Abstract

Abstract
Lightning hit a transmission powerline outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1m households. The power outage raised questions about the ability of the GB electricity grid to withstand rapid changes in frequency caused by outages and surges on the network. Grid inertia has been changing in recent years due to the emergence of renewable generation as a significant contributor to the energy mix. As part of climate change mitigation efforts, there has been an acceleration in the deployment of distributed renewable generation replacing conventional thermal power plants in grids across the world. As a result, there has been a change in the aggregate and regional inertial capacity, with consequences for the stability of these networks and their ability to withstand large variations in frequency. Measures to mitigate the consequences of this change to grid stability need to be evaluated and the level of investment required to prevent a reoccurrence of an event such as that of 9 August quantified. Simulating frequency events on the GB grid using a single-bus model involves a system of differential equations representing the overall generation and load present at the time. The standard model based on the swing equation assumes unlimited capacity in aggregated resources, the availability of these services for the duration of the frequency excursion and a homogeneous response without local variation. In simulating the effect of outages on the GB Grid frequency on 9 August 2019 and other events in the period 2018--2019, the effect of limiting these services to the capacity of resources engaged during the event is examined. Taking resource limitations into account enables the approximation of the frequency trace for documented network perturbations. Enhancing this model so that it represents a networked grid using an algebraic differential system of equations facilitates the simulation of the effects of localized variation in inertia and frequency response services on the propagation of transients across a network. Using this model, the effects of varying responses to transients can be investigated, and grids of varying scales and topologies can be compared to determine differences in their response to outages. The propagation of disturbances across domains within the network that have different frequency response characteristics can thereby be examined with a view to drawing conclusions about the optimal deployment of frequency response services, and their relative cost-effectiveness in delivering a stable supply as the proportion of renewable generation in the energy mix grows. The model is demonstrated to be generalizable by its application to simulating an outage on the Italian grid, with the results compared to similar results on that network. This demonstrates the facility of applying the model to examining power systems of different topologies and characteristics, and evaluating plans for their migration to zero-carbon generation. Insight is gained into the responses of various characteristics of the grid and how they interact with unplanned generation imbalances. Using this adapted model, events on the GB grid are examined to validate the influence of these features and evaluate the anticipated response to similar events in the future using energy-mix scenario projections. With the effectiveness of the model validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

Abstract
Abstract Lightning hit a transmission power line outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1 m households. Simulating such events typically involves a system of differential equations representing the overall generation and load present at the time. A standard model based on the swing equation assumes unlimited capacity in aggregated resources, and the availability of these services throughout the duration of the frequency deviation. In simulating the effect of outages on the GB Grid frequency on 2019/8/9, the effect of limiting these services to the capacity of resources engaged during the event is examined. It is shown that by taking these refinements into account the timing and extent of the frequency nadir can be successfully estimated. Insight is gained into the responses of various grid characteristics and how they interact with unplanned generation imbalances. Using this adapted model, further events on the GB grid are examined to validate the influence of these features. With the model’s effectiveness validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

Abstract
This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network.

Abstract
Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water-energy-social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

Abstract
The transition from the current energy architecture to a new model is evident and inevitable. The coming future promises innovative and increasingly rigorous projects and challenges for everyone involved in this value chain. Technological developments have allowed the emergence of new concepts, such as renewable energy communities, decentralized renewable energy production, and even energy storage. These factors have incited consumers to play a more active role in the electricity sector and contribute considerably to the achievement of environmental objectives. With the introduction of renewable energy communities, the need to develop new management and optimization tools, mainly in generation and load management, arises. Thus, this paper proposes a platform capable of clustering consumers and prosumers according to their energy and geographical characteristics to create renewable energy communities. Thus, this paper proposes a platform capable of clustering consumers and prosumers according to their energy and geographical characteristics to create renewable energy communities. Moreover, through this platform, the identification (homogeneous energy communities, mixed energy communities, and self-sufficient energy communities) and the size of each community are also obtained. Three algorithms are considered to achieve this purpose: K-means, density-based spatial clustering of applications with noise, and linkage algorithms (single-link, complete-link, average-link, and Wards' method). With this work, it is possible to verify each algorithm's behavior and effectiveness in clustering the players into communities. A total of 233 members from 9 cities in the northern region of Portugal (Porto District) were considered to demonstrate the application of the proposed platform. The results demonstrate that the linkage algorithms presented the best classification performance, achieving 0.631 by complete-ink in the Silhouette score, 2124.174 by Ward's method in the Calinski-Harabasz index, and 0.329 by single-link on the Davies-Bouldin index. Additionally, the developed platform demonstrated adequacy, versatility, and robustness concerning the classification and sizing of renewable energy communities.