69 results found
Sarabia Escriva EJ, Hart M, Acha Izquierdo S, et al., 2021, Techno-economic evaluation of integrated energy systems for heat recovery applications in food retail buildings, Applied Energy, ISSN: 0306-2619
Acha Izquierdo S, Shah N, Soler A, 2021, Best practices to mitigate CO2 operational emissions: A case study of the Basque Country energy ecosystem, Ekonomiaz Basque Economic Review, ISSN: 0213-3865
This work reviews the best practices to reduce CO2 emissions in energy intensive organizations and energy value-chains by highlighting the synergy that can be built with like-minded organizations via collaborations; taking the Basque Country as a case study. An academic review covers how corporate strategies are attempting to curtail emissions in a systematic manner. The study is then complimented by findings obtained from interviews of key stakeholders in the Basque Country responsible for playing an important role in implementing a green agenda. The interviews allow us to highlight flagship projects and assess the collaborative framework strengths and challenges. Results indicate that organizations are well underway in implementing and researching low carbon solutions, but issues surrounding governance, strategy, and regulatory challenges can slow progress of goals.
Ayoub AN, Gaigneux A, Le Brun N, et al., 2021, Corrigendum to “The development of a low-carbon roadmap investment strategy to reach science based targets for commercial organisations with multi-site properties” [J. Build. Environ. 186 (2020) 107311], Building and Environment, Vol: 190, Pages: 107540-107540, ISSN: 0360-1323
Hart M, Austin W, Acha S, et al., 2020, A roadmap investment strategy to reduce carbon intensive refrigerants in the food retail industry, Journal of Cleaner Production, Vol: 275, Pages: 1-17, ISSN: 0959-6526
High global warming potential (GWP) refrigerant leakage is the second-highest source of carbon emissions across UK supermarket retailers and a major concern for commercial organizations. Recent stringent UN and EU regulations promoting lower GWP refrigerants have been ratified to tackle the high carbon footprint of current refrigerants. This paper introduces a data-driven modelling framework for optimal investment strategies supporting the food retail industry to transition from hydrofluorocarbon (HFC) refrigeration systems to lower GWP systems by 2030, in line with EU legislation. Representative data from a UK food retailer is applied in a mixed integer linear model, making simultaneous investment decisions across the property estate. The model considers refrigeration-system age, capacity, refrigerant type, leakage and past-performance relative to peer systems in the rest of the estate. This study proposes two possible actions for high GWP HFC refrigeration systems: a) complying with legislation by retrofitting with an HFO blend (e.g. R449-A) or b) installing a new natural refrigerant system (e.g. R744). Findings indicate that a standard (i.e. business-as-usual) investment level of £6 m/yr drives a retrofitting strategy enabling significant reduction in annual carbon emissions of 71% by the end of 2030 (against the 2018 baseline), along with meeting regulatory compliance. The strategy is also highly effective at reducing emissions in the short term as total emissions during the 12-year programme are 59% lower than would have been experienced if the HFC emissions continued unabated. However, this spending level leaves the business at significant risk of refrigeration system failures as necessary investments in new systems are delayed resulting in an ageing, poorly performing estate. The model is further tested under different budget and policy scenarios and the financial, environmental, and business-risk implications are analysed. For example, under a more agg
Ayoub AN, Gaigneux A, Le Brun N, et al., 2020, The development of a low-carbon roadmap investment strategy to reach Science Based Targets for commercial organisations with multi-site properties, Building and Environment, Vol: 186, Pages: 1-17, ISSN: 0360-1323
The Paris Climate Agreement has motivated commercial organisations to set and work towards Science Based Targets, a realignment of greenhouse gas emissions in line with climate science. This work presents a modelling framework to develop cost-effective decarbonisation investment programs that address electricity and heat carbon emissions in organisations with multiple properties. The case study takes a set of 60 supermarkets in the UK and evaluates the techno-economic viability of installing biomethane combined heat and power engines and photovoltaic panels to make them zero carbon buildings. Simulation results from the batch of buildings offer the financial and environmental benefits at each site and generates a set of regression coefficients which are then applied into an optimisation problem. Solving the optimisation yields the decarbonisation investment strategy for the estate up to 2050; indicating the preferred sequence of investments the company needs to undertake to embark upon an effective low-carbon roadmap. A sensitivity analysis compliments the study to understand how market and policy externalities may influence roadmaps. Results suggest a CAPEX ranging from £57-£80 million is required to deliver an ambitious decarbonisation plan, while OPEX and carbon savings benefits range between £197 and £683 million and 461–715 ktCO2e; respectively. The case study highlights that although carbon targets can be achieved by 2030, the 2050 targets are more challenging to meet; suggesting additional technologies and policies should be considered and implemented. The framework serves as a blueprint of how modelling can assist decision-makers in reducing their carbon footprint cost-effectively to reach Science Based Targets.
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.
Acha Izquierdo S, Le Brun N, Damaskou M, et al., 2020, Fuel cells as combined heat and power systems in commercial buildings: A case study in the food-retail sector, Energy, Vol: 206, Pages: 1-13, ISSN: 0360-5442
This work investigates the viability of fuel cells (FC) as combined heat and power (CHP) prime movers in commercial buildings with a specific focus on supermarkets. Up-to-date technical data from a FC manufacturing company was obtained and applied to evaluate their viability in an existing food-retail building. A detailed optimisation model for enhancing distributed energy system management described in previous work is expanded upon to optimise the techno-economic performance of FC-CHP systems. The optimisations employ comprehensive techno-economic datasets that reflect current market trends. Outputs highlight the key factors influencing the economics of FC-CHP projects. Furthermore, a comparative analysis against a competing internal combustion engine (ICE) CHP system is performed to understand the relative techno-economic characterisitcs of each system. Results indicate that FCs are becoming financially competitive although ICEs are still a more attractive option. For supermarkets, the payback period for installing a FC system is 4.7–5.9 years vs. 4.0–5.6 years for ICEs when policies are considered. If incentives are removed, FC-CHP systems have paybacks in the range 6–10 years vs. 5–8.5 years for ICE-based systems. A sensitivity analysis under different market and policy scenarios is performed, offering insights into the performance gap fuel cells face before becoming more competitive.
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
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., 2020, Pre-feasibility modelling and market potential analysis of a cloud-based CHP optimiser, 2020 ASHRAE Annual Conference (Virtual), Publisher: ASHRAE, Pages: 300-307
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., 2020, 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.
Soh QY, O'Dwyer E, Acha S, et al., 2020, Optimization and Control of a Rainwater Detention and Harvesting Tank, Editors: Pierucci, Manenti, Bozzano, Manca, Publisher: ELSEVIER SCIENCE BV, Pages: 547-552
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., 2019, 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.
Sorrentino A, Pantaleo AM, Markides C, et al., 2018, Energy performance and profitability of biomass boilers in commercial sector: the case study of Sainsbury’s stores in the UK, 73rd Conference of the Italian Thermal Machines Engineering Association (ATI 2018), Publisher: Elsevier, Pages: 539-646, ISSN: 1876-6102
Commercial buildings or shopping malls are characterized by large thermal and electrical energy consumptions with high variability of energy demand. Therefore, there is a large interest to explore novel renewable energy generation systems for these applications. A novel flexible configuration of biomass-fired CHP system with organic Rankine cycle(ORC) is here proposedand applied to the case study of Sainsbury’s supermarkets in the UK.The proposed configuration adoptsa molten salt (MS) circuitto transfer heat from the biomass furnace to the ORC plant. A thermal Energy Storage (TES) is proposedtoimprove the flexible operation of the plantand reduce the size of the biomass boiler. Molten salts have been preferredto thermal oil as they have no fire risks and low environmental impactand can be used as medium for a Two Tank TES with a “direct heating” scheme. The planthas beenanalysedusing real input data of biomass boiler installed, conversion efficiency and heat demand from the store. The model is informed by hourly energy costs and electricity feed in tariff in order to define optimal size and operation of the bottoming ORC for the specific case study of large commercial energy end userin the UK.The results show that the use of thermal storage in a biomass-fired ORC plant can improve the boiler efficiency and reduce the biomass consumption in thermal-load following operating mode and increase the investment profitability.
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
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.
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