This week’s energy seminar was given by Dr Julia Sachs from the Department of Chemical Engineering. Her talk focussed on her work on MUSE, a new energy systems model and it’s application to the residential building sector in the UK.
Scientists and researchers use a number of modelling tools, known as Integrated Assessment Models (IAM), to understand how the energy systems works and the best ways to tackle climate change. Based in the Sustainable Gas Institute (SGI) at Imperial College London, I am part of an international and interdisciplinary team of five postdocs and four PhD students who are developing a new novel IAM model, known as MUSE (ModUlar energy system Simulation Environment). MUSE is a data-driven model which generates long term projections of the energy market to identify plausible pathways of the energy systems transition to a low-carbon economy and the technologies within.
What is an IAM?
Integrated assessment modelling is a type of scientific modelling often used in environmental science and environmental policy analysis, characterized by the integration of knowledge from several domains as techno-economics and environmental science. Apart from MUSE, a variety of IAMs exist, e.g. TIMES, all differing in methodology, level of technical detail, geographical scope and in the questions they answer.
We use IAMs to examine the complex interaction of several fields (see figure 1). IAMs can be used to determine the effect that different technologies have on the environment (e.g. implications of a temperature increase), which then causes changes in policy regulation which in turn can affect the society and influences their investment decision in new technologies.
IAMs can be used to make sense of all these complexities, and can be used to study how these changes can be managed. They have a huge range of potential uses from industry and politics, from building an understanding around the key characteristics of technologies through to assessing the economic impact of energy and climate policy.
How does MUSE differ from other IAMs?
Most models lack clarity on the input assumptions and underlining functionality. The main focus of MUSE lies in the development of an accurate description of the investment and operational decision making in each geographical region within a sector, where a variety of methods are implemented to account for cultural differences and sector specify objectives. . Most models either use a central planning approach to suggest optimal energy system changes, or use a single investment metric across the economy. MUSE is comprised of a number of different sectors, and each sector model (e.g. natural gas or renewables) is built using a bottom-up technologically-rich approach capturing a comprehensive picture of all technologies across that particular sector. This enables us to ask challenging questions regarding specific costs and performance of specific systems and technologies like electric vehicles or Carbon Capture and Storage.
What does MUSE look like?
MUSE is a model of the whole energy system (i.e. including demand, transformation/conversion and supply sectors), on a global scale, which is divided into 28 regions and a t temporally divided into a number of yearly sub-intervals which varies depending on the sector to account for variations in temporal resolution of energy demand and consumption.
MUSE has been built using a simulation approach coupled with an imperfect foresight to model the real-world decision making of investors as realistically as possible. This framework allows sector-specific modelling and thus the use of the most appropriate methodology for each energy sectors. Besides giving a new perspective on the energy system transitions, MUSE is designed to enable transparent and flexible analysis of all sectors of the energy market as a whole or separately. For example a recent study focused on the transition on the UK building sector where other work looked at the use of bioenergy in the industrial sector. It also includes all sources of CO2 emissions and shows the complex relationships within the energy system among technology, economics, and impact on the environment.
The energy equilibrium of MUSE is given by the market clearing algorithm (MCA) which connects all parts of the model and is responsible for the information flow between all sectors. The MCA iterates between all sector modules until a system equilibrium on price and quantity for each energy commodity in each region and time period is achieved.
Why is the building sector so unique?
While some low-carbon technologies have been shown to be economically competitive, a limited understanding of the consumer perspective can block large scale commercialisation. While some technologies are implemented rather seamlessly in society (e.g., energy efficient boilers, compact fluorescent lamps), other technologies have encountered diverse amounts of resistance from the public (e.g., hydrogen technologies).There are many barriers that affect the uptake of technologies such as overseen technical problems and unwillingness to abandon the status-quo. Agent-based modelling, which is incorporated into MUSE, allows us to examine these complexities to understand diffusion of technologies and related investment decision-making process more than other models.
The decisions made by individuals in the buildings sector when it comes to their energy-related investment choices is incredibly varied and more complex that other sectors. For example, the outcome for each individual varies according to budget, values, and perception of a technology, even if they are faced with an identical decision or task. Understanding how people make decisions is really important and needs to be included in models. Beside several important economic and environmental criteria that people take into consideration when making a decision, investment behaviour is also influenced by social and psychological factors.
Please contact us at email@example.com if you want more information on MUSE.
Dr Julia Sachs
Dr Julia Sachs is a Research Associate in the Department of Chemical Engineering at Imperial College London.
She previously studied Engineering Cybernetics at the University in Stuttgart in Germany and graduated with a Diploma in 2012.
Julia then went on to do a PhD in model-based optimization of hybrid energy systems in rural areas also at the University of Stuttgart in collaboration with the Research Centre of Bosch in Singapore until 2016.
Julia is currently working on the MUSE energy systems model at the Sustainable Gas Institute and her research interests include mathematical programming, modelling, optimization, system dynamics and control theory.