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This paper presents a novel agent-based, stochastic model, which uses game-theoretic principles to simulate Contract for Difference (CfD) auctions. The framework has use cases and implications for policymakers and renewable generators alike, and can be used by developers to prepare bidding strategy and for policymakers to empirically test auction design. The model is demonstrated through replication of the offshore wind CfD Allocation Round 3 (AR3) pot, and utilises high-level cost modelling distribution data to estimate bid prices for the competing projects. The model produces a distribution of most likely results which better categorises uncertainty, and through comparison of AR3 and simulation results, demonstrates how outcomes can be predicted with reasonable confidence by developers. Analysis show that the transmission network and grid connection charges are a significant barrier for projects in some geographical regions to be awarded a CfD contract, potentially hindering renewable deployment in those areas. Moreover, this paper demonstrates how players can use probability theory to select an optimum bidding strategy which maximises expected profit while factoring the uncertainty inherent in CfD auctions. Results show that a 1200 MW wind farm development can increase potential profits by £135 million over the CfD contract length in exchange for a 25 p.p. chance reduction in being awarded a subsidy. • A novel simulation approach is introduced for analysing the UK's CfD auction • We show how players can attempt to optimise their bid based on expected profits • We demonstrate how uncertainty in auctions can be characterised by developers • Transmission charges act as a barrier for offshore wind projects in some regions [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the application of complexity science methods in understanding energy systems and system change. The challenge of moving to sustainable energy systems which provide secure, affordable and low-carbon energy services requires the application of methods which recognise the complexity of energy systems in relation to social, technological, economic and environmental aspects. Energy systems consist of many actors, interacting through networks, leading to emergent properties and adaptive and learning processes. Insights on these type of phenomena have been investigated in other contexts by complex systems theory. However, these insights are only recently beginning to be applied to understanding energy systems and systems transitions. The paper discusses the aspects of energy systems (in terms of technologies, ecosystems, users, institutions, business models) that lend themselves to the application of complexity science and its characteristics of emergence and coevolution. Complex-systems modelling differs from standard (e.g. economic) modelling and offers capabilities beyond those of conventional models, yet these methods are only beginning to realize anything like their full potential to address the most critical energy challenges. In particular there is significant potential for progress in understanding those challenges that reside at the interface of technology and behaviour. Some of the computational methods that are currently available are reviewed: agent-based and network modelling. The advantages and limitations of these modelling techniques are discussed. Finally, the paper considers the emerging themes of transport, energy behaviour and physical infrastructure systems in recent research from complex-systems energy modelling. Although complexity science is not well understood by practitioners in the energy domain (and is often difficult to communicate), models can be used to aid decision-making at multiple levels e.g. national and local, and to aid understanding and allow decision making. The techniques and tools of complexity science, therefore, offer a powerful means of understanding the complex decision-making processes that are needed to realise a low-carbon energy system. We conclude with recommendations for future areas of research and application. [ABSTRACT FROM AUTHOR]

To ensure security of supply and incentivize reliable investment in generation capacity, capacity markets (CMs) have been implemented or are being considered. However, demand response (DR) and electrical energy storage (EES) also contribute to system adequacy. In this paper, we analyse the change in the need for a CM if DR and EES are available, in the presence of a growing portfolio share of intermittent renewable energy sources electricity (RES-E). We present a novel hybrid electricity market model of the transition to a low-carbon electricity system which uses optimization for short-term market operations and agent-based simulation of long-term decisions. DR and EES may significantly reduce the risk of shortages in an energy-only market, even if investment decisions are myopic, like in our model, as compared to an energy-only market without flexibility options. We also present a novel mechanism for contribution of EES to the CM. This reduces the cost of the CM and improves the business case for EES. In our model, DR and EES achieve almost the same improvement of security of supply as a CM, but they do so at a lower cost. Therefore, the case for a centralized CM is weakened in a system with even a limited share of DR and medium-term EES, as presented in our model. These results depend on the duration of scarcity events and the cost of EES and DR. Refinement of the model representation will be required to extrapolate these conclusions to real markets with other types of DR, EES and CMs. [ABSTRACT FROM AUTHOR]

• Develop an agent-based model of electric vehicle (EV) and vehicle-to-grid (V2G) adoption. • The EV-V2G model can explicitly simulate social influences on the EV and V2G adoption. • Only 3.3% of survey participants were familiar with V2G. • V2G selling price and advertisement were not very influential to the diffusion of V2G. The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent's preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several "what-if" scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment. [ABSTRACT FROM AUTHOR]

• Unscheduled simulation of the occupant behaviour in buildings. • Simulation of individual occupant behaviour by the agent-based modelling. • Support for the personalising of the designed building space. • Possibility to investigate individuals comfort during building design phase. Simulation of occupant behaviour (OB) in buildings is a challenging task. Available software uses a broad spectrum of tools that try to reproduce the patterns of human activity. From building energy perspective, the main emphasis in research has been focused on discovering behaviour directly related to energy. In recent years, more attention has been given to simulating occupant actions that are indirectly influencing building energy use. In most of the cases, this is achieved with the use of agent-based modelling which allows describing occupant actions on a room level. According to the existing methodologies review, it is a proper step, but to include occupant behaviour in energy simulations, spatial and temporal resolution of the occupant behaviour model must be improved. Addressing this issue requires the development of a comprehensive model supported by numerous modules that would cover various significant occupant actions. This paper focuses on the development of the high-resolution, data-driven movement engine of occupants. It is one of the fundamental modules necessary to simulate occupant behaviour with high granularity. Once the model is developed within its essential functionalities, it will deliver a bottom-up model capable of testing various energy use strategies. It will allow for testing different heat, ventilation and air conditioning solutions and the responses provided by simulated occupants. The data used to develop this module was obtained thru in-situ measurements, with the use of depth registration. Information obtained from experiments is similar to previous research, but it also extends the investigation scope with an additional transition-based variable. [ABSTRACT FROM AUTHOR]