Implications of Limited Foresight and Sequential Decision Making for Long-term Energy System Planning: An Application of the Myopic MESSAGE Model

This paper presents the development and demonstration of a limited foresight energy system model. The presented model is implemented as an extension to a large, linear optimization model, MESSAGE. The motivation behind changing the model is to provide an alternative decision framework, where information for the full time frame is not available immediately and sequential decision making under incomplete information is implied. While the traditional optimization framework provides the globally optimal decisions for the modeled problem, the framework presented here may offer a better description of the decision environment, under which decision makers must operate. We further modify the model to accommodate flexible dynamic constraints, which give an option to implement investments faster, albeit with a higher cost. Finally, the operation of the model is demonstrated using a moving window of foresight, with which decisions are taken for the next 30 years, but can be reconsidered later, when more information becomes available. We find that the results do demonstrate some of the pitfalls of short term planning, e.g. lagging investments during earlier periods lead to higher requirements later during the century. Furthermore, the energy system remains more reliant on fossil based energy carriers, leading to higher greenhouse gas emissions. reliant on fossil based energy carriers, leading to higher greenhouse gas emi

Incorporating homeowners' preferences of heating technologies in the UK TIMES model

Hot water and space heating account for about 80% of total energy consumption in the residential sector in the UK. It is thus crucial to decarbonise residential heating to achieve UK's 2050 greenhouse gas reduction targets. However, the decarbonisation transitions determined by most techno-economic energy system models might be too optimistic or misleading for relying on cost minimisation alone and not considering households' preferences for different heating technologies. This study thus proposes a novel framework to incorporate heterogeneous households' (HHs) preferences into the modelling process of the UK TIMES model. The incorporated preferences for HHs are based on a nationwide survey on homeowners' choices of heating technologies. Preference constraints are then applied to regulate the HHs' choices of heating technologies to reflect the survey results. Consequently, compared to the least-cost transition pathway, the preference-driven pathway adopts heating technologies gradually without abrupt increases of market shares. Heat pumps and electric heaters are deployed much less than in the cost optimal result. Extensive district heating using low-carbon fuels and conservation measures should thus be deployed to provide flexibility for decarbonisation. The proposed framework can also incorporate preferences for other energy consumption technologies and be applied to other linear programming-based energy system models

Energy scenario choices: insights from a retrospective review of UK energy futures

Since the 1980s, there has been a shift in energy research. It has shifted from approaches that forecast or project the future to approaches which make more tentative claims and which explore several plausible scenarios. Due to multiple uncertainties in energy systems, there is an infinite amount of plausible scenarios that could be constructed and scenario developers therefore choose smaller, more tangible sets of scenarios to analyse. Yet, it is often unclear how and why this scenario choice is made and how such choices might be improved. This paper presents a retrospective analysis of twelve UK energy scenarios developed between 1978 and 2002. It investigates how specific scenarios were chosen and whether these choices captured the actual UK energy system transition. It finds that scenario choice reflected contemporary debates, leading to a focus on certain issues and limiting the insights gleaned from these exercises. The paper argues for multi-organisation and multi-method approaches to the development of energy scenarios to capture the wide range of insights on offer. Rather than focus on uncertainty in model parameters, greater reflection on structural uncertainties, such as shifts in energy governance, is also required

Estimating the impact of variable renewable energy on base-load cycling in the GB power system

Between 2009 and 2017 the share of wind and solar energy sources in the GB electricity generation mix increased from 2.5% to 17%. Due to the variable nature of these renewable sources, large thermal power stations designed for constant base-load operation have been required to operate more flexibly to compensate for fluctuations in renewable generation. This flexible operation results in increased thermal stress and reduced efficiency causing increased operation, maintenance and fuel costs for these assets. In this paper we present the results of what is, to the best of our knowledge, the first empirical study on the impact of renewables generation on startups, ramping and part-loading (collectively, ‘cycling’) of base-load generators. We develop regression models using half-hourly generation data from 2009 to 2017 that capture the impact of increased renewable penetration while taking into account confounding factors including seasonality and demand. We find that with 2009-levels of renewable generation, cycling in 2017 would have been less severe, with 20% fewer startups. We also present estimates for cycling under National Grid Future Energy Scenarios to 2030 with implications for investment in generation assets. Additionally, the dataset derived in this research is made available and comprises the first open-access dataset on cycling

Investigating UK consumers’ heterogeneous engagement in demand-side response

Demand-side response (DSR), the incentivised time-shifting of energy use by consumers away from peak times, is regarded as a potentially effective measure to balance electricity supply and demand. This will be even more important in the low-carbon energy system of the future, with a high share of non-dispatchable power, such as variable renewable energy and nuclear power. Most DSR programmes require consumers’ active engagement in shifting end-use activities. Previous studies have, however, rarely revealed socio-demographic factors influential for consumers’ willingness-to-shift specific end-use activities. This study thus aims to fill this research gap and, using a multinomial logistic model to analyse a nationwide survey, identify factors influential for DSR-related decisions. The nationwide survey for 1004 respondents was carried out to collect data about consumers’ willingness-to-shift their daily activities. We focused on the activities that constitute the major part of domestic energy consumption, i.e. cooking, dish-washing, entertainment, heating, laundry and showering. According to the results, consumers’ original timing of the end-use activities, socio-demographic factors, ownership of specific appliances and level of concern for energy-saving are influential for their willingness-to-shift activities. These findings can not only help policymakers make more targeted DSR promotion plans but also help to improve broader modelling tools to better consider consumers’ willingness-to-shift their demand

Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models

In this study we describe a novel formulation of the so-called modelling to generate alternatives (MGA) methodology and use it to explore the near cost optimal solution space of the global energy-environment-economy model TIAM-UCL. Our implementation specifically aims to find maximally different global energy system transition pathways and assess the extent of their diversity in the near optimal region. From this we can determine the stability of the results implied by the least cost pathway which in turn allows us to both identify whether there are any consistent insights that emerge across MGA iterations while at the same time highlighting that energy systems that are very similar in cost can look very different. It is critical that the results of such an uncertainty analysis are communicated to policy makers to aid in robust decision making. To demonstrate the technique we apply it to two scenarios, a business as usual (BAU) case and a climate policy run. For the former we find significant variability in primary energy carrier consumption across the MGA iterations which then projects further into the energy system leading to, for example, large differences in the portfolio of fuels used in and emissions from the electricity sector. When imposing a global emissions constraint we find, in general, less variability than the BAU case. Consistent insights do emerge with oil use in transport being a robust finding across all MGA iterations for both scenarios and, in the mitigation case, the electricity sector is seen to reliably decarbonise before transport and industry as total system cost is permitted to increase. Finally, we compare our implementation of MGA to the so-called Hop-Skip-Jump formulation, which also seeks to obtain maximally different solutions, and find that, when applied in the same way, the former identifies more diverse transition pathways than the latter

The Contribution of Renewable Energy to a Sustainable Energy System

This report provides an overview of the main results from the scenarios analysed in the CASCADE MINTS project to assess the role of renewables in solving global and European en-ergy and environmental issues. The main conclusion is that renewable energy can make a sub-stantial contribution to reducing greenhouse gas emissions and improving diversification of the European energy production portfolio, although other technologies will also be needed in order to achieve post Kyoto targets. The report outlines the impacts, costs and benefits of ambitious renewables targets for Europe in the medium term. It also presents lessons learned from taking the global perspective

Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights