Integrated modelling framework for the analysis of demand side management strategies in urban energy systems

Influenced by environmental concerns and rapid urbanisation, cities are changing the way they historically have produced, distributed and consumed energy. In the next decades, cities will have to increasingly adapt their energy infrastructure if new low carbon and smart technologies are to be effectively integrated. In this context, advanced planning tools can become crucial to successfully design these future urban energy systems. However, it is not only important to analyse how urban energy infrastructure will look like in the future, but also how they will be operated. Advanced energy management strategies can increase the operational efficiency, therefore reducing energy consumption, CO2 emissions, operational costs and network investments. However, the design and analysis of these energy management strategies are difficult to perform at an urban scale considering the spatial and temporal resolution and the diversity in users energy requirements. This thesis proposes a novel integrated modelling framework to analyse flexible transport and heating energy demand and assess different demand-side management strategies in urban energy systems. With a combination of agent-based simulation and multi-objective optimisation models, this framework is tested using two case studies. The first one focuses on transport electrification and the integration of electric vehicles through smart charging strategies in an urban area in London, UK. The results of this analysis show that final consumer costs and carbon emissions reductions (compared to a base case) are in the range of 4.3-45.0% and 2.8-3.9% respectively in a daily basis, depending on the type of tariff and electricity generation mix considered. These reductions consider a control strategy where the peak demand is constrained so the capacity of the system is not affected. In the second case study, focused on heat electrification, the coordination of a group of heat pumps is analysed, using different scheduling strategies. In this case, final consumer costs and carbon emissions can be reduced in the range of 4-41% and 0.02-0.7% respectively on a daily basis. In this case, peak demand can be reduced in the range of 51-62% with respect to the baseline. These case studies highlight the importance of the spatial and temporal characterisation of the energy demand, and the level of flexibility users can provide to the system when considering a heterogeneous set of users with different technologies, energy requirements and behaviours. In both studies, trade-offs between the environmental and economic performance of demand-side management strategies are assessed using a multi-objective optimisation approach. Finally, further applications of the integrated modelling framework are described to highlight its potential as a decision-making support tool in sustainable and smart urban energy systems.Open Acces

Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach

Cities account for around 75% of the global energy demand and are responsible for 60-70% of the global greenhouse gasses emissions. To reduce this environmental impact it is important to design efficient energy infrastructures able to deal with high level of renewable energy resources. A crucial element in this design is the quantitative understanding of the dynamics behind energy demands such as transport, electricity and heat. In this paper an agent-based simulation model is developed to generate residential energy demand profiles in urban areas, influenced by factors such as land use, energy infrastructure and user behaviour. Within this framework, impact assessment of low carbon technologies such as plug-in electric vehicles and heat pumps is performed using London as a case study. The results show that the model can generate important insights as a decision support tool for the design and planning of sustainable urban energy systems

An agent-based decision support framework for a prospective analysis of transport and heat electrification in urban areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities

Supporting an integrated transportation infrastructure and public space design: A coupled simulation method for evaluating traffic pollution and microclimate

Traditional urban and transport infrastructure planning that emphasized motorized transport has fractured public space systems and worsened environmental quality, leading to a decrease in active travel. A novel multiscale simulation method for supporting an integrated transportation infrastructure and public space design is presented in this paper. This method couples a mesoscale agent-based traffic prediction model, traffic-related emission calculation, microclimate simulations, and human thermal comfort assessment. In addition, the effects of five urban design strategies on traffic pollution and pedestrian level microclimate are evaluated (i.e., a “two-fold” evaluation). A case study in Beijing, China, is presented utilizing the proposed urban modeling-design framework to support the assessment of a series of transport infrastructure and public space scenarios, including the Baseline scenario, a System-Internal Integration scenario, and two External Integration scenarios. The results indicate that the most effective way of achieving an environmentally- and pedestrian- friendly urban design is to concentrate on both the integration within the transport infrastructure and public space system and the mitigation of the system externalities (e.g., air pollution and heat exhaustion). It also demonstrates that the integrated blue-green approach is a promising way of improving local air quality, micro-climatic conditions, and human comfort

Modelling Real-Time Pricing of Electricity for EnergyConservation Measures in the UK Commercial Sector

Electricity bills in the UK are increasing year after year due to power market conditions and they will most likely continue to rise. These high costs are reducing the profitability of businesses and thus efforts on understanding and mitigating these charges are a key concern for companies in order to improve their bottom line. This paper focuses on detailing a comprehensive bottom-up model of electricity commercial bills that generates real-time price curves; thus allowing customers to comprehend the true cost of the electricity they consume. The model provides profiles for different UK regions across various seasons. These insights are valuable because they can be used to inform more accurately energy efficiency programs in terms such as return on investment. By knowing where energy is more expensive it makes it easier to prioritize investments. Results overall show Yorkshire has the highest rates, while the South West has the most expensive peaks. Meanwhile, London and Southern England have the cheapest rates

Estimating plug-in electric vehicle demand flexibility through an agent-based simulation model

In the future context of smart grids, plug-in electric vehicles (PEVs) can be seen not only as a new spatial and temporal distributed load, but also as an electricity storage system. In this sense, the storage capacity can be aggregated and made an active participant in the power market to provide ancillary services. The estimation of this capacity over time and space is challenging as it depends on many factors such as vehicle owner driving profiles, charging behavior, and charging infrastructure features, etc. In this paper the demand flexibility potential of a PEV fleet is estimated using an agent-based modelling approach in which different scenarios of participation in flexible charging mechanisms are evaluated. The case study depicted in this work is based on current technology and demographic data from an urban area in London (UK)

A methodology for simulating synthetic populations for the analysis of socio-technical infrastructures

Modelling socio-technical systems in which a population of heterogeneous agents generates demand for infrastructure services requires a synthetic population of agents consistent with aggregate characteristics and distributions. A synthetic population can be created by generating individual agents with properties and rules based on a scenario definition. Simulation results fine-tune this process by comparing system level behaviour with external data, after which the emergent behaviour can be used for analysis and optimisation of planning and operation. An example of electricity demand profiles is used to illustrate the approach

Simulation and analysis of Indian residential electricity consumption using agent-based models

The increasing demand of residential consumption and the integration of renewable energy sources have motivated researchers to develop grid simulations for testing energy management strategies. Agent-based modelling is one such methodology with the capability of mimicking the emergent and complex behaviour of grids over time. Thus, we have utilized this concept to model and predict the energy consumption of a house. Results from the simulation indicate the proposed approach closely mimics the fine-grained energy data obtained from the residential unit in India. This model possesses the flexibility to be extended to estimate the electricity demand of different localities in India and, in step, to understand the behaviour of the agent with the integration of low carbon technology.by Sohini Dhar, Babji Srinivasan and Rajagopalan Srinivasa

Hierarchical price coordination of heat pumps in a building network controlled using model predictive control

Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm’s behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time