Reference electric distribution network modelling and integration of electric vehicle charging stations

Smartcities,withprosumersatthecentre,areatthefrontlineoftheenergytransition. The national and international policies should encourage then this transition by promoting, among many aspects, energy digitalization, massive penetration of renewable energies and electrification of the transport sector. To embrace all these changes, a holistic view, covering not only the distribution system, is necessary to plan, design and reorganize in particular urban areas. The radical distribution networks transformation is monitored and presented, both considering technical and non-technical aspects, which aims at encouraging potential directions that distribution system operators can pursue. The thesis work has three main objectives. From the distribution system operator (DSO) perspective, the main objective is to investigate how the technical and non-technical features vary among distribution system networks in Europe. From the modelling perspective, the second main objective is firstly to define a method which incorporates the previous findings to properly design a tool able to reproduce representative urban networks and secondly to validate the results through a statistical methodology. From the electric vehicle’s infrastructure perspective, the thirdmainobjectiveisfirstlytounderstandtheelectricvehiclesdemandbehaviour and develop models capable of reproducing them, and secondly to assess, through a dedicated methodology, the electric vehicles charging infrastructure features and performance. Theresultsfromthisthesisindicatesthattheincreasingattentiontowardthedistribution sector should not be underestimated by the main actor, distribution system operator, which appears to have different approaches in smartening and digitalizing their network especially concerning electric mobility, demand response and data management between distribution and transmission system operators (TSO). It is urgent for policy makers and stakeholders involved to align distribution system operators to a common strategy to tackle the introduction in the distribution network grids of new players. Tools like DiNeMo platform applied in this thesis may be used to perform preliminary research studies concerning the installation of newcharginginfrastructure, renewableenergygeneratorsornetworkreinforcement analysis. Indeed, it is crucial for regulators to take into account the physical layer of distribution grids when designing new policies and incentives in order to address challenges of tomorrow’s cities

Estimating national and local low-voltage grid capacity for residential solar photovoltaic in Sweden, UK and Germany

The electric grid\u27s available capacity to accommodate solar photovoltaic on national scales is currently uncertain. This makes decisions about grid capacity expansion, which can be very costly for local grid operators, difficult to make. Yet, knowledge of national solar photovoltaic grid capacity is central in order to formulate realistic solar PV targets and strategies. We present a methodology based on publicly available data to estimate the grid\u27s hosting capacity of residential solar photovoltaic at both the national and local scale. The model is applied to Sweden, Germany and the UK and shows that low-voltage grid capacity for residential solar photovoltaic is very large, 33 (+5/-7) GW (Sweden), 248 (+5/-24) GW (Germany) and 63 (+1/-14) GW UK, and similar to current total generation capacity. Based on our estimations, we find that with the capacity of the present grid Sweden can supply 24%, Germany 60% and UK 21% of their current annual net electricity consumption from residential solar photovoltaic. In addition, we find that the grid-supported individual solar PV system sizes increase as population density decreases. Finally, our work highlights the importance of implementing sizing incentives for customers when installing their solar PV systems

The Right Tools for the Job: The Case for Spatial Science Tool-Building

This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open-source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability

Generating low-voltage grid proxies in order to estimate grid capacity for residential end-use technologies: The case of residential solar PV

Due to data restrictions and power system complexity issues, it is difficult to estimate grid capacity for solar PV on regional or national scales. We here present a novel method for estimating low-voltage grid capacity for residential solar PV using publicly available data. High-resolution GIS data on demographics and dwelling dynamics is used to generate theoretical low-voltage grids. Simplified power system calculations are performed on the generated low-voltage grids to estimate residential solar PV capacity with a high temporal resolution. The method utilizes previous developments in reference network modelling and solar PV hosting capacity assessments. The method is demonstrated using datasets from Sweden, UK and Germany. Even though the method is designed to estimate residential solar PV grid capacity, the first block of the method can be utilized to estimate grid capacity or impacts from other residential end-use technologies, such as electric heating or electric vehicle charging. This method presents: • A method for estimating peak demand based on population density and dwelling type. • Generation of low-voltage grids based on peak demand. • Sizing of transformers and cables based on national low-voltage regulations and standards

Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches

A framework for regional smart energy planning using volunteered geographic information

peer reviewedThis study presents a framework for regional smart energy planning for the optimal location and sizing of small hybrid systems. By using an optimization model - in combination with weather data - various local energy systems are simulated using the Calliope and PyPSA energy system simulation tools. The optimization and simulation models are fed with GIS data from different volunteered geographic information projects, including OpenStreetMap. These allow automatic allocation of specific demand profiles to diverse OpenStreetMap building categories. Moreover, based on the characteristics of the OpenStreetMap data, a set of possible distributed energy resources, including renewables and fossil-fueled generators, is defined for each building category. The optimization model can be applied for a set of scenarios based on different assumptions on electricity prices and technologies. Moreover, to assess the impact of the scenarios on the current distribution infrastructure, a simulation model of the low- and medium-voltage network is conducted. Finally, to facilitate their dissemination, the results of the simulation are stored in a PostgreSQL database, before they are delivered by a RESTful Laravel Server and displayed in an angular web application

Geospatial Inference and Management of Utility Infrastructure Networks

Ph. D. Thesis.Modern cities consist of spatially and temporally complex networks that connect urban infrastructure assets to the buildings they service. Critical infrastructure networks include transport, electricity, water supply, waste water and gas, all of which play a key role in the functioning of modern cities. Understanding network spatial connectivity, resource flow, dependencies and interdependencies is essential for infrastructure planning, management, and assessment of system robustness and resilience. However, there is a sparsity of fine spatial scale data from which such understanding can be derived or inferred. Often data is held within commercially sensitive organisations and may be incomplete topologically and/or spatially. Thus, there is an urgent need to develop new approaches to the integrated inference, management and analysis of the complex utility infrastructure networks. Such approaches should allow the highly granular representation of utility network connectivity to be represented in a spatially explicit manner, employing methods of data and information management to ensure they are scalable and generic. This thesis presents the development of such an approach, one that employs a geospatial ontology to formally define the key entities, attributes and relationships of fine spatial scale utility infrastructure networks. This ontology is used as the conceptual framework for the development of a suite of algorithms that allow the heuristic inference of the spatial layout of utility infrastructure networks for any urban conurbation within the UK. This is demonstrated via several case studies where the electricity feeder network between substations and buildings is generated for several different cities within the UK. Validation against the known network for the city of Newcastle upon Tyne indicates that the network can be inferred to high levels of accuracy (about 90%). Moreover, the algorithm is shown to be a transferable to the inference and integration of other utility infrastructure networks (gas, water supply, waste water, and new road layouts). ii The representation, management and analysis of such spatially complex and large utility networks is, however, a major challenge. The efficient storage, management and analysis of such spatial networks is explored via a comparison of a traditional RDMS approach (PgRouting within Postgres), spatial database (PostGIS) and a NoSQL graph-database (Neo4j), as well as a bespoke hybrid spatial-graph framework (combination of PostGIS and Neo4j). A suite of comparison tests of data writing, data reading and complex network analysis demonstrated that significant performance benefits in the use of the NoSQL graph database approach for data read (around 210% faster) and network analysis (between 420 and 1170 % faster). However, this was at the expenses of data writing which was found to be between 135 and 150% slower.MISTRAL project, School of Engineering at Newcastle University

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

RISK-BASED ASSESSMENT AND STRENGTHENING OF ELECTRIC POWER SYSTEMS SUBJECTED TO NATURAL HAZARDS

Modern economic and social activities are dependent on a complex network of infrastructure systems that are highly interdependent. Electric power systems form the backbone of such complex network as most civil infrastructure systems cannot function properly without reliable power supply. Electric power systems are vulnerable to extensive damage due to natural hazards, as evident in recent hazard events. Hurricanes, earthquakes, floods, tornados and other natural hazards have caused billions of dollars in direct losses due to damage to power systems and indirect losses due to power outages, as well as social disruption. There is, therefore, a need for a comprehensive framework to assess and mitigate the risk posed by natural hazards to electric power systems. Electric power systems rely on various components that work together to deliver power from generating units to customers. Consequently, any reliable risk assessment methodology needs to take into account how the different components interact. This requires a system-level risk assessment approach. This research presents a framework for system-level risk assessment and management for electric power systems subjected to natural hazards. Specifically, risk due to hurricanes and earthquakes, as well as the combined effect of both is considered. The framework incorporates a topological-based system reliability model, probabilistic and scenario-based hazard analysis, climate change modeling, component vulnerability, component importance measure, multi-hazard risk assessment, and cost analysis. Several risk mitigation strategies are proposed; their efficiency and cost-effectiveness are studied. The developed framework is intended to assist utility companies and other stakeholders in making a risk-informed decision regarding short- and long-term investment in natural hazard risk mitigation for electric power systems. The framework can be used to identify certain parts of the system to strengthen, compare the efficiency and cost-effectiveness of various risk mitigation strategies using life-cycle cost analysis, compare risks posed by different natural hazards, and prioritize investment in the face of limited resources