Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather

Abstract

The design of cost effective power systems with high shares of variable renewable energy technologies (VREs) requires a modelling approach that simultaneously represents the whole energy system combined with the spatiotemporal and inter-annual variability of VREs. Here we soft-link a long term energy system model, that explores new energy systems configurations from years to decades, with a high spatial and temporal resolution power system model that captures VRE variability from hours to years. Applying this methodology to Great Britain for 2050, we find that VRE focused power system design is highly sensitive to the inter-annual variability of weather and that planning based on a single year can lead to operational inadequacy and failure to meet long term decarbonisation objectives. However, some insights do emerge that are relatively stable to weather year. Reinforcement of the transmission system consistently leads to a decrease in system costs while electricity storage and flexible generation, needed to integrate VREs into the system, are generally deployed close to demand centres.

A new perspective on global renewable energy systems: why trade in energy carriers matters

“…PV and Wind) are possible on a country or continental level. [50][51][52] Different technological options in the energy system, such as sectoral integration, 53 spatial and technological diversification of VRES generation, 52 and integration of different generation and storage technologies 54 allow for operation of electricity systems almost fully based on VRES. Yet, the system levelized costs of electricity 55 are lowest at a VRES penetration well below 100%, [56][57][58] as depicted in Fig.…”

Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective

“…Whole energy system models are more aggregated and typically have a stylised temporal representation and low spatial resolution, in order to limit computational costs; both of these limitations can have a significant impact on the results [48]. As a result, they are less able to represent flexibility options, particularly the role of technologies in meeting half-hourly/hourly demand-supply fluctuations, and spatial factors influencing the energy supply, such as renewable resources [65] or the costs of infrastructure [37]. Some of these limitations are addressed via linking to other models, for example, to further assess flexibility requirements in specific system configurations through linking to models with a higher spatiotemporal resolution, to assess system feasibility [66][67][68], to parameterise system requirements without structural changes [68], to increase the temporal resolution [69], or to assess changes to the code to introduce flexibility options such as demand side response [26].…”

Importance subsampling: improving power system planning under climate-based uncertainty

“…Various approaches to reduce computational cost without lowering temporal resolution exist. One strategy is the soft-linking of a long-term planning model with a more detailed simulation operating on a shorter scale (Ringkjøb et al, 2018;Zeyringer et al, 2018;Collins et al, 2017). Another is to run a model with a smaller number of representative periods (e.g.…”