This paper explores whether negative electricity prices can change the rationale that efficient energy storage devices are more economical for arbitrage in electricity markets. An established model algorithm to determine the maximum available arbitrage revenue and optimum schedule of electrical energy storage (EES) operation is used to simulate storage with a time-series of electricity prices which includes some negative prices. Our results suggest that at any likely frequency of negative electricity prices, inefficient EES is not encouraged, and can only be encouraged for EES devices with very low energy capacity to power ratios.
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.
<p>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.</p>
Abstract 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.
This article explores the possibility of coupling a tidal current energy converter (TCEC) with an energy storage system. The purpose of this study is two-fold: first, to show that storage can decrease the loss of output from a TCEC, when there are transmission constraints present. Second, to specify the properties of the storage system (efficiency, capacity, input/output power limit, and self-discharge rate) required in order to produce either demand-matching or base-load output from a TCEC. Models of such systems are constructed. These are run over several spring/neap cycles, to determine the time dependence of the whole system. It is shown that a 1.2 MW tidal current energy converter associated with a 1-MWh storage system of modest efficiency can offer significant advantages over the generator working alone.
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.
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