Modelling aggregate demand-side flexibility in distribution networks with electrified heat and transport
Lead Research Organisation:
University of Manchester
Department Name: Electrical and Electronic Engineering
Abstract
This project aims to model demand-side flexibility coming from aggregation of a large
number of residential and small and medium-size commercial end-users in the distribution network (DN). The
algorithms developed through this project will facilitate more flexible operation of the DN by assessing the time
varying capacity available from flexible loads, in response to flexible services currently procured by the
distribution system operator (DSO), namely: Sustain, Secure, Dynamic and Restore. The aggregate flexibility will
be described as the amount of available capacity and its duration, as a result of aggregating individual loads with
different operating modes, start times, maximum deferral times, etc., driven by the end-users' daily behaviour
and constrained by their comfort. Such flexibility profiling, corresponding to that of larger flexible resources
already employed in practice (e.g., distributed generators or storage), will make provision of multiple flexible
services accessible to small and medium-size end-users. This will result in increased flexibility of the DN as a
whole. Furthermore, harnessing flexibility potential of residential and commercial users would have significant
environmental implications, as these contribute to a large share to both, electrical usage and global greenhouse
gas emissions. The findings of the project could be further complemented with smart meter data to develop
tariffs and incentives for residential and commercial users, supporting more coordinated procurement of
flexibility by reducing uncertainty of efficiency and outcome of the demand response (DR) programmes.
The main beneficiaries of the research would be DSOs, aggregators and other DR responsible parties at the DN
level. The question of flexibility modelling is not only important for reporting DR potential at the demand side
(commonly, an aggregator's role), but also for more confident estimation of the outcome of DR programmes, tariff design and flexibility assessment, which are highly relevant to DSOs. One of the main benefits for DSOs
brought by this project would be in supporting decision making when investing into incentives and infrastructure
allowing network-wide control of flexible loads.
number of residential and small and medium-size commercial end-users in the distribution network (DN). The
algorithms developed through this project will facilitate more flexible operation of the DN by assessing the time
varying capacity available from flexible loads, in response to flexible services currently procured by the
distribution system operator (DSO), namely: Sustain, Secure, Dynamic and Restore. The aggregate flexibility will
be described as the amount of available capacity and its duration, as a result of aggregating individual loads with
different operating modes, start times, maximum deferral times, etc., driven by the end-users' daily behaviour
and constrained by their comfort. Such flexibility profiling, corresponding to that of larger flexible resources
already employed in practice (e.g., distributed generators or storage), will make provision of multiple flexible
services accessible to small and medium-size end-users. This will result in increased flexibility of the DN as a
whole. Furthermore, harnessing flexibility potential of residential and commercial users would have significant
environmental implications, as these contribute to a large share to both, electrical usage and global greenhouse
gas emissions. The findings of the project could be further complemented with smart meter data to develop
tariffs and incentives for residential and commercial users, supporting more coordinated procurement of
flexibility by reducing uncertainty of efficiency and outcome of the demand response (DR) programmes.
The main beneficiaries of the research would be DSOs, aggregators and other DR responsible parties at the DN
level. The question of flexibility modelling is not only important for reporting DR potential at the demand side
(commonly, an aggregator's role), but also for more confident estimation of the outcome of DR programmes, tariff design and flexibility assessment, which are highly relevant to DSOs. One of the main benefits for DSOs
brought by this project would be in supporting decision making when investing into incentives and infrastructure
allowing network-wide control of flexible loads.
Publications
Ponocko J
(2022)
Coordinated, sensitivity-based DSM for enhanced cross-border power transfers
in International Journal of Electrical Power & Energy Systems
Ponocko J
(2022)
Cross-Border Dsm as a Complement to Storage and Res in Congestion Management Markets
in SSRN Electronic Journal
Ponocko J
(2023)
Cross-border DSM as a complement to storage and RES in congestion management markets
in International Journal of Electrical Power & Energy Systems
Nukic E
(2023)
Data-driven assessment of aggregated EV charging potential for flexibility procurement
in IET Conference Proceedings
Churkin A
(2024)
Interpreting the value of flexibility in AC security-constrained transmission expansion planning via a cooperative game framework
in Electric Power Systems Research
Churkin A
(2024)
Interpreting the value of flexibility in AC security-constrained transmission expansion planning via a cooperative game framework
in Electric Power Systems Research
| Description | The work so far has modelled demand side flexibility and investigated its impact on distribution network performance in the presence of renewable generation. The main contributions stemming from this work are as follows: 1) When performing photovoltaic (PV) power forecasting in distribution network using artificial intelligence (AI) methods, the critical data types, i.e., the input data required to improve the forecasting performance (e.g., active power, wind speed, temperature, humidity, global horizontal radiation, etc.), depend on the chosen method, but also on the timescale of the historical data. With the appropriate AI method, there is no need for long historical data to be stored for training. 2) When assessing demand response potential of a fleet of electric vehicles (EVs), data driven approach can be taken to estimate the lower and upper boundaries of the number of EVs than can potentially participate in demand response, as well as the potential to additionally utilise public charging stations during the off-peak hours. The suggested approach to initially assess aggregated EV charging demand composition can increase the confidence in accuracy and applicability of the assessed EV flexibility potential in demand response programmes. 3) The results of the sensitivity analyses in a power network have shown that the ranking list of most influential flexibility providers for demand response programmes is highly dependent on the penetration level of renewables as it can visibly change the level of influence of the considered flexible providers. 4) Linearisation of the current sensitivity of distribution network lines to load changes triggered by DR programmes has shown that different factors are more influential in radial network compared to meshed. Sensitivities in the radial network are more susceptive to changes in net demand and operating uncertainties. On the other hand, conductor temperature and network topology are more impactful on sensitivities in the meshed network. This emphasises that appropriate probabilistic modelling is more critical for DR planning in radial distribution networks than in meshed, which are more sensitive to temperature and topology changes. 5) A comprehensive analysis of the effects of Future Energy Scenarios (FES), published by National Grid Electricity System Operator (NGESO), on a typical distribution network (DN) in the UK has been performed. It identified the challenges of future DNs from the aspects of steady state voltage performance and line (cable) overloading and assessed the extent to which demand response (DR) can mitigate these. The results have shown that DR can be an effective means for voltage regulation in future DNs, but also a potential source of congestion due to the energy payback. 6) As part of our research on optimised scheduling of DR programmes, we introduced "available payback potential" as a metric to explicitly capture the payback associated with DR. The results demonstrated how the optimised activation of flexible loads and subsequent, scheduled, payback, improves the overall performance of the DN (e.g., meeting network limits and reducing losses), and facilitates the integration of available distributed generation. 7) Our work on AI-based active and reactive demand forecasting has shown that, with appropriate tuning of the hyperparameters of the artificial neural networks, day-ahead forecasting of demand at substation level can be replicated in different geographical areas even without changing the original training data set. To what extent were the award objectives met? If you can, briefly explain why any key objectives were not met. The project has met most of its objectives related to estimating flexibility of aggregate electric vehicles and the analysis of the impact of demand response on distribution network performance. The project has not considered heat pumps in the methodologies developed, mainly as the assessment of EV flexibility has shown to be rather challenging. Modelling of heat pumps requires different approach from modelling of EVs and the remaining time on the project would not allow this to be performed to a satisfactory level. How might the findings be taken forward and by whom? Researchers working in the following areas can benefit from the contributions of this project: distribution network operation and planning, demand response and demand side management, demand side flexibility assessment, artificial intelligence based methods for time series forecasting, electrification of transport, sensitivity analyses. The findings made in this project can also be used by distribution network operators (when they are scheduling DR programmes, planning reinforcement in line with FES, prioritising flexible providers to save costs or forecasting solar power generation in parts of the network for enhanced network operation) and flexibility aggregators (when they are assessing available flexibility from EV fleets and planning demand payback). |
| Exploitation Route | Several (nine) conference papers have been published so far, which will serve further research in this and complementary areas. A journal paper is entering its final stages before submission for peer review. Further details on how the findings might be taken forward and by whom have been provided in the previous section. |
| Sectors | Energy Environment Transport |