60 results found
Yang L, van Dam KH, Zhang L, 2020, Developing Goals and Indicators for the Design of Sustainable and Integrated Transport Infrastructure and Urban Spaces, SUSTAINABILITY, Vol: 12
Yang L, Zhang L, Philippopoulos-Mihalopoulos A, et al., 2020, Integrating agent-based modeling, serious gaming, and co-design for planning transport infrastructure and public spaces, URBAN DESIGN INTERNATIONAL, Vol: 26, Pages: 67-81, ISSN: 1357-5317
Yang L, Zhang L, Stettler MEJ, et al., 2020, Supporting an integrated transportation infrastructure and public space design: A coupled simulation method for evaluating traffic pollution and microclimate, Sustainable Cities and Society, Vol: 52, ISSN: 2210-6707
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.
Miu LM, Mazur CM, Van Dam KH, et al., 2019, Going smart, staying confused: perceptions and use of smart thermostats in British homes, Energy Research and Social Science, Vol: 57, ISSN: 2214-6296
Given the significant contribution of housing to energy consumption, research into how residents use energy-saving technologies has been gathering pace. In this study, we investigate the perception and use of domestic smart heating controls by a small group of residents in London, UK. These residents are supplied by a district heat network (DHN) through underfloor heating systems, and took part in a trial where their controls were upgraded from traditional thermostats to smart thermostats. Pre- and post-trial interviews were used to assess changes in how residents interacted with and perceived their controls and heating systems. After the upgrade, more residents were satisfied with the usability of their controls and programmed heating schedules which matched their actual occupancy patterns, but they also made ad-hoc temperature and schedule adjustments more frequently. These changes provide insight into how a unique sample of residents, “twice removed” from the most intuitive methods of heating control, adjusted their behaviour and perceptions following a technology upgrade. Although the small sample size and lack of long-term monitoring limits the generalizability of our results, the findings open avenues for further research into whether smart heating controls change user behaviour in a way that improves the predictability of heating demand, a crucial aspect of improving DHN operation and reducing related emissions.
Wang X, van Dam KH, Triantafyllidis C, et al., 2019, Energy-water nexus design and operation towards the sustainable development goals, COMPUTERS & CHEMICAL ENGINEERING, Vol: 124, Pages: 162-171, ISSN: 0098-1354
Mazur C, Hoegerle Y, Brucoli M, et al., 2019, A holistic resilience framework development for rural power systems in emerging economies, Applied Energy, Vol: 235, Pages: 219-232, ISSN: 0306-2619
Infrastructure and services within urban areas of developed countries have established reliable definitions of resilience and its dependence on various factors as an important pathway for achieving sustainability in these energy systems. However, the assessment, design, building and maintenance of power systems situated in rural areas in emerging economies present further difficulties because there is no a clear framework for such circumstances. Aiming to address this issue, this paper combines different visions of energy-related resilience both in general and under rural conditions in order to provide a robust practical framework for local and international stakeholders to derive the right actions in the rural context of emerging economies. An in-depth review is implemented to recompile information of resilience in general, in energy systems and in rural areas in particular, and a number of existing frameworks is also consulted. In order to acknowledge the particular circumstances and identify the important factors influencing the resilience of rural electrification in emerging economies, a holistic rural power system resilience framework is developed and presented. This consists of twenty-one indicators for technical resilience, eight indicators for social resilience, and thirteen indicators for economic resilience. This framework can be used by system owners and operators, policy makers, NGOs and communities to ensure the longevity of power systems. This work also paves the way for the creation of appropriate and effective resilience standards specifically targeted for application in these regions - aiming to achieve the delivery of global and local sustainability goals.
van Dam KH, Feng B, Wang X, et al., 2019, Model-based decision-support for waste-to-energy pathways in New South Wales, Australia, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 1765-1770, ISBN: 978-0-12-819940-4
Wang X, Yang W, Noor S, et al., 2019, Blockchain-based smart contract for energy demand management, 10th International Conference on Applied Energy (ICAE), Publisher: ELSEVIER SCIENCE BV, Pages: 2719-2724, ISSN: 1876-6102
Noor S, Yang W, Guo M, et al., 2018, Energy Demand Side Management within micro-grid networks enhanced by blockchain, APPLIED ENERGY, Vol: 228, Pages: 1385-1398, ISSN: 0306-2619
Kis Z, Koppelaar RHEM, Sule MN, et al., 2018, Framework for WASH sector data improvements in data-poor environments, applied to Accra, Ghana, Water, Vol: 10, ISSN: 2073-4441
Improvements in water, sanitation and hygiene (WASH) service provision are hampered by limited open data availability. This paper presents a data integration framework, collects the data and develops a material flow model, which aids data-based policy and infrastructure development for the WASH sector. This model provides a robust quantitative mapping of the complete anthropogenic WASH flow-cycle: from raw water intake to water use, wastewater and excreta generation, discharge and treatment. This approach integrates various available sources using a process-chain bottom-up engineering approach to improve the quality of WASH planning. The data integration framework and the modelling methodology are applied to the Greater Accra Metropolitan Area (GAMA), Ghana. The highest level of understanding of the GAMA WASH sector is achieved, promoting scenario testing for future WASH developments. The results show 96% of the population had access to improved safe water in 2010 if sachet and bottled water was included, but only 67% if excluded. Additionally, 66% of 338,000 m3 per day of generated wastewater is unsafely disposed locally, with 23% entering open drains, and 11% sewage pipes, indicating poor sanitation coverage. Total treated wastewater is <0.5% in 2014, with only 18% of 43,000 m3 per day treatment capacity operational. The combined data sets are made available to support research and sustainable development activities.
Noor S, Guo M, van Dam KH, et al., 2018, Energy demand side management with supply constraints: Game theoretic approach, Applied Energy Symposium and Forum on Renewable Energy Integration with Mini/Microgrid Systems (REM), Publisher: Elsevier, Pages: 368-373, ISSN: 1876-6102
The management of energy supply and demand is becoming more challenging in regions where the demand continues to grow rapidly and more intermittent renewable supply sources are added to the energy infrastructure. In this context, Demand Side Management (DSM) can be employed to improve reliability of supply and stretch the capacity limits of the existing grid infrastructure. A game theoretic approach for DSM model incorporating storage components is suggested in this paper for environments with supply constraints. The proposed model is able to not only reduce the Peak-to-Average ratio to benefit the electric grid, but also smoothen the dips in load profile caused by supply constraints.
Liu M, van Dam KH, Pantaleo AM, et al., 2018, Optimisation of integrated bioenergy and concentrated solar power supply chains in South Africa, 28th European Symposium on Computer-Aided Process Engineering (ESCAPE), Publisher: Elsevier, Pages: 1463-1468, ISSN: 1570-7946
Climate change and energy security are complex challenges whose solutions depend on multi-faceted interactions between different actors and socio-economic contexts. Energy innovation through integration of renewable energies in existing systems offers a partial solution, with high potential identified for bioenergy and solar energy. In South Africa there is potential to further integrate renewable energies to meet local demands and conditions. Various concentrated solar power (CSP) projects are in place, but there is still land available to generate electricity from the sun. In combination with sustainable biomass resources these can offer synergetic benefits in improving the power generation’s flexibility. While thermodynamic and thermo-economic modelling for hybrid CSP-Biomass technology have been proposed, energy modelling in the realm of supply chains and demand/supply dynamics has not been studied sufficiently.We present a spatially and temporally Mixed Integer Linear Programming (MILP) model, to optimize the choice and location of technologies in terms of economic cost while being characterised by realistic supply/demand constraints as well as spatially-explicit environmental constraints. The model is driven by electricity demand, resource availability and technology costs as it aspires to emulate key energy and sustainability issues. A case study in the South African province of Gauteng was implemented over 2015-2050 to highlight the potential and challenges for hybrid CSP-Biomass and integrated systems assessment and the applicability of the modelling approach.From the range of hybrid CSP-Biomass technologies considered, based on detailed techno-economic characteristics from the literature, the Biomass only EFGT plant is identified as the cost optimal. When distributed generation (DG) technologies, small-scale Solar PV and Wind Turbines were introduced to the model as a competing alternative, they were demonstrated to be more economically optimal (&eur
Bieber N, Kee JH, Wang X, et al., 2018, Erratum to "Sustainable planning of the energy-water-food nexus using decision making tools" [Energy Policy 113, (2018) 584, 2018], Energy Policy, Vol: 116, Pages: 289-289, ISSN: 0301-4215
Wang X, Guo M, Koppelaar RHEM, et al., 2018, A nexus approach for sustainable urban energy-water-waste systems planning and operation, Environmental Science and Technology, Vol: 52, Pages: 3257-3266, ISSN: 0013-936X
Energy, water and waste systems analyzed at a nexus level is key to move towards more sustainable cities. In this paper, the “resilience.io” platform is developed and applied to emphasize on waste-to-energy pathways, along with the water and energy sectors, aiming to develop waste treatment capacity and energy recovery with the lowest economic and environmental cost. Three categories of waste including wastewater (WW), municipal solid waste (MSW) and agriculture waste are tested as the feedstock for thermochemical treatment via incineration, gasification or pyrolysis for combined heat and power generation, or biological treatment such as anaerobic digestion (AD) and aerobic treatment. A case study is presented for Ghana in Sub-Saharan Africa, considering a combination of waste treatment technologies and infrastructure, depending on local characteristics for supply and demand. The results indicate that the biogas generated from waste treatment turns out to be a promising renewable energy source in the analyzed region, while more distributed energy resources can be integrated. A series of scenarios including the business-as-usual, base case, natural constrained, policy interventions and environmental and climate change impacts demonstrate how simulation with optimization models can provide new insights in the design of sustainable value chains, with particular emphasis on whole-system analysis and integration.
Triantafyllidis CP, Koppelaar RHEM, Wang X, et al., 2018, An integrated optimisation platform for sustainable resource and infrastructure planning, Environmental Modelling and Software, Vol: 101, Pages: 146-168, ISSN: 1364-8152
It is crucial for sustainable planning to consider broad environmental and social dimensions and systemic implications of new infrastructure to build more resilient societies, reduce poverty, improve human well-being, mitigate climate change and address other global change processes. This article presents resilience.io, 2 a platform to evaluate new infrastructure projects by assessing their design and effectiveness in meeting growing resource demands, simulated using Agent-Based Modelling due to socio-economic population changes. We then use Mixed-Integer Linear Programming to optimise a multi-objective function to find cost-optimal solutions, inclusive of environmental metrics such as greenhouse gas emissions. The solutions in space and time provide planning guidance for conventional and novel technology selection, changes in network topology, system costs, and can incorporate any material, waste, energy, labour or emissions flow. As an application, a use case is provided for the Water, Sanitation and Hygiene (WASH) sector for a four million people city-region in Ghana.
Bieber N, Ker JH, Wang X, et al., 2017, Sustainable planning of the energy-water-food nexus using decision making tools, Energy Policy, Vol: 113, Pages: 584-607, ISSN: 0301-4215
Developing countries struggle to implement suitable electric power and water services, failing to match infrastructure with urban expansion. Integrated modelling of urban water and power systems would facilitate the investment and planning processes, but there is a crucial gap to be filled with regards to extending models to incorporate the food supply in developing contexts. In this paper, a holistic methodology and platform to support the resilient and sustainable planning at city region level for multiple sectors was developed for applications in urban energy systems (UES) and the energy-water-food nexus, combining agent-based modelling - to simulate and forecast resource demands on spatial and temporal scales - with resource network optimization, which incorporates capital expenditures, operational costs, environmental impacts and the opportunity cost of food production foregone (OPF). Via a scenario based approach, innovative water supply and energy deployment policies are presented, which address the provision of clean energy for every citizen and demonstrate the potential effects of climate change. The results highlighted the vulnerability of Ghanas power generation infrastructure and the need for diversification. Feed-in tariffs and investment into supporting infrastructure and agriculture intensification will effectively increase the share of renewable energy and reduce carbon emissions.
Gurguc Z, O'Connor J, Van Dam K, 2017, Delivering Urban Transformation through Collaborative Frameworks: Future Cities in the UK, Academy of Management
Wang X, Guo M, Van Dam KH, et al., 2017, Waste-Energy-Water systems in sustainable city development using the resilience.io platform, 27th European Symposium on Computer Aided Process Engineering
Riveros M, Guo M, van dam, et al., 2017, Carbon Arbitrage with Stationary Batteries in the City of London, 27th European Symposium on Computer Aided Process Engineering, Publisher: Elsevier, Pages: 529-534, ISSN: 1570-7946
Stationary batteries could facilitate provision of carbon arbitrage services in cities. Such services offer a smart solution to integrate low-carbon energy technology into grid electricity supply and help tackle climate change. In this paper the environmental implications and overall profitability of this approach are assessed. A modelling framework has been developed to design an energy storage system with optimal capacity to maximise carbon savings. The City of London was used as a case study to demonstrate model applicability and analyse the potential effect of intermittent renewable energy sources in the supply system. The total savings obtained for the carbon arbitrage service were economically valorised using carbon market prices. In addition, a critical profitability thresholds for carbon trading prices are identified. Results show that this approach could bring environmental benefits depending on the carbon intensity of the grid, but that high carbon trading prices are required before it is economically feasible.
Crémel S, Guo M, Bustos-Turu G, et al., 2017, Optimal design of urban energy systems with demand side management and distributed generation, 27th European Symposium on Computer Aided Process Engineering, Publisher: Elsevier, Pages: 2371-2376, ISSN: 1570-7946
To tackle prominent societal challenges such as increasing energy demands and climate change due to greenhouse gas (GHGs) emissions demand side management (DSM) and distributed generation (DG) have been proposed as effective solutions particularly for urban areas with high energy densities and diverse types of energy demands. However, urban energy systems are complex, involving supply-demand interconnections, interaction of whole system level with various stakeholders (e.g. end-users, centralised suppliers) and regulation effects of policy instruments. The potential roles of integrated DSM and DG for the climate change mitigation under the urban energy system context haven been yet well understood. Our research aims to advance the understanding of such promising urban energy solutions and generate new insights via modelling framework development. In this study, an optimisation model is developed to simulate the combined effects of DSM and DG strategies in the optimal design of urban energy systems, and investigate the trade-offs between environmental and economic targets. The results of our case study on a hypothetical urban area suggest that the effects of just DSM on climate change mitigation are relatively low whereas urban system would benefit significantly from the introduction of more carbon efficient and economically competitive DG technologies. The set of Pareto optimal solutions derived from the model provide insights into the trade-offs between conflicting GHG and economic objectives: the environmentally optimal solutions with up to 39-43% of the GHG reduction are derived at the expenses of a cost increase by 73-87%; relatively cost efficient systems with marginal increase in economic profiles (4-5%) but significant GHG reductions (32-33%) are achievable. This study demonstrates the insights such model could provide for the decision-making and paradigm shifts towards sustainable urban energy systems and smart operational strategies.
Wang X, Van Dam KH, Triantafyllidis C, et al., 2017, Water and energy systems in sustainable city development: a case of Sub-saharan Africa, Urban Transitions Conference 2016, Publisher: Elsevier, Pages: 948-957, ISSN: 1877-7058
Current urban water and energy systems are expanding while increasing attention is paid to their social, economic and environmental impacts. As a research contribution that can support real-world decision making and transitions to sustainable cities and communities, we have built a model-based and data-driven platform combining comprehensive database, agent-based simulation and resource technology network optimization for system level water and energy planning. Several use cases are demonstrated based on the Greater Accra Metropolitan Area (GAMA) city-region in Ghana, as part of the Future Cities Africa (FCA) project. The outputs depict an overall resource landscape of the studied urban area, but also provide the energy, water, and other resource balance of supply and demand from both macro and micro perspectives, which is used to propose environmental friendly and cost effective sustainable city development strategies. This work is to become a core component of the resilience.io platform as an open-source integrated systematic tool gathering social, environmental and economic data to inform urban planning, investment and policy-making for city-regions globally.
Wang X, Guo M, van dam K, et al., 2017, Waste-Energy-Water systems in sustainable city development using the resilience.io platform, 27th European Symposium on Computer Aided Process Engineering
Crémel S, Guo M, Bustos-Turu G, et al., 2017, Optimal design of urban energy systems with demand side management and distributed generation, 28th European Symposium on Computer Aided Process Engineering
van Dam KH, Bustos-Turu G, Shah N, 2017, A methodology for simulating synthetic populations for the analysis of socio-technical infrastructures, Social Simulation 2015 conference, Publisher: Springer, Pages: 429-434, ISSN: 2194-5357
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.
Acha Izquierdo S, Van Dam KH, Markides C, et al., 2016, Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach, Energycon 2016, Publisher: IEEE
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.
Delval F, Guo M, van Dam KH, et al., 2016, Integrated multi-level bioenergy supply chain modelling applied to sugarcane biorefineries in South Africa, 26th European Symposium on Computer Aided Process Engineering, ISSN: 1570-7946
Bustos G, Guo M, van Dam KH, et al., 2016, Incorporating life cycle assessment indicators into optimal electric vehicle charging strategies: An integrated modelling approach, Editors: Kravanja, Bogataj, Publisher: ELSEVIER SCIENCE BV, Pages: 241-246
Bustos-Turu G, van Dam KH, Acha S, et al., 2014, Estimating Plug-in Electric Vehicle Demand Flexibility through an Agent-Based Simulation Model, 5th IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Publisher: IEEE, ISSN: 2165-4816
Acha Izquierdo S, Van Dam KH, Shah N, 2013, Spatial and Temporal Electric Vehicle Demand Forecasting in Central London, 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013), Publisher: IET
If electricity infrastructures are to make the most of electric vehicle (EV) technology it is paramount to understand how mobility can enhance the management of assets and the delivery of energy. This research builds on a proof of concept model that focuses on simulating EV movements in urban environments which serve to forecast EV loads in the networks. Having performed this analysis for a test urban environment, this paper details a case study for London using an activity-based model to make predictions of EV movements which can be validated against measured transport data. Results illustrate how optimal EV charging can impact the load profiles of two areas in central London - St. John's Wood & Marylebone/Mayfair. Transport data highlights the load flexibility a fleet of EVs can have on a daily basis in one of the most stressed networks in the world, while an optimal power flow manages the best times of the day to charge the EVs. This study presents valuable information that can help in begin addressing pressing infrastructure issues such as charging point planning and network control reinforcement.
Acha S, van Dam KH, Shah N, 2012, Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks, 2012 IEEE Power and Energy Society General Meeting, Publisher: IEEE, Pages: 1-8
The ability to determine optimal charging profiles of electric vehicles (EVs) is paramount in developing an efficient and reliable smart-grid. However, so far the level of analysis proposed to address this issue lacks combined spatial and temporal elements, thus making mobility a key challenge to address for a proper representation of this problem. This paper details the principles applied to represent optimal charging of EVs by employing an agent-based model that simulates the travelling patterns of vehicles on a road network. The output data is used as a reliable forecast so an optimal power flow model can devise optimal charging scenarios of EVs in a local electrical network. The effectiveness of the model is illustrated by presenting a multi-day case study in an urban area. Results show a high level of detail and variability in EV charging when a present-day carbon fuel mix is compared to one with lower carbon intensity.
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