(331e) Integrated Power Systems Capacity and Transmission Planning with High Spatial and Temporal Resolution

Integrated modelling can combine different scales with regards to time and space. For power systems such integrated planning is crucial to optimal investment and operational decision-making. Driven by a growing level of network interconnections, electrication (e.g., in transport and heating), and digitalisation, there is increasing interest to model and investigate future energy systems at a high spatial and temporal resolution.

To explore spatially-dependent future power systems pathways, we have developed the Electricity Systems Optimisation Model with high temporal and spatial granularity (ESONE). This contribution is based on previous work by Heuberger et al. [1, 2] and open source models available at https://zenodo.org/record/1212298. ESONE is a mixed-integer linear program that minimises total system cost subject to operational constraints, system-wide requirements and carbon emissions targets. On a planning time horizon (years) we perform optimal capacity expansion and grid reinforcement, while on an operational time horizon (hours) we determine the optimal dispatch and unit commitment schedule for up to 2000 units of 16 different types of power generation and energy storage technologies. Optimal power flow is modelled as simplified direct current considering energy loss as a function of transmission volume and distance of zone centres, i.e., 29 nodes for a case study on the United Kingdom (UK). Additionally, we account for system ancillary services in form of spinning reserve, as well as an approximation of frequency and voltage control.

In order to enable model feasibility we develop a multi-dimensional k-means clustering approach and apply this to the spatially and temporally dependent data (i.e., solar, onshore/offshore wind availability [3, 4], electricity demand, power import price [5]). In addition, we introduce a convex hull reformulation [6] for the integer scheduling constraints and apply an iterative solution method implemented in GAMS 25.0.3/CPLEX 12.8. This results in a reduced model size of 2.15 10⁶ compared to 8.6 10⁶ variables. We parametrise the ESONE model to the UK's power system and explore future scenarios from 2015 to 2050 based on regional resource availability, technical, economic, and environmental drivers. We highlight the role of Carbon Capture and Storage (CCS) equipped power plants, including bio-energy and CCS, in decarbonising future energy systems. While we see a push for offshore wind in the northern half of the UK, we also see a signficant usage of CCS in the south and northeast, with gas-fired CCS plants coming online in 2025 and bio-energy CCS plants appearing in 2030. This contribution presents the methodology behind the ESONE model and showcases its capabilities for the UK power system.

[1] Heuberger, C. F., Rubin, E. S., Staffell, I., Shah, N., Mac Dowell, N. Power Capacity Expansion Planning Considering Endogenous Technology Cost Learning, Applied Energy 204, 831-845 (2017)
[2] Heuberger, C. F., Staffell, I., Shah, N., Mac Dowell, N. A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks. Computers & Chemical Engineering 107, 247256 (2017)
[3] Pfenninger, S., Staffell, I. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy 114, 1251-1265 (2016)
[4] Staffell, I., Pfenninger, S. Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output. Energy 114, 1224-1239 (2016)
[5] National Grid, UK. Data Explorer - Real Time Demand Data. http://tinyurl.com/kbe65ca, (2015)
[6] Hua, B., Baldick, R., Wang, J. Representing Operational Flexibility in Generation Expansion Planning Through Convex Relaxation of Unit Commitment, IEEE Transactions on Power Systems 33(2), 2272-2281 (2017)