Frequency control of future power systems: reviewing and evaluating challenges and new control methods
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Integration of more renewable energy resources introduces a challenge in frequency control of future power systems. This paper reviews and evaluates the possible challenges and the new control methods of frequency in future power systems. Different types of loads and distributed energy resources (DERs) are reviewed. A model representation of a population of the water heater devices for the demand side frequency response is considered. A model representation of a population of battery energy storage system (BESS)-based DERs such as smart electric vehicles (EVs) charging, large-scale BESSs, and residential and non-residential BESSs, are highlighted. The simplified Great Britain power system and the 14-machine South-East Australian power system were used to demonstrate the effectiveness of the new methods in controlling power system frequency following a disturbance. These new methods are effective in recovering the fallen frequency response and present a great potential in controlling the frequency in future power systems.
KeywordsBattery energy storage system (BESS) Distributed energy resource (DER) Electric vehicle Home-based BESS Large-scale BESS aggregation Demand side response Frequency control Markov chain
Due to the integration of renewable energy resources (RESs), performing the frequency control from only the conventional generation becomes more expensive. The aggregation of the demand side controllable devices to regulate the frequency is a new method to alleviate the expanding need in the conventional power generators [1, 2, 3]. The distributed energy resources (DERs) are becoming more attractive to supply local loads alongside with the conventional generators . The DERs have different power dynamics compared with the classical power generators. Some DERs had no rotational inertia and are connected to the grid via power electronics interface. The whole power system stability with the integration of DERs is an important issue in the modern systems. DERs and their interactions have to be well coordinated. The DERs with a well-coordinated control can significantly improve the stability of the power system frequency [1, 2, 3]. The uses of the emergency power amount from the load side for the frequency reserve services presents a new challenge. The challenge is associated with the control of large distributed loads . Especially, with the electric vehicles (EVs), Residential battery energy storage systems (BESSs), water heaters, and cloth dryers. Therefore, the DER allocation is also important to enhance the integration of these power sources and improving the power system frequency .
The load control with the integration of EVs and distributed generators was presented in  for the power regulation. The load-shifting optimisation problem was solved according to technical and market conditions. This approach is applicable for various DERs such as the EVs smart charging . Also, a review of the congestion management methods was presented in  for the distribution network with high penetrations of DERs. The methods covered the market and direct control methods. Furthermore, a review of the power electronics-based DERs and their stability problem under the smart grid scenario was proposed in . Some renewable energy sources and intelligent loads were considered as an example of the power electronics-based DERs. The conventional power system and power electronics stability theorems were used to define the potential problems. The stability challenges were identified with the possible solutions from the steady state, small-signal, and large-signal stability criteria . Intelligent electronic devices (IEDs) are the standard protection and control equipment that is being used nowadays in power systems. These smart devices are used for different applications such as system control and protections, hence, can boot the system modelling and analysis of power systems .
In the Great Britain power system, the demand side frequency control (DSFC) and BESSs were considered in [9, 10, 11, 12]. They are among the DER key factors for the modern power system. BESSs are considered in the previous work for the application of the frequency regulation in the power system [13, 14]. It presented a fast dynamic response and compensated the load change on the grid side. Therefore, the aggregated BESSs can participate in regulating the frequency of both low and high-frequency reserve services. Therefore, the objectives of this paper are: ① Review the frequency control in the Great Britain power system; ② Identify the inertia, the source of inertia, and the future challenge of an inertia reduction due to RESs; ③ Classify the new control methods for controlling the frequency such as demand response and DERs; ④ Use some model representation of a population of controllable loads; ⑤ Demonstrate the effectiveness of the controllable loads in controlling the frequency of a power system.
2 Frequency control in power systems
Frequency containment policy of Great Britain power system
Frequency limits (Hz)
System frequency under normal operating conditions and the maximum frequency deviation for a loss of generation or a connection of demand up to ± 300 MW
Maximum frequency deviation for a loss of generation more than 300 MW and less than or equal to 1320 MW
Maximum frequency deviation for a loss of generation more than 1320 MW and less than or equal to 1800 MW. The frequency must be restored to at least 49.5 Hz within 1 minute
Many of the interventions of the Great Britain system operator should be adopted for balancing the frequency. This can be carried out by integrating different balancing services, such as reserve services, system security services and frequency response services. These services aim to maintain the frequency within the acceptable limits and restore the frequency after sudden changes in the demand or generation. The services involve both generation and demand. The frequency response services include firm frequency response (FFR), mandatory frequency response (MFR) and enhanced frequency response (EFR), as indicated below .
2.1 Firm frequency response
This provides a dynamic or non-dynamic response to the changes in the frequency. This service is acquired from generators, except for in generators that provide MFR. In addition, it is provided from the demand through a competitive process of tenders. These tenders can be assigned for a low or high-frequency event or both .
2.2 Mandatory frequency response
The primary frequency response is an automatic 10% increase in the output of a generator in response to a frequency drop within ten seconds and can be sustained for a further twenty seconds.
The secondary frequency response is an automatic 10% increase in the output of a generator in response to a frequency drop within thirty seconds and can be sustained for up to thirty minutes.
The high-frequency response is an automatic reduction in the output of a generator in response to a frequency rise within ten seconds and can be sustained indefinitely.
2.3 Enhanced frequency response
3 Inertia in power systems
System inertia can be defined by the availability of the energy in the rotating mass of generators that are directly coupled to the power system . System inertia determines the response of a power system to a frequency disturbance, such as a sudden loss of generation or load.
3.1 Source of inertia in power system
Large-capacity synchronous generators, i.e. in the Great Britain power system provide about 70% of the system inertia. The rest is provided by smaller synchronous generators and synchronous demand . The National Grid is currently instructing conventional generators to run continuously, even if there are no economic profits since they are part-loaded. This creates a minimum level of available inertia to secure a capacity for frequency response . This capacity is expected to be 30%-40% more than the current capacity in the next five years . However, these generators are expensive to operate and produce large amounts of greenhouse gas emissions.
Payments for different frequency response services by National Grid in July 2016 and January 2017
Payment cost (million £)
FFR plus frequency control by demand management
3.2 Challenges of inertia reduction
The absence of direct coupling between the machine and the power system in some RESs, e.g. wind and solar due to their power electronics, prevent their rotating mass from contributing to system inertia . Therefore, RESs reduce the total system inertia, and hence, prompt decreased power system stability and increment the difficulties of the operation and control of the power system. RESs have power fluctuations due to the change of the wind speed and solar power, causing a significant impact on the stability of the frequency deviation.
Frequency response requirements for different values of inertia and generation loss
System inertia (GVA·s)
Response requirement (MW)
500 MW loss
600 MW loss
A reduction in the system inertia will increase the rate of change of frequency (RoCoF) when the system is subjected to sudden disturbances such as loss or increase in the demand or generation. In this situations, it is highly recommended to minimise the settling time during the disturbance period [10, 25, 26, 27]. Therefore, the need for additional frequency control is increased [20, 28]. A fast frequency response from the generation side is one of the recommended solutions to mitigate the increased frequency deviation issue [26, 29]. Also, the RESs alongside with the classical generators have potential to provide frequency control as ancillary service [30, 31].
The control system, which is responsible for controlling the frequency, must provide a fast and stable response [9, 32]. A rapid response to a high RoCoF is strongly recommended; however, a very quick response has a risk of system oscillations . A flexible embedded real-time controller that offers higher flexibility versus low cost is required with the ability of event detection and response algorithm to any disturbance. The designed controller is preferable to have scalable parameters and fast controller latency to create a new adaptive protection system that is capable of standing against frequency collapse in future energy networks . This scheme is intended to supplement local control, rather than replace it. Existing load shedding and governor-frequency control processes continue to be in place, but new forms of frequency control will decrease the degree to which the conventional response would be approached. This stage will allow the control scheme to be fine-tuned based on real measurements [25, 26].
4 Demand side frequency response
With the expanding needs of renewable energy resources, performing primary frequency control utilising just the generation side becomes noticeably expensive but also technically difficult. In addition, the combination of high wind and solar output alongside with a low demand means that a significant number of interventions by the Great Britain system operator should be taken for balancing and operability reasons. Therefore, there are opportunities to further develop demand-side services during both periods of low and high demand .
Demand side frequency response presents a novel way to mitigate the increasing need in the conventional power generators [33, 34, 35]. The uses of the emergency power amount from the load side for the frequency reserve services presents a new challenge. The challenge is associated with the control of large distributed loads . Especially, with the EVs, residential BESSs, water heaters, and cloth dryers.
4.1 Demand-side integration
Demand-side integration (DSI) measures how to use the loads and local generations to support system management and to improve power supply. The term ‘demand-side integration’ refers to the relationship between the power systems, energy supply and end users. This relationship includes demand-side management (DSM) and demand-side response (DSR) . The potential of DSI relies upon customer’s, such as the duration and the timing of their demand response, the availability and the timing of the information provided to them, the automation of end-use equipment, metering, pricing/contracts, and the performance of the communications infrastructure .
There are two types of programs for the application of DSI: price-based programs and incentive-based programs [37, 38]. In price-based programs, consumers adjust their energy consumption about the changes in electricity market price. In contrast, the latter is provided through curtailment or interruptible contracts where consumers are paid to shift or reduce their energy consumption .
In the Great Britain power system, a project on demand-side integration estimated that the programs of demand-side integration are more commercially viable for distribution network operators at medium voltage level than lower levels in term of investments .
However, it is important to address the challenges associated with the demand side integration, such as changing the natural diversity of loads, which can create more unpredictable and undesirable effects. For example, the amount of recovered energy through the DSR may be larger than the required load reduction .
4.2 Control methods of demand for frequency response
Estimated flexible demand in Great Britain power system during a peak hour of a winter day
Other non-domestic sectors
Two control methods were used in the literature to control flexible demand units: centralised and decentralised control methods. The loads with a thermal storage showed suitable characteristics to provide a provision of demand-side frequency response than other types of loads [41, 45, 46, 47].
Centralised control of the demand units relies on the infrastructure of information and communication technology (ICT) to provide communications between the unit and the centralised control of the aggregator . For instance, a centralised frequency controller presented in  sends a signal to turn ON/OFF domestic air conditioning units and water heaters after a pre-set value of frequency rise/dip. The centralised controller reduces the uncertainty in the response of controllable units. However, the establishment of communications in the centralised method presents challenges, such as cost and latency.
To overcome these challenges, decentralised frequency controllers were developed. A decentralised controller, presented in , regulated the set-points of the temperature of refrigerators according to the variation in frequency deviation and its power consumption was controlled. A dynamic decentralised controller was developed in  to change the aggregated power consumption of refrigerators in a linear relationship with a frequency change. The controller planned to abstain from influencing the primary cold supply function of refrigerators. Similar controllers were developed to provide a frequency response from industrial bitumen tanks  and melting pots .
The required availability of refrigerators to provide frequency response was estimated by work presented in . It was estimated that approximately 1.5 million refrigerators are required to provide 20 MW of response. The total cost of frequency controllers added to each refrigerator was calculated in 2007 at a price of approximately £3 million (£2 million of an estimated cost for each controller) .
4.3 Thermostatically based controllable loads
In Great Britain’s power system, the DSR was evaluated and considered in applications of the frequency control [56, 57, 62]. The aggregation of the TCLs for the DSFC in the Great Britain power system was investigated in [56, 57]. The DSR model was used to regulate the dynamic of the TCLs. This model was used to obtain the optimal power consumption and allocated sufficient ancillary services. This model was developed for a multi-stage stochastic unit commitment and integrated into a mixed integer linear programming formulation. It was proposed to deal with the future inertia reduction under future low-carbon scenarios. The study cases were focused on the total system cost and the produced amount of the CO2 emission .
In addition, domestic refrigerators as an example of the TCLs DSFC in the Great Britain power system was proposed in  to deal with the future inertia reduction. The method presented a non-real-time communication-controlled TCLs. The aggregated power of the TCLs was controlled as a linear function of the local frequency change. A novel technique was developed in  for estimating the infeed loss and post-fault in a power system.
Markov chain model was applied to represent the aggregated power consumption of the TCLs population for DSR [33, 34, 35, 63]. A hierarchal DSR framework with two layers was presented in . The top layer is used to obtain the control gain of the drooping amount. This value was sent to the local layer, which involves a population model including different devices. The local layer changed their power consumption of the controllable loads to meet the value of the control gain. The local layer had a Markov chain-based frequency controller to change the power consumption to meet the gain value probabilistically. The TCLs were designed according to three operation states, ON, OFF, and LOCK . Similarly, the same framework for DSR was used in [33, 35] to represent the TCLs but with four operation states, ON, OFF-LOCKED, OFF, and ON-LOCKED.
4.4 Water heaters
Electric water heaters are ideal home appliances, which can be controlled to provide frequency response services by turning ON/OFF the device in response to a pre-set value of frequency deviation [64, 65, 66, 67, 68]. There are two main types of water heaters: the electric resistance water heater (ERWH) and the heat pump water heater (HPWH). In addition, a hybrid type of water heater has both types incorporated in the same unit .
There is a large population of water heaters in the present and future power system. The water heater has a power consumption higher than other home appliances, such as dryers, washing machines and refrigerators. For example, in certain areas in the USA, water heaters consume about 30% of the household load, which contributes significantly to the peak load.
Water heaters can be used as energy storage devices by heating up water to a higher temperature than its normal range. Hence, no energy is wasted in providing balancing services, and there is no impact on customers’ comfort.
The modelling and control of water heater devices are widely covered in the literature to support the frequency in power systems [64, 71, 72]. Markov chain was used to represent the aggregated power for a various controllable population of water heaters for DSFC [33, 34, 35, 63]. A hierarchal control framework for the demand side frequency control with two decision layers was presented in [73, 74, 75]. The top layer is the supervisory control of the aggregator, while the local layer is for the devices population and a frequency controller. The dynamic behaviour of the controllable load was represented by using Markov-based states [73, 74, 75, 76]. The electric resistive water heater was represented by two states (ON and OFF) while the heat pump water heater was represented by four states ON, OFF-LOCKED, OFF, and ON-LOCKED . Markov chain-based states are representing the dynamic behaviour of the switching in the end-user controllable water heater devices. Hence, it represents the population of those controllable devices. The controller changed the power consumption of controllable loads with an amount according to the gain value sent by the supervisory control Layer. The gain value was calculated according to the number of the system loads and the controllable loads (see further details in [73, 74, 75]).
4.5 Electric vehicles
Recently, an increasingly ambitious target for a high level of EV integration was announced around the world. An internationally high priority target was placed on deploying and developing the technology for EVs [31, 77]. It is assumed that the annual production of EVs would be over 100 million by 2050 .
The UK government has declared that EVs are anticipated to play a major role in future transport sectors. The increased interest in EVs leads to a significant impact on power systems .
However, the high uptake of EVs introduces a new challenge to the planning and operation of current and future power systems. This challenge relates to the uncontrolled charging of EVs, or so-called ‘dump charging’. This uncontrolled charging may create a new peak load, such as charging when EV owners return home from their last day trip .
EVs’ load can be controlled to provide frequency response service in a power system. However, providing a primary frequency response from EVs in certain cases can introduce a negative impact on power system stability. This impact is due to insufficient load estimation of aggregated EVs . The common approach to provide a demand-side frequency response from EVs is to control the charging/discharging rates of vehicle-to-grid (V2G). There are many types of control and management of loads (including EVs), such as reducing users’ bills, charging coordination of EVs and charging scheduling .
Load control with the integration of EVs and distributed generators was presented in  for the power regulation. The load-shifting optimisation problem was solved according to technical and market conditions. This approach is applicable for various DERs, such as the EVs’ smart charging .
4.6 Battery energy storage systems
Energy storage systems are among key factors for future smart grids [9, 29, 80]. BESSs are evaluated and considered in the literature for the frequency regulation [13, 14, 29]. Also, the estimated growth of storages in the Great Britain power system by 2050 will be about 10.7 GW based on the ‘consumer power scenario’ . Also, residential and non-residential BESSs are growing up day by day due to the technical developments and cost reduction as well as high levels of photovoltaic (PV) integration [14, 81]. A large number of these batteries are connected to distribution networks installed behind the meter . The BESSs present a fast dynamic response to compensate the load variations in distribution networks. In the Great Britain power system, many tenders were taken into consideration by the National Grid to provide an enhanced frequency response from BESSs [81, 82].
The application of BESSs in direct load control (DLC) is proposed in . The combination of electrical load, the load level in the building, and their controllable devices were considered to investigate the DLC application. The problem of controlling many distributed small-scale BESSs was highlighted as well. The scheme presented in  is reducing the frequency deviation by controlling the state of charge (SOC) of the batteries installed behind the meters . A coordination method of batteries charging was presented in  for controlling neighbouring batteries to regulate the frequency and voltage.
Markov-chain was previously depicted to represent dynamic behaviour of the battery SOC for EV batteries  or PV charging-based batteries . The modelling of the batteries SOC for the power supply availability from PV was presented in . The model was used to improve the availability of PV generation and to understand the nature of the charge/discharge rates of the batteries supplied by PV. The dynamic representation of BESS’s SOC was designed according to many states transitions, from zero to full charge and vice versa . Various types of batteries and their applications were presented such as behind meter BESSs (home-based) , smart charging of EVs , and large-scale BESSs (grid-scale BESS) . The aggregation of these types is important in regulating the power system frequency [83, 86, 87].
5 Control of DERs
DERs include energy storage systems, demand response and distributed generation (DG). Different approaches are presented in the literature to control and coordinate the operation of DERs. Many of these approaches aimed to actively integrate DERs into distribution networks rather than through a conventional passive connection to achieve a more secure and economical operation than with conventional methods.
Breaking the distribution network into smaller areas, such as microgrids, or wider control areas, such as cells, is one of the active approaches to manage DERs . Both cells and nicrogrids are pointed at managing and coordinating the DERs to supply their local demand. Virtual power plant (VPP) is another control approach, which was established to manage DERs. VPP intended to aggregate different types of DERs to represent a special type of power unit to participate in the energy market .
A microgrid is a small area of a distribution network that involves different types of DERs and can operate in the island or grid-connected mode to supply local energy demand . The control in a microgrid means to regulate both frequency and voltage. The coordination of DERs within a microgrid presents a novel way to increase the benefits to the overall system performance, such as reducing losses of feeders, compensating the fluctuation of RESs, improving power quality and supporting local frequency and voltage [36, 88].
Autonomous microgrids for an instant, has both renewable energy generation and energy storage system. Both of them have to be coordinated to regulate the frequency within the Microgrid by compensating the mismatch of load and generation. This control or functionality called or known as load frequency control (LFC) of a microgrid [89, 90].
Therefore, automated and robust balancing mechanism is required especially in the islanded situation. Microgrids have different inverter-based DERs, and therefore, controlling these inverter-based DERs is the key point in the stability of the frequency . Centralised and decentralised control solutions were introduced to control a microgrid. The centralised method requires an expensive communication infrastructure [90, 91, 92].
The decentralised control structure reduces the cost of the communications. This method considered the grid-connected mode, or the nature of the inverter’s primary source has not been considered. Distributed control of microgrids is growing up day by day as it compromises both positive features of centralized and decentralized controls . During grid-connected mod, the control of a microgrid is simple, since the large grid dominates the microgrid dynamics .
5.2 Wider control area (cells)
The ‘cells’ concept was introduced to overcome the challenges when more than 50% of the total generation capacity is from DG. The high penetration of DG introduces a fluctuated impact on the power system, as this is the case in the Danish power system [36, 94].
Therefore, a cell is a wide area in a distribution system with a group of controlled DERs . Like the microgrids, the control in this area covers both frequency and voltage and can work on the island or grid-connected modes. In the normal operation mode, cell effectively manages its DERs. In the case of a regional emergency, such as a real risk of a blackout, it disconnects itself from the grid and moves to the islanded mode [36, 94].
5.3 Virtual power plant
VPP aggregates different types of DERs to make them visible to the system operator as a single controlled unit to participate in the ancillary services [95, 96]. The output of the aggregated DERs in a VPP is arranged to be as a central generation unit with commercial and technical roles . The commercial role of a VPP is driven by the activity of market participation, such as energy supplier. In contrast, the technical role of a VPP was driven by the activity of the system management and support .
5.4 Wind turbine generators
Wind turbine generators (WTGs) were widely installed and its capacity in power systems around the world is growing day by day due to the improvements in technology and cost reduction [97, 98, 99, 100]. In the US, for example, WTGs represented a 33% of the total additional power generation since 2007. By the end of 2013, the total installed WTGs in the USA was over 61.1 GW and about 12 GW was under constructions .
However, higher penetration of WTGs in a power system introduces new challenges for the operation and control of power systems. These challenges are mainly due to the interact of different WTG capacities with the power system. Particularly, the stability of the frequency response decreases due to power electronics interface decouples the WTGs inertias from the system [97, 99, 101, 102].
It was found that the integration of wind turbine generation without frequency control leads to a negative impact on the frequency response. Therefore, frequency regulation mode shode carried out .
5.5 Load shedding for stability support
The last defence line to achieve a stable and safe operation of a power system is under frequency load shedding (UFLS) and under voltage load shedding (UVLS) [103, 104]. These two techniques are also useful and necessary to avoid any collapse in frequency or voltage.
The developments in large power systems, such as integrating more RESs, as well as the complex network refurbishments, the safety of the system operation is facing more challenges. Therefore, current traditional UFLS/UVLS methods can lead to inadequate load shedding, thus leading to more economic losses . As a result, the traditional methods for load shedding are unable to meet the growing needs of modern power systems. In this situation, smart devices such as such as wide-area measurement systems and synchrophasor concept can be used effectively to support the frequency and voltage [103, 104].
The wide-area monitoring system (WAMS) is rapidly increasing due to its importance in modern power systems operators. Wide-area monitoring, protection and control (WAMPAC) has already been used by many systems operator especially with UFLS .
6 Summary of challenges and new control methods
The frequency control limits in the Great Britain power system are defined by the system operator using two main levels: the operational limit, (i.e. equal to ± 0.2 Hz), and the statutory limit, (i.e. equal to ± 0.5 Hz). The frequency response services in the Great Britain power system are used by the system operator to maintain frequency within the acceptable limits and to restore frequency following sudden changes in demand/generation [24, 74].
Large-capacity synchronous generators provide the majority of inertia in the Great Britain power system. The rest is provided by smaller synchronous generators and demand. Conventional generators in the Great Britain power system are continuously run to create a minimum level of inertia to secure a and adequate capacity for the stability of frequency response. This capacity is expected to be increased more than current capacity in the next five years. However, these generators are expensive to operate and produce large amounts of greenhouse gas emissions.
The absence of direct coupling between the rotating machine and the power system in some RESs, e.g. wind and solar due to the power electronics leads to a reduced inertia. Therefore, increase the difficulties of the power system operation and control. In addition, RESs have power fluctuations due to unpredictable environmental conditions causing a significant impact on the stability of the frequency. A reduction in the system inertia will increase the RoCoF when the system is subjected to abrupt disturbances such as loss or increase in the demand or generation. In this situation, it is recommended to minimise the settling time of the response during the disturbance period. As a result, the need for additional frequency control is increased due to an increased level of RESs. A fast frequency response from the generation side is one of the recommended solutions to overcome the issue of the increased frequency deviation.
The new control system of the frequency, must provide a fast and stable response to a high RoCoF. This is highly recommended; however, a fast response has a risk of system oscillations. A flexible embedded real-time controller is required with the ability of event detection and response algorithm to any disturbances. This controller offers higher flexibility versus low cost and is preferable to have scalable parameters and fast controller latency. This is to create a new adaptive protection system which is capable of standing against frequency collapse in modern power systems. This control scheme is intended to supplement local frequency control, rather than replace it.
There are opportunities to further develop demand-side services during both periods of low and high demand due to the increasing needs of RESs. However, using the power from the load side for the frequency reserve services presents a new challenge. The challenge is about the control of these many distributed loads, especially, with the EVs, residential BESSs, water heaters, and cloth dryers.
The estimated level of storage in the Great Britain power system by 2050 will be about 10.7 GW based on ‘consumer power scenario of UK system operator’. Also, residential and non-residential BESSs are growing daily due to the developments in cost reduction as well as high levels of PV integration. A large number of these batteries are in distribution connected to the meter. The BESSs present a fast dynamic response to compensate the load variations in distribution networks. In the Great Britain power system, National Grid, which is the system operator, considered many tenders to provide enhanced frequency response from BESSs .
7 Evaluation and simulation results
The evaluation of the DERs integration for the frequency control was carried out in the MATLAB PowerSim. The simulation results were saved as vectors to visualise the comparison.
7.1 Modelling of some controllable loads
DSFC presents a novel way to mitigate the difficulties of the increased need for the active power generation. The TCLs such as refrigerators, air-conditioners, and water heaters have been widely considered due to the short-term modulation of their aggregated power consumption [10, 12]. TCLs modulate the used power for cooling/heating to maintain the temperature nearly to the desired level. In Great Britain power system, the DSFC was considered in the applications of the modern power system’s frequency control [10, 12]. A novel technique was proposed in  for the domestic refrigerators, as an example of the TCLs demand side frequency control in the Great Britain power system. It was proposed to deal with the future inertia reduction and for estimating the infeed loss and post-fault restoration. Also, the method presented a non-real-time communication-controlled TCLs. Aggregated power of the TCLs was controlled as a linear function of the local frequency change .
BESSs are seen as key technologies for the future smart grids [9, 80, 84, 105, 106, 107, 108]. BESSs are evaluated and considered in the literature for the frequency regulation [13, 14]. The BESSs presented a fast dynamic response to compensate the load variations on the grid side and hence regulate the frequency. The application of the BESSs DLC is proposed in  highlighted the problem of integrating small-scale BESSs. The scheme was effective in reducing the frequency deviation by controlling the behind-meters BESS SOC . Also, a coordinated BESS was presented in  to control the neighbouring BESS and to regulate the frequency and voltage.
7.2 Evaluation assumptions
The BESS inverters are smart, and a high controllability of the SOC is available.
- 2)The SOC level of the population is different during the same day and the year. Therefore, there are three different initial conditions for the aggregated population, representing three levels of SOC: high, medium, and low SOC shown in Table 5.Table 5
BESS SOC initial conditions study cases
SOC population level (%)
- 3)There are three aggregators with three different BESS aggregation types, which are aggregation of residential-based BESS, aggregation of a large-scale BESS, and aggregation of the EVs’ in either home-based or station-based smart charging, shown in Table 6.Table 6
Power assumption of each aggregator and their controllable BESS-based DERs
Population of aggregated controllable amount (MW)
- 4)There are three aggregators considering three different DSFC aggregation types, which are ERWH and HPWH, shown in Table 7.Table 7
Power assumption of each aggregator and their controllable DSFC
Population of aggregated controllable amount (MW)
- 5)Three study cases are considered for the integration of the aggregators to represent DERs availability shown in Table 8.Table 8
Study cases for simulation results with South East Australian power system
1-Bus 206 (Area 2)
2-Bus 312 (Area 3)
3-Bus 408 (Area 4)
The service is available for 24 hours and provides its full controllable amount of the power for the Great Britain primary frequency reserve period (10–30 s).
The initial values of all cases were (30% ON, 10% OFF-LOCKED, 50% OFF, 10% ON-LOCKED), and (30% ON, 70% OFF) for HPWH and ERWH, respectively.
7.3 Simplified Great Britain power system
7.4 South-East Australian power system
Scenario 1: different capacity of DSFC
Scenario 2: different BESS SOC initial conditions
Scenario 3: different capacity of BESSs
The paper presented a review and evaluated of the integration of DERs for the applications of the frequency regulation in future power systems. The integration of the demand side frequency control and the BESS as types of DERs were considered. The aggregations of ERWHs and HPWHs were modelled based on models presented in the literature. The representation of a population of BESS and their applications such as aggregation of residential-based BESS, aggregation of a large-scale BESS, and aggregation of the EVs’ in either home-based or station-based smart charging were used. The Great Britain power system and the 14-machine South-East Australian power system were employed to demonstrate the effectiveness of the new methods in controlling the frequency. Different study cases of DERs availability were modelled, and real disturbances were utilised. The frequency response was highly improved by integrating various DERs. Aggregation of 172 MW and 282 MW of water heaters and BESS-based DERs reduced the frequency deviation and error following a disturbance in the South-East Australian power system with 14.5 GW system load base.
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