Evaluation and allocation of distribution network hosting capacity for connecting renewable generation and new loads

Abstract

Further growth of distributed generation (DG) and emergency of new loads due to electrification of road transportation and heating sectors necessitates a comprehensive understanding and evaluation of hosting capacity (HC) of distribution networks (DNs) for their connection. The study presented in this Ph. D. thesis highlights the importance of HC analysis as one of the very fundamental tools for planning and operation of existing and future distribution networks, i.e., actively managed “smart grids”. Focusing on both deterministic and probabilistic methodologies, the presented research addresses the dynamics and stochastic outputs of renewable energy generation sources (e.g., wind and PV generation), varying load patterns in both controlled and uncontrolled demand scenarios, and their impact on main distribution network components (e.g., transformers and overhead lines). Ability to control DG and connected load allowed to evaluate “firm” and “non-firm” HC allocation scenarios, considering both coincidental and non-coincidental minimum and maximum generation and demand patterns, providing useful insights for network planners in terms of utilising and upgrading existing distribution networks and building the new ones, and policymakers to incentivise and support these efforts. With the energy paradigm continuing to shift towards increased decentralization, the HC analysis is not only crucial for the optimal integration of additional DG and new loads, but it also emerges as an essential input for the evaluation of technical and non-technical constraints, improvement of network resilience and realisation of more flexible and highly secure distribution networks.

The analysis in the thesis starts with an overview of literature and state-of-the-art research on HC for DGs and Loads with deterministic and probabilistic approaches. The primary focus is on defining HC, drawing insights from existing literature and aligning them with the specific context of the thesis. Subsequently, a comprehensive exploration of various constraints (technical and non-technical), both commonly encountered and less frequently addressed, is undertaken, as they play a pivotal role in defining the HC allocation. The thesis then delves into the impact of weather conditions and parameters on renewable generation, particularly in wind and PV-based DG systems. The thesis extends to the implications for loading limits of network components and meeting energy demands, with an emphasis on the anticipated electrification of transport.

The thesis's main chapter delves into HC allocation for DG distinguishing between "initial" and "further" evaluation cases. The “initial” analysis focuses on integrating wind and PV-based DG, which are the predominant renewable-based DG technologies. It entails a comprehensive examination, considering variations in network load and detailed evaluations of solar and wind energy resources, as well as impact of these and other weather conditions (e.g., ambient temperature) on the static thermal ratings (STRs) and dynamic thermal ratings (DTRs) of main network components. Notably, this "initial" HC analysis relies on actual measured weather and demand data from Scottish/UK networks, facilitating a detailed comparative analysis between wind and PV-based DGs under their respective HC conditions. As a baseline, the "initial" analysis considers only thermal ratings of network components as its main constraint.

Next, the thesis introduces “further” HC evaluation of DG, in which a general methodology for assessing technical and non-technical/economic constraints were added, allowing to determine optimal network HC for wind and PV-based DG from the perspective of both network operators and DG owners. This analysis involves three stages: a) firm Locational Hosting Capacity (LHC), considering unrestricted DG output at a specific network bus, b) non-firm LHC, considering increased installed DG capacity (above firm HC limit) at a specific bus, which however should be controlled/curtailed, and c) network hosting capacity (NHC), considering DG connections at all (available) network buses. Additionally, the analysis incorporates, operation of on-load tap changing (OLTC) transformer, power factor control of DG, voltage constraints, and electricity prices, providing a comprehensive evaluation for fair HC allocations at individual network buses and at the whole network level for both wind and PV-based DG.

In terms of analysing HC for the connection of new loads, the study extends its focus to EV charging in distribution networks, which is rapidly increasing. Similarly, the methodology introduces a general approach to determine network EV HC which is divided into “initial” and “further” analysis cases. For both cases, the analysis implements Monte Carlo simulations on actual uncontrolled EV charging data from the UK and generates daily charging profiles for various EV scenarios. The analysis again considers STRs and DTRs of main network components, typical UK weather conditions and pre-EV demands. The “initial” EV HC assessment begins with “firm” EV HC analysis in which the EV charging demand is uncontrolled. This serves as a base case defining lower limits of EV HC. In the "further" EV HC allocation, assuming full control of EV charging demand enables evaluating the ranges of maximum number of hosted EVs in a given distribution network. Results provide insights into the range of network EV HC values, considering lower and upper limits for uncontrolled and controlled charging, considering spatial and temporal variations in aggregate EV demands. The analysis is illustrated using IEEE 33-bus test network and typical UK/Scottish MV networks, with both voltage-independent (“constant power”) EV charger models and voltage-dependent EV charging demands. The presented methodology can be easily applied to the other types of new/future loads.

In retrospect, the thesis' HC analysis enables confident HC selection for DG allocations and new loads across varied HC ranges. Additionally, with the proposed methodology, computationally efficient solutions were provided. In summary, the thesis contributes a comprehensive analysis of HC in distribution networks, addressing renewable energy integration and the increasing impact of EV charging. Valuable insights are provided for network planners and policymakers, facilitating efficient deployment of future DG and EV within distribution networks.