Importance subsampling for power system planning under multi-year demand and weather uncertainty

This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models. Recent studies indicate that reliably determining optimal electricity (investment) strategy under climate variability requires the consideration of multiple years of demand and weather data. However, solving planning models over long simulation lengths is typically computationally unfeasible, and established time series reduction approaches induce significant errors. The importance subsampling method reliably estimates long-term planning model outputs at greatly reduced computational cost, allowing the consideration of multi-decadal samples. The key innovation is a systematic identification and preservation of relevant extreme events in modeling subsamples. Simulation studies on generation and transmission expansion planning models illustrate the method’s enhanced performance over established "representative days" clustering approaches. The models, data and sample code are made available as open-source software

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

Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling, allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios

Efficient quantification of the impact of demand and weather uncertainty in power system models

This paper introduces a new approach to quantify the impact of forward propagated demand and weather uncertainty on power system planning and operation models. Recent studies indicate that such sampling uncertainty, originating from demand and weather time series inputs, should not be ignored. However, established uncertainty quantification approaches fail in this context due to the data and computing resources required for standard Monte Carlo analysis with disjoint samples. The method introduced here uses an m out of n bootstrap with shorter time series than the original, enhancing computational efficiency and avoiding the need for any additional data. It both quantifies output uncertainty and determines the sample length required for desired confidence levels. Simulations and validation exercises are performed on two capacity expansion planning models and one unit commitment and economic dispatch model. A diagnostic for the validity of estimated uncertainty bounds is discussed. The models, data and code are made available

Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory. To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology. In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating. We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available

The importance of weather and climate to energy systems: a workshop on next generation challenges in energy-climate modelling

Over 80 international participants, representing weather, climate, and energy systems research, joined two 4-hour remote sessions to highlight and prioritize ongoing and future challenges in energy-climate modelling. The workshop had two primary goals: to build a deeper engagement across the “energy” and “climate” research communities, and to identify and begin to address the scientific challenges associated with modelling climate risk in energy systems

Subsampling for renewable electricity system optimisation

Decarbonisation efforts are making power (electricity) systems - which require a continuous matching of supply and demand across time and throughout a network known as the grid - increasingly weather-dependent. Historically, this balance was maintained by adjusting dispatchable (mostly fossil fuel) generation. However, as fossil fuels are displaced by weather-dependent renewables such as solar and wind, neither supply nor demand can be fully controlled. This complicates the maintenance of supply-demand balance, known informally as "keeping the lights on". Power system planning models are simplified representations of electricity systems used to inform investment strategy, such as whether to build a wind farm, solar farm, gas plant, transmission line or battery. They involve an optimisation problem that minimises the sum of install and subsequent operation costs. Their outputs depend on demand and weather patterns, which enter the model as time series of e.g. demand levels, wind speeds and solar irradiances. The use of relatively short demand and weather time series (e.g. a single year) in power system planning studies has recently been criticised. This is because many conclusions - such as whether a 100% renewable power system is feasible and affordable - depend on the choice of sample; picking an unrepresentative one provides an unrealistic impression of cost or reliability. At the same time, computing resources usually prevent the consideration of longer samples, since planning problems take too much time or memory to solve. In this thesis, we introduce subsampling methods to reduce and quantify the impact of demand and weather sampling uncertainty in power system planning models. Our algorithms compress long time series into shorter data sets - enhancing computational efficiency - and provide uncertainty bounds on model outputs. They help prevent incorrect conclusions by (1) allowing the consideration of larger amounts of data, reducing sampling uncertainty, and (2) indicating to users whether their outputs are statistically robust or a result of the particular demand and weather sample. We combine and extend techniques from the power systems, statistics and optimisation literature. This is because power system planning models involve optimisation problems solved across demand and weather time series, which we view - as is common in statistics - as samples from an underlying distribution. Our contributions are statistical subsampling methods, such as importance sampling and the bootstrap, adapted to power system optimisation problems.Open Acces

The importance of weather and climate to energy systems: a workshop on next generation challenges in energy-climate modelling: A workshop on next generation challenges in energy–climate modeling

The challenges emerging from the workshop highlight the need for increased interaction. Weather and climate scientists must first begin to understand how climate information is used by energy researchers in practice, ensuring that the data provided can interface with the tools and techniques being used. This understanding requires atmospheric scientists to investigate how the processes involved in energy modeling relate to the impacts of weather and climate, rather than focusing on the climate itself. In parallel, energy scientists should seek to develop a better appreciation of climate uncertainty, addressing its importance for oth historical and future simulations. A key step is therefore to develop the tools and understanding required to quantify the effects of climate uncertainty in highly complex energy systems, and to understand the importance of climate relative to the contributions from other sources of uncertainty

The importance of weather and climate to energy systems: a workshop on next generation challenges in energy-climate modelling: A workshop on next generation challenges in energy–climate modeling

The challenges emerging from the workshop highlight the need for increased interaction. Weather and climate scientists must first begin to understand how climate information is used by energy researchers in practice, ensuring that the data provided can interface with the tools and techniques being used. This understanding requires atmospheric scientists to investigate how the processes involved in energy modeling relate to the impacts of weather and climate, rather than focusing on the climate itself. In parallel, energy scientists should seek to develop a better appreciation of climate uncertainty, addressing its importance for oth historical and future simulations. A key step is therefore to develop the tools and understanding required to quantify the effects of climate uncertainty in highly complex energy systems, and to understand the importance of climate relative to the contributions from other sources of uncertainty