Publications

Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability.

To solve the conflicting interests over the two timescales, this paper: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as ‘active’ actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. 

Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

This paper provides a detailed overview of the current snapshot of available open data for modelling the impacts of electric vehicles (EVs) on the UK distribution network, highlighting opportunities for a digital spine. We are the first to review open data available for UK distribution networks, focusing on spatial data. We also explore data for census small geographies, vehicle ownership, EV charger locations and data on their usage. Several issues are identified, including inconsistencies in dataset availability, file naming conventions, feature definitions and geographic discrepancies.

We specifically analyse EV charger connection data for secondary distribution substations from two UK Distribution Network Operators (DNOs). The validity of the data is assessed by comparing it to known public charger locations from OpenChargeMap. While one DNO provides data coverage for >95% of its substations, it is valid for only 24.1% of substations with at least one public charger. Conversely, the other DNO provides data coverage for 1% of its substations due to privacy-related obfuscation, with data valid for 98.3% of substations with at least one public charger.

Addressing these challenges through standardised data-sharing practices and implementing a digital spine could enhance the accuracy and reliability of EV-grid integration models. These improvements are essential for facilitating the seamless integration of EVs into the grid and supporting the transition to a sustainable energy system.

Diversifying and decarbonising energy production by investing in renewables and clean energy is the UK Government's blueprint to power Britain from Britain.

Technological developments and deployment are progressing rapidly, however, the whole-system approach—bringing together organisations across the traditional boundaries to provide the country with an increasing capability to source affordable, clean and home-grown energy—is still lacking. 

A key barrier to the whole-system approach is lack of a data sharing infrastructure (DSI), which allows standardised and interoperable data to be securely shared between key stakeholders, helping to align giga watt, mega watt and kilo watt renewable and clean energy with end-user demand. Development of a DSI covering the entire problem and organisation space is a complex and costly undertaking. 

This paper advocates for a minimum viable product (MVP) that takes an early, continuous engagement of influencing and impacting stakeholders, facilitates the discovery of desired system functional properties at the earliest possible stage of system development to meet diverse users' needs, mitigate potential risks, and inform the future development. If an MVP offers genuine benefits for early adoptions and the opportunity to address mission critical challenges, it will propel mass collaboration and innovation to accelerate net zero transition and green growth.

This paper reviews electric vehicle (EV) profiling at the distribution substation level in the UK, focusing on eight EV charging trials and projects conducted between 2010 and 2022. It examines current profiling methods and highlights key challenges and opportunities in mapping EV charging demand across distribution substations, including the risk of data drift, the need to consider the interaction between multiple sociodemographic and locational factors, poor visibility of EV adoption, and the inherent uncertainty in the system. The pace of change in EV adoption, technology and policy means that trial datasets quickly become outdated. The multitude of factors influencing EV charging demand requires the collection of rich datasets to quantify explicit relationships between them. While visibility of EV adoption on distribution networks remains prone to inaccuracies, ongoing research is advancing methods, presenting new opportunities to address these gaps through leveraging new insights and address the numerous sources of uncertainty inherent in such a complex and dynamic system. The findings emphasise the need for improved data collection and the development of predictive profiling techniques to support seamless EV integration and inform future network planning.