- Email: [email protected]
Use of Heating Loads for Grid Frequency Control
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 103 (2016) 135 – 140
Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid, 19-21 April 2016, Maldives
Use of Heating Loads for Grid Frequency Control Meng Chenga*, Jianzhong Wua, Stephen J. Galsworthyb, Nikola Gargovc, William Hungd a
Cardiff University, Cardiff, CF24 3AA, UK b Open Energi, London, EC4A 3BG, UK c National Grid, Warwick, CV34 6DA, UK d WH Power System Consultant Ltd, Leamington Spa, CV32 6PJ, UK
Abstract Power generation from the renewable energy sources is usually intermittent and uncontrollable which challenges the grid frequency stability. The smart control of loads is an effective means to mitigate the challenge. A decentralized control of heating loads -- industrial melting pots (MPs), was developed which dynamically changes the power consumption of loads in response to grid frequency. A thermodynamic model of MPs was developed and validated based on site measurements by Open Energi. An aggregation of MP models with the control was integrated with a simplified Great Britain power system model. Results showed that MPs are able to provide frequency response in a way similar to generators, which provide a means for the system operator to quantify the benefits of demand response. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-reviewofunder responsibility of REM2016 Peer-review under responsibility the scientific committee of the Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid. Keywords: Type your keywords here, separated by semicolons ; Max 6 keywords
1. Introduction The integration of Renewable Energy Sources (RES) will reduce the capacity of fossil-fuel generators and therefore reduce the Green House Gas (GHG) emissions. However, the intermittency of RES causes uncertainties in the power supply which challenges the real-time power balance between generation and demand of the entire system and leads to the stability issue of grid frequency. The conventional solution of regulating grid frequency relies on the spinning reserve of frequencysensitive generators which is costly. The Great Britain power system operator, National Grid, calculated
* Meng Cheng. Tel: +44 29208 70422 E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid. doi:10.1016/j.egypro.2016.11.262
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
that the cost of frequency response only in July 2014 was £14.27 million [1]. In addition, these generators are mainly partly-loaded fossil-fuel generators and therefore aggravate the GHG emissions. One alternative low carbon solution is the use of grid-scale energy storage system (ESS), such as the flywheels and battery energy storage, when a large amount of variability exists in the system. However, most gridscale ESS is still considered as a high cost technology [2]. Recent research shows the potential of using existing devices -- the flexible loads, to mitigate the risks caused by the integration of RES. By intelligently managing the power consumption of flexible loads over time, power consumption of loads is able to be scheduled to achieve the peak shaving and load shifting [3]. This reduces the congestions in the electric network during the peak demand period. Demand is also able to be controlled to change the power consumption in response to regulation signals. Ref [4] coordinates the demand response and battery energy storage in order to provide tie-line smoothing of a Microgrid. Ref [5] developed an event-driven smart home controller in response to the Time of Use tariff which reduces the customer bills. In this paper, a local control of loads was developed which dynamically and autonomously changes the power consumption in response to grid frequency. A simplified thermodynamic model of heating loads, using industrial melting pot loads as an example, was developed which is a general model and can be easily modified to model other time-flexible loads. The benefits of using demand to provide frequency response are quantified. 2. Thermodynamic Model of Melting Pot Loads and Validation Melting pots (MPs) are industrial heating loads which are used for storing molten metal in readiness for casting. A hysteresis temperature control maintains the molten metal at the specified temperature and controls the On/Off state of a MP. The specified temperature is usually high, for example, the molten aluminum is required to be maintained at around 730±25 °C. The power consumption of a MP is typically 30 kW when it is at On-state. Normally, MPs are turned off outside operating hours as shown in Fig. 1 (i.e. before 0 h) which depicts the field measurements on a MP. The model and control of MPs in this paper only consider the hours when a MP is operating within the specified temperature set-points. 800
700
Temperature (degrees Celsius)
600
500
400
300
200
100
0
0
10
20
30 Time (h)
40
50
60
Fig. 1. Field measurements of temperature of a MP from UK’s demand aggregator, Open Energi
2.1. Define t ON and t OFF of a melting pot As depicted by the zoomed figure in Fig. 1, the increase and decrease of temperature with time show a nearly linear relationship. A schematic diagram of heating and cooling of a MP is shown in Fig. 2. Please note temperature is denoted as ‘T’ while the On/Off period of a MP is denoted as italic ‘T’. The MP is switched On at T low (point A) and the MP temperature (T) follows the curve AB. Conversely, the MP is
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
switched Off at T high (point B) and the MP temperature follows the curve BC. However, when a MP is used for frequency control, the On/Off state of a MP will be interrupted. The MP may be switched On/Off at any intermediate temperature. For example, in Fig. 2(a), MP is On at D. If the heater remains On, temperature will follow the curve DB for a time t ON until T high is reached at B. However, if the heater is switched OFF at D, temperature will follow the curve DE for a time t OFF until T low is reached at E. A similar process occurs as shown in Fig. 2(b) when a MP is initially Off at point M. The minimum possible value of t ON is zero and occurs when temperature reaches T high . The maximum possible value of t ON is the On-period T ON when temperature is at T low . Similarly, the range of t OFF is from zero to T OFF . T (°C)
T (°C) tON B
Thigh T
Thigh F
D T
Tlow
E tOFF
A
TON
shift
Tlow
C
TOFF
t (min)
tON
shift
N
H
M G tOFF
TOFF
TON
t (min)
Fig. 2. (a) t ON and t OFF of during the heating of a MP; (b) t ON and t OFF of during the cooling of a MP
t ON and t OFF was measured on two 32 kW and two 44 kW real MPs at different times of several days. At different temperatures of a MP, a pair of t ON and t OFF were measured by recording the time of the MP to remain On and remain Off. The measured pair of t ON and t OFF at each temperature is depicted by a cross in Fig. 3. The analytical expression of the relationship between t ON and t OFF in Fig. 3 was derived by the curve-fitting tool (cftool) of Matlab and is shown by (1) and (2). (1) (2)
Fig. 3. Relationship of t ON and t OFF of a MP from field measurements (t ON is normalised with maxon (i.e. T ON ) and t OFF is normalised with maxoff (i.e. T OFF ))
2.2. Simplified curve-fit model of a MP A simplified curve-fit model of each MP was developed in Matlab/Simulink based on the relationship of t ON and t OFF as shown in Fig. 3. Thermodynamics of a MP, i.e. variations of the MP temperature over time, were modelled using variations of t ON and t OFF over time as illustrated by Fig. 2.
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
Table 1 is used to model the variations of t ON and t OFF IRUDOOSRVVLEOHWKHUPDOVWDWHVǻt is the time step (e.g. 10 ms in simulations). Equation (4) was based on (2) and equation (5) was based on (1). Table 1. t ON and t OFF of a MP model Variations of t ON and t OFF
On/Off of a MP Remain On at t
(3)
Switched On at t (4) Remain Off at t (5) (6)
Switched Off at t
MPs were assigned different initial t ON and t OFF in order to reflect the diversity amongst MPs. t ON was assigned randomly in the range of 0 to T ON . t OFF was assigned randomly in the range of 0 to T OFF . The value of T ON was distributed randomly within the range of 6 to 41 min, which was measured on different MPs in the field tests. Similarly, T OFF was distributed randomly within the range 19 to 69 min. The simplified curve-fit model was validated with site measurements by calculating the temperature at each pair of t ON and t OFF . In addition, a thermal model of each MP was also developed based on the heat transfer principle to depict the variations of temperature at each time step as illustrated in [6]. Fig. 4 shows the temperature variations obtained from the curve-fit model, the thermal model and the field measurements. The temperature variations and On/Off cycles of the three nearly match each other. The simplified curve-fit model is validated to represent the thermodynamics of MPs and shows high computational efficiency when studying the impacts of grid-scale MP loads for frequency response. Temperature (ºC)
750
Site Measurement Thermal Model
745
Simplified Model
740 735 730 725 0
20
40
60 Time (min)
80
100
120
Fig. 4. Comparison of temperature of a MP in the site measurement, thermal model and simplified model
3. Dynamic Control of Melting Pots for Frequency Response Fig. 5 briefly depicts the control strategy of a MP for frequency response. Inherent hysteresis temperature control constantly measures temperature and compares with the specified temperature setpoint which determines On/Off state of the MP. A dynamic controller is added on each MP which constantly measures the grid frequency and compares with a pair of frequency set-points of a MP, and then determines to switch On/Off the MP. The pair frequency set-points, namely F ON and F OFF , are randomly distributed within the range of 49.5 to 50 Hz and of 50 to 50.5 Hz. If frequency drops lower than F OFF , the MP is switched Off. If frequency rises higher than F ON , the MP is switched On. The frequency control can therefore override the temperature control if the temperature stays within the temperature set-points. If temperature is outside the set-points, the MP is switched according to temperature control regardless of frequency deviations. Similar as explained in [6], when frequency starts to recover back to 50 Hz after a frequency incident, F ON or F OFF of each MP are updated dynamically to 50 Hz. At each time step, the change of F ON or F OFF is in proportion to the change of frequency. When F ON reaches 50 Hz, F ON will be updated to 50.5 Hz and revert the MP back to Off-state. Similarly when F OFF reaches 50 Hz, F OFF will be updated to 49.5 Hz and revert the MP back to On-state. Otherwise, F ON and F OFF remain unchanged.
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
Temperature
Hysteresis Control Switch (e.g. Temperature Control)
Switch On/Off
Relay
Request
Melting Pot
Switch Request Grid Frequency
Monitor Power Consumption
On/Off State
Dynamic Controller
Database
Fig. 5. Control strategy of one melting pot for frequency response
The dynamic control ensures equal opportunity amongst MPs to be switched in response to frequency deviations. The entire control takes the temperature control as a priority which will not undermine the normal use of loads. The control strategy can be applied to other heating loads and water pumping loads. 4. Case Studies Case studies were carried out by connecting an aggregation of 50,000 MPs (30 kW per MP) with a one-bus GB power system model from National Grid. The model includes frequency-responsive generator models, e.g. coal and gas generators, and generator models without frequency control, e.g. wind and nuclear generators. The system demand was 20 GW. A loss of generation of 1.8 GW was firstly applied to the power system. Results are shown in Fig. 6. MP response (MW)
Frequency (Hz)
50.5 without MP response with MP response
50 49.5 49
0
10
20
30 Time (s)
40
50
60
0 -200 -400 -600
0
10
20
30 Time (s)
40
50
60
df/dt (Hz/s)
0.5
0
-0.5
without MP response with MP response 0
10
20
30 Time (s)
40
50
60
Fig. 6. (a) Variations of grid frequency after sudden loss of generation; (b) Reduction of MP power consumption; (c) Variations of rate of change of frequency
It can be seen, MPs provide approx. 496 MW of reduction when frequency drops to 49.38 Hz. With the response from MPs, the maximum drop of frequency is reduced by 0.18 Hz. In addition, Fig. 6(c) depicts that, with MPs’ response, the rate of frequency change is decreased which indicates that the frequency response from demand is a fast response compared to the response from frequency sensitive generators. A sudden increase in generation of 1 GW in the power system was also simulated. Results are given by Fig. 7. When frequency increases, the power consumption of MPs is increased by 772 MW which reduces the maximum rise of frequency by 0.2 Hz. 1000 without MP response with MP response
50.4 50.2 50 49.8
0
10
20
30 Time (s)
40
50
60
MP response (MW)
Frequency (Hz)
50.6
500
0
-500
0
10
20
30 Time (s)
40
50
60
Fig. 7. (a) Variations of grid frequency after sudden increase in generation; (b) Increase in MP power consumption
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
5. Conclusions The model and control of MPs for frequency response was developed. The model is a simplified and generalised thermodynamic model based on field measurements which facilitates the grid-level study of demand response with high computational speed. The frequency control is a local control which dynamically and autonomously changes power consumption of MPs without undermining the inherent temperature requirements of each MP. Case studies were carried out to quantify the benefits of demand response for frequency control. Results showed that MPs are able to provide dynamic frequency response in a manner similar to frequency-sensitive generators. This will also contribute to smoothing the supply fluctuations from RES. Future work will be carried out to implement the frequency control with other load types such as the water pumping loads. More case studies will be carried out to study the impacts of the demand response. Acknowledgements The work was supported in part by NIA project, by RESTORES project under the grant of EPSRC, by P2P-SmarTest project under the grant of EU commission. References [1] National Grid plc, ‘Monthly Balancing Services Summary – July 2014’, [Online]. Available: http://www2.nationalgrid.com/WorkArea/DownloadAsset.aspx?id=31962. [2] G. Strbac, M. Aunedi and et.al, ”Strategic assessment of the role and value of energy storage systems in the UK low carbon energy future,” Rep. Carbon Trust. Energy Futur. Lab, Imp. Coll. EDF UK R&D Cent., pp. 9, 2012. [3] Mohammad Rastegar, Mahmud Fotuhi-Firuzabad, Farrokh Aminifar, Load commitment in a smart home, Applied Energy, vol. 96, Aug 2012, pp. 45-54. [4] Dan Wang et al., "A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services," in IEEE Transactions on Sustainable Energy, vol. 5, no. 2, pp. 476-486, Apr 2014. [5] Alessandro Di Giorgio, Laura Pimpinella, An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management, Applied Energy, vol. 96, Aug 2012, pp. 92-110. [6] Meng Cheng, Jianzhong Wu and et al, “Power system frequency response from the control of bitumen tanks”, IEEE Trans. on Power System, vol. PP, no. 99, pp. 1-10, Jun 2015.
ScienceDirect Energy Procedia 103 (2016) 135 – 140
Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid, 19-21 April 2016, Maldives
Use of Heating Loads for Grid Frequency Control Meng Chenga*, Jianzhong Wua, Stephen J. Galsworthyb, Nikola Gargovc, William Hungd a
Cardiff University, Cardiff, CF24 3AA, UK b Open Energi, London, EC4A 3BG, UK c National Grid, Warwick, CV34 6DA, UK d WH Power System Consultant Ltd, Leamington Spa, CV32 6PJ, UK
Abstract Power generation from the renewable energy sources is usually intermittent and uncontrollable which challenges the grid frequency stability. The smart control of loads is an effective means to mitigate the challenge. A decentralized control of heating loads -- industrial melting pots (MPs), was developed which dynamically changes the power consumption of loads in response to grid frequency. A thermodynamic model of MPs was developed and validated based on site measurements by Open Energi. An aggregation of MP models with the control was integrated with a simplified Great Britain power system model. Results showed that MPs are able to provide frequency response in a way similar to generators, which provide a means for the system operator to quantify the benefits of demand response. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-reviewofunder responsibility of REM2016 Peer-review under responsibility the scientific committee of the Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid. Keywords: Type your keywords here, separated by semicolons ; Max 6 keywords
1. Introduction The integration of Renewable Energy Sources (RES) will reduce the capacity of fossil-fuel generators and therefore reduce the Green House Gas (GHG) emissions. However, the intermittency of RES causes uncertainties in the power supply which challenges the real-time power balance between generation and demand of the entire system and leads to the stability issue of grid frequency. The conventional solution of regulating grid frequency relies on the spinning reserve of frequencysensitive generators which is costly. The Great Britain power system operator, National Grid, calculated
* Meng Cheng. Tel: +44 29208 70422 E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid. doi:10.1016/j.egypro.2016.11.262
136
Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
that the cost of frequency response only in July 2014 was £14.27 million [1]. In addition, these generators are mainly partly-loaded fossil-fuel generators and therefore aggravate the GHG emissions. One alternative low carbon solution is the use of grid-scale energy storage system (ESS), such as the flywheels and battery energy storage, when a large amount of variability exists in the system. However, most gridscale ESS is still considered as a high cost technology [2]. Recent research shows the potential of using existing devices -- the flexible loads, to mitigate the risks caused by the integration of RES. By intelligently managing the power consumption of flexible loads over time, power consumption of loads is able to be scheduled to achieve the peak shaving and load shifting [3]. This reduces the congestions in the electric network during the peak demand period. Demand is also able to be controlled to change the power consumption in response to regulation signals. Ref [4] coordinates the demand response and battery energy storage in order to provide tie-line smoothing of a Microgrid. Ref [5] developed an event-driven smart home controller in response to the Time of Use tariff which reduces the customer bills. In this paper, a local control of loads was developed which dynamically and autonomously changes the power consumption in response to grid frequency. A simplified thermodynamic model of heating loads, using industrial melting pot loads as an example, was developed which is a general model and can be easily modified to model other time-flexible loads. The benefits of using demand to provide frequency response are quantified. 2. Thermodynamic Model of Melting Pot Loads and Validation Melting pots (MPs) are industrial heating loads which are used for storing molten metal in readiness for casting. A hysteresis temperature control maintains the molten metal at the specified temperature and controls the On/Off state of a MP. The specified temperature is usually high, for example, the molten aluminum is required to be maintained at around 730±25 °C. The power consumption of a MP is typically 30 kW when it is at On-state. Normally, MPs are turned off outside operating hours as shown in Fig. 1 (i.e. before 0 h) which depicts the field measurements on a MP. The model and control of MPs in this paper only consider the hours when a MP is operating within the specified temperature set-points. 800
700
Temperature (degrees Celsius)
600
500
400
300
200
100
0
0
10
20
30 Time (h)
40
50
60
Fig. 1. Field measurements of temperature of a MP from UK’s demand aggregator, Open Energi
2.1. Define t ON and t OFF of a melting pot As depicted by the zoomed figure in Fig. 1, the increase and decrease of temperature with time show a nearly linear relationship. A schematic diagram of heating and cooling of a MP is shown in Fig. 2. Please note temperature is denoted as ‘T’ while the On/Off period of a MP is denoted as italic ‘T’. The MP is switched On at T low (point A) and the MP temperature (T) follows the curve AB. Conversely, the MP is
137
Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
switched Off at T high (point B) and the MP temperature follows the curve BC. However, when a MP is used for frequency control, the On/Off state of a MP will be interrupted. The MP may be switched On/Off at any intermediate temperature. For example, in Fig. 2(a), MP is On at D. If the heater remains On, temperature will follow the curve DB for a time t ON until T high is reached at B. However, if the heater is switched OFF at D, temperature will follow the curve DE for a time t OFF until T low is reached at E. A similar process occurs as shown in Fig. 2(b) when a MP is initially Off at point M. The minimum possible value of t ON is zero and occurs when temperature reaches T high . The maximum possible value of t ON is the On-period T ON when temperature is at T low . Similarly, the range of t OFF is from zero to T OFF . T (°C)
T (°C) tON B
Thigh T
Thigh F
D T
Tlow
E tOFF
A
TON
shift
Tlow
C
TOFF
t (min)
tON
shift
N
H
M G tOFF
TOFF
TON
t (min)
Fig. 2. (a) t ON and t OFF of during the heating of a MP; (b) t ON and t OFF of during the cooling of a MP
t ON and t OFF was measured on two 32 kW and two 44 kW real MPs at different times of several days. At different temperatures of a MP, a pair of t ON and t OFF were measured by recording the time of the MP to remain On and remain Off. The measured pair of t ON and t OFF at each temperature is depicted by a cross in Fig. 3. The analytical expression of the relationship between t ON and t OFF in Fig. 3 was derived by the curve-fitting tool (cftool) of Matlab and is shown by (1) and (2). (1) (2)
Fig. 3. Relationship of t ON and t OFF of a MP from field measurements (t ON is normalised with maxon (i.e. T ON ) and t OFF is normalised with maxoff (i.e. T OFF ))
2.2. Simplified curve-fit model of a MP A simplified curve-fit model of each MP was developed in Matlab/Simulink based on the relationship of t ON and t OFF as shown in Fig. 3. Thermodynamics of a MP, i.e. variations of the MP temperature over time, were modelled using variations of t ON and t OFF over time as illustrated by Fig. 2.
138
Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
Table 1 is used to model the variations of t ON and t OFF IRUDOOSRVVLEOHWKHUPDOVWDWHVǻt is the time step (e.g. 10 ms in simulations). Equation (4) was based on (2) and equation (5) was based on (1). Table 1. t ON and t OFF of a MP model Variations of t ON and t OFF
On/Off of a MP Remain On at t
(3)
Switched On at t (4) Remain Off at t (5) (6)
Switched Off at t
MPs were assigned different initial t ON and t OFF in order to reflect the diversity amongst MPs. t ON was assigned randomly in the range of 0 to T ON . t OFF was assigned randomly in the range of 0 to T OFF . The value of T ON was distributed randomly within the range of 6 to 41 min, which was measured on different MPs in the field tests. Similarly, T OFF was distributed randomly within the range 19 to 69 min. The simplified curve-fit model was validated with site measurements by calculating the temperature at each pair of t ON and t OFF . In addition, a thermal model of each MP was also developed based on the heat transfer principle to depict the variations of temperature at each time step as illustrated in [6]. Fig. 4 shows the temperature variations obtained from the curve-fit model, the thermal model and the field measurements. The temperature variations and On/Off cycles of the three nearly match each other. The simplified curve-fit model is validated to represent the thermodynamics of MPs and shows high computational efficiency when studying the impacts of grid-scale MP loads for frequency response. Temperature (ºC)
750
Site Measurement Thermal Model
745
Simplified Model
740 735 730 725 0
20
40
60 Time (min)
80
100
120
Fig. 4. Comparison of temperature of a MP in the site measurement, thermal model and simplified model
3. Dynamic Control of Melting Pots for Frequency Response Fig. 5 briefly depicts the control strategy of a MP for frequency response. Inherent hysteresis temperature control constantly measures temperature and compares with the specified temperature setpoint which determines On/Off state of the MP. A dynamic controller is added on each MP which constantly measures the grid frequency and compares with a pair of frequency set-points of a MP, and then determines to switch On/Off the MP. The pair frequency set-points, namely F ON and F OFF , are randomly distributed within the range of 49.5 to 50 Hz and of 50 to 50.5 Hz. If frequency drops lower than F OFF , the MP is switched Off. If frequency rises higher than F ON , the MP is switched On. The frequency control can therefore override the temperature control if the temperature stays within the temperature set-points. If temperature is outside the set-points, the MP is switched according to temperature control regardless of frequency deviations. Similar as explained in [6], when frequency starts to recover back to 50 Hz after a frequency incident, F ON or F OFF of each MP are updated dynamically to 50 Hz. At each time step, the change of F ON or F OFF is in proportion to the change of frequency. When F ON reaches 50 Hz, F ON will be updated to 50.5 Hz and revert the MP back to Off-state. Similarly when F OFF reaches 50 Hz, F OFF will be updated to 49.5 Hz and revert the MP back to On-state. Otherwise, F ON and F OFF remain unchanged.
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
Temperature
Hysteresis Control Switch (e.g. Temperature Control)
Switch On/Off
Relay
Request
Melting Pot
Switch Request Grid Frequency
Monitor Power Consumption
On/Off State
Dynamic Controller
Database
Fig. 5. Control strategy of one melting pot for frequency response
The dynamic control ensures equal opportunity amongst MPs to be switched in response to frequency deviations. The entire control takes the temperature control as a priority which will not undermine the normal use of loads. The control strategy can be applied to other heating loads and water pumping loads. 4. Case Studies Case studies were carried out by connecting an aggregation of 50,000 MPs (30 kW per MP) with a one-bus GB power system model from National Grid. The model includes frequency-responsive generator models, e.g. coal and gas generators, and generator models without frequency control, e.g. wind and nuclear generators. The system demand was 20 GW. A loss of generation of 1.8 GW was firstly applied to the power system. Results are shown in Fig. 6. MP response (MW)
Frequency (Hz)
50.5 without MP response with MP response
50 49.5 49
0
10
20
30 Time (s)
40
50
60
0 -200 -400 -600
0
10
20
30 Time (s)
40
50
60
df/dt (Hz/s)
0.5
0
-0.5
without MP response with MP response 0
10
20
30 Time (s)
40
50
60
Fig. 6. (a) Variations of grid frequency after sudden loss of generation; (b) Reduction of MP power consumption; (c) Variations of rate of change of frequency
It can be seen, MPs provide approx. 496 MW of reduction when frequency drops to 49.38 Hz. With the response from MPs, the maximum drop of frequency is reduced by 0.18 Hz. In addition, Fig. 6(c) depicts that, with MPs’ response, the rate of frequency change is decreased which indicates that the frequency response from demand is a fast response compared to the response from frequency sensitive generators. A sudden increase in generation of 1 GW in the power system was also simulated. Results are given by Fig. 7. When frequency increases, the power consumption of MPs is increased by 772 MW which reduces the maximum rise of frequency by 0.2 Hz. 1000 without MP response with MP response
50.4 50.2 50 49.8
0
10
20
30 Time (s)
40
50
60
MP response (MW)
Frequency (Hz)
50.6
500
0
-500
0
10
20
30 Time (s)
40
50
60
Fig. 7. (a) Variations of grid frequency after sudden increase in generation; (b) Increase in MP power consumption
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Meng Cheng et al. / Energy Procedia 103 (2016) 135 – 140
5. Conclusions The model and control of MPs for frequency response was developed. The model is a simplified and generalised thermodynamic model based on field measurements which facilitates the grid-level study of demand response with high computational speed. The frequency control is a local control which dynamically and autonomously changes power consumption of MPs without undermining the inherent temperature requirements of each MP. Case studies were carried out to quantify the benefits of demand response for frequency control. Results showed that MPs are able to provide dynamic frequency response in a manner similar to frequency-sensitive generators. This will also contribute to smoothing the supply fluctuations from RES. Future work will be carried out to implement the frequency control with other load types such as the water pumping loads. More case studies will be carried out to study the impacts of the demand response. Acknowledgements The work was supported in part by NIA project, by RESTORES project under the grant of EPSRC, by P2P-SmarTest project under the grant of EU commission. References [1] National Grid plc, ‘Monthly Balancing Services Summary – July 2014’, [Online]. Available: http://www2.nationalgrid.com/WorkArea/DownloadAsset.aspx?id=31962. [2] G. Strbac, M. Aunedi and et.al, ”Strategic assessment of the role and value of energy storage systems in the UK low carbon energy future,” Rep. Carbon Trust. Energy Futur. Lab, Imp. Coll. EDF UK R&D Cent., pp. 9, 2012. [3] Mohammad Rastegar, Mahmud Fotuhi-Firuzabad, Farrokh Aminifar, Load commitment in a smart home, Applied Energy, vol. 96, Aug 2012, pp. 45-54. [4] Dan Wang et al., "A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services," in IEEE Transactions on Sustainable Energy, vol. 5, no. 2, pp. 476-486, Apr 2014. [5] Alessandro Di Giorgio, Laura Pimpinella, An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management, Applied Energy, vol. 96, Aug 2012, pp. 92-110. [6] Meng Cheng, Jianzhong Wu and et al, “Power system frequency response from the control of bitumen tanks”, IEEE Trans. on Power System, vol. PP, no. 99, pp. 1-10, Jun 2015.