to operate the production units at the most fuelefficient load and to store the surplus heat. If the prime movers aregas turbines or spark-ignited gas engines the most fuel-efficientoperation is full load [7].

The advantage of the Danish operating strategy becomes evenmore obvious when acting under market conditions with hourlyprices. The larger the difference between peak and off-peak pricesthe more attractive it becomes for flexible plants.

In Germany, one of the largest energy markets in Europe, high-efficiency cogeneration especially in combination with districtheating/cooling is regarded as strategic technology to support thegovernment’s energy and climate policy goals. However, targetsset in the CHP act of 2002 have not been achieved. In June 2008German Parliament has approved a new CHP law, which aims atdoubling the total share of CHP electricity to 25% by 2020. The esti-mated economic potential for CHP electricity production in Ger-many is lying between 300 and 350 TW h-el per year. Thistranslates into about 35 GW-el of CHP capacity [8].

CHP electricity is promoted by bonus payments for the pro-duced electricity paid on top of the achieved electricity sales price.Besides the bonus and the commodity price CHP operators will re-ceive a compensation for avoided grid use if connected at a lower

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* Corresponding author. Tel.: +370 5 274 4718; fax: +370 5 274 4731.E-mail address: [email protected] (G. Streckiene).

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voltage grid. The setup results in the CHP-plant being exposed tothe electricity market. Electricity spot prices in Germany show adistinct daily pattern with very high peak prices, thus the applica-tion of a flexible operating strategy can be expected to be highlyattractive in the German market. Also, Germany is one of the EUleaders in renewable energies. Especially wind and solar powerproduction is fluctuating, hence increasing the demand for flexibleresponse from other producers.

Two aspects of the German CHP market are conspicuousthough: despite the support scheme decentralized CHP electricityproduction is still rather low, and interestingly, only few decentral-ized CHP-plants in Germany are designed to operate in a flexible,market oriented way. Thermal storage tanks are rarely built or rel-atively small.

This paper shows that the German power market is highlyattractive for decentralized CHP development if the plants are de-signed and operated as flexible units that take advantage of the factthat electricity is priced differently at different times of the day andweek. This method proposes a plant design that differs signifi-cantly from common practice in Germany.

2. Optimized operation of a CHP-plant with thermal store

To optimize the operation of CHP-plants with thermal stores isfar from being a new problem. It has been analyzed in a vast num-ber of studies and programmes [1,2,9–19]. However, the focuses inthese analyses are very different.

Some studies focus mainly on the modelling approach, othersmore on the effects of changing the design of the CHP system.Those focussing more on the system design differ especially inthe way they make use of a thermal store. In this context it makessense to differentiate between storages that enable more steadyand extended operation of CHP-units (operational storage utiliza-tion) and storages that are aimed at shifting operation to the com-mercially most viable points in time (commercial storageutilization). While the first ones are aimed mostly at allowing ex-tended full load operation of small-scale CHP-units, the latter onesare meant to improve economic feasibility especially for biggerunits facing variable electricity prices.

Marchand et al. [9] presented a static linear programming mod-el of a district heating system with a cogeneration station. Themodel was applied to a medium-sized city. It was shown that oper-ations research models could be used to answer the key issuesraised by the introduction of cogeneration.

Gustafsson and Karlsson [10] showed that thermal stores mightbe of interest for CHP networks. They showed that sources of fuelscould have influence on the attractiveness of the thermal store,especially if waste heat was utilized and it covered the base load.Ito et al. [11] analyzed a diesel engine cogeneration plant withthermal store using a dynamic programming method withmixed-integer programming. They showed that the installation ofthe thermal store and the adaptation of the optimal operation pol-icy reduced the daily and the annual operation costs of the totalplant.

Dotzauer and Holmström [12] showed that finding the optimalproduction of both heat and electricity and the optimal use of thethermal storage was a complex optimization problem. They solvedthe short-term production-planning problem using a combinationof dynamic programming, general-purpose solvers and theLagrangian relaxation method. Electricity prices play an importantrole in their solution. Lee et al. [13] analyzed a daily operationscheduling method for industrial CHP systems with thermal sto-rages, electricity chargers and auxiliary boilers. The authors pro-posed an operation scheduling scheme for cogeneration systemsusing fuzzy linear programming. They showed that scheduling of

buying and selling of electricity influences the calculation of totaloperation cost.

Khan et al. [14] carried out a feasibility study of cogenerationusing a double-effect absorption chiller and cogeneration coupledwith a thermal energy storage of chilled water. They showed thaton-site cogeneration using the double-effect absorption chillerprovides a potential of about 13% peak-demand reduction and sav-ings in energy consumption of about 16%. Khan et al. concludedalso that thermal store coupled with cogeneration was more eco-nomic compared to cogeneration only. Rolfsman [15] made a studyof district heating supplied via boilers and CHP-plants. It presentedan optimization model for short-term planning of the operation ofthermal store together with CHP-plants. It was shown that varia-tions in the electricity prices can influence the investment poten-tial. The commercially operated store is the driver behind that.Siddiqui et al. [16] found that for a specific site with a small heatingload, there was no incentive to use a thermal store at all. On theother hand, a customer with a medium ratio of heating to electric-ity loads used thermal store to meet 30% of the heating loads.Again commercial store utilization is not applicable in this case.However, every particular case needs detailed analysis.

Schaumburg-Müller [17] presented a model that created day-ahead production schedules for a CHP-plant using CPLEX 10.0.1under GAMS. The CHP-unit was divided into several segments toallow taking into account varying efficiency and production costsat different load levels. Such segmentation concepts may be usedfor the modelling of CHP-plants at partial load. Obara [18] analyzeda residential CHP system consisting of a methanol-steam-reform-ing fuel cell, a geothermal heat pump, both electrical and thermalstore. The complex energy system was analyzed using a geneticalgorithm. The thermal store was connected to the heat pumpand the stored heat was supplied to the heat load during the daytime. The analysis showed that thermal storage capacity must bechosen according to the available technologies for heat and powerproduction. Bogdan and Kopjar [2] assessed the influence of a dis-trict thermal store on a big-scale CHP-plant using an optimizationcode ACOM, which has been specially developed for simulating andoptimizing the EL-TO plant in Zagreb. They could show that theintroduction of a district thermal store may substantially improvethe economic performance of the CHP-plant when electricity pricesare governed by the dual-time tariff system, with significant differ-ences between day- and night-time tariffs. Here the thermal storeis used commercially, though in a rather simple tariff system.

Onovwiona et al. [19] presented a model for a residential CHPwith internal combustion engine. The chosen thermal store of300 kg hot water was too small for a 6 kW capacity engine becausethe CHP system did not utilize its full potential. Having a largeCHP-unit with a small thermal store may lead to excess heat out-put which is dumped. A 2 kW-el CHP-unit reduces heat dumpand is more efficient as compared to a 6 kW-el CHP-unit. These re-sults indicate that it is important to make a proper selection of thecapacity of both thermal and electrical devices in the CHP system.However, the system is optimized under operational rather thancommercial aspects.

Hongbo et al. [1] developed a mixed integer nonlinear program-ming model, using a commercial software package LINGO for a res-idential CHP system with thermal store. That analysis wasconducted to find the economically optimal CHP investment for aprototypical residential building. It was found that an optimal stor-age tank can extend the operating time of the CHP-plant; however,an oversized tank leads to less economic merits because of in-creased energy losses. Also in this case the thermal store is usedoperationally rather than commercially.

The number of studies shows that finding the optimal configu-ration of a CHP-plant is a frequently addressed topic. In generalmost of the previous works focus on modelling and optimization.

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From a practical point of view sufficient modelling approaches ex-ist and applied research is needed that helps implementing, oper-ating and designing strategies for decentralized CHP-plants.

It is obvious that the practical design and operation of CHP-plants in Germany rarely is aimed at the spot market. We discusshow a thermal storage can help to improve the economy of a plantand how the capacity of the thermal store influences the design ofthe production unit under spot market conditions. For this analysisan existing optimization tool, energyPRO, is applied to find theoptimal plant design. The modelling tool is used to simulate theplant operation and to calculate the operational income of theplant.

The energyPRO tool developed by EMD [20] is an input/outputmodel typically calculating annual production in steps of 1 h. Itoptimizes the operation of plants against technical and financialparameters. Adding a thermal store to a CHP system the complex-ity of finding the optimal operation and design of a plant increases.

3. Spot market

In order to introduce and promote distributed CHP-plants, it isimportant to underline the fact that the design of market regula-tion plays a major role in defining the optimal performance ofCHP-plants when maximizing the income from selling electricity.Consequently, such conditions also influence the design of newCHP-plants when optimizing the size of the CHP-units and whendeciding if and how much thermal store capacity should be added[21,22].

A thermal store is one way to partially decouple heat supplyfrom electricity production. Without a thermal store, the CHP oper-ation is driven directly by the heat demand, while the marketprices paid for produced or consumed electricity vary significantlywith the time of the day. The option to exploit the variation of elec-tricity prices raises the demand for flexible operation of a CHP-plant. The flexibility increases with store capacity and operationcan be scheduled more and more independently from the heat de-mand profile [22,23]. The conditions of the daily spot market thushave a strong influence on the configuration and capacities of theCHP-plant.

Competitive electricity spot markets are common all over Eur-ope, and CHP-plant operators will have to adapt to these condi-tions. This clearly applies to countries like Denmark and

Germany but also to less developed electricity markets like Lithu-ania, which plans to link its market to the Nordic market by build-ing an electricity bridge to Sweden. Moreover an expansion of CHPcapacity is likely to be necessary as Lithuania has to close the nu-clear power plant that generates most of the country’s electricity.

The European Energy Exchange (EEX) is the market place for en-ergy and related products in Germany, Austria and Switzerland.The EEX operates spot markets for power, gas and emission rightsas well as a derivatives market. On every exchange trading day,closed hourly auctions regarding delivery of electricity on the fol-lowing day take place [24].

Electricity markets exhibit a number of typical features that arealso found in the financial markets to some degree, such as pricespikes and complex seasonality patterns. Spot prices may evenshow extreme price spikes that are the result of unplanned outagesor capacity limits of generation or transmission assets or a sudden,unexpected and substantial change in demand [25,26]. Marketmechanism failure and capacity constraints in the network maycause spikes, because they lead to temporary deviations from effi-cient competition in the market [27].

The variation of the electricity spot price is under influence ofmany factors. They tend to fluctuate more as a consequence ofmarket deregulation, i.e. higher prices during the day when themarkets’ aggregated demand is high and lower prices during theevening and the night when markets’ aggregated demand is low[28]. It is observed that spot prices generally are higher during spe-cific peak hours (8.00–20.00 on working days) and lower in theremaining hours of the week (off-peak) in the EEX market. Duringweekends and on public holidays, spot prices sometimes can behigher in the evening than in the middle of the day [29]. Especiallythis is observed on Sundays and on such public holidays as NewYear, Easter and Christmas days. Hourly variations of the spot priceon Sunday and Monday in winter (a), summer (b) and autumn (c)are shown in Fig. 1. It should also be noted that the German elec-tricity spot prices have several key features such as: long memorypersistence with seasonality and non-constant volatility [30].

In Table 1 it can be seen that the average spot price has in-creased from 2001 to 2007 and that there is a big difference be-tween peak and off-peak hours in the EEX market every year[29]. In the last couple of years the ratio between annual averagepeak and off-peak has been around two: 1.91 in 2006 and 2.02 in2007. Shifting electricity production from off-peak hours to peakhours would result in higher revenues from electricity production;

Fig. 1. Example of daily variation of spot price in EEX, Monday and Sunday: (a) winter-time, (b) summer-time, (c) autumn-time.

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for example, a difference between the annual average spot price inpeak and off-peak hours in 2007 was 27.83 €/MW h-el.

4. CHP-plant simulation model

4.1. General economic evaluation model of CHP

The optimization of a CHP-plant in a competitive market is amatter of design and the related investment costs as well as a mat-ter of operational performance. The schematic layout of the ana-lyzed CHP-plant is shown in Fig. 2.

The economic feasibility analysis of the chosen CHP-plant con-figuration is based on net present value (NPV) and simple paybacktime. Simple payback time is the simplest and most commonlyused key factor. It is, however, not the most suitable criteria for along term investment [31]. Due to its widespread application,though, it will be used as an indicator in the calculation results.The NPV is selected as the preferred criteria for the optimality ofthe CHP-plant configuration. To find the optimal size of the CHP-unit and the thermal store, a simple calculation sequence has beenused, shown in Fig. 3. The optimal capacity of the thermal storewill be found when the NPV no longer increases by increasingthe capacity. The CHP-plant configuration with the maximumNPV is the optimal solution from an economic point of view.

4.2. Energypro software

Various modelling tools for the economic analysis and optimaloperation of CHP-plants have been developed in recent years.Some of the professional software is focused on the design oflarge-scale CHP systems and some on small-scale CHP systems,many others are designed only for specific cases [6,31].

EnergyPRO is a modelling software which allows carrying outcomprehensive and detailed technical and financial analyses of en-ergy projects [32]. It has been used to design most decentralisedCHP-plants in Denmark [33]. Basically, this modelling tool is an in-put/output model calculating annual productions in time steps ofwhatever granularity is required – typically 1 h. The input param-

eters are fuels, capacities, efficiencies, time series for heat demandand electricity prices, the operational strategy, environmental data[34].

The model will derive a dispatch strategy for all heat and elec-tricity production units as well as store utilization based on netheat production costs. For a CHP-unit the net heat production costswill decrease with increasing electricity prices, while the net heatproduction costs of a boiler will be independent from the spotprices. When electricity prices are high enough to reduce the netheat production costs of the CHP below those of the boiler, CHPproduction will be preferred. Thus the energy and economy calcu-lations are tied together and affect each other [33,34]. To furtheroptimize the CHP production, price intervals are defined. Energy-PRO will place the production in the most favourable periods usinga non-chronological method. The whole planning period is dividedinto time steps with constant parameters, e.g. temperature, heatdemand, electricity prices, production capacities etc. based onthe predefined time series [20]. The time steps are divided intogroups, depending on which price interval they belong to, andevery step is tested for possible production.

4.3. Thermal store

Three types of thermal energy storage systems (TES) are preva-lent in practice, namely sensible TES, latent TES and thermochem-ical TES. The selection of the storage system is dependent on thestorage period required, i.e. diurnal or seasonal, economic viability,operating conditions, etc. [35]. However, at present storage of sen-sible thermal energy is used the most for the practical applications.

The amount of thermal energy stored by a sensible thermal en-ergy storage device is proportional to the difference between thestorage input and output temperatures, the mass of the storagemedium and specific heat of the material. The basic equation forstored heat in a material mass is calculated by

Q ¼ mcpDT ¼ qVcpDT ð1Þ

where m is a the mass of the material, cp is the specific heat of thematerial at a constant pressure, DT is the temperature difference, qis the density and V is the volume of the thermal store.

Stratified hot water tanks are designed for short term heat stor-age and allow an equalization of daily load profiles [36]. Thermalstratification or thermocline in a thermal store can be established

Table 1Annual spot price variation in EEX.

Year Average spotprice (€/MW h)

Average spotprice during peakhours (€/MW h)

Average spot priceduring off-peakhours (€/MW h)

Peak/off-peakspot price (%)

2001 24.07 34.50 18.30 1892002 22.49 32.41 17.00 1912003 29.41 42.89 21.95 1952004 28.48 37.79 23.32 1622005 45.99 63.11 36.52 1732006 50.79 73.30 38.34 1912007 37.99 56.35 27.83 202

Fig. 2. Schematic layout of the CHP-plant.

Fig. 3. Finding an optimal size of thermal store.

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due to the buoyancy forces, which ensure the highest temperatureat the top and the lowest temperature at the bottom of the tank[37]. During the charging process, hot water is supplied to thetop of the tank, while the same amount of cold water is takenout from the tank bottom. The charging of the thermal store startswhen the heat production is higher than the heat consumption.During the discharge of the thermal store, an opposite processtakes place.

The supply and return water generates a thermal layer insidethe thermal store. This thermal layer should be as thin as possibleto maximize the efficiency of the store [38].

In practice, the store capacity is about 90% of the calculatedcapacity due to degradation of thermal stratification that is causedby various heat transfer mechanisms. Four primary factors areknown to contribute to the loss of stratification and hence the deg-radation of stored energy: heat losses to the surrounding environ-ment, heat conduction from the hot portions of the storage fluid tothe colder portions, vertical conduction in the wall, and mixingduring charging and discharging periods. A number of studies ana-lyzed the degradation of thermal stratification in thermal stores[39–42].

The type and size of a thermal store depends on the CHP-unitcapacity and water temperatures in the system. In energyPRO athermal store is defined by a maximum content measured inMW h. The basic equation which is shown above is applied forthe calculation of the thermal content.

5. Case study

An analysis of the optimal size of a CHP-unit and a thermal storeis conducted for a municipal supplier in Germany (Stadtwerke),delivering 30,000 MW h-heat per year. The results depend verymuch on the underlying heat demand profile. In this analysis, itis assumed that the district heating system primarily supplies heatto private households. The annual heat delivered from the plantconsists of 18,000 MW h for space heating, 9000 MW h for hotwater and 3000 MW h for network losses.

The CHP-plant consists of a CHP-unit, a thermal store and a nat-ural gas boiler. It is assumed that the system is based on a naturalgas fired reciprocating engine and it is run only at rated power, i.e.full load operation. Partial load is not necessary when a thermalstore is available and it shall be avoided in this analysis due to low-er electric efficiency.

The reference situation is an existing district heating systemwithout CHP production in which all required heat is producedby a natural gas boiler. It is assumed that there is no CHP and ther-mal store. The size of the natural gas boiler is equal in all analyzedcases and it is assumed to be able to cover the complete heat de-mand single-handedly if necessary. The investment in the naturalgas boiler therefore is not included and analyzed.

In order to calculate the NPV and the simple payback time, thefollowing assumptions are made [43]:

– Real discount rate without inflation is 4.0% (equal to anapproximate nominal discount rate of 6.0%).

– Specific investment in a CHP-unit (equal to typical invest-ment costs in Denmark) is 0.67 mill. €/MW-el (5 mill. DKK/MW-el).

– Specific investment in a thermal store (equal to typicalinvestment costs in Denmark) is 268 €/m3 (2000 DKK/m3).

– Lifetime of the investment is assumed to be 20 years.– CO2 emission is 242.0 kg/MW h (received fuel – natural gas).

Main technical assumptions of the system are shown in Table 2[43]. These values are used directly for energyPRO input. Economic

inputs for the cases calculated are presented in Table 3 [43–46]. Itis assumed that natural gas prices for municipal suppliers lie some-what below the purchase prices of industrial customers as pub-lished by Eurostat [45]. Most of the calculations are madeindependently of the CHP-bonuses which are introduced in theNew CHP Law. The minimum operation time of the CHP-unit isdecided to be 3 h to achieve better technical performance of thegas engine.

The spot prices for 2008 used in the simulations were calculatedbased on the historical spot market variation during 2006 in theEEX market multiplied with a factor scaling it to a yearly averagespot price of 40.00 €/MW h-el.

The calculated heat production cost for the existing boiler is39.87 €/MW h-heat. The bid price on the spot market is 34.25 €/MW h-el, where the heat produced by the CHP-unit is assumedto replace heat produced at the boiler. Only relevant paymentsfor the investment analysis are modeled in energyPRO. Hence,not all costs have been described. Focus lies on the payments,which will change, when the investment in the CHP-plant is made.

The increased operational income due to these selected pay-ments is calculated as the difference between the operational in-come of the reference case (only gas boiler) and the operationalincome after installation of the appropriate configuration of theCHP-plant.

6. Results and discussion

6.1. CHP-unit and thermal store optimization

To find the optimal ratio between CHP-unit and thermal store anumber of pre-calculations have been made. It was found that withthe assumed heat demand of 30,000 MW h-heat there is no need touse a thermal store at this CHP-plant when the CHP-unit has acapacity of up to 1 MW-el. Such a CHP-unit could run all hoursof the year because it would only cover the base heat demand.

When the CHP-unit is bigger than 1 MW-el, it requires a ther-mal store that needs to be found case by case. At first, the optimalstore for a CHP capacity of 2 MW-el is found. Six calculations withincreasing thermal store volume are performed for the 2 MW-elCHP-plant. The calculation results are shown in Table 4. The opti-

Table 3Economical assumptions of the system.

Natural gas price 25.0 €/MW h-fuelFuel tax for gas boiler 5.5 €/MW h-fuelCO2 certificate 20.0 €/t CO2Gas boiler O&M costs 1.0 €/MW h-heatCHP-unit O&M costs 8.0 €/MW h-elCHP-unit starting cost 8.0 €/per 1 MW-elHeat sale 55.0 €/MW h-heatElectricity sale Spot priceAverage spot price for 2008 40.00 €/MW h-elNet using bonus (CHP-unit) 1.5 €/MW h-el

Table 2Technical assumptions of the system.

CHP-unit Type: gas engineHeat efficiency: 47.0%Electrical efficiency: 40.0%

Boiler Fuel input: 16.50 MW-gasHeat output: 15.0 MWHeat efficiency: 90.9%

Thermal store Temperature difference: 45 �CUtilization: 90%

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mal thermal store capacity for this CHP-plant is found to be250 m3.

Similar calculations are performed for CHP-plants with capaci-ties of 3, 4, 5, 6, 7, 8 and 10 MW-el. Polynomial (second order)coherence is found between the CHP capacity and the thermalstore volume for the analyzed case study. Results are shown inFig. 4. With this coherence between the CHP capacity and thermalstore size, the NPV and simple payback time are shown as functionof CHP capacity in Fig. 5.

From an economic point of view a 6 MW-el capacity CHP-unitwith 1400 m3 thermal store results in the best NPV (1.584 mill. €).For further analysis, however, a 4 MW-el capacity CHP-unit with650 m3 is chosen. While this configuration still shows good eco-nomic results, it is regarded to be less risky as compared to the5 MW-el and 6 MW-el CHP-plants. Bigger CHP-plants are moresensitive to such economic parameters as fuel and electricity priceswhich comprise a high level of uncertainty. Also, as the store is big-ger for the bigger CHP-unit there is more value to loose by notbeing able to apply a flexible strategy. The 4 MW-el CHP-plant

needs a much smaller thermal store and investment as comparedto the 6 MW-el CHP-plant.

It was found that the investment in a 4 MW-el CHP-unit withoptimal thermal store gives an NPV of 1.43 mill. €. A comparisonof the yearly operational results between the reference case andthe CHP-plant of 4 MW-el calculated in energyPRO is shown in Ta-ble 5.

As it can be seen from Table 5, the 4 MW-el CHP-plant with650 m3 thermal store shows a higher natural gas consumptionand higher expenditures for the purchase of fuel, CO2-certificates,operation and maintenance as compared to pure boiler production.Additionally, CHP-unit starting costs of 32.0 € per start are in-cluded. However, the CHP-unit sells its electricity to the spot mar-ket. Moreover, the CHP-plant receives a net using bonus for thedelivered electricity and does not have to pay fuel tax for the nat-ural gas consumption. These conditions make it possible to im-prove the operational income as compared to the reference casein which only heat is produced on a natural gas boiler. The highernatural gas consumption by the CHP-unit is justified because con-ventional electricity generation will be replaced by the combinedheat and power production having a better total energy conversionefficiency. Hence, the overall amount of fuel consumption is de-creased, which leads to a reduction of emissions.

Fig. 6 shows the operation of the optimal plant configuration,4 MW-el CHP-unit with 650 m3 thermal store, during one weekin spring. The upper graph shows the spot price variation, thegraph in the middle represents the heat production by the heatgeneration units and heat demand, the lower graph shows the levelof the thermal store. The engine runs when the spot price is higherthan the bid price and it is able to operate continuously for at least3 h. When the heat demand is lower than the heat production thesurplus heat is stored in the thermal store. During the hours with

Table 4Optimal thermal store, 2 MW-el CHP.

Thermal store CHP-plant investment Total starts of engine Increase in operation income Simple payback time NPV, 20 years(m3) (mill. €) (starts) (mill. €) (Years) (mill. €)

150 1.381 438 0.162 8.51 0.823200 1.394 416 0.165 8.44 0.850250 1.408 407 0.167 8.41 0.866300 1.421 405 0.168 8.45 0.864400 1.448 403 0.169 8.58 0.845

Fig. 4. Coherence between CHP capacity and thermal store volume.

Fig. 5. CHP-unit with optimal thermal store.

Table 5Comparison of main operation results between reference case and 4 MW-el CHP-plant (calculation results from energyPRO).

Yearly parameters Referencecase (RC)

4 MW-el,650 m3

Difference between4 MW-el CHP and RC

Electricity production (MW h) 0 16410.5 16410.5Total heat production (MW h) 27,000 27,000 0.0Natural gas consumption (MW h) 33000.0 52815.6 19815.6

RevenuesSale of ElectricitySpot market (€) 0 889,906 889,906Net using bonus (€) 0 24,616 24,616Total sale of electricity (€) 0 914,522 914,522Sale of heat (€) 1,485,000 1,485,000 0

ExpendituresNatural gas purchase (€) 825,000 1,320,391 495,391Fuel tax (€) 181,500 64,842 �116,658Operation and Maintenance (€) 30,000 142,001 112,001CO2-certificate (€) 159,720 255,628 95,908CHP-unit starting costs (€) 0 12,480 12,480

Operation income of selectedpayments (€)

288,780 604,180 315,400

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low spot prices, the CHP-unit does not run and the heat demand issupplied from the thermal store.

6.2. High efficiency CHP-unit

This analysis is based on CHP-units with average efficiencies. Ifwe instead choose a high efficiency 5.1 MW-el CHP-unit (Rolls-Royce, Bergen B35:40-V12 AG) with a nominal electrical efficiencyof 46.3%, a thermal efficiency of 48.2% and a specific investment of0.76 mill. €/MW-el [47], economic results can be improved. Theoptimal thermal store capacity is then found to be 1200 m3.

When the high efficiency CHP-unit with 1200 m3 thermal storeoperates under nominal conditions, the NPV becomes 2.684 mill. €.The new NPV value is significantly higher than the NPV for the4.0 MW-el capacity CHP-unit calculated above; although the highefficiency CHP-unit needs more capital investment.

6.3. CHP-plant with CHP-bonus

This subsection analyzes the impact of CHP-bonuses introducedin the new German CHP-law. The crucial improvement in the newlaw is that also completely new plants of sizes above 2 MW-el willget a CHP-bonus of 15 €/MW h-el for 5 years (max. for 30,000 fullload hours), whereas before bonuses were only paid to existingplants or smaller new units. The plants are getting an even higherbonus of 21 €/MW h for the capacity share below 2 MW. However,this could not be taken into account as the legislation process wasstill ongoing when the analysis was conducted. Other assumptionsfor the calculations remain the same as the previous. The conse-

quences are calculated for the CHP-plants of 3, 4, 5, 6 and 5.1(Rolls-Royce) MW-el capacities.

The heat production cost for the boiler remains the same aspresented in Chapter 4 (39.87 €/MW h-heat). However, getting aCHP-bonus, the bid price on the spot market decreases signifi-cantly from 34.25 €/MW h-el to 19.25 €/MW h-el for a CHP-plant.Getting the CHP-bonus, the CHP-plant shows far better economicresults, presented in Table 6. The CHP-plants with CHP-bonusshow double the NPV compared to the situation without CHP-bo-nuses. Simple payback time decreases to approximately half thetime and the investment in the CHP-unit with thermal store be-comes more attractive. For example, simple payback time for theCHP-plant of 5 MW-el capacity decreases from 9.5 years to5.1 years.

As it can be seen, adding CHP-bonuses to the CHP-plant for pro-duced electricity results in a much better NPV for the 5 MW-elcapacity CHP-plant as compared to the 4 MW-el capacity CHP-plant. Hence such bonus schemes make it possible to install largerCHP-units.

6.4. Influence of daily spot price variation

A higher variation of spot prices during a day has an influenceon the CHP-plant production. In this analysis, the EEX daily spotprice variation of 2006 was used to forecast a daily spot price var-iation of 2008. If we instead choose the daily price variation of2007, the NPV of the 4 MW-el capacity CHP-plant increases from1.43 mill. € to 2.04 mill. €. The average spot price for both scenariosis set to the same 40.0 €/MW h.

Fig. 6. Production graph from the energyPRO of the optimal plant configuration under the spot market.

Table 6Comparison of CHP-plants, with and without CHP-bonus.

CHP-unit capacity(MW-el)

Thermal store(m3)

Total investment(mill. €)

NPV, 20 years (mill. €) Simple payback time (years)

With CHP-bonus Without CHP-bonus With CHP-bonus Without CHP-bonus

3.0 400 2.118 2.206 1.186 4.38 8.714.0 650 2.855 2.821 1.431 4.65 9.055.0 1100 3.646 3.187 1.564 5.08 9.516.0 1400 4.397 3.287 1.585 5.56 9.995.1 1200 4.209 4.589 2.684 4.68 8.28

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Obviously the daily spot price variation has been higher in 2007as compared to 2006 (see Table 1). The CHP-plant will generatebetter economic results, when the spot price series is based onprice variations from 2007. This shows that high volatility on theelectricity market may significantly contribute to the income of aflexible CHP-plant. As production will take place during the higherpriced hours anyway, the plant will profit from the price increasein those hours to a greater extent, than it suffers from lower pricesduring off-peak hours. Higher daily price variation will support theuse of thermal stores in CHP-plants.

6.5. Sensitivity analysis

A sensitivity analysis is carried out on some of the economicassumptions. The influence of changes in the real discount rate,investment costs (CHP-unit and thermal store), electricity and nat-ural gas price are analyzed.

The real discount rate is varied from 4.0% to 7.0%. The impact ofthe real discount rate on the NPV for the 4 MW-el CHP-plant isshown in Fig. 6. As it can be seen, the real discount rate has a biginfluence on the NPV. Higher values of the real discount rate re-duce the present value of the selected payments and thus, the totalNPV. However, a 4 MW-el CHP-plant still has a positive NPV(0.486 mill. €) at a real discount rate of 7%.

To show the impact of changing investment cost, natural gasand electricity prices, values for these parameters are varied indi-vidually ±10% of the values in the base case. The NPV and the sim-ple payback time are calculated for these values.

Final electricity prices are derived from marginal power gener-ation costs (which reflect the price of primary fuel inputs to gener-ation, the costs of hydropower, nuclear energy and renewable-based generation), and non-generation costs of supply. Althoughthe price paths follow smooth trends, prices are in fact likely to re-main volatile [48].

An influence of the variation of investment costs, electricity andnatural gas price on NPV and simple payback time is shown in Figs.7 and 8. Here it can be seen that a variation of investment costs hasthe smallest influence on the NPV as compared to a variation ofnatural gas and electricity prices. Even if the investment costs wereincreased by 10% of the reference price, a 4 MW-el capacity CHP-plant shows good NPV results. In such a case the NPV decreasesto 1.146 mill. €. Simple payback time increases slightly. See Fig. 9.

Increases in the natural gas and electricity price have the largestimpact on NPV. An increased natural gas price reduces NPV and in-creases simple payback time significantly. However, contrary to ahigher natural gas price, an increase in electricity prices raisesthe NPV. If electricity prices increase by 10% of its reference price,the NPV becomes 1.85 times higher.

As fuel prices are a major driver of electricity prices, it is likelythat natural gas prices and electricity prices will be correlated tosome extent. What really affects the economics of the plant isthe spread between the two prices. Thus if the spread stays atthe same level, this will decrease the effects of the individual pricechanges on the NPV and keep the NPV more stable.

7. Conclusions

Having a thermal store, CHP-plants gain flexibility and mayachieve improved economic results if managed properly. Moreoveroperators of CHP-plants will gain an increase in security whenplanning their day ahead schedules as fluctuations in the heat de-mand can be compensated with the store. The analyses in this pa-per lead to the following conclusions:

– The German electricity market contains an incentive todesign and operate decentralized CHP-plants flexibly.

– Analyzing a German energy plant delivering 30,000 MW h-heat per year it is found that with the assumed economicconditions a 4 MW-el capacity CHP-unit with 650 m3 ther-mal store is feasible.

– Adding a CHP-bonus for the delivered electricity improvesthe economic feasibility of the CHP-plant. A bonus of 15 €/MW h-el doubles the NPV and reduces simple payback timeFig. 7. Impact of real discount rate on NPV.

Fig. 8. Impact of variation of investment cost, electricity and natural gas price onNPV.

Fig. 9. Impact of variation of investment cost, electricity and natural gas price onsimple payback time.

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from 9 to 10 years down to 5 years. It was found that a CHP-bonus would enable the installation of bigger capacity CHP-plants.

– Higher variations in electricity prices allow achieving bettereconomic results for a CHP-plant with thermal store.

– Installing a highly efficient gas engine improves the feasibil-ity of the plant. This highly efficient CHP-unit of 5.1 MW-elcapacity shows an NPV that is 1.9 times better as comparedto the NPV of a 4 MW-el capacity CHP-plant.

– The economics of a CHP-plant are very sensitive to fuel andelectricity prices. However, as long as both electricity andnatural gas prices move in the same direction and the spreadstays the same, the negative impact on the NPV is limited.

The applied model provides a tool to evaluate the effects of add-ing flexibility to a plant. However, it is still necessary to be able tocapture that value during operation. So adding a thermal store andbuilding bigger units creates opportunities on the market but alsoincreases the operational risk. As operators of decentralized plantstend to be very risk averse this might keep them from applyingthese methods.

In general, the feasibility of CHP-plants with thermal store de-pends on the individual conditions, such as electricity and fuelprices, their variation over time, investments costs, national energypolicy, taxes and the local energy demand. A change in the volatil-ity of spot prices will influence the design of CHP-plants; i.e. espe-cially the capacities of the CHP-unit and the thermal store. Highvariations in spot prices provide a significant incentive for theuse of thermal stores at CHP-plants. A CHP-bonus scheme mayhelp to increase economic feasibility of CHP-plants. After all how-ever, each individual plant has to be designed according to its spe-cific framework conditions.

Acknowledgements

The article is a part of the EU-funded MASSIG (Market Access forSmaller Size Intelligent Electricity Generation) project and LongLife Learning/Erasmus programme.

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