The possibilities of combined heat and power production balancing large amounts of wind power in Finland

Energy 82 (2015) 1034e1046

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The possibilities of combined heat and power production balancing large amounts of wind power in Finland S. Rinne*, S. Syri Aalto University, Department of Energy Technology, School of Engineering, FIN-00076 Aalto, Finland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 July 2014 Received in revised form 16 December 2014 Accepted 1 February 2015 Available online 25 February 2015

We simulate Finnish future energy system with large amounts of CHP (combined heat and power) and wind power. The Nordic countries have ambitious wind power targets, which means a substantial need for balancing power. One third of electricity in Finland is produced by CHP, and a large amount of nuclear power is running constantly as base load. There is significant correlation in wind power patterns across a large geographical area in Northern Europe, so the interconnected networks don't solve the balancing problem completely. A precautionary principle is to have ability to balance the electricity production and consumption on a national level. CHP with thermal storages could be economical and technically easy option for balancing. This alternative has been largely neglected in European studies. We simulate future wind power by upscaling existing hourly data. The economically optimal storage size was here found to be from the current 0.3% up to 30% of the total annual heat demand, depending on the wind power share and carbon trade price. We find that the use of economically optimal thermal storage can increase CHP production by 15% in the case of wind energy providing 24% of the total electricity production in Finland. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Combined heat and power Energy storage Thermal storage Load management Power system dynamics Wind energy

1. Introduction Similarly to Sweden and Denmark, Finland has ambitious wind power targets and a feed-in tariff in force since 2011 [1]. At least environmental impact assessment was under process for a total of 9400 MW of wind power capacity in Finland in 2014 [2]. This would mean an annual production of about 20 TWh, while the estimated total power consumption in 2020e2030 is estimated to be slightly under 100 TWh/a [3]. The target is to stabilise the consumption to about that level. In scenarios here we suppose maximum 8000 MW of wind power to be built, which could be realised in 2030e2040. Without extensive and, probably also in the future, economically unrealistic storage of electricity there is a need to balance the mismatch between the timing of power production and consumption [4,5]. This problem gets more pronounced, the more there is fluctuating RE (renewable electricity) production and capital intensive baseload electricity production with nuclear power or with CCS (carbon capture and storage) technology, compared to the easily adjustable and investment-wise cheap

* Corresponding author. Tel.: þ358 400 543 835. E-mail address: samuli.rinne@aalto.fi (S. Rinne). http://dx.doi.org/10.1016/j.energy.2015.02.002 0360-5442/© 2015 Elsevier Ltd. All rights reserved.

fossil-fuel condensing power without CCS. Also CHP (combined heat and power) running strictly after heat needs increases the problem [6]. The investment cost of electricity storage starts from about 50 V/kWh storage capacity for pumped hydro, not taking into account the easy but limited above-ground sites, and from about 200 V/kWh for batteries [7e9]. The same cost for heat storage in large scale is 1e5 V/kWh [10e12]. Thus heat storages may be a very good alternative to even out the mismatches between production and consumption, which has been noted also at EU level [13]. Kiviluoma et al. [14] have studied the effects of heat storages, heat pumps, electrical heating and electric vehicles on the costoptimal shares of different power production methods. Also in these studies one of the starting points is that the share of wind power may be increased to the extent where some additional balancing methods are needed instead of only using thermal power plants and hydro power for balancing. The results show that these flexibility mechanisms, especially those related with heat production, may prove a feasible solution to smoothen the variable wind energy production. When the wind power share was very high (e.g. 40% of the annual electricity production), it was feasible to replace even CHP by a combination of heat pumps, electric boilers and heat storages. These kinds of scenarios, applied to the Danish conditions, have also been presented in Refs. [15,16].

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Wagner [17] found that the capacity value of wind and solar electricity is e.g. in Germany less than 10% of their maximum output, so the conventional back-up capacity is still needed to be able to supply over 90% of the RE capacity when needed. With the optimal ratio between wind and solar power they can produce 63% of the electrical energy demand, so 37% must be covered by back-up production. Moreover, the same amount, 37%, would be excess production. The FLH (full load hours) for back-up plants are only 1800 compared to about 6000 in the case without RE. Pfluger & Wietschel [18] have made a European wide, longterm, least-cost scenario study about high RE penetration effects on the power system. In the base scenario for year 2050 about 33% of the total electricity production capacity would be thermal power and it would produce about 50% of the total electricity. In the “strengthened intensities”-scenario these shares would be 25 and 19%, respectively. The curtailed shares of electricity were small compared to the Wagner's study, only 1.1% in the base case and 5.6% in the strengthened intensities-case. These good figures result from the use of pumped hydro and compressed air storages [19], the total use being at about current level, about 5% of the whole electricity production capacity [20], and from a strengthened transmission network [21]. Czisch [22] presents 100% or nearly 100% RE production scenarios with different kinds of assumptions concerning e.g., the prices of technologies and minimising the system cost. As a result, the magnitude of the needed thermal backup production is about 20% of the total electricity production, i.e. round 800 TWh out of 4000 TWh. The results of Pfluger&Wietschel's and Czisch's studies are close to each other, but a bit different compared those of Wagner's. Steinke et al. [23] in turn found that the energy share of backup power is 20 / 40% depending on the electricity storage sizes and interconnections of the European electricity network. Altogether, it can concluded that backup power would be needed roughly at 20 / 40% of the total electricity production. Average FLH (full load hours) for backup plants in Czisch's study are nearly 5000, the difference to Wagner's study being significant. Czisch has used 3 h' time step and Wagner 15 min, which may explain a part of the difference. Czisch's study also covers whole Europe and even e.g. some Northern African countries are thought to be connected quite strongly with high voltage direct current transmission lines. However, the need for backup power production still would exist despite this interconnection. Tafarte et al. [24] conclude that upwards from about 40% share of solar and wind power (variable renewables, VRES) the produced excess electricity increases progressively. If VRES would cover 80% of the electricity needs, about 45% of the VRES production is excess. In that article it is shown that e.g. orientation of solar panels and low specific rated power of wind mills (i.e. ability to utilise also low wind speeds) reduce the excess. However, there is still need for flexibility and heat storages can apply to that. If the mentioned high RE scenarios were realised, there would still be a need of about 700e2000 TWhel thermal power in the European level in 2050. The waste heat amount from this production would be roughly 1500-3000 TWhheat. For comparison, the usable energy needed for space heating and domestic hot water in EU27 is about 2600 TWh/a and about 80% of it is produced by burning fuels directly to produce heat [25]. Thus, on an annual level, the waste heat from a necessary thermal power production could theoretically handle a large share of the European space heating needs also in the future. To make use of this possibility in practice, the timing of power and heat production should be to some extent separated from the heat use, i.e. the plants should be run at “power mode”. This was also an

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assumption made on the Czisch's study and he found that a very large share (>90%) of the waste heat from CHP could be used for heating with the aid of one-day-lasting heat storages. In addition to hydropower, conventional condensing power has this far been mostly the regulating power, as it has the highest marginal cost of electricity production methods. Further, the share of conventional condensing power has this far been so high (40% of the total electricity production [20]) in the EU27 that the use of CHP for power system regulating purposes has not been significantly needed. However, in energy efficient power systems, there would be as little condensing power as possible. Thus the regulation must be arranged in some other way. Hydropower with reservoirs can partly handle the task, but problems may arise, when the share of renewables is high enough, as described above. In Finland the relative amount of condensing power production will be quite low especially if the planned substantial nuclear and wind power increase is realised [26]. This is advantageous in terms of emissions and cost effectiveness, but poses a potential problem with hourly balancing. CHP has about 50% market share of the space heating in Finland. Consequently, there is a significant potential for CHP to take more balancing role in the power market in Finland, by using heat storages. Recent studies in Finland also present the same conclusion [27,28]. Maintaining the role of CHP would also be recommendable from the point of view of system CO2 emissions, as studied in Ref. [29]. In this paper, we study the following questions: - How large thermal storages are optimal if DH (district heating) CHP plants are used for power balancing purposes and thus run according to the power needs, not directly according to the heat needs? - What will be the ramping rate needed for regulating plants to balance the wind power fluctuations in the near future? - Will the thermal plants in Finland be able to meet these ramping requirements? We present the methodology in Section 2. The Finnish energy system and data used are introduced in Section 3. The resulting optimal thermal storage capacities are presented in Section 4. In Section 5, we analyse, whether the thermal plants in Finland will be able to meet the forthcoming ramping requirements. In Sections 6 and 7, we discuss the results and possible shortcomings in this analysis, and present conclusions. 2. Methods The general, simplified principle of CHP thermal storages is given in Fig. 1a and b. We illustrate here a situation, where 24% of the total electricity production is wind. The pictures show only calculated CHP and condensing power production during the selected week, other generation forms are omitted. Nuclear power, industrial CHP, wind power and also river hydro power are here supposed to be inflexible, i.e. they are not regulated. In (a) DH CHP plants are run to meet the calculated heating needs without thermal storages, i.e. they run in the heat mode, and (b) to meet the power needs of the system as much as possible, i.e. they run in the power mode. In (b) there is supposed to be a thermal storage. Now less condensing power is needed and there is less excess production, but CHP heat must be stored. The produced CHP heat amount per week is the same in the both cases. The installed power of all DH CHP plants is here all together 3500 MW, which explains the sharp limit in the production. The total electricity consumption is about 9000…12000 MW. Consumption and fixed production data is from year 2009. Wind power production is extrapolated from the real data.

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Fig. 1. a, b. Electricity production (MW) in the 24% wind scenario in a winter week in Finland.

Thus, storing CHP heat enables maximising the use of CHP production instead of condensing power and heat only boilers. CHP electricity replaces condensing power and CHP heat in turn replaces heat-only-boilers and even electrical heating. This leads to a system efficiency increase, i.e. less heat is wasted in the form of cooling water in condensing power plants. However, the required ramping rates for the CHP plants may be increased in this case, which is also visible in Fig. 1a and b. Heat storing technique is well known as a common form of hot water in steel tanks or in special cases rock caveats. Already now about 2/3 of the Finnish CHP DH is produced in the networks which has storage possibility [30]. Storages altogether have a capacity of about 8 h of the average heat delivery in the networks. In this paper, we simulate the possible Finnish future energy system with large amounts of wind power. As far as applicable, we use as a basis the realised hourly electricity production and consumption data from the year 2009. In the scenarios, wind or nuclear power replaces other power production so that the total system cost is minimised. The aim is to find an optimal balance so that each hour the demand of electricity and heat is fulfilled. The electricity production methods used for balancing are, preferably in this merit order: 1. hydropower with reservoirs, 2. CHP with thermal storages, 3. condensing power. Industrial CHP processes have also some possibilities to store heat. The industrial heat production is however connected to the process heat use, which is not directly predictable from the outside air temperature (like DH use) and thus the study should be done separately for different types of processes. Thus, in this study the scope is solely the DH CHP systems. In addition to heat storages, there are also other possibilities to level the RE fluctuations. These are discussed in Section 6. Our basic production data consists of the realised hourly values of nuclear and industrial CHP electricity. The hourly production of these is fixed as it was in Finland in 2009. In addition we use the realised electricity use in 2009 and heat consumption estimated from the known temperatures. In the simulations we use wind power shares of 12 or 24% of the electricity consumption in Finland, similarly to the EU-27 plans for the period 2020 / 2030, according to the national renewable energy plans [31] and more long-term plans in Finland and in the EU area, see e.g. Refs. [3,32].

We use a strict assumption that no imports or exports are allowed, so the domestic power production must take care of the regulation. This strictness can be justified, because wind power production coincides over quite large areas, which is discussed more in Section 7. Also, adding flexibility to the system and not relying solely on the electricity exchange as a balancing method is a feasible choice to the national economy. This follows from the electricity price fluctuations. If there is excess power production and it is exported during the moments of low power market price (which may result from excess production in neighbouring countries also), the export income is less than what can be achieved by better matching of production and consumption by flexibility [33,34]. To optimise the use of the available power and heat production capacity and other electricity production methods, we have used energyPLAN model, which is used and described in more detail in e.g. Refs. [34e38]. The model allocates e.g. the hydro reservoir and thermal storage use in the most economical manner. EnergyPLAN is an analysis tool for different kind of energy systems including both heat and electricity production and consumption. As input data, aggregated capacities and properties of condensing and CHP power plants and RE generation are given. Also electricity and heat uses are given aggregated, i.e. whole Finland is supposed to be one area with average temperature and without internal limits for electricity transmission. The model optimises the use for each type of production hour by hour to get the minimum total operating cost or minimum fuel use in the system level. The model is deterministic, so the result with the same basic assumptions is always the same. The model cannot be used to track the plant utilisation models for single hours or short timeframes in general, but rather to estimate energy balances and trends over the year. The point here is the expected intra-annual and shorter-term variation. These are largely determined by the natural properties of wind energy, if there is a high share of wind energy in the network and there is not enough hydropower to level all the variation (see also Section 3). The energyPLAN model is used to simulate the distribution of power production among the different methods, presented in Section 4. The model does not take into account the possible ramping rates of the power plants. Therefore, the needed ramping rates of the regulating power plants due to the large wind energy production increase are calculated separately using MS Excel, also hour by hour, and this is presented in Section 5. The ramping rate calculation is done by simply subtracting the fixed hourly production values from the corresponding demand values. The rest is the needed power output from the regulating power plants and the speed of the changes is further analysed. 3. The energy system data and elements to be optimised Finland is part of the Nordic electricity market since the mid1990's. The market consists of Denmark, Finland, Norway and Sweden [39]. In Finland, the electricity supply mix is quite balanced, consisting of nuclear power (28%), coal (13%), hydropower (16%), biomass (10%), natural gas (12%), peat (5%), waste (1%), oil (0.6%), wind (0.3%) and imports (15%), in 2009. The majority of conventional generation is produced in CHP (combined heat and power) plants, which supply also heat to all cities in Finland. Net electricity imports to Finland are mainly from Russia, but this has recently been changing due to the electricity market reform in Russia [40]. At the moment, there is one 1600 MW nuclear reactor under construction at Olkiluoto, with estimated completion in 2016 [41]. In addition, the Finnish Parliament gave positive decisions-inprinciple to two other new reactors. They can be connected to the

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grid earliest in the early 2020's. However, in this study these are not taken into account. The possible new nuclear plants can be thought to cover the increase in the baseload consumption of electricity and thus they do not change the big picture concerning the wind power balancing. Fig. 2 shows the calculated heat consumption in DH CHP networks. Heat use is calculated as a function of temperature and average domestic hot water heating share. The heating need in 2009 was about 4% less than the average in 1981e2010 [42]. There is also the realised electricity consumption in Finland in 2009 [43]. These are used as basic values in the simulation. Fig. 3 shows the electricity use and production in Finland, as average hourly values from years 2006e2009. Two kinds of variation in the consumption can be seen: weekly and seasonal. The consumption is about 20e25% lower in the weekends than in weekdays. In the summertime the consumption is about one third lower than in wintertime. The figure does not include imports or exports. Nuclear power is running at base load with the exception of fuel element changes in Spring and in Autumn (there are two plants with altogether four reactors). Also industrial CHP (CHP ind), mainly originating from forest industries, runs at quite constant power. Some decrease in the summertime production can be seen due to e.g. the smaller heating needs for process raw materials. DH CHP follows naturally the DH consumption pattern since there are no seasonal storages. Hydropower is partly not adjustable run-ofriver power, but largely regulating power concerning diurnal regulation, Fig. 4. The rest is produced by conventional condensing power. Wind power was not at a high enough level in Finland during those years to be visible in the chart. For the calculations, we use the realized 2009 hourly production data when possible (Figs. 3 and 4 are averages of four years, due to the data publicity limitations). The fixed hourly production values are used for wind power, nuclear power, industrial CHP power and a part of hydropower. Actually nuclear and industrial CHP power are dispatchable, but the dispatching of these mostly leads to the economical sub-optimisation due to the low marginal production cost. Thus the assumption of fixed production also for these. The fixed part of hydropower is calculated by taking minimum values of a moving average of 24 h (following the hour under definition) for the realised hydropower production, Fig. 5. Use above the minimum concentrates on the autumn and winter periods, where electricity prices are usually higher. The horizontal axis is a timescale of one year and the distance between vertical lines represents one month. The effect of snow melting in April and May can be seen.

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The ability for hydropower to even out the variation depends on the hydro reservoir size, inflow profile during the year and the installed capacity of the turbines. For the seasonal variation levelling (measured in TWh), the hydro reservoir size sets a maximum. Respectively, for the diurnal (or in general short-time) levelling, the produced total amount per year with hydro naturally sets the absolute maximum. The reservoir size in Finland is about 5 TWh and the total hydropower production in 2009 was about 13 TWh. The installed, usable capacity is 2350e3000 MW, depending on the water situation. Here we use the approximate average, 2500 MW. The approximate inflow profile used here is adapted from the Nordpool [43] and shown in Fig. 6. The sum over the year in absolute terms is 15 TWh, of which about 90% can be converted to electricity. Wind power production is extrapolated from the 2009 data, Fig. 7. The smoothly curved 2nd degree equation line shows the seasonal trend of wind power producing more in the wintertime. The bold black curve in turn shows the daily averages. The grey area in the background shows the hourly values. The increase of wind capacity during the year (3%) has been taken into account. From seasonal distribution point of view, wind power is a good companion with e.g. electricity driven, smartly controlled heat pump and can unleash seasonal hydro reservoirs for other balancing purposes. On the other hand, the shorter-term variation may still make a challenge in balancing without extra measures. The nature of the large changes taking place in the interval of some days can be seen. As the scope is in the domestic electricity production balance, imports or exports are not considered, i.e. the whole demand must be fulfilled with domestic production. After the fixed production, the rest must be covered by DH CHP power, condensing power and the regulable part of the hydro power. Also the flexibility in the consumer side can be used to optimise the production, but to a rather limited extent. In Finland the electricity use flexibility occurs mainly in the form of night-time electrical heating, both for space and domestic hot water heating. When modelling the energy production and consumption here, we assume only this flexible electricity consumption. The estimated annual flexible electricity use of these is 1 TWh, maximum input power 1000 MW. It is assumed that the flexibility can change the hours of consumption within one day, which is the current practice. In the simulation the values shown in Table 1 were used [10,30,40,42]. Fuel prices for CHP are higher than those for condensing plants, since there are taxes for fuels used for district heating. ETS (emission trade system) price of CO2 is here supposed to be 11 V/ton, as it was on average in 2009.

Fig. 2. Hourly consumption of electricity (upper, grey curve) and DH CHP heat (lower, black curve) in Finland in 2009.

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Fig. 3. The average hourly production of electricity in Finland in years 2006e2009. CHP ind ¼ industrial CHP, mainly from forest industries.

Fig. 4. The hourly electricity production in Finland in the first week of January, average in years 2006e2009.

Fig. 5. The estimated fixed hydropower production in Finland as shown on the lowest, black curve. This one is used in the simulation. The realised total production (the average of 2006e2009) is shown on the grey, fluctuating curve.

Fig. 6. The relative monthly inflow to the Finnish hydro reservoirs [42].

Using these values, a cost-optimal way of using the power and heat production capacity is calculated for different combinations of the maximum wind power and heat storage size. Further, the following CO2 emissions and total costs can be calculated. 4. The optimal CHP thermal storage capacities The simulation results are presented in Table 2. CO2-emission comparison indicates the reduction compared to the largest

emission case, which is the one without heat storages and the realised, small amount of wind power. Additional price of CO2 emission reduction means the price, which is required to be added to the realised ETS price of 11 V/CO2-tonne (in 2009), for the specific alternative to be the optimal solution. The EnergyPLANmodel do not account for ramping rates, so these values for wind power may be underestimated and respectively those for storages overestimated, see also Table 4. In the cost calculation both the investment and operation costs are considered, but it

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Fig. 7. The relative hourly wind production in Finland in 2009.

must be noticed that only wind power and heat storage investments are different in different scenarios. Otherwise the plant fleet is the same. Wind power investment is supposed to be 1500 e/kW, lifetime 25 years and interest rate 5%. The largest emission reductions are achieved by a large wind power share and large storages. Overall, the results are quite predictable, i.e. the shares of heat-only boilers and condensing production are the smaller, the larger is the storage capacity. In economic terms, with this low carbon price, wind power does not seem to be profitable, neither do the larger-than-present (17 GWh) storages. Yet, if the carbon price is e.g. over 30 V/tonne, even the 8000 MW/24% wind scenario with very large 500 GWh heat storage seems to be near the profitability limit.

Table 1 The properties of the system used in the simulation. Otherwise the starting point is roughly as it was in Finland 2009, but the amount of wind power, DH CHP and condensing power are varied and imports/exports are not used. Max MWs

Annual energy, TWhe

Variable operation and maintenance cost, fuel not incl.

Electricity use and supply Total use 14,130 Nuclear 2730 Industrial CHP 1730 Wind 100/4000/8000

81 (see Fig. 2) 22.7 (see Fig. 3) 9.5 (see Fig. 3) ~0.2/10/20 (see Fig. 7) Hydro 2500 13 (see Fig. 5) DH CHP 3500 Calculated Condensing 2500 Calculated Heat use and supply in CHP DH networks Total use 9570 29.6 (see Fig. 2) DH CHP 5800 Calculated Heat pumps 100 Calculated Oil boilers 11,600 Calculated Fuels for power plants Fuel shares, %

DH CHP

Condensing power

DH CHP network heat storage Max energy content Variable part of storage cost Lifetime

Coal 47 Oil 2 Natural gas 34 Biomass 16 Coal 86 Oil 2 Natural gas 2 Biomass 10

5 V/MWhe 6 V/MWhe 5 V/MWhe

5 V/MWhe 5 V/MWhth 2 V/MWhth Fuel prices, V/MWh, incl. CO2 price Coal 18 Oil 34 Natural gas 26 Biomass 18 Coal 14 Oil 28 Natural gas 22 Biomass 18

0/17/100/500/1000/5000/10,000 GWh 33 V*volume (m3) 25 years, interest rate in calculation 5%, no residual value

In these simulations, the excess production of wind energy leads to the use of electric boilers and reduction in CHP production. In 4000 MW (12%) scenarios this is not an important issue, but in 8000 MW (24%) scenarios the part converted to heat is up to 1.2 TWh, 6% of the production. Another way to express the flexibility is to show the overproduction without any additional flexibility mechanisms of this kind. This method is presented by e.g. Lund [44]. As can be seen, the increase of the storage size increases the CHP share of the total heat supply quickly with high wind power shares. From 5000 GWh storage size on (about 20% of the annual heat demand), the larger size does not increase the CHP share any more, considering even the 8000 MW wind power case. The same applies to the CHP electricity and system level CO2 emissions. The economical size of the thermal storages altogether is about 100 GWh according to this estimate, if the target is to minimise the total costs. This size is about fivefold compared to the current aggregated size. The storages have several cycles per year, which improves their profitability. The use depends on e.g. the magnitude and frequency of the electricity price fluctuation. An example of the storage use pattern in DH CHP networks is given in Fig. 8. The CHP plants are run as much as possible to meet the regulating power needs. Thus excess heat (which is not needed for DH at that moment) is stored and used later. The maximum storage heat content is 100 GWh. With current storage size and DH CHP running in the usual heat mode, the DH CHP power production would be 13.6 TWh/year if there is 8000 MW of wind power. In the power mode, the DH CHP power production can go up to 16.9 TWh if the storages are large enough, the additional 3 TWh coming from the increased production of storable CHP heat to replace the heat production from heatonly-boilers. However, this large storages may not be economically feasible if they cannot be built using especially cheap storage modes like aquifers, already made caverns etc. [45,46].

5. Are the power plants fast responding enough for variable wind production and flexible CHP production? An important issue is the ramping speed needed from the regulating power plants. The energyPLAN simulation program does not take the practical limitations into account, so we present a brief separate study of the issue. We assess, how much a high share of wind power would increase the ramping rate needed from the regulating plants (condensing & CHP) and whether this need can be met with current plants. The most challenging situations would be a strong increase in wind power production and a simultaneous decrease in electricity consumption e.g. in the evening, or vice

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Table 2 Cost-optimal heat and electricity productions in Finland, when the size of CHP heat storages and the amount of wind power maximum are varied. 4000 MW and 8000 MW wind power correspond to 12% and 24% of the annual consumption in the simulated year, respectively. Heat storages GWh

Wind MW

Wind TWhel

DH CHP TWhel

Condensing TWhel

Heat only boilers TWhheat

Electric boilers TWhheat

Cost compared to the cheapest alt. MV/a

0 0 0 17 17 17 100 100 100 500 500 500 1000 1000 5000 5000 10000 10000

100 4000 8000 100 4000 8000 100 4000 8000 100 4000 8000 4000 8000 4000 8000 4000 8000

0.2 9.8 19.5 0.2 9.8 19.5 0.2 9.8 19.5 0.2 9.8 19.5 9.8 19.5 9.8 19.5 9.8 19.5

17.1 16.3 12.9 17.3 16.8 13.7 17.4 17.1 14.7 17.6 17.4 15.4 17.5 15.7 17.5 17.6 17.5 17.6

19.7 11.2 6.1 19.6 10.7 5.3 19.4 10.4 4.4 19.2 10.1 3.7 10.0 3.4 10.0 1.5 10.0 1.5

0.8 2.0 6.8 0.6 1.2 5.6 0.3 0.7 4.2 0 0.2 3.2 0 2.7 0 0 0 0

0 0 1.2 0 0 1.0 0 0 0.7 0 0 0.6 0 0.6 0 0.5 0 0.5

3 91 278 0 79 261 1 76 246 22 93 255 121 279 380 497 704 821

Table 3 The maximum capacities of current electricity transmission lines to/from Finland and the planned situation in 2020 [61]. For comparison, the electricity consumption, as hourly measured values, in Finland in 2009 was 4350…14,129 MW, averaging 9280 MW.

Sweden Norway Russia Estonia Sum

2012, MW

2020, MW

2850 50 1460 350 4710

3550 50 1460 1000 6060

versa. In these situations, the regulating speed capacity of the regulating plants might be exceeded. Also, the speed of wind power output changes as such should be considered. To find out the frequency of co-incidents of ramp-ups or downs of wind energy and electricity consumption, we first define, what kind of electricity production profile we must provide when the non-dispatchable production is subtracted from the usage profile. As in the simulation presented in Section 3, we use fixed hourly values for nuclear power, industrial CHP, a base part of hydro power and extrapolated wind power for the whole year. The regulable hydropower use is here allocated so that it cuts the peak needs when possible. The hydro storage level is modelled so that it is always over zero and does not exceed 5 TWh, which is the

CO2emission reduction Mt

%

0 7 12 0 7 13 0 7 13 0 7 14 8 14 8 15 8 15

0 11 20 0 12 21 0 12 22 0 12 22 12 23 12 24 12 24

CO2-emission reduction, additional price V/ton

13 23 0 11 21 6 10 19 90 13 19 16 20 51 34 94 56

maximum of Finnish reservoirs. The maximum output of hydropower is limited to 2600 MW (run-of-river þ regulating part) and that of DH CHP power to 3500 MW. These are the approximate current outputs. Since we suppose in this part of analysis that CHP has heat storages large enough, we do not consider the heat balance or heat demand ramping speeds. What cannot be covered by DH CHP or hydro power, is left for condensing power. Fig. 9 shows an example of the principle used here. In other words, the picture shows the regulating part of the electricity production. Only the Finnish wind production profile has been taken into account when calculating the production needs. In addition, if the production of wind power exceeds the demand after the fixed production, the overproduction is supposed to be curtailed as described in Section 4. Fig. 10 shows the calculated necessary ramp-up speeds, expressed as percentage of the total installed DH CHP and condensing power capacity. In 2009 with 0.3% wind also imports/ exports are considered, in 12% or 24% scenarios not. A conventional solid fuel power plant (either condensing or CHP) has a practical ramp up time of about 4e6 h from minimum to maximum output. There is variation in this, but in Finland 4 h is a rule of thumb. The Finnish CHP and condensing power plants together have a maximum output of about 3600 and 2000 MW, respectively. If the thermal power plants are all running at

Table 4 Possible error sources with their estimated effect to the result (direction and magnitude). Possible error source

Effect when corrected (þ increase the storage profitability,  decrease)

Significance (x ¼ less significant, xxx ¼ may be very significant)

Maximum ramping rates or start-up costs of power plants are not taken into account in the balance optimisation The real timing logic of the hydropower production is not known accurately Flexibility in the other parts of the system are not considered Heat use profile is supposed to be the same over the country Power plant grouping does not consider the internal variation in the group; e.g. coal, natural gas and wood-fired CHP plants are considered as one aggregated “average” group Nuclear power can technically also be used for regulation Storage losses are not considered

þ

xxx

?

xx

e ? ?

xx x x

e e

x x

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Fig. 8. The calculated energy content of the heat storages in Finland if 24% of the total electricity production is wind power.

Fig. 9. Electricity production, which has to be covered by other production methods than wind (maximum production supposed to be 8000 MW), nuclear power, industrial CHP and run-of-river hydro power, example of the first week of March.

Fig. 10. The durability curves for maximum needed output power change, of DH CHP and condensing power plants, in the time (hours) specified in the first numbers in the legend. The second number indicates the energy share of wind power of the total electricity production in percentages.

minimum power, about 30e40% of the maximum, they could ramp up or down at a rate of round 3500e4000 MW per 4 h. According to Table 3, this would be enough, as the maximum required 4 h change would be 3281 MW. In practice the ramp-up speeds of thermal plants in Finland are normally slower than the above mentioned rule. For example, in 2009 the largest output change from DH CHP plants in Finland was about 600 MW and that for condensing plants 1300 MW in 4 h, i.e. together 1900 MW in 4 h, if ramped at the same time. If imports or exports are allowed, this is still enough, but if the idea is to prepare for the self-sufficiency in regulation capability, there are occasions, when the domestic capacity may not be fast enough if the current practice sets the standard. However, even in the 8000 MW wind-scenario, about 95% of the calculated ramp-up needs could be handled with the current maximum ramping speed of the power plants. The prerequisite to achieve this is, however, that the power plants would be already running and

ready for ramp-ups, which is not always true. This is discussed more in Section 6.1. Another way to indicate the impact of wind power on the ramping needs of thermal power plants is the number of annual cycles [14]. This is the sum of hourly changes in the power output over one year, divided by the maximum output and further divided by two. In this case the cycle number of DH CHP and condensing plants together was about 50 in 2009. Had there been 4000 MW wind power, there would have been about 110 cycles, and in the case of 8000 MW wind power, 130 cycles. 6. Discussion 6.1. The profitability and advantages of storages The increasing production of RE (renewable electricity) with wind and solar will need a considerable amount of regulating

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power or large-scale electricity storages. Electricity storages other than hydro have not been this far profitable, so for the time being there is only thermal power production in addition to the limited hydro power resources. Contrary to electricity storage, heat storages have in many cases proved successful. Especially together with DH networks they are feasible, thanks to the economy of scale [47]. DH networks also allow to exploit efficiently solar heat, large-scale HPs, geothermal heat and waste heat from industry, refrigeration etc. [48]. In Heat Roadmap Europe it is calculated that if more CHP and HPs are used instead of individual heat-only boilers and excess renovations, the heating cost in EU27 would be about 600 billion euros per year instead of 700 [49]. Heat storages and DH could also be used to utilise waste heat from possible future synthetic methane production [50]. Here we study how well heat storages work together with CHP plants, to level the fluctuations of wind and solar power, when there is heat storage to partly disconnect the timing of electricity production from that of heat demand. If there is plenty of condensing power available (or rather has to be used), there is no need to store CHP heat as condensing power plants can be used for regulating purposes. However, if ambitious renewable energy plans of the EU take place, it is not clear that condensing power can handle the regulating task. Another option is to curtail or dump excess electricity for flexible purposes, like some forms electrical heating. A strong transmission network balances the fluctuation of wind power, but as the wind speeds coincide in large spatial areas, this does not solve the problem completely. When the marginal power production is mostly coal or gas condensing, CHP power replaces mainly the condensing production, which is also environmentally preferable. If there is a lot of wind, solar, CCS or nuclear power, the situation will remain the same, if CHP DH networks then have the possibility to store heat for long enough time. Then the CHP plants can run at full power when there is the highest need for electricity and vice versa. If the heat-only-boilers are to be largely replaced by stored CHP heat (or heat pumps, which are not considered here, but especially in non-CHP DH networks probably a good alternative), it would be advantageous that the network operates at a maximum of about 95  C temperature. A problem may be that most of the DH networks are dimensioned for about 115  C temperature in the coldest weather. Only very few customers need high temperatures in their in-house heating networks [51], but the transmission capacity of DH network may require a large temperature difference, which is achieved for example by high outgoing water temperature. However, lowering the temperatures is one present development target in the DH industry and the EU [52]. The balancing aspect with thermal storages is one more to foster the development.

6.2. The required ramping rates for DH CHP and condensing power plants and their ability to cope with it In this study we found also that in the Finnish system with a 24% wind power share, the ramping rate required in principle would mostly not prevent from operating CHP with heat storages more in power mode. However, the problem may arise from the constant need of the plants to be ready to level the wind power fluctuations since the cold start time of a solid fuel steam turbine plant is about 48 h. The condensing plants are already now not running all the time to be ready for ramping up and in the system with more nondispatchable production, the full load hours for condensing plants can be still reduced significantly. If they must run all the time to be ready for regulating purposes as a spinning reserve, constant running even at minimum power causes a considerable reduction of system efficiency. This issue should be further studied. Further, not all CHP power plants are running during the periods outside coldest winter time. In spring and winter some plants in the larger networks which have several CHP plants may not be ready to ramp up quickly. In addition, in the summertime there are annual revisions of the plants. The use of hydropower for cutting the peaks can however help to get the thermal production needed in the summertime to lower level, Fig. 11. However, there is still quite a high continuous need to have plants on-line and ready for balancing. The cut at 2700 MW occurs due to the usage pattern of hydropower in the simulation: it cuts the peaks from just about 2700 MW. Even if there is a period of less need for calculated regulating power in the summer, there are gaps in the wind power production to be filled with CHP or condensing power. The time available for start-up is rather hours than days, which mean that the plants cannot be shut down completely. In principle, nearly the whole fleet should be ready to meet the demand all the time. This in turn means that there is either unprofitable condensing power running or CHP plants produce excess heat, which weakens also their profitability. Also it is possible that both of these produce excess electricity during ramp-ups or edowns or while they are running at minimum effect to be ready for ramp-ups. Taking into account that regulating needs of several thousands of MWs occur only a few times per year, it seems plausible that DH CHP plants and condensing power plants could cover the regulating needs for most of the time what comes to the nominal available capacity. However, the required plant readiness for ramp-ups may cause problems. Some possibilities to handle this are presented in Section 6.5. Here we have simulated the energy system at hourly level. However, for a quicker adjustment of electricity production (which is not visible with this 1-h interval data) there may be need for fast starting reserves, which are usually needed at 10e40% of the maximum wind power capacity [53,54] and power production with

Fig. 11. The hourly need for thermal power during the simulated year, if 24% of the total electricity production is wind power.

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a higher possible ramping rate, i.e. gas turbines or gas engines. Also, as mentioned earlier, e.g. heat pumps can offer a quick on-off network frequency control possibility, if their life length is not too much reduced due to the possibly increased number of starts and stops. 6.3. Other applications for thermal storages Heat storages are also useful in the moments when there is excess electricity production and CHP use is not feasible. The storages can then be heated with electrical heat pumps or in extreme cases with electrical resistances, again with flexible timing [5,14,55,56]. The possibility to use the excess electricity for some useful purpose makes it possible to overdimension RE production without just curtailing the excess. Thus the RE share and its absolute capacity value can be increased. Actually the same applies to nuclear power and CCS technology, which also profit from having a possibility to produce as much electricity as they can. The storages also enable decreasing the use of heat-only-boilers for peak loads, especially when combined with low-temperature DH networks. 6.4. Correlation of wind power production over large areas The question of taking into account imports/exports is here discussed a little more in detail since it may have a very significant impact on the result. In Fig. 12 there is an example of the wind power variation difference between one country (Finland) and the average of eight areas (one for Finland, four for Sweden, two for Denmark and one for North-Eastern Germany), i.e. if all the areas would be limitlessly connected and there were equal amounts of maximum hourly output in each country. The most difficult situation and highest ramping rate needed from the regulating power plants is when the quick change in the wind power production and in the electricity consumption coincide and the direction is the opposite. In our example year this does not seem to be the case, if we compare the results to the maximum wind power variations presented by Holttinen [60]. The change of the wind power production in Finland in 4 h' time was found at maximum about ±40% of installed capacity, in the Nordic area ±35%. For 12 h' time the maximum change was found to be 70% in Finland and 50% in the Nordic area, respectively. In absolute terms, the 8000 MW installed wind power capacity may change the output by 3200 MW in four hours' time, which is quite the same result as shown here in Table 4. According to this, also in a large multinational network it is possible that the wind power production is sometimes practically zero and the change from the low production moments to roughly 70e80% of the theoretical maximum capacity can take place in about 24 h. This means that there must be enough other capacity

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for low wind periods even if the transmission network between regions is strong. Also the production peaks must be taken into account, however slightly less than in the case of no exchange. Even if there is a risk of over-investments or additional costs for keeping old plants in reserve, it is safe to design the system so that each country is able to balance its own production and consumption. The real-world option of electricity imports and exports adds reliability and economy to the power system. The Finnish electricity transmission capacity is, however, more limited than in many other countries. In addition, the Russian capacity market limits the economic use of this connection. In Table 3 we show the capacity of current and planned lines in 2020. In other words, if there are large shares of wind energy and the wind power variation in turn is evened out by imports and exports, there may be not enough transfer capacity to handle other balancing needs. The wind power variation levelling may well reserve nearly the whole transmission capacity, since e.g. in 10% wind share scenario the wind power output in Finland is at maximum 4000 MW, which is nearly as much as the electricity transmission capacity in or out from Finland at present. To be on the safe side, too much of the variation levelling cannot be counted on the power exchange. 6.5. Other possibilities for adding flexibility to the energy system Electricity use and production in industry can also be flexible to some extent. For example, in the forest industries the timing of the most electricity demanding processes (e.g. wood grinding in mechanical pulp industries) can in some occasions be shifted to the moments when the electricity is at its cheapest in the specified timeframe. Also, industrial CHP electricity production can be a bit shifted to the moments of the expensive electricity. Special optimisation programs are used for these purposes. Since industrial cases differ significantly from each other, we have left them out of scope here. In addition to the CHP DH heat storages and energy use flexibility in industry, there are also other possibilities give some extra flexibility, which may still be needed. These may be, presented in approximate order of commonness: - allowing fluctuation of room temperature, especially in buildings with intermittent usage - the use of thermal mass of building structures - consumer information or dynamic pricing according to the marginal cost of production - decentralised heat storages in the consumers' premises - biomass fuel drying [62]. - sorption coolers with cool storages and the building thermal mass utilisation

Fig. 12. Relative hourly production of wind power as a percentage of the maximum achieved hourly production [42,57e59].

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- snow melting for e.g. improved pedestrian safety in cold climate urban areas For electricity, electrical vehicles may give daily flexibility to a significant extent. Also stationary batteries can be profitable in special cases, like shaving the sharpest peaks or controlling frequency [63,64]. One conclusion from the possible overproduction problem of CHP heat during summertime, due to the need to be ready for quick ramp-ups for wind power fluctuations, is that the profitability of solar thermal collectors in CHP networks is questionable. In this situation solar energy replaces cheap CHP heat, which is not the most effective way of using it. However, this should be studied in more detail in a case with real life constraints. The user end measures in general offer in principle a large potential to solve the optimisation problem from the whole energy system point of view. However, even if the investments in the user end may in many cases be feasible in monetary terms, the scattered nature of these energy efficiency improvements limit their realisable potential. They require knowledge and consciousness from the end user. If this capability is lacking, the transaction cost of interventions may prove too high to get the investments realised [65]. One way to get also the small consumers involved is to allow the electricity distribution company to temporarily limit the provision of network electricity. The required equipment for this is cheap and the arrangement does not require user attention. Since the timescale of the storage needs can be days instead of hours, the possibilities in the end user facilities are limited and the same applies to electricity storage, in that case due to the expensive investments and low FLH [66]. This favours the idea that more investments for e.g. energy efficiency and emission reduction will in practice be done in the production end of the whole energy chain, heat storage as a form or another being one key element. In larger buildings there are however more possibilities, also for small-scale CHP with heat storages [67]. On the other hand, a mass customisation of smart devices, which guide the consumption towards the possible overproduction or otherwise cheapest moments, may change this. In practice these smart devices could be e.g. smart charging of heat boilers by electricity or heat pumps or, exceptionally as a form of electricity, electric smart charging of electric vehicles [37,68]. Especially interesting is the possibility to use of different storage sizes for the most suitable purposes: - larger heat storages in DH networks can handle the balancing needs in timescale of some days and more (see Figs. 7 and 8) and - smaller storages like building thermal mass use and consumers' own heat storage tanks can add to daily balancing. Even in the domestic size class it is becoming possible to have smart steering of electricity use, which considers not only the time of the day, but also the actual electricity balance situation. E.g. some heat pumps have a readiness for taking in external signals whether to be in the power save, normal or intensified storage charging mode. The external signal can be fed directly from the local electricity network operator or the public hourly power market price can be used. Both of these steer the consumption towards the hours of the high electricity production. 6.6. Possible error sources Possible errors in this study and an estimate of their supposed effect on the profitability of the CHP heat storage is shown in

Table 4. Also the significance of the topic is estimated, i.e. how much does it increase or reduce the demand for storages. In addition, the simulation is done by using data from only one year. However, even if the wind production and e.g. the heat consumption profiles vary from year to year, the relative error due to this is small compared to the other possible error sources. The small difference between the years concerning excesses electricity production is shown for example in Ref. [44]. The most important issue is the amplitude and the frequency of the fluctuations in production and consumption. One quality indicator of the whole power and heat system could be the ability to balance the fluctuations by e.g. storages and demand side management and this seems to be exactly enough measurable from one year's data. 7. Conclusions We studied the future energy system in Finland, assuming the realisation of the present ambitious wind power plans. More specifically, we analysed the possibilities of CHP to provide regulation to the electricity system by using larger heat storages than at present. In addition, we analysed whether the required ramping rates would be realistic for the conventional power plant fleet in Finland. This kind of production timing shift is already done in many CHP plants in Finland or Denmark in diurnal time scale, but larger storages than presently existing would help to take the most advantage out of this. In this study we found that the optimal size of the storages could be manifold compared to the present level. The suitable aggregated capacity of the storages may be e.g. about 0.3% of the annual heat consumption while it is now 0.06%. In absolute terms, this would mean the increase from the current heat storage capacity of 17 GWh in the Finnish DH CHP networks to about 100 GWh. Kiviluoma [14] has found in his scenarios, which include a strong wind power increase, the feasible storage size to be 50 / 150 GWh, i.e., the same magnitude as we found here. Also Czisch gives about the same size for the appropriate thermal storage in conjunction with CHP, in his study of 100% renewable energy production in Europe [22]. In smaller scale, e.g. the Skagen CHP plant in Denmark has a storage size of about 0.4% of the annual heat demand [69]. According to our results, by increasing the storage size to 0.3% of the annual heat consumption, the system-level CO2 emissions would be reduced 3% more than what would be achieved only by increasing wind power share from zero to 24% of the total electricity production. If the wind power increase is only to 12% of the electricity production, the extra gain from increased storage size is much smaller. The decrease in CO2 emissions results from lower use of condensing power and separate heat boilers, and consequently the increased share of CHP, which enhances the system efficiency. IEA [70] has estimated that to reach the 450 ppm CO2 target, the price of CO2 emission tonne should be about 100 V in 2035. Already if the price of CO2 tonne is increased to 30e40 V, even storage sizes of about 3% of the annual heat demand would be profitable in Finland. This would mean that about half of the condensing power and separate heat production could be replaced by CHP, and CO2 emissions reduced by 7% in the 24% windscenario. The emission impacts mentioned here are valid if the fuel shares in the different kind of plants remain constant also after the CO2 price rise. In reality, with this price level, biomass would already significantly replace coal use. Thus, the CO2 reductions presented above are overestimates. However, the primary energy saving due to the storages can free the limited biomass resources for other purposes.

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