Spatially and Temporally Explicit Energy System Modelling to Support the Transition to a Low Carbon Energy Infrastructure – Case Study for Wind Energy in the UK

Renewable energy sources and electricity demand vary with time and space and the energy system is constrained by the location of the current infrastructure in place. The transitioning to a low carbon energy society can be facilitated by combining long term planning of infrastructure with taking spatial and temporal characteristics of the energy system into account. There is a lack of studies addressing this systemic view. We soft-link two models in order to analyse long term investment decisions in generation, transmission and storage capacities and the effects of short-term fluctuation of renewable supply: The national energy system model UKTM (UK TIMES model) and a dispatch model. The modelling approach combines the benefits of two models: an energy system model to analyse decarbonisation pathways and a power dispatch model that can evaluate the technical feasibility of those pathways and the impact of intermittent renewable energy sources on the power market. Results give us the technical feasibility of the UKTM solution from 2010 until 2050. This allows us to determine lower bounds of flexible elements and feeding them back in an iterative process (e.g. storage, demand side control, balancing). We apply the methodology to study the long-term investments of wind infrastructure in the United Kingdom

Energy-led, non-domestic building refurbishment : decision support for a whole-building approach to improvement of operational performance

Pressure is growing upon non-domestic building owners and occupiers to measure and improve the energy performance, and associated carbon emission levels, of the portfolio in which they operate. In line with this, the need for energy-led refurbishment of existing buildings is increasingly evident, with approximately 60% of the current building stock expected to still exist in 2050 and less than 1% being replaced annually. However, energy-led refurbishment of existing non-domestic property faces a number of barriers, including an ill-defined decision-making process and a lack of low carbon skills required to guide building owners in this complex transition. This thesis examines first, the need for a re-alignment of disciplines within the construction industry to fulfil the growing requirement for low carbon skills, specific to energy-led refurbishment. A comprehensive desk study was undertaken, evaluating the competencies of the established construction industry professions, as defined by their governing bodies. This was supported by structured interviews with users of large, nondomestic property and industry professionals to establish whether a need existed and how they proposed it be fulfilled. A deficiency in expertise was identified, and from this a competency specification for professionals leading energy-led refurbishment in existing, non-domestic property has been developed. Second, this thesis explores the different forms of automated decision support within the construction sector, identifying opportunities for a structured decision-making approach to energy-led refurbishment. An optimum decision support tool (DST) process was proposed, consisting of seven steps from assessment of the existing building’s state through to continuous evaluation and improvement of the refurbished building. A key module within this process was developed in detail to address the complex multiple attribute decision making (MADM) approach required during selection of energy performance improvement measure (EPIM). A set of assessment criteria, addressing a variety of performance characteristics, was designed using an online Delphi survey with a select group of ‘energy in buildings’ experts. The criteria range from short term impact (EPIM installation) to long term impact (EPIM operation and disposal) upon the existing property’s performance. Subsequent weighting of the assessment criteria in terms of their relative importance was undertaken using the same expert group through a paired comparison survey methodology. This revealed the relative importance of each criterion, consequently aiding prioritisation of EPIMs within the optimum DST and supporting decision-making

Implementation of a new bi-directional solar modelling method for complex facades within the ESP-r building simulation program

This paper provides an overview of a new method for modelling the total solar energy transmittance. It is implemented in the ESP-r building simulation program to model complex façades such as double glazed façades with external, internal or integrated shading devices. This new model has been validated and tested for several cases. The new model required changes to the solar control simulation algorithm and the user interface, so a new “Advanced optics menu” was also introduced into ESP-r. The paper presents the interface development and application of the new technique to different simulation configurations (especially different complex façades with shading devices) in a standard office building

The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition

The energy sector transition requires large financial investments in low-carbon generation technologies, to be delivered by a variety of actors with heterogeneous characteristics. Real-world actors have bounded-rationality, reflected by their limited foresight and heterogeneous expectations, and as past trends influence their investments. Agent-based models are highly suitable modelling frameworks to study such realistic and complex energy transition dynamics. This paper introduces BRAIN-Energy, a novel agent-based model which explicitly allows to explore the impacts of actors' heterogeneous characteristics, and of their interactions, on the transition pathways of the UK, German and Italian electricity sectors. Results show that actors' heterogeneous characteristics pose barriers to effective decarbonisation efforts, affect the speed of the transition, and impact the transition's security of supply and affordability dimensions. Limited foresight and path-dependency lead to investment cycles (both virtuous and vicious). The country comparison highlights how such effects are stronger in markets with more heterogeneous market players

The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors

Achieving electricity sector transitions consistent with stringent climate change mitigation under the Paris Agreement requires a careful understanding both of the coordinating role of national governments and of its interactions with the heterogeneous market players who will make the low-carbon investments in the electricity sector. However, traditional energy models and scenarios generally assume exogenous policy targets and fail to capture this co-evolution between policy-makers and heterogeneous private and public investors. This paper uses BRAIN-Energy, a novel agent-based model of investment in electricity generation to simulate and contrast government and investor dynamics in the transition pathways of the UK, German and Italian electricity sectors. Key findings show that a successful transition – which achieves the energy policy “trilemma” (low carbon, secure, affordable) – requires the co-evolution of the policy dimension (strong and frequently updatable CO2 price, renewable subsidies and capacity market) with the strategies of the heterogeneous market players. If this dynamic balance is maintained then incentives are politically feasible and suppliers learn and evolve (in what we term a virtuous cycle). If either the incentives are too weak to drive learning or too expensive so the policy regime collapses, then the transition fails on one of its key dimensions (in what we term a vicious cycle). Getting this balance right is harder in risky markets that also have players with more pronounced bounded rationality and path dependence in how they make investments

The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition

Market players in the energy sector transition are heterogeneous, have bounded rationality and are influenced by their own past failures, as well as imitating the successes of their competitors. However this agent heterogeneity and complex behaviour in investment choices is not taken into account in traditional energy-economy models used to inform energy sector policies. By using BRAIN-Energy, an agent-based model of investment in electricity generation, which enables to study the impact of actors’ heterogeneous characteristics on the transition pathways of the UK, German and Italian electricity sectors, this paper shows how historic path-dependency in investment choices displaces low-carbon in favour of high-carbon investments under a weak regulatory framework. By contrast, imitation can help the diffusion of renewable technologies, through a self-reinforcing positive feedback when government subsidies to low-carbon investments are in place

Modelling governance for a successful electricity sector decarbonisation

Early and deep electricity decarbonisation is critical to achieve the overall energy transition target of net-zero emissions by 2050. This paper extends an electricity agent based model to capture the inter-dependence of consistent governance with underpinning societal pressure and resultant investment strategies. Results show only with the strongest level of governance – reflected in the range of national/local policy mechanisms used, and their strength/timing when interim targets are met/missed – can near-zero electricity emissions be met well before 2050. Strong governance can also ensure a stable electricity system, with consistent policies mitigating the intensity of any investment cycles. Strong governance entails higher capital investments, but these can deliver lower electricity prices in the long-term. And strong governance means that a successful electricity decarbonisation does not need to be built solely on existing incumbents, but also via local cooperatives to aggregate household financing and demand side management. However, with inconsistent governance, a vicious cycle ensues with a weak rationale to enact ambitious policies at both the local and national levels, significant inertia in new electricity investments, and hence “failure” scenarios of decarbonisation. This challenges the prior findings of optimistic achievement of electricity decarbonisation scenarios by standard techno-economic optimisation models