Teesside University[1] – Written evidence (BAT0015)
Integrated Demand Response in EV Charging Network with Fast Frequency Response
Authors: Gill Lacey, Sean Williams, Michael Short
Historically national electrification followed a trajectory of increasing integration and centralisation[2]. In centralised electricity networks the transmission system operators (TSOs) are primarily responsible for balancing the network [3]. Recently however increasing concerns over environmental and energy problems, the concept ‘low-carbon economy’ has shifted opinion from traditionally dispatched fossil fuel generation plants to smart technologies for optimising renewables. Consequently, reserve capacity is diminished, which will impact energy security. This paper presents a conceptual framework to integrate electric vehicles charging into electric power systems for grid balancing. Moreover, a dynamic EV charging strategy may be used to perform primary frequency control, leading to improved short term grid stability. Furthermore, it may accelerate the deployment of EV charging in decentralised power systems ensuring an integrated flexible demand response service to local energy communities. The implementation for fast acting frequency response using similar technology intervention is demonstrated in buildings [4].
Generally, an EV is considered as a load on the system with no or very little interaction with the energy provider, so offer no grid reinforcement contributions. For decentralised primary fast acting frequency response, participating aggregated clusters of improved EV chargers will contribute to the immediate restoration of grid frequency equilibrium without any centralised signalling being required. Although the market could retrospectively signal appropriate remuneration according to the time of day and scale of service, this may be complicated to implement and manage. A simpler alternative is to offer enabled chargers a reduced electricity rate which can be determined based upon the average number and duration of frequency excursions in a particular seasonal period, the benefits to the supplier/operator, and the potential degradation costs of a typical Li-ion battery (see, e.g. [5]).
In the UK, the level of power loss that the transmission system must be able to sustain is 1800MW. For this higher level of loss, grid frequency may fall outside statutory limits of 49.5Hz to 50.5Hz. During frequency deviations caused by severe disturbance, interventions such as load shedding, generator control and isolator switching get activated to avoid infractions. Aggregating controlled EV charging in electric power systems can offer fault ride-through and grid stability.
The simplified block diagram shown in Fig 1 illustrates the general approach. Here, when a power disturbance attributed to a rapid fall in power output from an intermittent renewable energy source, () is applied to a single area power system, the decentralised frequency control embedded in the EV Charger, can arrest the measured frequency excursion in real-time.
Fig 1. Simplified block model of improved EV charger
The approach offered is not dependent on national ICT infrastructure and avoids discontinuous (on/off) switching of loads, this avoiding synchronisation and restoration loading issues. Figure 2 reports the effectiveness of the small gain regulator, reducing the immediate measured frequency deviation so that the gap on reserve generation capacity margins can be reduced and the time to restore frequency equilibrium is lessened.
Policymakers can capitalise on the opportunity to implement an integrated demand response with EV charging loop control by building a more dynamic market for provision of smart EV charging solutions. A proactive grid friendly EV charging infrastructure may play an important role in future grid security.
Fig 2. Integrated DR with EV Charger feedback loop frequency response (a) 1-15 min, (b) 0-5 min |
29 March 2021
[1] Teesside University is a public university founded in 1930 as Constantine College. Its main campus is in Middlesbrough in the North East of England. It has five schools. The University vision is that “Teesside will be a leading University with an international reputation for academic excellence that provides an outstanding student and learning experience underpinned by research, enterprise and the professions”, while the mission is that “Teesside University generates and applies knowledge that contributes to the economic, social and cultural success of students, partners and the communities we serve.”
The Centre for Sustainable Engineering at the University is concerned with carrying out original research and innovation to support the process of designing and operating systems such that they consume resources at a rate that does not compromise the natural environment. The research activities of the centre build upon over a decade of research excellence in sustainable engineering areas. Focus areas of research are the use of digitalization, informatics, cybersecurity, automation, energy management, artificial intelligence, distributed control and social science in energy related and construction applications.
This evidence and the recommendations made are partially based upon findings partly derived from research activities partially funded by the European Union Horizon 2020 Framework (Grant Agreement 696114), by Teesside University and also the Doctoral Training Alliance (DTA) scheme in Energy. We believe they are highly relevant to this call for evidence.
[2] P. Mallet et al, "Power to the People!: European Perspectives on the Future of Electric Distribution," IEEE Power and Energy Magazine, 2014.
[3] A. Snow, "The first National Grid," Engineering Science and Education Journal, vol. 2, (5), pp. 215-224, 1993.
[4] S. Williams, M. Short and T. Crosbie, "On the use of thermal inertia in building stock to leverage decentralised demand side frequency regulation services," Applied Thermal Engineering, vol. 133, pp. 97-106, 2018. Available: https://www.sciencedirect.com/science/article/pii/S1359431117370242. DOI: 10.1016/j.applthermaleng.2018.01.035.
[5] A. Gailani et al, "Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models," Electronics, vol. 9, pp. 90, 2020. . DOI: 10.3390/electronics9010090