RESULTS

At this section the power-driven operation will be compared to the heat-driven operation based on the reference scenario described before. The main comparison criterion will be the overall plant gains, since the work itself concerns an economic optimization. In addition to that, grid interactivity of both operations will be discussed. Lastly, effect of varying the size of the storage tank on overall costs and grid interactivity will be investigated.

8.1. Comparison with respect to overall plant gains

For comparison a yearly optimization has been carried out. Optimization parameters are given in Table 8.1.

Table 8.1 Optimization parameters for comparison scenario

𝐂_𝐩 24 ℎ𝑜𝑜𝑟𝑠

𝐩 24

Step size 1 ℎ𝑜𝑜𝑟

𝐭𝐦𝐝𝐦 1 ℎ𝑜𝑜𝑟

𝐦_𝐝 2

As seen in Table 8.1, prediction horizon is 24 hours. Therefore, 365 sequential optimizations are done to achieve a yearly result. Each optimization process is initialized with the end state of the previous optimization. A “Python” script, which carries out this initialization, was available at Fraunhofer ISE. All parameters are selected as the same values for both heat-driven and power-driven models. Initialization parameters are given in Table 8.2.

Table 8.2 Parameters values for initialization

Size of Storage Tank (𝐕) 45 m³

CHP Nominal Thermal Power (𝐐_{𝐂𝐂𝐂,𝐭𝐭}) 215 kW

CHP Nominal Electrical Power (𝐂_𝐞𝐞) 198 kW

CHP thermal efficiency (𝛈_{𝐂𝐂𝐂,𝐭𝐭}) 0.45

CHP electrical Efficiency (𝛈𝐂𝐂𝐂,𝐞𝐞) 0.41

Boiler Efficiency (𝛈_{𝐁𝐥𝐝𝐞𝐞𝐁}) 0.90

Start Temperature of Storage tank (𝐓_{𝐝𝐭𝟏𝐁𝐭}) 45℃

High Temperature Level (𝐓𝐭𝐝𝐡𝐭) 80 ℃

Low Temperature Level (𝐓_𝐞𝐥𝐥) 45 ℃

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For this scenario heat demand of district “Gutleutmatten” considered. It is intended to show the differences between heat-driven and power-driven operation for the same scenario.

Nominal powers of the CHP and efficiency values are realistic and can be encountered in real plants.

Results of both operations are given in Table 8.3 and Fig. 8.1.

Table 8.3 Result of the future scenario (1)

Gains Heat-driven Power-driven Change

Storage Gain [€] 9880 4170 - 57 %

CHP Gain [€] 29285 33193 +13 %

Boiler Gain [€] 33186 51843 + 56 %

Total Gain [€] 72351 89206 + 23 %

Fig. 8.1 Results of the future scenario (2)

As can be seen in Table 8.3 and Fig. 8.1, the total gain of the power-driven model is higher (by 23 %) than that of heat-driven model. It can be also stated that when the CHP operation is optimized, then the gain of the CHP rises by 13 % itself. Threshold electricity price value, when the CHP and the boiler gain are the same can be found as

𝐽CHP=𝐽Boiler , (𝟖.𝟏𝟏) 𝑑el∙ 𝑐el + 𝑄CHP,th∙ 𝑐heat− 𝑄fuel,CHP∙ 𝑐fuel =𝑜b∙ 𝑐heat− 𝑄fuel,CHP∙ 𝑐fuel , (𝟖.𝟏𝟏)

𝑐_el =− 𝑄CHP,th∙ 𝑐heat+𝑄fuel,CHP∙ 𝑐fuel+𝑜b∙ 𝑐heat− 𝑄fuel,CHP∙ 𝑐fuel

𝑑_el , (𝟖.𝟏𝟏)

𝑐_el =− 𝑄_CHP,th∙ 𝑐_heat+𝑄_fuel,CHP∙ 𝑐_fuel+𝑜_b∙ 𝑐_heat− 𝑄_fuel,CHP∙ 𝑐_fuel

𝑑el . (𝟖.𝟏𝐝)

0 20000 40000 60000 80000 100000 Heat Driven

Power Driven

Gains [€/year]

Storage Gain CHP Gain Boiler Gain

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When the Eq. (8.1d) is solved for the same amount of heat supply by the CHP and the boiler (𝑜b =𝑄CHP,th), then the threshold level of electricity price can be found as 3.25 𝑐𝑐./𝑘𝑘ℎ. That means when the electricity price is lower than this level, operating the boiler is more profitable. Electricity price is generally lower than this threshold price level (can be seen in Fig. 7.4). That’s why; the power-driven operation makes the highest profit from the boiler.

Fuel consumptions at the boiler and storage tank, operation hours of the CHP and specific gains of the CHP are given in Table 8.4.

Table 8.4 Comparison of specific gains based on gas consumptions and operation hours

Comparison Factors Heat-driven Power-driven

Gas Consumption at Boiler[𝒎^𝟑] 164788 257435

Gas Consumption at CHP [𝒎^𝟑] 419930 234189

Operation hours of CHP [h] 6893 4566

Specific Gain of the CHP [€/𝒎^𝟑] 0.069 0.141

Specific Gain of the CHP [€/h] 4.24 7.26

As can be seen in Table 8.4, gas consumption at boiler is proportional to the gain of the boiler, as the heat price is constant. On the other hand, the gas consumption at CHP is higher at heat-driven model while the gain of the CHP is lower at heat-driven model. In addition, total operation hours are also higher at heat-driven model, as the CHP operates as long as there is heat demand without considering any knowledge of prices. It can be also seen by comparing the specific gains that the power-driven operation outperforms the heat-driven operation.

It can be deduced that in the future, heat-driven operation would not be a smart solution for meeting residential heat demand considering the financial gains. Systems, which are capable of supplying heat and electricity concurrently, should consider electricity prices and develop further strategies to respond to fluctuations in the electricity market.

8.2. Comparison with respect to grid interactivity

First and foremost grid interactivity is a concept corresponding with how good the electricity production or consumption is scheduled. It concerns whether the electricity is produced at desired or undesired times with respect to the demand. Residual load 𝐺 is a good indicator to determine desired and undesired time periods. It is calculated by taking away the electricity productions of fluctuating renewable energy plants from the total electricity demand (Shammugam 2014, p. 9):

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𝐺= 𝑑_load− 𝑑_wind− 𝑑_PV , (𝟖.𝟐) where 𝑑_load is the electrical power demand; 𝑑_wind is the power produced by wind farms and 𝑑_PV is the power produced by photovoltaic plants.

In order to quantify the term grid interactivity, Klein et al. has proposed a coefficient called Absolute Load-Grid Matching Coefficient (𝐿𝐺𝐿𝐿abs), which is derived as follows (Klein et al.

2014, p. 5):

𝐿𝐺𝐿𝐿abs=∫ 𝑑el(𝑐)∙ 𝐺(𝑐)∙ 𝑑𝑐

𝑘_el∙ 𝐺̅ , (𝟖.𝟑𝟏) 𝑘el=� 𝑑el(𝑐)∙ 𝑑𝑐 , (𝟖.𝟑𝟏) where 𝑑_el is time-resolved electricity production; 𝐺 is time-resolved residual load; 𝑘_el is the total electricity production for an evaluation period and 𝐺̅ is the average residual load during the evaluation period.

𝐿𝐺𝐿𝐿_abs values can be interpreted as follows:

• 𝐿𝐺𝐿𝐿_abs> 1 indicates that electricity is produced at favorable times. This kind of

operation known as “positively grid-interactive” or “grid favorable” behavior (Klein et al. 2014, p. 5).

• 𝐿𝐺𝐿𝐿_abs= 1 can be interpreted as “grid-neutral” behavior. By definition, LGMC value approaches to 1, when electricity is continuously produced during a reference time (Shammugam 2014, p. 13).

• 𝐿𝐺𝐿𝐿abs< 1 is regarded as “negatively grid-interactive” or “grid-adverse” behavior. It

means that electricity production by conventional power plants takes place when renewable power plants also produce high amount of electrical power (Klein et al.

2014, p. 5).

In addition to the financial aspects, grid interactivity of both operations is also analyzed for the same future scenario. Fig. 8.2 shows monthly 𝐿𝐺𝐿𝐿abs values of both operations.

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Fig. 8.2 Comparison of 𝐋𝐋𝐋𝐂𝟏𝟏𝐝 values

As seen in Fig. 8.2, heat-driven operation demonstrates a “grid-neutral” behavior, as can be seen the 𝐿𝐺𝐿𝐿abs value ranges around “1.0”. However, power driven operation results in better 𝐿𝐺𝐿𝐿abs values throughout the year. Apart from monthly based calculation, 𝐿𝐺𝐿𝐿abs is also calculated for reference time of one year. It has been found out that power-driven operation is 33 % more grid interactive than the heat-driven operation.

It can be commented that it is possible to increases the grid interactivity of the system by optimizing the operation of the CHP based on electricity prices. As electricity prices mainly depend on the residual load, operating the CHP when electricity prices are high means also operating it when the residual load is high. This is why power-driven operation shows “grid-favorable” behavior.

8.3. Comparison with respect to size of the storage tank

At this section, effect of the storage size on grid interactivity and financial gains of the power-driven operation is discussed. To do that, three months at summer season are considered, as in the winter season storage tanks are generally not used.

The reference scenario includes the same heat demand, residual load and electricity prices of the future scenario. The effect of varying the size of the storage tank can be seen in Fig. 8.3.

0,9 1 1,1 1,2 1,3 1,4 1,5 1,6

1 2 3 4 5 6 7 8 9 10 11 12

LGMC

Months LGMC_Power Driven

LGMC_Heat Driven

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Fig. 8.3 Optimization results of the same summer scenario with different storage sizes

Except the storage volume, all other parameters including the optimization horizon (24 hours) stay consistent for comparison of all cases. However, these results would vary depending on the length of the optimization horizon.

As can be seen in Fig. 8.3, changing the size of the storage tank increases both the gains and the grid interactivity up to a certain level. In summer season, the heat demand is generally lower than the CHP thermal nominal power. Increasing the size of the storage tank enables the CHP to operate longer. Therefore, when the electricity prices are high enough, then the CHP stores the excess heat at the storage tank without causing any mismatch between the demand and supply. However, when the size of the storage tank reaches a saturation volume, which is 150 𝑚³ in this case, then it should be well reasoned whether it makes sense to have a storage tank with a larger size. It is worth commenting that this size should be determined by taking into account the average expected heat demand and the nominal thermal capacity of the CHP.

1,12 1,14 1,16 1,18 1,2 1,22 1,24

0 5000 10000 15000 20000 25000

5 15 45 60 100 150 200

LGMC [-]

Gains [€]

Storage Volume (m^3)

Storage Gain CHP Gain Boiler Gain LGMC

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Im Dokument Supervisory control of a combined heat and power plant by economic optimization (Seite 71-77)