Integrated sustainable energy solutions for large- and small-scale communities

Devising sustainable energy for large- and small-scale communities requires two different perspectives. While, in the former, the main challenge is the allocation of relatively abundant resources to meet the supply for generic demands, in the latter, the detailed properties of the resources and demands should be identified and matched. First, this thesis focuses on one of the challenges of energy development simulations for large-scale communities. A novel CSP performance model is proposed for whole-of-grid simulations, that despite the usual simplified models in the literature does not compromise the major performance parameters. The model is first used to estimate the performance of a new CSP project and then its results are compared to those from a simplified CSP model in a whole-of-grid simulation. The comparison shows a significant overestimation in the case of the simplified model. The second part of the thesis focuses on two sustainable solutions for small-scale communities. In the first proposed solution for Nauru, the recovered waste heat from the diesel generator-based power plant of the island is utilised to supply cooling power required for the preservation of imported food in the island and increase the production of permeate water of the island’s desalination units by heating its inlet water. The solution supplies the whole required cooling power, reduces the carbon emission of the island by 1.1 kilotonnes and increases the annual freshwater production by 81 million litres. In the second proposed solution, several combined cooling and power (CCP) units, fuelled by agriculture residue and household waste supply electricity and cooling power for different districts of Tonga. The results show that the optimum combination of the CCP units supplies 38% of the total electricity demand of Tonga and provides 3700 m2 of cold storage area, sufficient for preserving the prospective yield of the exploitation of 27% of the total fallow land

Global socio-economic losses and environmental gains from the Coronavirus pandemic

On 3 April 2020, the Director-General of the WHO stated: “[COVID-19] is much more than a health crisis. We are all aware of the profound social and economic consequences of the pandemic (WHO, 2020)”. Such consequences are the result of counter-measures such as lockdowns, and world-wide reductions in production and consumption, amplified by cascading impacts through international supply chains. Using a global multi-regional macro-economic model, we capture direct and indirect spill-over effects in terms of social and economic losses, as well as environmental effects of the pandemic. Based on information as of May 2020, we show that global consumption losses amount to 3.8$tr, triggering significant job (147 million full-time equivalent) and income (2.1$tr) losses. Global atmospheric emissions are reduced by 2.5Gt of greenhouse gases, 0.6Mt of PM2.5, and 5.1Mt of SO2 and NOx. While Asia, Europe and the USA have been the most directly impacted regions, and transport and tourism the immediately hit sectors, the indirect effects transmitted along international supply chains are being felt across the entire world economy. These ripple effects highlight the intrinsic link between socio-economic and environmental dimensions, and emphasise the challenge of addressing unsustainable global patterns. How humanity reacts to this crisis will define the post-pandemic world

Mass-conserving dynamic organic Rankine cycle model to investigate the link between mass distribution and system state

This study presents a mass-conserving dynamic numerical model to capture dynamic response of ORCs (organic Rankine cycles) when the system experiences a change in expander's rotational speed, pump's capacity factor, and conditions of hot and cold heat transfer fluids. ORC's dynamic response is tracked specifically considering evaporator pressure, condenser pressure, degree of superheating at the evaporator exit, degree of sub-cooling at the condenser exit, and the mass distribution along evaporator, condenser and liquid receiver tank. The developed model is novel due to the way subcomponent models are integrated together. Specifically, this integration includes fully coupled tank and condenser models. The model is validated against an experimental benchmark study for various steady state conditions and further verified considering mass and energy conservation principles. A parametric study is carried out to identify parameters which can be used for devising a new autonomous control strategy for organic Rankine cycles. It is illustrated through simulations that the mass distribution over ORC sub-components are linked to the system's overall state; and both the liquid level in the tank and the degree of sub-cooling at the tank exit can be used to devise a control system by changing pump's capacity factor and expander's rotational speed

Cooling and Power from Waste and Agriculture Residue as a Sustainable Strategy for Small Islands—A Case Study of Tonga

Identifying local energy sources and devising a circular economy could improve self-sufficiency in many Pacific Islands. On the islands with significant agriculture, the residue from the cultivation of plants has promising energy potential. The waste stream is another potential source of energy that otherwise should undergo proper treatment. Additionally, cold-storage capacity improves the preservation of crops and increases the agricultural exports of these islands. This study proposes a combined cooling and power (CCP) system driven by biomass from agriculture residue and waste streams as fuel for different districts in Tonga. The units supply a fraction of the districts’ electricity demand and provide sufficient cold-storage capacity to preserve the prospective yield of a fraction of fallow lands. The technical and economic performance of the CCP units was analysed for different fractions of electricity demand and fallow land exploitation in each district during a year of operation. The results show that the optimum combination of the CCP units supplies 38% of the total electricity demand of Tonga and prevents the annual consumption of 7.4 million litres of diesel and emission of 20 kilotonnes of CO2. In addition, it provides 3700 m2 of cold-storage area, which is sufficient for preserving the prospective yield of the exploitation of 27% of the total fallow land of Tonga. Annual export revenue of about AU$10 million is expected from such a cold-storage capacity for Tongan farmers. Furthermore, the units consume 10,000 tonnes of annual waste, significantly reducing waste management costs. This study presents an example of a comprehensive circular-economy solution for a remote island state that improves its socioeconomic and environmental condition by supplying the community’s local needs from its available and abundant resources under a viable business model. The solution presented in this study can be adapted to many island communities with significant agriculture in the economy and crucial energy and cooling needs

Assessment of solar data estimation models for four cities in Iran

The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated global horizontal insolation is monthly. The results show that the developed NN model has promising performance and considering the calibration process for Angstrom's model it can be used as an alternative. The NN model uses climatic data to estimate the solar insolation which makes it more flexible in terms of being applicable for different regions. (C) 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Assessment of solar data estimation models for four cities in Iran

The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated global horizontal insolation is monthly. The results show that the developed NN model has promising performance and considering the calibration process for Angstrom's model it can be used as an alternative. The NN model uses climatic data to estimate the solar insolation which makes it more flexible in terms of being applicable for different regions. (C) 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei