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  • Journal article
    García Kerdan I, Giarola S, Hawkes A, 2019,

    A novel energy systems model to explore the role of land use and reforestation in achieving carbon mitigation targets: A Brazil case study

    , Journal of Cleaner Production, Vol: 232, Pages: 796-821, ISSN: 0959-6526

    Due to its low global share of direct energy consumption and greenhouse gas emissions (1–2%), the implications of technological transitions in the agricultural and forestry sector on the energy system have been overlooked. This paper introduces the Agriculture and Land Use Sector module part of the ModUlar energy System Environment (MUSE), a novel energy system simulation model. The study presents a generalisable method that enables energy modellers to characterise agricultural technologies within an energy system modelling framework. Different mechanisation processes were characterised to simulate intensification/extensification transitions in the sector and its wider implications in the energy and land use system aiming at providing reliable non-energy outputs similarly to those found in dedicated land use models. Additionally, a forest growth model has been integrated to explore the role of reforestation alongside decarbonisation measures in the energy system in achieving carbon mitigation pathways. To illustrate the model's capabilities, Brazil is used as case study. Outputs suggest that by 2030 under a 2 °C mitigation scenario, most of Brazil agricultural production would move from ‘transitional’ to ‘modern’ practices, improving productivity and reducing deforestation rates, at the expense of higher energy and fertiliser demand. By mid-century Brazil has the potential to liberate around 24.4 Mha of agricultural land, where large-scale reforestation could have the capacity to sequester around 5.6 GtCO2, alleviating mitigation efforts in the energy system, especially reducing carbon capture and storage technology investments in the industry and power sector.
  • Journal article
    Sachs J, Moya D, Giarola S, Hawkes Aet al., 2019,

    Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector

    , Applied Energy, Vol: 250, Pages: 48-62, ISSN: 0306-2619

    Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment)but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available.
  • Journal article
    Sachs J, Meng Y, Giarola S, Hawkes Aet al., 2019,

    An agent-based model for energy investment decisions in the residential sector

    , Energy, Vol: 172, Pages: 752-768, ISSN: 0360-5442

    Energy-related investment decisions in the buildings sector are heterogeneous in that the outcome for each individual varies according to budget, values, and perception of a technology, even if an apparently identical decision task is faced. In particular, the rate of adoption of new energy-efficient technologies is often hard to model and underlines the need for an advanced approach to capture diversity in decision-making, and enable the inclusion of economic, comfort, environmental and social aspects. This paper presents an enhanced agent-based model that captures several characteristics of consumer behaviour that influence investment decisions. Multiple agents with different objectives, search strategies, and decision methods are implemented. A case study is presented which illustrates the benefits of the approach for the residential sector in the UK. The agent-based method shows diversity in investment decisions, without requiring the constraints on uptake needed in many models. This leads to a range of technologies in the market during a transition phase, continuous investment in low capital cost technologies, and eventually the emergence of a low carbon system based on new mass market technologies. The system that emerges is vastly different from one observed when economically rational investment is assumed and uptake constraints are applied.
  • Journal article
    Panteli A, Giarola S, Shah N, 2018,

    Supply chain mixed integer linear program model integrating a biorefining technology superstructure

    , Industrial & Engineering Chemistry Research, Vol: 57, Pages: 9849-9865, ISSN: 0888-5885

    A crucial element of the quest of curbing carbon dioxide emissions is deemed to rely on a biobased economy, which will rely on the development of financially sustainable biorefining systems enabling a full exploitation of lignocellulosic biomass (and its macrocomponents such as cellulose, hemicellulose, and lignin) for the coproduction of biofuels and bioderived platform chemicals. In this work, a general modeling framework conceived to steer decision-making regarding the strategic design and systematic planning of advanced biorefining supply networks is presented. The design task is formulated as a mixed integer linear program which accounts for the maximization of the supply chain profit, considering multiechelon, multiperiod, multifeedstock, and multiproduct aspects as well as spatially explicit features. The applicability of the proposed model, along with the use of a bilevel decomposition approach, are demonstrated with a case study of lignocellulose-based biorefining production systems in the South-West of Hungary. Results show the effectiveness of the tool in the decision-making regarding the systematic design of advanced biorefining SC networks. An economic analysis of different design configurations (i.e., centralized and distributed scenarios) through a holistic evaluation of the entire biobased SC, integrating technology superstructure, shows that both instances generate profitable investment decisions that could be equally trusted by the decision-maker unless regional restrictions are applied.
  • Journal article
    Crow DJG, Giarola S, Hawkes AD, 2018,

    A dynamic model of global natural gas supply

    , Applied Energy, Vol: 218, Pages: 452-469, ISSN: 0306-2619

    This paper presents the Dynamic Upstream Gas Model (DYNAAMO); a new, global, bottom-up model of natural gas supply. In contrast to most “static” supply-side models, which bracket resources by average cost, DYNAAMO creates a range of dynamic outputs by simulating investment and operating decisions in the upstream gas industry triggered in response to investors’ expectations of future gas prices. Industrial data from thousands of gas fields is analysed and used to build production and expenditure profiles which drive the economics of supply at field level. Using these profiles, a novel methodology for estimating supply curves is developed which incorporates the size, age and operating environment of gas fields, and treats explicitly the fiscal, abandonment, exploration and emissions costs of production. The model is validated using the US shale gas boom in the 2000s as a historic case study. It is found that the modelled market share of supply by field environment replicates the observed trend during the period 2000–2010, and that the model price response during the same period – due to lower capacity margins and the financing of new projects – is consistent with market behaviour.
  • Journal article
    Giarola S, Forte O, Lanzini A, Gandiglio M, Santarelli M, Hawkes Aet al., 2018,

    Techno-economic assessment of biogas-fed solid oxide fuel cell combined heat and power system at industrial scale

    , Applied Energy, Vol: 211, Pages: 689-704, ISSN: 0306-2619

    Wastewater treatment plants (WWTP) are currently very energy and greenhouse gas intensive processes. An important opportunity to reduce both of these quantities is via the use of biogas produced within the treatment process to generate energy. This paper studies the optimal energy and economic performance of a wastewater treatment facility fitted with a solid oxide fuel cell (SOFC) based combined heat and power (CHP) plant. An optimisation framework is formulated and then applied to determine cost, energy and emissions performance of the retrofitted system when compared with conventional alternatives.Results show that present-day capital costs of SOFC technology mean that it does not quite compete with the conventional alternatives. But, it could become interesting if implemented in thermally-optimised WWTP systems. This would increase the SOFC manufacturing volumes and drive a reduction of capital and fixed operating costs.
  • Conference paper
    Garcia Kerdan I, Hawkes AD, Giarola S, 2018,

    Implications of Future Natural Gas Infrastructure on Bioenergy Production, Land Use Change and Related Emissions: A Brazil Case Study

    , 1st SDEWES Latin America

    Due to its low global share of direct energy consumption (3-5%) and greenhouse gas emissions (1-2%), energy systems models (ESM) have unfairly overlooked the implications of technological transitions in the agricultural sector. In fact, if the demand of agrochemicals and land use changes (LUC) due to expansion of bioenergy crops and increasing food demand are considered, the sector is indirectly responsible for up to 30% of global emissions. This paper introduces the Agriculture and Land Use Sector Simulation Module (Ag&LU-SM) which is integrated in a novel ESM, called MUSE, the ModUlar energy systems Simulation Environment. The Ag&LU-SM simulates the investments in agricultural energy technologies through the concept of mechanisation diffusion to meet the demand of sector’s commodities, such as crops, animal and forestry products, as well as the implications due to LUC when arable or forest land is allocated to bioenergy crops. The aim is to study the sector’s dynamics and resource competition between bioenergy and natural gas at a country level. Brazil, one of the major bioenergy producers and with large amounts of oil and natural gas reserves, is used as a case study to study the implications in terms of land use change in two different scenarios. One scenario explores a ten-fold expansion of bioenergy production by 2050, which means a 6% annual growth rate. The second scenario explores the expansion of natural gas production while halving bioenergy production (3% annual growth rate). Results show that, in order to meet the future food and bioenergy demand, the agricultural sector should move from transitional to modern agricultural practices, improve the productivity at the expense of higher energy consumption, invest in efficient agricultural practices to reduce land-related emissions and have the opportunity to liberate crop and pasture land that could be used for dedicated energy crops. Finally, the development of a gas infrastructure coul
  • Book chapter
    Sachs J, Hidayat S, Giarola S, Hawkes Aet al., 2018,

    The role of CCS and biomass-based processes in the refinery sector for different carbon scenarios

    , Editors: Friedl, Klemes, Radl, Varbanov, Wallek, Publisher: ELSEVIER SCIENCE BV, Pages: 1365-1370
  • Book chapter
    Sechi S, Giarola S, Lanzini A, Gandiglio M, Oluleye G, Santarelli M, Hawkes Aet al., 2018,

    An optimization method to estimate the SOFC market in waste water treatment

    , Editors: Friedl, Klemes, Radl, Varbanov, Wallek, Publisher: ELSEVIER SCIENCE BV, Pages: 415-420
  • Conference paper
    Budinis S, Giarola S, Sachs J, Hawkes ADet al., 2017,

    Modelling the impacts of investors' decision making on decarbonisation pathways in industry

    , 10th Annual Meeting of the IAMC, Publisher: IAMC

    The Paris Climate agreement calls for dramatic changes in the energy system. This will be challenging for demand sectors like industry, which is notoriously energy intensive. Although increased efficiency has proven to be suitable options to reduce energy and environmental impacts, stringent regulations on carbon will require this sector to undergo an unprecedented innovation effort, which will go well beyond cost efficiency measures to include the deployment of novel technologies and, most likely, of carbon capture and storage (CCS).This manuscript focuses on the uptake of novel technologies in the industrial sector and the barriers which might prevent or slow down the pace of innovation. Some of these barriers are technological as they depend on the availability and the technology readiness level of a specific technology. Others are instead related to the attitude that investors show towards innovative and the inherent level of risk. Among the many innovation options in the industrial sector, the focus here is on the uptake of the carbon capture and storage technologies.The industrial sector is modelled including the top-energy intensive industries, such as those manufacturing pulp and paper, iron and steel, chemicals and petrochemicals, the non-ferrous metals as well as non-metallic minerals. The simulations are carried out using a novel energy systems model, MUSE, the Modular energy systems Simulation Environment.

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