61 results found
Le Brun N, Simpson M, Acha S, et al., 2020, Techno-economic potential of low-temperature, jacket-water heat recovery from stationary internal combustion engines with organic Rankine cycles: A cross-sector food-retail study, Applied Energy, Vol: 274, Pages: 1-14, ISSN: 0306-2619
We examine the opportunities and challenges of deploying integrated organic Rankine cycle (ORC) engines to recover heat from low-temperature jacket-water cooling circuits of small-scale gas-fired internal combustion engines (ICEs), for the supply of combined heat and power (CHP) to supermarkets. Based on data for commercially-available ICE and ORC engines, a techno-economic model is developed and applied to simulate system performance in real buildings. Under current market trends and for the specific (low-temperature) ICE + ORC CHP configuration investigated here, results show that the ICE determines most economic savings, while the ORC engine does not significantly impact the integrated CHP system performance. The ORC engines have long payback times (4–9 years) in this application, because: (1) they do not displace high-value electricity, as the value of exporting electricity to the grid is low, and (2) it is more profitable to use the heat from the ICEs for space heating rather than for electricity conversion. Commercial ORC engines are most viable (payback ≈ 4 years) in buildings with high electrical demands and low heat-to-power ratios. The influence of factors such as the ORC engine efficiency, capital cost and energy prices is also evaluated, highlighting performance gaps and identifying promising areas for future research.
Olympios AV, Le Brun N, Acha S, et al., 2020, Stochastic real-time operation control of a combined heat and power (CHP) system under uncertainty, Energy Conversion and Management, Vol: 216, Pages: 1-17, ISSN: 0196-8904
In this paper we present an effort to design and apply a multi-objective real-time operation controller to a combined heat and power (CHP) system, while considering explicitly the risk-return trade-offs arising from the uncertainty in the price of exported electricity. Although extensive research has been performed on theoretically optimizing the design, sizing and operation of CHP systems, less effort has been devoted to an understanding of the practical challenges and the effects of uncertainty in implementing advanced algorithms in real-world applications. In this work, a two-stage control architecture is proposed which applies an optimization framework to a real CHP operation application involving intelligent communication between two controllers to monitor and control the engine continuously. Since deterministic approaches that involve no measure of uncertainty provide limited insight to decision-makers, the methodology then proceeds to develop a stochastic optimization technique which considers risk within the optimization problem. The uncertainty in the forecasted electricity price is quantified by using the forecasting model’s residuals to generate prediction intervals around each forecasted electricity price. The novelty of the proposed tool lies in the use of these prediction intervals to formulate a bi-objective function that represents a compromise between maximizing the expected savings and minimizing the associated risk, while satisfying specified environmental objectives. This allows decision-makers to operate CHP systems according to the risk they are willing to take. The actual operation costs during a 40–day trial period resulting from the installation of the dynamic controller on an existing CHP engine that provides electricity and heat to a supermarket are presented. Results demonstrate that the forecasted electricity price almost always falls within the developed prediction intervals, achieving savings of 23% on energy costs against
Acha Izquierdo S, Le Brun N, Damaskou M, et al., Fuel cells as combined heat and power systems in commercial buildings: A case study in the food-retail sector, Energy, ISSN: 0360-5442
Georgios M, Emilio Jose S, Acha Izquierdo S, et al., 2020, CO2 refrigeration system heat recovery and thermal storage modelling for space heating provision in supermarkets: An integrated approach, Applied Energy, Vol: 264, ISSN: 0306-2619
The large amount of recoverable heat from CO2 refrigeration systems has led UK food retailers to examine the prospect of using refrigeration integrated heating and cooling systems to provide both the space heating and cooling to food cabinets in supermarkets. This study assesses the performance of a refrigeration integrated heating and cooling system installation with thermal storage in a UK supermarket. This is achieved by developing a thermal storage model and integrating it into a pre-existing CO2 booster refrigeration model. Five scenarios involving different configurations and operation strategies are assessed to understand the techo-economic implications. The results indicate that the integrated heating and cooling system with thermal storage has the potential to reduce energy consumption by 17–18% and GHG emissions by 12–13% compared to conventional systems using a gas boiler for space heating. These reductions are achieved despite a marginal increase of 2–3% in annual operating costs. The maximum amount of heat that can be stored and utilised is constrained by the refrigeration system compressor capacity. These findings suggest that refrigeration integrated heating and cooling systems with thermal storage are a viable heating and cooling strategy that can significantly reduce the environmental footprint of supermarket space heating provision and under the adequate circumstances can forsake the use of conventional fossil-fuel (natural gas) boiler systems in food-retail buildings.
Hart MBP, Olympios A, Le Brun N, et al., Pre-feasibility modelling and market potential analysis of a cloud-based CHP optimiser, 2020 ASHRAE Annual Conference (Virtual), Publisher: ASHRAE
Smart control system technologies for combined heat and power (CHP) units arenot previously reported in literature, and have potential to generate significant savings. Only minimal capital investment is required in infrastructure and software development. A live cloud-based solution has therefore been developed,and installed in a real UK supermarket store, to optimise CHP output based upon predicted price forecasts,and live electricity and head demand data. This has allowed validation of the optimiser price forecasts, and predicted cost savings, anda model of the optimiser has therefore been applied to three case study sites. The model itself has also been validated against the installed optimiser data.The pre-feasibility analysis undertaken indicates cost savings between 2% and 12%.CHP units sized within the feasible operating range, above a part loadlevelof 0.65, generate the greatest percentage savings. This is because the optimiser has the greatest flexibility to control the CHP output. However, larger units, even though less nearly optimal,may actually generate greater overall savings and would therefore be targeted for earlier optimiser implementation. Installation costs are not expected to vary greatly from site-to-site. Some stores, though,show no material improvement over the existing control systems, demonstrating the valueof the pre-feasibility analysis using the model.Though waste heat increases significantly with all strategies, the propensity to sell this heat within the UK is likely to improvein the near future.
Sarabia EJ, Acha Izquierdo S, Le Brun N, et al., Modelling of a CO2 refrigerant booster system for waste heat recovery applications in retail for space heating provision, 2020 ASHRAE Annual Conference (Virtual), Publisher: ASHRAE
This paper compares and quantifies the energy, environmental and economic benefits of various control strategies for recovering heat from a supermarket’s CO2 booster refrigeration system. There covered heat is used for space heating, with the goal of displacing natural gas fueled boilers. A theoretical model with thermal storage is presentedbased on a previous validated model from an existing refrigeration system in a food-retail building located in the UK. Sixheat recovery strategies are analysed by modifying thermal storage volumes and pressure levels in the gas-cooler/condenser. The model shows that a reduction of 30-40% in natural-gasc onsumption is feasible by the installation of a de-superheater and without any advanced operating strategy, and 40-50% by using a thermal storage tank. However, the CO2 system can fully supply the entire space-heating requirement by adopting alternative control strategies, albeit by penalising the coefficient of performance (COP) of the compressor. Results show that the best energy strategy can reduce total consumption by 35%, while the best economic strategy can reduce costs by 11%. Findings from this work suggest that heat recovery systems can bring substantial benefits to improve the overall efficiency of energy-intensive buildings,although trade-offs need to be carefully considered and further analysed before embarking on such initiatives.
Langshaw L, Ainalis D, Acha Izquierdo S, et al., 2020, Environmental and economic analysis of liquefied natural gas (LNG) for heavy goods vehicles in the UK: A Well-to-Wheel and total cost of ownership evaluation, Energy Policy, Vol: 137, Pages: 1-15, ISSN: 0301-4215
This paper evaluates the environmental and economic performance of liquefied natural gas (LNG) as a transition fuel to replace diesel in heavy goods vehicles (HGVs). A Well-to-Wheel (WTW) assessment based on real-world HGV drive cycles is performed to determine the life-cycle greenhouse gas (GHG) emissions associated with LNG relative to diesel. The analysis is complemented with a probabilistic approach to determine the total cost of ownership (TCO) across a range of scenarios. The methodologies are validated via a case study of vehicles operating in the UK, using data provided by a large food retailer. The spark-ignited LNG vehicles under study were observed to be 18% less energy efficient than their diesel counterparts, leading to a 7% increase in WTW GHG emissions. However, a reduction of up to 13% is feasible if LNG vehicles reach parity efficiency with diesel. Refuelling at publicly available stations enabled a 7% TCO saving in the nominal case, while development of private infrastructure incurred net costs. The findings of this study highlight that GHG emission reductions from LNG HGVs will only be realised if there are vehicle efficiency improvements, while the financial case for operators is positive only if a publicly accessible refuelling network is available.
Pozo C, Limleamthong P, Guo Y, et al., 2019, Temporal sustainability efficiency analysis of urban areas via data envelopment analysis and the hypervolume indicator: Application to London boroughs, Journal of Cleaner Production, Vol: 239, Pages: 1-14, ISSN: 0959-6526
Transitioning towards a more sustainable society calls for systematic tools to assess the sustainability performance of urban systems. To perform this task effectively, this work introduces a novel method based on the combined use of Data Envelopment Analysis (DEA) and the hypervolume indicator. In essence, DEA is applied to (i) distinguish between efficient and inefficient urban systems through the identification of best practices; and to (ii) establish improvement targets for the inefficient urban systems that, if attained, would make them efficient. Meanwhile, the hypervolume indicator is employed in conjunction with DEA to evaluate how urban systems evolve with time. The capabilities of this approach are illustrated through its application to the sustainability assessment of London boroughs between 2012–2014. Results reveal that most boroughs tend to perform well in terms of the indicators selected, with 20–25 of the 32 boroughs found efficient depending on the year. Regarding the temporal assessment, a global improvement in sustainability performance was found, with a strong relationship between the boroughs’ performances and their locations. The method proposed opens new pathways of social and environmental research for the application of advanced multi-criteria decision-support tools in the assessment and optimisation of urban systems.
Acha Izquierdo S, Le Brun N, Shah N, et al., 2019, Assessing the modelling approach and datasets required for fault detection in photovoltaic systems, IEEE Industry Applications Society Annual Meeting, Publisher: IEEE
Reliable monitoring for photovoltaic assets (PVs) is essential to ensuring uptake, long term performance, and maximum return on investment of renewable systems. To this end this paper investigates the input data and machine learning techniques required for day-behind predictions of PV generation, within the scope of conducting informed maintenance of these systems. Five years of PV generation data at hourly intervals were retrieved from four commercial building-mounted PV installations in the UK, as well as weather data retrieved from MIDAS. A support vector machine, random forest and artificial neural network were trained to predict PV power generation. Random forest performed best, achieving an average mean relative error of 2.7%. Irradiance, previous generation and solar position were found to be the most important variables. Overall, this work shows how low-cost data driven analysis of PV systems can be used to support the effective management of such assets.
Gonzato S, Chimento J, ODwyer E, et al., 2019, Hierarchical price coordination of heat pumps in a building network controlled using model predictive control, Energy and Buildings, Vol: 202, ISSN: 0378-7788
Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm’s behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time.
Escriva EJS, Acha S, LeBrun N, et al., 2019, Modelling of a real CO2 booster installation and evaluation of control strategies for heat recovery applications in supermarkets, International Journal of Refrigeration, Vol: 107, Pages: 288-300, ISSN: 0140-7007
This paper compares and quantifies the energy, environmental and economic benefits of various control strategies recovering heat from a CO2 booster system in a supermarket for space heating with the purpose of understanding its potential for displacing natural gas fuelled boilers. A theoretical steady-state model that simulates the behaviour of the CO2 system is developed and validated against field measurements obtained from an existing refrigeration system in a food-retail building located in the United Kingdom. Five heat recovery strategies are analysed by modifying the mass flows and pressure levels in the condenser. The model shows that a reduction of 48% in natural-gas consumption is feasible by the installation of a de-superheater and without any advanced operating strategy. However, the CO2 system can fully supply the entire space-heating requirement by adopting alternative control strategies, albeit by penalising the coefficient of performance (COP) of the compressor. Results show that the best energy strategy can reduce total consumption by 32%, while the best economic strategy can reduce costs by 6%. Findings from this work suggest that heat recovery systems can bring substantial benefits to improve the overall efficiency of energy-intensive buildings; although trade-offs need to be carefully considered and further analysed before embarking on such initiatives.
O'Dwyer E, Pan I, Acha Izquierdo S, et al., 2019, Modelling and evaluation of multi-vector energy networks in smart cities, International Conference on Smart Infrastructure and Construction 2019, Publisher: ICE Publishing
Energy demand growth and the rapid rate of technological changein an urban contextare already having an impact on our energy systems. Considering global ambitions to reduce carbon emissions and minimise the rate and impacts of climate change, this demand will need to be met with energy from low carbon sources. Increased electrification of heat and transport networks is anticipated, however, the cross-sectoral impacts of different interventions in these systems must be better understood to prevent gains in one system leadingto losses in another while ensuring financial benefits for producers and consumers. As such, evaluating the impacts of specific interventions can be a challenge, with analyses typically focussed on individual systems. In this paper, asimulation environment is developed to capture the behaviour of interconnected heat, power and transport networks in an urban environment to act as a ‘digital twin’ for the energy systems of a district or city. The modelling environment illustrated here is based on the smart city interventions in Greenwich (London), with model validation carried out using real data measurements. Building retrofit and heat electrification interventions are demonstrated in terms of costs, energy consumption and CO2 emissions, considering constraints on power and thermal systems.
Howard B, Acha Izquierdo S, Shah N, et al., 2019, Implicit sensing of building occupancy count with information and communication technology data sets building and environment, Building and Environment, Vol: 157, Pages: 297-308, ISSN: 0360-1323
Occupancy count, i.e., the number of people in a space or building, is becoming an increasingly important measurement to model, predict, and minimize operational energy consumption. Explicit, hardware-based, occupancy counters have been proposed but wide scale adoption is limited due to the cost and invasiveness of system implementation. As an alternative approach, researchers propose using data from existing information and communication technology (ICT) systems to infer occupancy counts.In the reported work, three different data streams, security access data, wireless connectivity data, and computer activity data, from ICT systems in a medium sized office building were collected and compared to the counts of a commercially available occupancy counter over 59 working days. The occupancy counts from the ICT systems are compared to the commercial counter with and without calibration to determine the ability of the data sets to measure occupancy. Various transformations were explored as calibration techniques for the ICT data sets. Training sets of 24, 48, and 120 hours were employed to determine how long an external calibration system would need to be installed.The analysis found that calibration is required to provide accurate counts. While each ICT data set provides similar magnitudes and time series behavior, incorporating all three data streams in a two layer neural network with 1 week of training data provides the most accurate estimates against 5 performance metrics. Whilst 1 week of data provides the best results, 24 hours is sufficient to develop similar levels of performance.
Chakrabarti A, Proeglhoef R, Bustos-Turu G, et al., 2019, Optimisation and analysis of system integration between electric vehicles and UK decentralised energy schemes, Energy, Vol: 176, Pages: 805-815, ISSN: 0360-5442
Although district heat network schemes provide a pragmatic solution for reducing the environmental impact of urban energy systems, there are additional benefits that could arise from servicing electric vehicles. Using the electricity generated on-site to power electric vehicles can make district heating networks more economically feasible, while also increasing environmental benefits. This paper explores the potential integration of electric vehicle charging into large-scale district heating networks with the aim of increasing the value of the generated electricity and thereby improving the financial feasibility of such systems. A modelling approach is presented composed of a diverse range of distributed technologies that considers residential and commercial electric vehicle charging demands via agent-based modelling. An existing district heating network system in London was taken as a case study. The energy system was modelled as a mixed integer linear program and optimised for either profit maximisation or carbon dioxide emissions minimisation. Commercial electric vehicles provided the best alternative to increase revenue streams by about 11% against the current system configuration with emissions effectively unchanged. The research indicates that district heating network systems need to carefully analyse opportunities for transport electrification in order to improve the integration, and sustainability, of urban energy systems.
Olympios A, Le Brun N, Acha Izquierdo S, et al., Installation of a dynamic controller for the optimal operation of a CHP engine in a supermarket under uncertainty, ECOS2019: 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
This work is concerned with the integration and coordination of decentralized combined heat and power (CHP) systems in commercial buildings. Although extensive research has been performed on theoretically optimizing the design, sizing and operation of CHP systems, less effort has been devoted to an understanding of the practical challenges and the effects of uncertainty in implementing advanced algorithms to real-world applications. This paper provides details of an undergoing field trial involving the installation of a dynamic controller for the optimal operation of an existing CHP engine, which provides electricity and heat to a supermarket. The challenges in developing and applying an optimization framework and the software architecture required to implement it are discussed. Deterministic approaches that involve no measure of uncertainty provide limited useful insight to decision makers. For this reason, the methodology here develops a stochastic programming technique, which performs Monte Carlo simulations that can consider the uncertainty related to the exporting electricity price. The method involves the formation of a bi-objective function that represents a compromise between maximizing the expected savings and minimizing the associated risk. The results reveal a risk-return trade-off, demonstrating that conservative operation choices emerging from the stochastic approach can reduce risk by about 15% at the expense of a noticeably smaller reduction of about 10% in expected savings.
Ayoub AN, Gaigneux A, Le Brun N, et al., 2019, The development of a carbon roadmap investment strategy for carbon intensive food retail industries, International Conference on Sustainable Energy and Resource Use in Food Chains including Workshop on Energy Recovery Conversion and Management, Publisher: Elsevier, Pages: 333-342, ISSN: 1876-6102
This work presents an approach to develop an innovative decarbonisation investment strategy framework for carbon intensive UK industries by using statistical analysis and optimisation modelling. The case study focuses on taking a representative sample of retail buildings and assesses the financial viability of installing low-carbon Combined Heat and Power units (CHPs) and Photovoltaic Solar Panels (PVs) across a portfolio of buildings. Simulation of each building are initially conducted, and the results generate a set of regression coefficients, via a multivariate adaptive regression splines (MARS), which are inputted into a Mixed Integer Linear Programming (MILP) problem. Solving the MILP yields the optimal decarbonisation investment strategy for the case study up to 2050, considering market trends such as electricity prices, gas prices and policy incentives. Results indicate the level of investment required per year, the operational and carbon savings associated, and a program for such investments. This method is reiterated for several scenarios where different parameters such as utility prices, capital costs and grid carbon factors are forecasted up to 2050 (following the Future Energy Scenarios from National Grid). This work shows how a clear mathematical framework can assist decision-makers in commercial organisations to reduce their carbon footprint cost-effectively and thus reach science-based targets.
Efstratiadi M, Acha Izquierdo S, Shah N, et al., 2019, Analysis of a closed-loop water-cooled refrigeration system in the food retail industry: A UK case study, Energy, ISSN: 0360-5442
Refrigeration in supermarkets accounts between 30% and 60% of total electricity demand in UK stores. The aim of this study is to conduct a pre-feasibility analysis of whether the use of a water-cooled configuration rejecting heat to the soil can improve the overall cooling performance of commercial refrigeration systems against air-cooled designs. In this work, a model simulating the operation of an existing refrigeration system is presented and validated against field data measurements taken from a supermarket. The examined system is used as a baseline and then modified to evaluate the impact of installing a water-cooled gas cooler. Results indicate that the use of water-cooled gas coolers has the potential to reduce electrical consumption of refrigeration systems by up to a factor of 5 when external temperatures are high. Overall, annual operation indicates the water-cooled alternative uses 3% less electricity than the air-cooled approach. A hybrid system is also considered consisting of coupled air-cooled and water-cooled units operating in parallel, for which an energy reduction of 6% is obtained compared against the baseline system. An economic evaluation of these systems shows promising results with a payback period of about 5 years for systems installed in new stores, although retrofits are costlier.
O'Dwyer E, Pan I, Acha S, et al., 2019, Smart energy systems for sustainable smart cities: Current developments, trends and future directions, Applied Energy, Vol: 237, Pages: 581-597, ISSN: 0306-2619
Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.
Georgiou S, Acha S, Shah N, et al., 2018, A generic tool for quantifying the energy requirements of glasshouse food production, Journal of Cleaner Production, Vol: 191, Pages: 384-399, ISSN: 0959-6526
Quantifying the use of resources in food production and its environmental impact is key to identifying distinctive measures which can be used to develop pathways towards low-carbon food systems. In this paper, a first-principle modelling approach is developed, referred to as gThermaR (Glasshouse-Thermal Requirements). gThermaR is a generic tool that focuses on the energy requirements of protected heated production, by integrating holistic energy, carbon, and cost modelling, food production, data analytics and visualization. The gThermaR tool employs historic data from weather stations, growing schedules and requirements specific to grower and product needs (e.g. set-point temperatures, heating periods, etc.) in order to quantify the heating and cooling requirements of glasshouse food production. In the present paper, a case study is reported that employs a database compiled for the UK. Another relevant feature of the tool is that it can quantify the effects that spatial and annual weather trends can have on these heating and cooling requirements. The main contribution of this work, therefore, concerns the development a tool that can provide a simple integrated approach for performing a wide range of analyses relevant to the thermal requirements of heated glasshouses. The tool is validated through collaborations with industrial partners and showcased in a case study of a heated glasshouse in the UK, offering the capacity to benchmark and compare different glasshouse types and food growth processes. Results from the case study indicate that a significant reduction in the heating requirement and, therefore, carbon footprint, of the facility can be achieved by improving key design and operational parameters. Results indicate savings in the peak daily and annual heating requirements of 44-50% and 51-57% respectively, depending on the region where the glasshouse is located. This improvement is also reflected in the carbon emissions and operating costs for the different en
Acha Izquierdo S, Shah N, Markides C, et al., 2018, Fuel cells as CHP systems in commercial buildings: a case study for the food retail sector, 2018 ASHRAE Annual Conference, Publisher: ASHRAE
This study investigates fuel cells as combined heat and power systems (CHPs) for distributed applications in commercial buildings, specifically supermarkets. Up-to-date technical data from a specialized manufacturing company wasinvestigated and used to conduct a case study analysis on several food retail buildings using half-hourly historical data. A detail mathematical model, described in previous publications (Cedillos et al. 2016, Achaet al.2018), was used to simulate the performance of fuel cells through a year of operation in each supermarket. The simulations employ comprehensive energy market costing data and practical informationto evaluate project feasibility such as installation workcosts. The results of the simulations are discussed and a techno-economic assessment is conducted to evaluate the main factors affecting the economics of fuel cell projects.In addition, a comparative analysis with competing CHP technologies (internal combustion engines) is covered. Results show that fuel cells are becoming financially competitivealthough combustion engines are still amoreviableoption. For large-size supermarketsthe payback time forinstalling a fuel cell system is 4.7-5.6years versus 3.6-5.6years for internal combustion engines. The work alsodiscusses the prospects of fuel cells under different market and policy scenariosas well astechnologicalimprovements; thus,offering insights in what are the key aspects which can foster fuel cell installations
Acha Izquierdo S, Lambert R, Le Brun N, et al., 2018, Optimal CHP investments applying sensitivity analyses and financial risk management indicators, 2018 ASHRAE Annual Conference, Publisher: ASHRAE
Evaluating combined heat and power (CHP) investments for commercial applications is not a straightforward task.This work assesses the impact multipletechno-economicuncertainties can have on CHP investments.Understanding the impact these uncertaintiescanhave on projectviabilityallows decision-makers to make informed decisions on capital intensive projects. In this work,amathematical model described previously (Cedillos et al.)wasused to select the optimal CHP size and calculate a reference solution on the financial viability of such investments in a set of buildings. After generating these reference CHP solutions, the impact of uncertainty is then assessed by applying Monte-Carlo based sensitivity analyses and financial and risk management key performance indicators (KPIs). Results suggest thefour most influential parameters in CHP priojects areheating demand, installation costs, electricity prices, and electricity demand. For attractive investments cost uncertainty made projects vary in their payback from2.6 to 6.9years, while for unattractive investments payback ranged from 4 to 11.3 years. Furthermore, Monte-Carlo results illustrate thedifferent distributions of each project, with significant variations in tails risks; indicating which sites are more suitable than others. Results presented show the economic impactuncertainties have onCHP projects, hence allowing decision-makers to make informed decisions before committing their resources to such capital-intensive projects.
Chimento J, Gonzato S, O’Dwyer E, et al., 2018, District-level coordination of predictive control strategies for urban residential heating networks
© 2018 University of Minho. All rights reserved. Urban energy systems represent a significant proportion of global energy consumption, with residential and commercial buildings accounting for around 40%. This could be significantly reduced through the use of smarter control strategies for space heating. In district heating networks, the supply limitations associated with the electrical grid can lead to a misalignment between the locally optimal objectives of the individual dwellings and the globally optimal objective of the district. In this paper, a methodology is developed for coordinating a decentralised set of building heating subsystems within a district heating network by deriving a dynamic electricity price signal and communicating it to the network in order to satisfy a power supply limit. The price signal is transmitted by a high-level coordinator which discourages power usage when necessary. It was found that power supply limits could be satisfied in this way but only above a threshold value of 180 kW for the particular district, while lower values on the limit led to instability in the form of power shortages and occupant discomfort.
Acha Izquierdo S, Mariaud A, Shah N, et al., 2017, Optimal Design and Operation of Distributed Low-Carbon Energy Technologies in Commercial Buildings, Energy, Vol: 142, Pages: 578-591, ISSN: 0360-5442
Commercial buildings are large energy consumers and opportunities exist to improve the way they produce and consume electricity, heating and cooling. If energy system integration is feasible, this can lead to significant reductions in energy consumption and emissions. In this context, this work expands on an existing integrated Technology Selection and Operation (TSO) optimisation model for distributed energy systems (DES). The model considers combined heat and power (CHP) and organic Rankine cycle (ORC) engines, absorption chillers, photovoltaic panels and batteries with the aim of guiding decision makers in making attractive investments that are technically feasible and environmentally sound. A retrofit case study of a UK food distribution centre is presented to showcase the benefits and trade-offs that integrated energy systems present by contrasting outcomes when different technologies are considered. Results show that the preferred investment options select a CHP coupled either to an ORC unit or to an absorption chiller. These solutions provide appealing internal rates of return of 28–30% with paybacks within 3.5–3.7 years, while also decarbonising the building by 95–96% (if green gas is used to power the site). Overall, the TSO model provides valuable insights allowing stakeholders to make well-informed decisions when evaluating complex integrated energy systems.
Acha S, Mariaud A, Shah N, et al., 2017, Optimal design and operation of low-carbon energy technologies in commercial buildings, 30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems - ECOS 2017
© 2017 IMEKO Non-domestic buildings are large energy consumers and present many opportunities with which to enhance the way they produce and consume electricity, heating and cooling. If energy system integration is feasible, this can lead to significant reductions in energy use and emissions associated with building operations. Due to their diverse energy requirements, a broad range of technologies in flexible solutions need to be evaluated to identify the best alternative. This paper presents an integrated energy-systems model that optimizes the selection and operation of distributed technologies for commercial buildings. The framework consists of a comprehensive technology database, half-hourly electricity cost profiles, conventional fuel costs and building features. This data is applied to a mixed-integer linear programming model that optimizes the design and operation of installed technologies based on a range of financial and environmental criteria. The model aims to guide decision makers in making attractive investments that are technically feasible and environmentally sound. A case study of a food distribution centre in the UK is presented to illustrate the economic and environmental benefits the proposed integrated energy systems model could bring against a business as usual (BaU) approach. The technology portfolio considered in the case study includes combined heat and power (CHP) and organic Rankine cycle (ORC) engines, absorption chillers, photovoltaic (PV) panels, and battery systems. The results clearly illustrate the different outcomes and trade-offs that can emerge when stakeholders champion different technologies instead of making an exhaustive assessment. Overall, the model provides meaningful insights that can allow stakeholders to make well informed investment decisions when it comes to the optimal configuration and operation of energy technologies in commercial buildings.
Georgiou S, Acha Izquierdo S, Shah N, et al., 2017, Assessing, benchmarking and analyzing heating and cooling requirements for glasshouse food production: a design and operation modelling framework, 1st International Conference on Sustainable Energy and Resource Use in Food Chains, ICSEF 2017, Publisher: Elsevier, Pages: 164-172, ISSN: 1876-6102
Growing populations, increase in food demand, society’s expectations for out of season products and the dependency of the food system on fossil fuels stress resources due to the requirements for national production and from importation of products from remote origins. Quantifying the use of resources in food production and their environmental impacts is key to identifying distinctive measures which can develop pathways towards low carbon food systems. In this paper, a modelling approach is presented which can quantify the energy requirements of heated glasshouse food production. Based on the outputs from the model, benchmarking and comparison among different glasshouse types and growers is possible. Additionally, the effect of spatial and annual weather trends on the heating and cooling requirements of glasshouses are quantified. Case study results indicate that a reduction in heating requirements of about 50%, and therefore an equivalent carbon footprint reduction, can be achieved by replacing a single glass sealed cover with a double glass sealed cover.
Acha Izquierdo S, lambert R, Shah N, et al., 2017, Modelling and optimising the marginal expansion of an existing district heating network, Energy, Vol: 140, Pages: 209-223, ISSN: 0360-5442
Although district heating networks have a key role to play in tackling greenhouse gas emissions associated with urban energy systems, little work has been carried out on district heating networks expansion in the literature. The present article develops a methodology to find the best district heating network expansion strategy under a set of given constraints. Using a mixed-integer linear programming approach, the model developed optimises the future energy centre operation by selecting the best mix of technologies to achieve a given purpose (e.g. cost savings maximisation or greenhouse gas emissions minimisation). Spatial expansion features are also considered in the methodology.Applied to a case study, the model demonstrates that depending on the optimisation performed, some building connection strategies have to be prioritised. Outputs also prove that district heating schemes' financial viability may be affected by the connection scenario chosen, highlighting the necessity of planning strategies for district heating networks. The proposed approach is highly flexible as it can be adapted to other district heating network schemes and modified to integrate more aspects and constraints.
Dahmm H, Bustos-Turu G, Acha S, et al., 2017, Techno-economic analysis and trade-offs of cooperative community energy planning schemes in the UK, 30TH INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS
© 2017 IMEKO Community Energy (CE) is the ownership of distributed energy resources by local communities. this paper describes a modelling framework for how communities can realize new opportunities through cooperation. The model disaggregates a community into sectors, having unique combinations of energy profiles, financial conditions, and available technologies. A modified Nash Bargaining technique was used to optimize the selection and operation of boiler, heat pump and CHP technologies, while also managing the exchange of energy vectors within the community; sectors cooperated first by exchanging electricity generation, exchanging heat, and then exchanging electricity and heat together. This approach adds to the literature by comparing forms of energy integration while considering diverse community interests. A case study was developed from London residential data, combined with profiles of five non-residential sectors. Results show that the exchange of electricity or heat can increase CO2 mitigation relative to independent action, but exchanging electricity and heat together magnifies the possible mitigation. Economic benefits vary between sectors and scenarios, but exchanging electricity and heat together maximizes the objective. Cooperation increases community resilience to reductions in government support, and increasing existing subsidies might reduce the incentive to cooperate. Future work should consider business plans for realizing environmental and economic benefits.
Mariaud A, Acha S, Ekins-Daukes N, et al., 2017, Integrated optimisation of PV and battery storage systems for UK non-domestic buildings, ASHRAE Annual Conference, Publisher: ASHRAE, ISSN: 2578-5257
his paper presents an end-user technology selection and operation (TSO) optimisation model which simultaneously optimizes the selection, design and operation of photovoltaic(PV)and battery systems in the context of commercial buildings integration. Thismodelserves to guidebusiness decision makers by assessing investment attractivenessand strategies. A strong feature of themodel is that it encompasses whole life costing and carbon emissionswhile taking into accountbuilding loads and features. Input data relies on historic metered electricity demand and irradiance levels combined with real-time and forecasted UK electricity pricesat half-hourly intervals, hence providing more comprehensive results than previous works. For a selected building and from a portfolio of technologies, a mixed-integer linear programming model selects an optimal combination of technologies and capacities while establishing the optimum operational strategy that provides the best return on investment.The case study of aretail distribution centre is provided to highlight the capabilities of the model. Results showcase the model provides valuable insights into project evaluation and thus reducesthe uncertainty associated with high capital projects. Overall,attractive PV and battery storage systems investments can be identified for UK commercial buildingsthrough the implementation of integratedoptimization models.
Garcia AS, Acha S, Shah N, et al., 2017, Half-Hourly Regional Electricity Price Modelling for Commercial End Users in the UK, ASHRAE Annual Conference, Publisher: ASHRAE, ISSN: 2578-5257
This paper details a methodology to model half-hourly electricity priceprofiles fortheUK commercial end-users; thus allowing consumersto visualize and calculate more accurately thecost of the electricitythey consume.The methodologyconsistsin a bottom-up model that definesindividuallyall the tariff components of the bill and thenaggregatesthemto quantify the cost of a kWh across the day. In this work ,‘representative day’ electricity price curves for different months, day types, voltage levels connections, and regionsinthe UKfrom 2015 up to 2020are presented.Outputs from this work can informcompaniesto better understand their energy costs and accurately perform economic assessments of investments inschemes that reduce emissions andaim towardsreachingnet zero energy buildings. In addition, the disaggregated structure of the model allows specific analysis ofindividual components thus highlighting which elements carry a larger weight on costs across the year; such as network charges originatedfrom distribution and transmission system operators. Due to its multi-variable dependency, the model produces more than5,000 representative day curves. The results show that thosecommercial buildingsconnected to Low Voltage (LV) in North Wales and Merseyside, Northern Scotland, the South West and South Wales face the highest average electricity prices, whereas consumers connected to HV in London and Yorkshire have the cheapest electricity in the UK.
Efstratiadi M, Acha Izquierdo S, Shah N, et al., 2017, Analysis of a closed-loop water-cooled refrigeration system in the food retail industry: A UK case study, 2017 ASHRAE Annual Conference, Publisher: ASHRAE, ISSN: 2578-5257
The need for refrigeration in the food retail industry and specifically in supermarkets, currently accounts for about 30% to 60% of the total energy consumed in the UK stores. A key characteristic of this consumption, is the high amount of low-grade heat rejected by the condensation units to the ambient air. The aim of this study, which focuses on transcritical CO2 (R744) refrigeration cycles, is to assess whether the use of a water-cooled condenser rejecting heat to the soil via an intermediate closed-loop water-circuit, can improve the overall cooling performance, while also considering the economic implications of this modifications. In this work, a detailed model simulating the operation of an existing supermarket refrigeration system is presented and validated against field data measurements taken from a refrigeration system in a UK supermarket. The examined direct-expansion system comprises an air-cooled condenser coupled with two sets of compressors for the provision of intermediate and low-temperature cooling. This baseline model is then modified and used to evaluate the performance of a similar system, in which a water-cooled condenser is used instead of the existing air-cooled unit or in parallel to it. Preliminary results indicate that the use of water-cooled condensers has the potential to reduce the energy consumption of these refrigeration systems by up to a factor of 5 when the external temperature is high. However, in cold ambient conditions, the air-cooled condensers reject 10% less heat, resulting in a better system performance. Furthermore, a more thorough case study is developed in order to examine the yearly operation of the existing system, and to compare this to various water-cooled alternatives. The analysis indicates a reduction of approximately 3% in the energy consumed by the water-cooled system (compared to the reference benchmark air-cooled system), and a reduction of almost 6%, for a hybrid system with coupled air-cooled and water-coole
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