10 results found
Olympios AV, McTigue JD, Sapin P, et al., 2021, Pumped-thermal electricity storage based on Brayton cycles, Reference Module in Earth Systems and Environmental Sciences, ISBN: 9780124095489
Pumped-thermal electricity storage (PTES) based on a reversible (Joule-)Brayton cycle is a promising grid-scale energy storage technology, whose working principle is to store electricity in the form of high-grade thermal energy. This chapter provides an overview of the inner workings, operating principle and current development status of the many PTES variants, as proposed to date in the scientific literature or by manufacturers. The potential and competitiveness of the various candidate designs is quantified by – and discussed thanks to the definition of – specific techno-economic indicators. Investment cost and thermodynamic performance estimates are reported and used to assess the value of this technology as a potential large-scale, long-duration and long-lifetime energy storage option with unique sector-coupling features and low geographical constraints.
Olympios AV, McTigue J, Farres Antunez P, et al., 2021, Progress and prospects of thermo-mechanical energy storage – A critical review, Progress in Energy, Vol: 3, Pages: 1-44, ISSN: 2516-1083
The share of electricity generated by intermittent renewable energy sources is increasing (now at 26% of global electricity generation) and the requirements of affordable, reliable and secure energy supply designate grid-scale storage as an imperative component of most energy transition pathways. The most widely deployed bulk energy storage solution is pumped-hydro energy storage (PHES), however, this technology is geographically constrained. Alternatively, flow batteries are location independent and have higher energy densities than PHES, but remain associated with high costs and low lifetimes, which highlights the importance of developing and utilizing additional larger-scale, longer-duration and long-lifetime energy storage alternatives. In this paper, we review a class of promising bulk energy storage technologies based on thermo-mechanical principles, which includes: compressed-air energy storage (CAES), liquid-air energy storage (LAES) and pumped-thermal electricity storage (PTES). The thermodynamic principles upon which these thermo-mechanical energy storage (TMES) technologies are based are discussed and a synopsis of recent progress in their development is presented, assessing their ability to provide reliable and cost-effective solutions. The current performance and future prospects of TMES systems are examined within a unified framework and a thermoeconomic analysis is conducted to explore their competitiveness relative to each other as well as when compared to PHES and flow battery systems. This includes carefully selected thermodynamic and economic methodologies for estimating the component costs of each configuration in order to provide a detailed and fair comparison at various system sizes. The analysis reveals that the technical and economic characteristics of TMES systems are such that, especially at higher discharge power ratings and longer discharge durations, they can offer promising performance (round-trip efficiencies higher than 60%) along wit
Olympios AV, Pantaleo AM, Sapin P, et al., 2020, On the value of combined heat and power (CHP) systems and heat pumps in centralised and distributed heating systems: Lessons from multi-fidelity modelling approaches, Applied Energy, Vol: 274, Pages: 1-19, ISSN: 0306-2619
This paper presents a multi-scale framework for the design and comparison of centralised and distributed heat generation solutions. An extensive analysis of commercially available products on the UK market is conducted to gather information on the performance and cost of a range of gas-fired combined heat and power (CHP) systems, air-source heat pumps (ASHPs) and ground-source heat pumps (GSHPs). Data-driven models with associated uncertainty bounds are derived from the collected data, which capture cost and performance variations with scale (i.e., size and rating) and operating conditions. In addition, a comprehensive thermoeconomic (thermodynamic and component-costing) heat pump model, validated against manufacturer data, is developed to capture design-related performance and cost variations, thus reducing technology-related model uncertainties. The novelty of this paper lies in the use of multi-fidelity approaches for the comparison of the economic and environmental potential of important heat-generation solutions: (i) centralised gas-fired CHP systems associated with district heating network; (ii) gas-fired CHP systems or GSHPs providing heat to differentiated energy communities; and (iii) small-scale micro-CHP systems, ASHPs or GSHPs, installed at the household level. The pathways are evaluated for the case of the Isle of Dogs district in London, UK. A centralised CHP system appears as the most profitable option, achieving annual savings of £13 M compared to the use of decentralised boilers and a levelised cost of heat equal to 31 £/MWhth. However, if the carbon intensity of the electrical grid continues to reduce at current rates, CHP systems will only provide minimal carbon savings compared to boilers (<6%), with heat pumps achieving significant heat decarbonisation (55–62%). Differentiating between high- and low-performance and cost heat pump designs shows that the former, although 25% more expensive, have significantly lower annualised
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
Olympios A, Hoisenpoori P, Mersch M, et al., 2020, Optimal design of low-temperature heat-pumping technologies and implications to the whole energy system, The 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
This paper presents a methodology for identifying optimal designs for air-source heat pumps suitable for domestic heating applications from the whole-energy system perspective, accounting explicitly for a trade-off between cost and efficiency, as well as for the influence of the outside air temperature during off-design operation. The work combines dedicated brazed-plate and plate-fin heat-exchanger models with compressor efficiency maps, as well as equipment costing techniques, in order to develop a comprehensive technoeconomic model of a low-temperature air-source heat pump with a single-stage-compressor, based on the vapour-compression cycle. The cost and performance predictions are validated against manufacturer data and a non-linear thermodynamic optimisation model is developed to obtain optimal component sizes for a set of competing working fluids and design conditions. The cost and off-design performance of different configurations are integrated into a whole-energy system capacity-expansion and unit-dispatch model of the UK power and heat system. The aim is to assess the system value of proposed designs, as well as the implications of their deployment on the power generation mix and total transition cost of electrifying domestic heat in the UK as a pathway towards meeting a national net-zero emission target by 2050. Refrigerant R152a appears to have the best design and off-design performance, especially compared to the commonly used R410a. The size of the heat exchangers has a major effect on heat pump performance and cost. From a wholesystem perspective, high-performance heat pumps enable a ~20 GW (~10%) reduction in the required installed power generation capacity compared to smaller-heat-exchanger, low-performance heat pumps, which in turn requires lower and more realistic power-grid expansion rates. However, it is shown that the improved performance as a result of larger heat exchangers does not compensate overall for the increased technology cost, with
Sapin P, Simpson M, Olympios A, et al., 2020, Cost-benefit analysis of reversible reciprocating-piston engines with adjustable volume ratio in pumped thermal electricity storage, 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2020), Publisher: ECOS
Decarbonisation of heating, cooling and/or power services through the utilisation of renewable en-ergy sources relies on the development of efficient and economically-viable energy storage technolo-gies, ideally without geographical constraints. Pumped thermal electricity storage (PTES) is a strongcandidate technology – along with reversible Rankine cycle, (advanced adiabatic) compressed airenergy storage (CAES), and liquid air energy storage (LAES). One of the leading PTES variants isthe reversible Joule-Brayton cycle engine, where energy is stored as sensible heat in hot and coldthermal stores, while the temperature difference is achieved through gas compression and expansionprocesses. For cost reasons, and to achieve high round-trip efficiencies, it is advantageous for thecompression and expansion machines used in PTES plants to be reversible. Positive-displacementdevices offer this possibility. In particular, recent developments in pneumatically or electromagneti-cally actuated intake and exhaust valves could pave the way for high-efficiency reversible reciprocat-ing compression-expansion devices based on variable-valve control in real time. Advanced variablevalve timing (VVT) is a promising feature that allows piston machines not only to be operated bothas reversible compression and expansion devices, but also to maintain high efficiencies over a widerange of operating conditions, thanks to the possibility of adjusting the built-in volume ratio of a par-ticular machine. With enhanced part-load performance, such disruptive piston machines offer greatpotential for round-trip efficiency enhancement and cost minimisation of PTES storage plants. In thiswork, a cost-benefit analysis of innovative VVT-fitted reciprocating-piston technology is performedusing: (i) comprehensive dynamic reduced-order models to predict the compressor-expander perfor-mance for design optimisation, and (ii) Schumann-style one-dimensional models for simulating heatand mass transf
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.
Olympios A, Pantaleo AM, Sapin P, et al., 2019, Centralised vs distributed energy systems options: District heating for the Isle of Dogs in London, ICAE2019: The 11th International Conference on Applied Energy
This work focuses on a multi-scale framework for the design and comparison of low-carbon heat generation solutions to serve the residential and commercial thermal energy demand of high energy density urban areas. The adopted methodology assesses the cost and performance of four configurations integrated in a district heating network: (i) centralised cogeneration with gas turbine and bottoming steam turbine with flexible heat-to-electricity ratio; (ii) centralised cogeneration with gas-fired internal combustion engine; (iii) distributed building-integrated ground-source heat pumps for domestic hot water only; and (iv) distributed building-integrated ground-source heat pumps for both domestic hot water and space heating. Cost and performance data were obtained by conducting relevant market research and developing a simplified heat pump thermodynamic model. The different configurations are evaluated utilizing whole-year space heating and hot water demand profiles for the Isle of Dogs area in East London, UK. Scale effects are included by considering various technology size scenarios and the results indicate that a 50 MW centralised internal combustion cogeneration system appears to be the most profitable option, while the competitiveness of building-integrated heat pumps is dependent on their size.
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.
Harraz AA, Najjaran A, Sacks R, et al., 2019, Experimentally validated simulations of a diffusion absorption refrigeration system, Pages: 1045-1056
Diffusion absorption refrigeration (DAR) is a small-scale, thermally-driven cooling technology that operates passively without the need for mechanical or electrical inputs. Due to the lack of a compressor, DAR systems are charged with an auxiliary gas to enable single-pressure operation. Although DAR units have a simple construction and are easy to operate, their modelling presents challenges arising from the complexity of the physical processes that take place and govern operation. Few experimentally validated models offer a reliable prediction of the DAR system performance over a wide range of operating conditions. This paper combines results from experimental investigations on a laboratory-scale ammonia-water-hydrogen DAR system with a nominal cooling output of ∼100 W with model predictions of the performance characteristics of this system. In previous work, the DAR cycle was modelled using first-law thermodynamic analysis in the gPROMS environment for a single cooling delivery temperature and a single-charge pressure, using a group-contribution equation-of-state based on the statistical associating fluid theory (SAFT). Here, extended model validation is performed to investigate the effect of key operating parameters on system performance, including the generator heat input (varied from 150 W to 700 W), the cooling delivery temperature (set to two levels: 5 ◦C and 23 ◦C) and the system charge pressure (18 bar and 21 bar). The measured coefficient of performance (COP) was between 0.02 and 0.29. The present model predicts the maximum COP well over the heat-input range from 250 W to 550 W. Hence, the model shows good agreement with experiments, particularly when the heat-input rate is at or below the system’s design-point. Conclusions are drawn concerning the ability of models to predict this complex technology, with emphasis on part-load and off-design operation which is crucial, e.g., in solar applications.
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