Adiabatic compressed air energy storage (ACAES) is an energy storage technology that has the potential to play an important role in the transition to a predominantly renewables-driven net-zero energy system. However, it has not yet achieved the performance necessary to be widely deployed. This paper undertakes an exergy analysis of isobaric and isochoric ACAES systems, tracking lost work through the components and exploring the influences of different design choices. Three different configurations are modelled: (1) 3 compression and 3 expansion stages; (2) 4 compression and 2 expansion stages; and (3) 2 compression and 4 expansion stages. These results illustrate that isobaric systems are likely to have higher round-trip efficiency and significantly higher energy density, at the cost of achieving isobaric storage. Exergy analysis reveals that most of the losses arise in the compressors, compressor aftercoolers and expanders. Losses in aftercoolers are exaggerated when compressors operate with high-pressure ratios, emphasizing that the choice of TES is a key system variable. With pressurised water as the coolant and TES fluid, it seems likely that the best system will have more compression than expansion stages. Increasing the number of compression stages decreases the off-design penalty when the system is isochoric.

For countries with cold winters, air and ground source heat pumps have the potential to heat buildings without the use of fossil fuels. However they increase peak electrical loads for the building, and if time-of-use electricity tariffs are used, potentially lead to high costs for the end user if heating is required at peak times. Motivated by recent work showing that thermal energy storage (TES) may provide useful energy flexibility to address these issues, we consider the problem of minimising operating cost for a heat pump equipped with TES. We apply Pontryagin’s principle to analyse the problem and characterise its solutions before developing practically applicable controllers via costate estimation. Simulation based on real-world demand data from a UK house with historical data on a time-of-use electricity tariff indicates that this controller could achieve a 20.1% reduction in running cost over one year of operation. A globally optimal controller with perfect knowledge of the future would achieve a 22.8% reduction in the same scenario, as calculated by dynamic programming.

Heat pumps offer a promising solution to the chal-lenge of low-carbon heating but will significantly increase the electricity requirements of buildings. Adding thermal energy storage to heat pumps is of interest as it can: (1) remove the need for electricity to drive the heat pump when the grid is under stress. (2) reduce the size of heat pump required for a house, since peak heat demands can be met by the thermal storage. Using the heat demands from a house in a UK field trial as a case study, it is found that 120 kWh of thermal storage is required to avoid running the heat pump on the worst day of the year, increasing to 620 kWh to keep the house from requiring grid electricity for the worst week. Simulating a generic thermal energy store, we find that the heat pump peak electrical demand can be reduced by more than 50% with a thermal storage capacity of 30kWh. Our work highlights the potential of thermal energy storage to aid with reducing the grid impacts of domestic heat pumps. The developed framework can be expanded in future to find results across thousands of households, yielding a much more general understanding of the variation in these numbers.

Thermal Energy Storage plays a significant role in Adiabatic Compressed Air Energy Storage. There is a limited understanding of how the operational conditions of Adiabatic Compressed Air Energy Storage affect the performance of packed-bed Thermal Energy Storage and vice versa. In this study, packed-bed rock Thermal Energy Storage units operating at different air pressures integrated into a large-scale, four-stage Adiabatic Compressed Air Energy Storage system were examined over a single charging and discharging cycle and over five consecutive cycles. A transient, two-dimensional axisymmetric numerical model of packed-bed rock Thermal Energy Storage was developed, and the charging, standby, and discharging phases were simulated. The results show that all packed-bed Thermal Energy Storage systems of the same size can effectively store and release thermal energy at various pressures. The thermal energy from the compressed air is absorbed and stored with no significant change in the outlet air temperature at around 298 K during the first two hours of charging. A slight rise in the outlet air temperature was observed at the end of the three-hour charging stage. Results also indicate that during five consecutive cycles in the Thermal Energy Storage unit at the lowest pressure, the amount of energy stored at the end of the three-hour charging stage increases slightly from 118.7 kJ/kg in the first cycle to 127.5 kJ/kg in the second cycle, 129.7 kJ/kg in the third cycle, 130.3 kJ/kg in the fourth cycle, and 130.5 kJ/kg in the fifth cycle.

ABSTRACT A combined heat store and heat exchanger unit (HSX) intended for compressed air energy storage (CAES) is presented. The unit is directly charged by the pressurised air emerging from the compression train, which removes the need for a secondary low‐pressure air circuit. Salt is used as the thermal storage medium due to its good heat capacity, thermal conductivity, and its ability to accommodate the thermal expansion of the stainless‐steel pipes. This paper uses a CAES system (15 MW, 12‐h discharge) driven by an offshore wind turbine as a case study. There are not many commercial CAES systems in operation; however, the levelized cost of the heat storage subsystem of a CAES system (i.e. heat store, set of heat exchangers and ancillary low‐pressure circuit) ranges between 45 and 48 £/MWh. Findings show that the most cost‐effective design for a HSX has a capital cost of ∼£55k. This translates into a levelized cost of storage of ∼31.5 £/MWh. The roundtrip exergy efficiency of this design is 93.7 %. This accounts for heat‐exergy and pressure‐exergy losses; losses to ambient are not considered. A HSX unit can considerably reduce the overall cost of a CAES system.

This study addresses a critical economic aspect in compressed air energy storage that has not been discussed much in existing literature: the impact of operating pressure on machinery capital cots. It aims to answer whether the cost per unit of power for power conversion systems changes with the maximum storage pressure. Considering that higher storage pressures are associated with greater energy density, enhanced energy storage capabilities and improved system efficiency. This paper helps clarify uncertainties in initial cost estimations for power-generation plants. Effects of operating pressure on the components and overall sizes and consequently costs of power conversion machinery are individually investigated in two parts. Part I encompasses the compressor and expanders, and part II comprehensively discusses the effects of the operating pressure on the costs of heat exchangers. The analysis employs a conceptual engineering approach, revealing that higher intake pressure reduces overall compressor/expander size, leading to cost savings. Additionally, increasing the number of compression stages for higher storage pressures enhances exergy storage cost-effectiveness. To establish an advanced adiabatic CAES plant with a storage pressure of 200 bar instead of 50 bar, there is potential for a 6 % reduction in $/kW expenditure.

The distribution of current/voltage can be further regulated by optimising the electrical connection topology, considering a particular battery thermal management systems. This study numerically investigates a 4P6S battery module with two connection topologies: 1) a straight connection topology, where the sub-modules consist of parallel-connected cells that are serial connected in a linear configuration, and 2) a parallelogram connection topology, where the sub-modules are serial connected in a parallelogram configuration. We find that the straight topology is more advantageous, as it allows the temperature gradient to be distributed among the parallel-connected cells in the sub-modules, mitigating over(dis)charging. Consequently, it achieves a 0.8% higher effective capacity than the parallelogram topology at 1C discharge, along with a higher state of health at 80.15% compared to 80% for the parallelogram topology. Notably, the straight topology results in a maximum current maldistribution of 0.24C at 1C discharge, which is considered an acceptable trade-off.

Medium and long-duration energy storage systems are expected to play a critical role in the transition towards electrical grids powered by renewable energy sources. ACAES is a promising solution, capable of handling power and energy ratings over hundreds of MW and MWh, respectively. One challenge with ACAES is achieving the required highly efficient operation in the compressor over the range of conditions encountered in the system as the pressure in the air store changes. In this paper, an application-oriented axial-flow compressor is designed, aiming towards efficient operation throughout the operation range, whilst also associating the performance prediction to a practical compressor geometry. A two-step design methodology based on inviscid, axisymmetric flow conditions has been implemented, leading to the flowtrack, blade-row geometries and the compressor performance map. The compressor model is integrated into an ACAES model, including two compression spools, two expansion stages with preheat, a constant volume high pressure storage operating between 5.5 and 7.7 MPa and two separate Thermal Energy Storage units. While the existing ACAES literature either ignores the transient off-design operation or uses generic numerical correlations (which are not associated to a particular geometry), the key novelty of this paper is the application of a detailed design method for turbomachinery to ACAES. The results indicate that the designed compressor requires 33 stages over the two spools, and is able to operate efficiently over the storage pressure range, showing that if the application-oriented design procedure is applied to the compressor, it does not stop ACAES reaching 70% round-trip efficiency, outputting 35MW for approximately 15 h. Importantly, the specific ACAES requirement of conserving heat at higher temperatures has been fulfilled by decreasing the number of intercoolers. Finally, it is recommended that a similar level of scrutiny is applied to the other components (i.e. expanders, heat exchangers and TES units), keeping in mind the unique set of operational requirements of ACAES. This work is an important step towards removing the common misconception that off-the-shelf components can be easily be used in typical ACAES designs.

Localised data aggregation in many countries including Great Britain (GB) is typically done to a geographical level with polygon boundaries that have a robust and trusted governance system in place. At a minimum this will mean there is confidence in a process to create a set of polygons that have unique identifiers coupled to geographical areas, and the ability to have these updated through a defined code of practice. Examples found across many countries are in the delivery of post, such as postcodes and zip codes, and of the definition of census areas and municipal boundaries. The confidence in these boundaries allows different data to be aggregated by third parties, which itself provides greater levels of data over comparable geographical areas to enhance wider analysis and decision making. Here we combine publicly available datasets published from the six regional electricity Distribution Network Operators of GB to produce a new geospatial dataset with 4436 unique polygons defining the areas served by electrical primary substations. An example is also presented of the use of these polygons to link postcode level open government datasets on domestic energy consumption (2015-2020) from the Department of Energy Security and Net Zero (DESNZ). This results in another dataset with energy statistics aggregated to the geographical areas served by each primary substation across Great Britain. Therefore, we believe there is a compelling argument for countries to set up processes to create and update polygons that have a meaningful relationship to energy systems. This would allow more accurate energy systems analysis to be performed, ultimately leading to an accelerated or potentially lower cost transition to a net-zero world.

In the field of compressed air energy storage, a critical economic aspect that has been overlooked in existing literature relates to the influence of storage pressure on the capital cost of power conversion system. In Part I, a comprehensive study was conducted to address this question focusing on compressors and expanders. This part is devoted to the heat exchangers and basically assesses the engineering rationale behind the relationship between the cost per kW for HXs and operating pressure. Based on the performed analysis, the operating pressure of a HX impacts two crucial cost-related factors: the heat transfer area and required tube thicknesses. Higher operating pressures are associated with the smaller heat transfer area tending to lower costs, but increasing pressure raises tube thickness requirements, tending to increase costs. Below approximately 200 bar, the former effect prevails over the latter, leading to cost reductions with rising pressure. Conversely, at higher pressures, the latter effect outweighs the former, resulting in cost increases with increasing pressure. On the other hand, as the number of compression stages is increased to attain higher storage pressures, there is a noteworthy variation in the cost contribution of HXs. Specifically, the contribution of HX costs within the PCS machinery escalates from 10 % at a storage pressure of 30 bar to approximately 35% at a storage pressure of 350bar. This cost increase is accompanied by a substantial reduction in costs associated with other PCS machinery components (compressors and expanders), ultimately justifying the advantages of operating at higher storage pressures.

The modification of the geometric configurations of heat transfer pipes in shell and tube Latent Heat Thermal Energy Storage (LHTES) systems not only enhances the melting process of the phase change material (PCM) but also improves the overall performance of these systems. This study aims to investigate ways to enhance the performance of LHTES systems by employing heat transfer pipes with various fin and twisted tape arrangements in a horizontal orientation. The Finite Volume Method and Enthalpy-Porosity method are employed to simulate the melting process. Stearic acid is used as the PCM material, while water serves as the heat transfer fluid. Eight different geometric configurations are modelled in the LHTES system: base case, horizontal fins, vertical fins, helical fins, horizontal tape, vertical tape, twisted tape and helical fins with twisted tape. The results show that within the time range of 0 and 29 min, the combined configuration of helical fins with twisted tape consistently demonstrates the fastest melting process. After 29 min, the configuration with vertical fins exhibits a marginally faster melting process than the combined configuration of helical fins with twisted tape. The configurations involving tapes also contribute to accelerated melting, although to a lesser extent than those with fins. Particularly, twisted tape proves highly effective in facilitating faster melting. The complete melting process times for configurations with vertical fins, helical fins, and combined helical fins with twisted tape are 38.7 %, 23.5 % and 32.7 % faster compared to the base case which is similar to 69 min. Among the configurations, using tapes results in higher flow resistance and surface area compared to the base case. The attractive features of these configurations make them ideal for creating efficient and space-saving energy storage systems. This study provides crucial insights into essential heat and mass transfer processes, which can be leveraged to develop advanced LHTES systems for enhanced performance and sustainable energy solutions.

Solar PV and battery energy storage (BES) costs for domestic consumers are constantly diminishing. On top of this, the end of the Feed-in-Tariff programme has significantly increased interest in combined PV + BES systems. In this paper, we explore the economics of domestic PV + BES systems, extending the current literature on the topic via the use of a large smart meter dataset and a demographic comparison. Predictably, time-varying tariffs and higher electricity prices generally strengthen the economic arguments for PV + BES systems, however our consumption data yields a wide range of Net Present Values for different consumers. In terms of demographics, we find that batteries are more favourable for more affluent households due to their larger consumption levels, though profitability becomes more uniform if the batteries are tailored to individual households. This is an important point for policy, since it indicates that if PV + BES systems become widely profitable this is unlikely to help the financial situation of lower-income households.

The primary challenge with big smart meter data is to gain actionable insights which ultimately enhance the sustainability of the power network. In this paper, we analyze 1 year of smart meter data from 326 households in Austin, TX. Via clustering, we find distinct daily usage patterns, illustrating heterogeneous consumption behavior which changes throughout the year. However, most consumers have at least 56% of days explained by 3 out of 24 identified load types. For each load type we estimate the value of PV and batteries under flat rate and Time-Of-Use electricity tariffs. We find that knowledge of a consumers most common load shapes can significantly improve estimates for PV viability compared to a control estimate based on population-wide averages. However, knowledge of the most common load shapes does not improve estimates of battery viability unless electricity prices are time-varying. This highlights that, in general, the information contained by load shape clusters is of high value when consumers face economic choices that depend on the timing of their consumption. This work builds on current knowledge by explicitly linking smart meter segmentation techniques to individual consumer suitability for different distributed energy technologies.

Recent theoretical studies have predicted that adiabatic compressed air energy storage (ACAES) can be an effective energy storage option in the future. However, major experimental projects and commercial ventures have so far failed to yield any viable prototypes. Here we explore the underlying reasons behind this failure. By developing an analytical idealized model of a typical ACAES design, we derive a design-dependent efficiency limit for a system with hypothetical, perfect components. This previously overlooked limit, equal to 93.6% under continuous cycling for a typical design, arises from irreversibility associated with the transient pressure in the system. Although the exact value is design dependent, the methodology we present for finding the limit is applicable for a wide range of designs. Turning to real systems, the limit alone does not fully explain the failure of practical ACAES research. However, reviewing the available evidence alongside our analytical model, we reason that underestimation of the system complexity, difficulty with the integration of off-the-shelf components, and a number of misleading performance claims are the primary reasons hindering ACAES development.

Graphical AbstractView Large Image Figure ViewerDownload Hi-res image Download (PPT)For decades, technical literature has appraised adiabatic compressed air energy storage (ACAES) as a potential long-duration energy storage solution. However, it has not reached the expected performance indicators and widespread implementation. Here, we reflect on the design requirements and specific challenges for each ACAES component. We use evidence from recent numerical, theoretical, and experimental studies to define the technology-readiness level (TRL). Lastly, we discuss promising new directions for future technology development. For decades, technical literature has appraised adiabatic compressed air energy storage (ACAES) as a potential long-duration energy storage solution. However, it has not reached the expected performance indicators and widespread implementation. Here, we reflect on the design requirements and specific challenges for each ACAES component. We use evidence from recent numerical, theoretical, and experimental studies to define the technology-readiness level (TRL). Lastly, we discuss promising new directions for future technology development.

While energy storage is key to increasing the penetration of variable renewables, the near-term effects of storage on greenhouse gas emissions are uncertain. Several studies have shown that storage operation can increase emissions even if the storage has 100% turnaround efficiency. Furthermore, previous studies have relied on national-level data and given very little attention to the impacts of storage on emissions at local scales. This is an important omission, as carbon intensities can vary very significantly at sub-national scales. We introduce a novel approach to calculating regional marginal emissions factors, based on a validated power system model and regression analysis. The techniques are used to investigate the impacts of storage operation on CO2 emissions in Great Britain in 2019, under a range of operating scenarios. It is found that there are significant regional differences in storage emissions factors, with storage tending to increase emissions when used for wind balancing in areas with little wind curtailment. In contrast, the greatest emissions reductions are achieved when charging storage with otherwise-curtailed renewables and discharging to reduce peak demands in areas consuming high volumes of fossil fuel power. Over all regions and operating modes studied, the difference between the highest reduction in emissions and the highest increase in emissions is considerable, at 741 gCO(2) per kWh discharged. We conclude that power system regulators should pay increased attention to the impact of storage operation on system CO2 emissions.

Energy storage is a key solution to supply renewable electricity on demand and in particular batteries are becoming attractive for consumers who install PV panels. In order to minimize their electricity bill and keep the grid stable, batteries can combine applications. The daily match between PV supply and the electricity load profile is often considered as a determinant for the attractiveness of residential PV-coupled battery systems, however, the previous literature has so far mainly focused on the annual energy balance. In this paper, we analyze the techno-economic impact of adding a battery system to a new PV system that would otherwise be installed on its own, for different residential electricity load profiles in Geneva (Switzerland) and Austin (U.S.) using lithium-ion batteries performing various consumer applications, namely PV self-consumption, demand load-shifting, avoidance of PV curtailment, and demand peak shaving, individually and jointly. We employ clustering of the household’s load profile (with 15-minute resolution) for households with low, medium, and high annual electricity consumption in the two locations using a 1:1:1 sizing ratio. Our results show that with this simple sizing rule-of-thumb, the shape of the load profile has a small impact on the net present value of batteries. Overall, our analysis suggests that the effect of the load profile is small and differs across locations, whereas the combination of applications significantly increases profitability while marginally decreasing the share of self-consumption. Moreover, without the combination of applications, batteries are far from being economically viable.

In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.

Interest in residential batteries to supply photovoltaic (PV) electricity on demand is increasing, however they are not profitable yet. Combining applications has been suggested as a way to increase their attractiveness, but the extent to which this can be achieved, as well as how the different value propositions may affect the optimal battery technology, remain unclear. In this study, we develop an open-source optimization framework to determine the best-suited battery technology depending on the size and the applications combined, including PV self-consumption, demand load-shifting, demand peak shaving and avoidance of PV curtailment. Moreover, we evaluate the impact of the annual demand and electricity prices by applying our method to representative dwellings in Geneva (Switzerland) and Austin (United States). Our results indicate that the combination of applications help batteries to become close to break-even by improving the net present value by up to 66% when compared with batteries performing PV self-consumption only. Interestingly, we find that the best-suited battery technology in Austin is lithium nickel cobalt aluminum oxide (NCA) as for Geneva lithium nickel manganese cobalt oxide (NMC) batteries reach in average a higher net present value than NCA-based batteries. However, NCA-based batteries could be a more promising alternative when applications are combined.

Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to -15% for residential buildings and by -4% to -21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types.