Barry Hayes

The potential vulnerability of demand response (DR) to cyber-attacks represents a significant challenge that can have detrimental impacts on the technical and economic performances of the distribution system (DS). This issue is addressed in the proposed grid-aware price-based DR framework under price integrity attack (PIA) as a bi-level defender-attacker problem. The bi-level comprises a distribution system operator (DSO) at the upper-level, and demand response providers (DRPs) and customers at the lower-levels. It uses a grid-aware, distribution locational marginal price as an interaction in DR pricing. The defender-attacker problem is constructed when the attacker devises an adversarial DR attack on the DRP’s price using PIA to manipulate the load consumption of the customers. In response, the DSO uses dynamic Network Reconfiguration (NRE) as a defender action to change the grid-aware price for mitigating adverse DR. The proposed problem is tested on the 33-bus and 119-bus DS and is solved using the nested reformulation & decomposition method. Numerical analyses demonstrate that the proposed methodology effectively shows the substantial change in economic and technical performance of DR under attack and defender action. It shows the significant impact of PIA which reduces DR contribution from 9.60 to 2.15 %, and then improves to 5.30 % with the defender action using dynamic NRE for 33-bus. For the 119 bus, it changes from 9.90 % to 3.73 % then to 7.49 %. Further, the proposed model shows its scalability and tractability on small- to large-scale systems effectively.

The restructuring of electricity markets has transformed the provision of ancillary services, which are essential to ensuring grid security, quality, and reliability. Traditionally supplied by large, centralized power plants, these services—encompassing frequency control, voltage regulation, and system restoration—are now increasingly delivered by distributed energy resources such as battery storage, electric vehicles, and microgrids. This comprehensive survey reviews the evolving definitions and classifications of ancillary services in modern power systems, emphasizing the impact of renewable energy integration and digitalization on grid stability. Specifically, it highlights the emergence of new ancillary services—such as fast frequency response and synthetic inertia—critical for managing the challenges posed by low-inertia grids in systems dominated by renewables. International case studies from regions including Ireland, Great Britain, and Japan are examined to illustrate how innovative operational strategies and regulatory reforms bolster system flexibility and resilience. The insights presented herein aim to guide policymakers and system operators in designing robust, sustainable power systems for the future.

The proliferation of Low-Carbon Technologies (LCTs), such as electric vehicles, heat pumps, and solar panels, poses a challenge for distribution system operators (DSOs). LCTs are installed within the household premises, i.e., behind the meter, and do not usually require a formal registration process. Since DSOs are generally unaware of their presence, automated detection techniques that can identify LCTs from electricity smart meters (SMs) measurements are becoming essential for network planning. Recent advances in Machine Learning (ML) have shown the potential of supervised learning techniques in energy demand forecasting, load profiling and segmentation, and smart metering. ML models can identify and classify usage patterns, providing immediate data insights and real-time feedback. This paper investigates the application of supervised learning approaches in LCT detection and develops two classifiers that can reliably detect their presence in SM profiles. The performance of both classifiers is demonstrated using a publicly available dataset SM measurements collected in Belgium and compared with other state-of-the-art techniques. Additionally, the generalisation ability of each classifier is examined on an unseen dataset from Ireland.

Increasing penetration of low carbon technologies in residential low voltage (LV) networks increases the need for modelling their state to preempt voltage issues. Due to the challenges in modelling vast numbers of feeders, LV network models are often simplified, incomplete, or even absent. The largescale roll-out of smart meters (SMs), creates the opportunity for generating accurate LV network models at scale at low cost. In this paper, a methodology for voltage estimation in LV networks without an electrical model is proposed and tested across 127 real distribution feeders. The approach uses machine learning and historical active power and voltage data from SMs to predict voltage at a node of interest. This approach shows promising results, particularly in its capability to generalise to different loading scenarios and estimate voltage under higher electric vehicle and solar photovoltaic penetration levels.

Demand response programmes are essential for the stability of a power system with increasing penetration of renewable generation. However, before the necessary technologies are deployed, the flexibility potential that consumers can offer to the grid operator must be evaluated. Although smart meters can support this process, aggregated consumption profiles do not provide insights into the individual appliances. Non-intrusive load monitoring techniques are an alternative, but most recent works only focus on increasing the performance of primarily supervised learning algorithms, reducing the applicability of their results. This paper proposes an end-to-end methodology to quantify flexibility potential and emissions intensity of large household appliances. The process relies solely on local aggregated measurements and low-complexity unsupervised learning techniques to identify major power blocks. This information is combined with information on wholesale market prices and electricity production mix to derive three novel metrics, dubbed peak price, peak emissions, and critical peak score. The results show that relatively simple unsupervised techniques can help characterise major consumption blocks from aggregated consumption, providing the per appliance flexibility potential.

In electrical power systems where the proportion of synchronous generators (SG) is gradually decreasing, grid-forming (GFM) converters need to be installed and controlled to meet all the system requirements that SGs have provided to date. Modeling, control, and implementation of GFM converters have been the subject of numerous studies in recent years, particularly in the context of ensuring grid stability during the transition to non-synchronous renewable energy sources. This paper provides a comprehensive literature review on the modeling and control of grid-connected converters. In particular, the focus is placed on GFM-type control structures, objectives, and applications. Both grid-following (GFL) and GFM control structures are detailed. Then, the objectives of controlling GFM converters in power systems are discussed in detail. Finally, some completed and ongoing GFM installation projects around the world are summarized under the subheadings of battery energy storage system (BESS), GFM wind, hybrid, and high voltage direct current (HVDC).

This article presents a novel numerical approach aimed at finding a distribution network expansion plan that prevents future congestion and voltage issues. Forecasted duration and intensity of thermal and voltage violation events are used to determine a pool of potential candidates for infrastructure (i.e., line/cable) upgrade, voltage regulator, and energy storage system installations. This is complemented with an algorithm to obtain the minimum-cost list of these candidates that solves all constraint violation events using binary linear programming. This approach is validated using the modified IEEE 33- bus network and a real 1171- bus feeder in the West of Ireland through numerous high-resolution quasi-static time series simulations. Three pools of candidates and three cost projections were considered to explore the method's sensitivity to different scenarios. Results show that the proposed methodology is a versatile tool for designers, planners and policymakers. The methodology can ensure that the investment plan solves all forecasted violation events. Nevertheless, we show that accepting a marginal degree of violations may be admissible and would significantly reduce investment costs.