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

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

Optimisation of building energy retrofit strategies using dynamic exergy analysis and exergoeconomics

Existing buildings represent one of the most energy intensive sectors in today’s society, where comprehensive building energy retrofit (BER) strategies play a major role in achieving national reduction targets. Despite the efforts made in recent decades through policies and programmes to improve building energy efficiency, the building sector (which proportionally has the highest demand for heat) has the lowest thermodynamic efficiency among all UK economic sectors. As other sectors have shown, exergy and exergoeconomic analyses can be indispensable tools for the design and optimisation of energy systems. Therefore, there is a need for modification of existing BER methods in order to include thermodynamic analysis with the aim improve true efficiency of buildings and minimise its environmental impact. However, a paradigm shift represents a big challenge to common building practice as traditional methods have prioritised typical energy and economic objectives. The aim of this thesis is to develop a methodological framework for the evaluation of BER strategies under exergy analysis and exergoeconomic accounting supported with the integration of the calculation framework into a typical dynamic building simulation tool. There are two original contributions to the knowledge of this research. First, the techno-economic appraisal of BER strategies, based on the typical energy-efficient and cost-benefit method, is enhanced by adding a whole-building exergy analysis combined with an exergoeconomic method (SPECO). Second, ExRET-Opt, a retrofit-oriented simulation tool based on dynamic exergy calculations and exergoeconomic analysis combined with a comprehensive and robust retrofit database, is developed and implemented for this research. In addition, a multi-objective optimisation module based on genetic algorithms is included within the simulation framework in order to improve BER design under different thermodynamic and non-thermodynamic conflicting cost objective functions. Three UK non-domestic case studies implementing a wide range of active and passive retrofit strategies are presented. Results suggest that under identical economic and technical constraints, the inclusion of exergy/exergoeconomic indicators as objective functions into the optimisation procedure has resulted in buildings with similar energy and thermal comfort performance as traditional First Law methods; while providing solutions with better thermodynamic performance and less environmental impact. The approach also demonstrates to provide BER designs with an appropriate balance between active and passive measures, while consistently accounting of irreversibilities and its costs along every subsystem in the building energy system. The developed framework/tool seems like a promising approach to introduce the Second Law into typical building energy practice and for the development of policies, incentives, and taxes based on exergy destruction footprints. Such policies could help highly thermodynamically-efficient or low exergy BER designs to become widely available

Parametric study and simulation-based exergy optimization for energy retrofits in buildings

The undertaking of building energy retrofits is essential for the reduction of energy use and carbon emissions at a national level. Nowadays, a number of construction methods and energy technologies that are available to practitioners require that the appropriate retrofit solution is identified to ensure long-term project success. A significant limitation of conventional methods that may be used to examine this (e.g. scenario by scenario) is that only a limited number of design scenarios can be evaluated which limits the potential for identifying the “best” designs. Furthermore, while the building sector has a large thermodynamic potential where most of the buildings' energy demands (especially space conditioning) can be met by low-grade sources, the associated exergy analysis method is rarely used in architectural practice. The following paper presents a simulation-based exergy optimization model, which aims to assess the impact of a diverse range of retrofit measures. Two non-domestic UK archetype case studies (a typical office and a primary school) are used to test the feasibility of the proposed framework. The objective optimization functions in this study are building energy use, exergy destructions throughout the building energy supply chain, and improvement of occupants’ thermal comfort levels. Different measures combinations based on retrofitting the insulation levels of the envelope and the application of different HVAC systems configurations (VAV, VRF, ground-source heat pump, air-source heat pump, district heating/cooling systems) are assessed. A large range of optimal solutions were achieved highlighting the framework capabilities. This approach can be extended by using the outputs in cost-benefit analysis and in thermoeconomic optimization

Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building

In recent years, surrogate modelling approaches have been implemented to overcome the time and computational power demands of traditional building energy modelling. Artificial neural networks (ANN), due to their potential to capture building energy systems complex interactions are regarded as powerful surrogate models; however, the definition of optimal ANN structures and hyperparameters have been overlooked causing substandard prediction performance. The aim of this study is to present a novel hybrid neuro-genetic modelling framework developed as an open source tool capable of identifying optimal multi-input/multi-output ANN structures for accurately predicting building thermodynamic performance. The ANN optimisation process uses a genetic algorithm that minimises the root mean squared error (RMSE) data difference between the target and predicted values for both the training and testing data. As a case study, an archetype social house located in different climatic regions in Mexico is used. The ANN training database has been generated by simulating a sample of high-resolution energy models considering a combination of different active and passive energy strategies (input data) while calculating building exergy destructions, occupant thermal comfort and life cycle cost (output data). After automatically evaluating thousands of different structures, the neuro-genetic tool has identified a single deep ANN structure (3 hidden layers with 18, 17, 20 neurons respectively) capable of predicting the model’s high output variability, achieving a prediction accuracy >0.95 for each of the outputs. The presented framework and tool can be adapted to further optimisation stages in the building design process and to solve similar problems in other research areas

Implications of future natural gas demand on sugarcane production, land use change and related emissions in Brazil

Due to its low share of energy-related emissions, energy systems models have overlooked the implications of technological transition in the agricultural sector and its interaction in the wider energy system. This paper explores the role of agriculture intensification by using a novel agricultural-based energy systems model. The aim is to explore the future role of Brazil’s agriculture and its dynamics with other energy sectors under two carbon constraint scenarios. The main focus has been to study resource competition between sugarcane and natural gas at a country level. Results show that in order to meet the future food and bioenergy demand, the agricultural sector would start intensifying by 2030, improving productivity at the expense of higher energy demand; however, land-related emissions would be minimised due to freed-up pasture land and reduction in deforestation rates. Additionally, the development of balanced bioenergy and natural gas markets may help limit the sugarcane expansion rates, preserving up to 12.6 million hectares of forest land, with significant emissions benefits

Spatially-resolved urban energy systems model to study decarbonisation pathways for energy services in cities

This work presents the COMET (Cities Optimisation Model for Energy Technologies) model, a spatially-resolved urban energy systems model that takes into account energy service demands for heating, cooling, electricity, and transport, and finds cost-effective pathways for supplying these demands under carbon constraints, trading-off energy supply, network infrastructure, and end-use technologies. Spatially-resolved energy service demands were obtained for the city of Sao Paulo, and six scenarios were modelled. Results show that district cooling is cost-effective in the highest linear cooling density zones, with full penetration in zones with over 1100 kWh/m by 2050. This threshold diminishes with tighter carbon constraints. Heating is electrified in all scenarios, with electric boilers and air-source heat pumps being the main supply technologies for the domestic and commercial sectors respectively by 2050. In the most carbon constrained scenario with a medium decarbonised electricity grid, ground source heat pumps and hydrogen boilers appear as transition technologies between 2030 and 2045 for the commercial and domestic sectors respectively, reaching 95% and 40% of each sector’s heat installed capacity in 2030. In the transport sector, ethanol cars replace gasoline, diesel, and compressed natural gas cars; compressed natural gas buses replace diesel and electric buses; and lorries continue using diesel. In carbon constrained scenarios, higher penetrations of electric cars and buses are obtained, while no change is observed for lorries. Finally, the most expensive scenario was only 6% more expensive than the reference scenario, meaning that achieving decarbonisation targets is not much costlier when comparing scenarios from a system-wide perspective

Thermodynamic and thermal comfort optimisation of a coastal social house considering the influence of the thermal breeze

Tropical coastal areas are characterised by high levels of wind and solar resources with large potentials to be utilised for low-energy building design. This paper presents a multi-objective optimisation framework capable of evaluating cost-efficient and low-exergy coastal building designs considering the influence of the thermal breeze. An integrated dynamic simulation tool has been enhanced to consider the impacts of the sea-land breeze effect, aiming at potentiating natural cross-ventilation to improve occupant's thermal comfort and reduce cooling energy demand. Furthermore, the technological database considers a wide range of active and passive energy conservation measures. As a case study, a two-storey/two-flat detached social house located in the North-Pacific coast of Mexico has been investigated. The optimisation problem has considered the minimisation of: i. annual exergy consumption, ii. life cycle cost, and iii. thermal discomfort. Optimisation results have shown that adequate building orientation and window opening control to optimise the effects of the thermal breeze, combined with other passive and active strategies such as solar shading devices, an improved envelope's physical characteristics, and solar assisted air source heat pumps have provided the best performance under a limited budget. Compared to the baseline design, the closest to utopia design has increased thermal comfort by 93.8% and reduced exergy consumption by 10.3% whilst increasing the life cycle cost over the next 50 years by 18.5% (from US$39,864 to US$47,246). The importance of renewable generation incentives is further discussed as a counter effect measure for capital cost increase as well as unlocking currently high-cost low-exergy technologies

An exergy-based simulation stock model: A new approach for policy making

This article presents the use of an exergy-based bottom-up stock model to investigate the impact of large-scale energy retrofit scenarios in the English and Welsh (E&W) non-domestic sector, with a modelling projection to 2050. The model consists of a combination of EnergyPlus as a first law analysis tool and a dynamic exergy analysis method. The aim of the paper is to illustrate the potential of exergy analysis in improving efficiency at a sectoral level. This preliminary study is composed by 6 different large-scale retrofit scenarios including low carbon and low exergy approaches. The results show that current regulations can reduce carbon emissions by up to 50% but only reduce exergy destructions by 8%. On the other hand, a low exergy scenario based on low temperature district systems was able to reduce carbon emissions by 68% and exergy destructions by 26%

Carbon sequestration potential from large-scale reforestation and sugarcane expansion on abandoned agricultural lands in Brazil

Since 1850, over 145 ± 16 PgC (μ ± 1σ) has been emitted worldwide due to land-use change and deforestation. Besides industrial carbon capture and storage (CCS), storing carbon in forestry products and in regenerated forest has been recognized as a cost-effective carbon sequestration option, with an estimated worldwide sink potential of about 50–100 PgC (15–36 PgC from tropical forest alone). This paper proposes the expansion of a Brazilian integrated assessment model (MUSE-Brazil) by integrating a non-spatial biomass-growth model. The aim is to account for carbon sequestration potential from either reforestation or sugarcane expansion in abandoned agricultural lands. Modelling outputs suggest that Brazil has the potential to liberate up to 32.3 Mha of agricultural land by 2035, reaching 68.4 Mha by mid-century. If a sugarcane expansion policy is promoted, by 2050, the largest sequestration rates would come from above and below ground biomass pools; gradually releasing to the atmosphere around 1.6 PgC or 1.2% of the current Brazilian land carbon stock due to lower SOC carbon pools when turning agricultural lands into sugarcane crops. On the other hand, a reforestation-only scenario projects that by 2035 the baseline year carbon stock could be recovered and by 2050 the country’s carbon stock would have been increased by 3.2 PgC, reaching annual net sequestration rates of 0.1 PgC y−1, mainly supported by natural vegetation regeneration in the Cerrado biome

A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design

This study presents a comparison of the optimisation of building energy retrofit strategies from two different perspectives: an energy/economic-based analysis and an exergy/exergoeconomic-based analysis. A recently retrofitted community centre is used as a case study. ExRET-Opt, a novel building energy/exergy simulation tool with multi-objective optimisation capabilities based on NSGA-II is used to run both analysis. The first analysis, based on the 1st Law only, simultaneously optimises building energy use and design's Net Present Value (NPV). The second analysis, based on the 1st and the 2nd Laws, simultaneously optimises exergy destructions and the exergoeconomic cost-benefit index. Occupant thermal comfort is considered as a common objective function for both approaches. The aim is to assess the difference between the methods and calculate the performance among main indicators, considering the same decision variables and constraints. Outputs show that the inclusion of exergy/exergoeconomics as objective functions into the optimisation procedure has resulted in similar 1st Law and thermal comfort outputs, while providing solutions with less environmental impact under similar capital investments. This outputs demonstrate how the 1st Law is only a necessary calculation while the utilisation of the 1st and 2nd Laws becomes a sufficient condition for the analysis and design of low carbon buildings