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Abstract Hydrothermal treatment (HT) is one of the efficient approaches for upgrading municipal solid waste (MSW). In the present study, emission characteristics of polycyclic aromatic hydrocarbons (PAHs) from hydrothermally treated municipal solid waste (H-MSW) combustion alone and H-MSW/coal co-combustion were investigated at different temperatures. The results showed that for all fuel combustion, the majority of PAHs were 3- or 4-ring PAHs. In addition, flue gas had the highest yields of PAHs followed by fly ash and bottom ash, while the ring number of dominated PAHs in fly ash was higher than those in flue gas and bottom ash. Compared to MSW, H-MSW combustion generated less PAHs at the value of 1131.95–7649.24 μg/g. The blending of H-MSW and coal reduced total PAH emissions and positive interactions were observed between H-MSW and coal during co-combustion. The toxicity equivalent quantity (TEQ) values of the PAHs from combustion were in the order MSW > H-MSW > H-MSW/coal, which was consistent with the total PAH emissions. The present study illustrated that significant reduction of PAH emissions and toxicity from combustion could be achieved by HT and the blending of H-MSW and coal.

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability.\ud \ud This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP), and utilises integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. The algorithms outperform the existing state-of-the-art commercial MINLP solver, which fails to find any solutions in two instances. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs. The optimisation framework and results can be used to inform stakeholders such as policy-makers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems. 47 pages, 10 figures, 14 Tables

Abstract In Australia, the government spending on public buildings’ energy and water consumption is considerable; however the building energy and water retrofit market potential has been diminished by a number of barriers, especially financial. In contrast, in other advanced economies there are several reported financing strategies that have been shown to accelerate retrofit projects implementation. In this study, a coupled Bayesian Network – System Dynamics model was developed with the core aim to assess the likely influence of those novel financing options and procurement procedures on public building retrofit outcomes scenarios in the Australian context. A particular case-study focusing on Australian public hospitals was showcased as an example in this paper. Stakeholder engagement was utilised to estimate likely preferences and to conceptualise causal relationships of model parameters. The scenario modelling showed that a revolving loan fund supporting an energy performance contracting procurement procedure was preferred. Subsequently, the specific features of this preferred framework were optimised to yield the greatest number of viable retrofit projects over the long term. The results indicated that such a financing scheme would lead to substantial abatement of energy and water consumption, as well as carbon emissions. The strategic scenario analysis approach developed herein provides evidence-based support to policy-makers advocating novel financing and procurement models for addressing a government’s sustainability agenda in a financially responsible and net-positive manner.

Abstract Efficient electric heat pumps have the potential to significantly reduce greenhouse gas emissions from heating and cooling buildings. However, heat pumps’ initial costs can be prohibitively high and their lifetime costs are only situationally competitive with incumbent technologies. Here we show that a business model based on heat purchase agreements could lower these barriers to heat pump adoption. In this business model, a user hosts a heat pump owned by an aggregator. The aggregator installs the heat pump at low or no initial cost to the user. The user buys the heat pump’s heat or cooling output from the aggregator. The aggregator buys the heat pump’s input electricity in the wholesale energy market and sells the flexibility of their aggregate electrical load in ancillary service markets. This paper presents the first economic analysis of heat purchase agreements as a third-party ownership model for electric heat pumps. We derive conditions under which a heat purchase agreement is mutually beneficial to the user and the aggregator. We also provide a method to fairly price heat and cooling. A case study of a typical United States home shows that a heat purchase agreement could more than double the value of a heat pump investment relative to the incumbent business model. The potential impact of this work is to reduce emissions both directly, by accelerating replacement of fossil-fueled or inefficient heating or cooling equipment, and indirectly, by helping power system operators reliably integrate wind and solar generation.

Abstract The intervention on the existing building envelope thermal insulation is the main and effective solution in order to achieve a significant reduction of the building stock energy needs. The infrared technique is the methodology of the energy diagnosis aimed to identify qualitatively the principal causes of energy losses: the presence of thermal bridges. Those weak parts of the building envelope in terms of heat transfer result not easy to treat with an energy efficiency intervention, while they are gaining importance in the buildings total energy dispersion, as the level of insulation of opaque and transparent materials is continuously increasing. It is generally possible to evaluate the energy dispersions through these zones with a deep knowledge of the materials and the geometry using a numerical method. Besides, authors proposed in the past a methodology to assess the flux passing through thermal bridges with an infrared image correctly framed. The analysis of surface temperatures of the undisturbed wall and of the zone with thermal bridge, allows to define the Incidence Factor of the thermal Bridge (Itb). This parameter is strongly affected by the thermographic image accuracy, therefore, this paper deals with the development and validation of an innovative mathematical algorithm to enhance the image resolution and the consequent accuracy of the energy losses assessment. An experimental campaign in a controlled environment (hot box apparatus) has been conducted on three typologies of thermal bridge, firstly performing the thermographic survey and then applying the enhancement algorithm to the infrared images in order to compare the Itb and the linear thermal transmittance ψ values. Results showed that the proposed methodology could bring to an accuracy improvement up to 2% of the total buildings envelope energy losses evaluated by quantitative infrared thermography. Moreover, the proposed algorithm allows the implementation of a further process applicable to the images, in order to extract the physical boundaries of the hidden materials causing the thermal bridge, so revealing itself as a useful tool to identify exactly the suitable points of intervention for the thermal bridge correction. The application of the imaging process on the quantitative infrared thermography is an innovative approach that makes more accurate the evaluation of the actual heat loss of highly insulating buildings and reaching a higher detail on the detection and treating of thermal bridges.

Abstract The optimized performance of two advanced CO2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.