Techno-Economic Analysis of On-Site Energy Storage Units to Mitigate Wind Energy Curtailment: A Case Study in Scotland

Wind energy plays a major role in decarbonisation of the electricity sector and supports achieving net-zero greenhouse gas emissions. Over the last decade, the wind energy deployments have grown steadily, accounting for more than one fourth of the annual electricity generation in countries like the United Kingdom, Denmark, and Germany. However, as the share of wind energy increases, system operators face challenges in managing excessive wind generation due to its nondispatchable nature. Currently, the most common practice is wind energy curtailment in which wind farm operators receive constraint payments to reduce their renewable energy production. This practice not only leads to wastage of large volumes of renewable energy, but also the associated financial cost is reflected to rate payers in the form of increased electricity bills. On-site energy storage technologies come to the forefront as a technology option to minimise wind energy curtailment and to harness wind energy in a more efficient way. To that end, this paper, first, systematically evaluates different energy storage options for wind energy farms. Second, a depth analysis of curtailment and constraint payments of major wind energy farms in Scotland are presented. Third, using actual wind and market datasets, a techno-economic analysis is conducted to examine the relationship between on-site energy storage size and the amount of curtailment. The results show that, similar to recent deployments, lithium-ion technology is best suited for on-site storage. As case studies, Whitelee and Gordon bush wind farms in Scotland are chosen. The most suitable storage capacities for 20 years payback period is calculated as follows: (i) the storage size for the Gordonbush wind farm is 100 MWh and almost 19% of total curtailment can be avoided and (ii) the storage size for the Whitlee farm is 125 MWh which can reduce the curtailment by 20.2%. The outcomes of this study will shed light into analysing curtailment reduction potential of future wind farms including floating islands, seaports, and other floating systems.

Potential benefits of real-time control to reduce urban flooding using distributed smart stormwater storage systems

<p>Stormwater infrastructure will require investments in the order of $100s of millions per local government area to maintain current levels of urban flood protection. This investment is likely to increase in the future as a result of the impact of climate change, population growth and increased urban densification. Traditional solutions aimed at increasing the capacity of stormwater systems have been directed towards pipe upgrades. An alternative approach is the use of smart storages, which have the following advantages:</p><ul><li>Extension of the lifespan of existing stormwater systems</li> <li>Provision of water supply</li> <li>Reduction in pollution levels in receiving waters.</li> </ul><p>The development of smart technologies enables the use of real-time control for increasing the effectiveness of storages. If forecasts of the timing and magnitude of impending rainfall events are available, storage outlet controls can be optimised to release stored water prior to and during the rainfall event to enable the peak flows to be reduced. In addition, by jointly controlling the outflows from multiple, distributed storages, rather than using a single storage or controlling multiple storages independently, coincident flood peaks from different sub-catchments can be minimised, further reducing peak flows at critical locations.</p><p>In this study, the potential benefits of real-time time control for distributed storages are compared with a system that uses storages without real-time controls. The impacts were assessed using a two-storage system, which is modelled using the software package SWMM with the real-time control schemes of the storages being optimised using a genetic algorithm. The case study was conducted for two storage sizes (2 and 10 m<sup>3</sup>) under a wide range of design rainfall conditions, with storm durations ranging from short (30mins) to long (24hrs), and annual exceedance probability ranging from frequent (50%AEP), to rare (1%AEP) for three different Australian climates (sub-tropical/Mediterranean). This results in a total of 75 different combinations. Results show there is a generic relationship between percentage peak flow reduction and the ratio of storage size to storm runoff volume irrespective of location and storm characteristics. The benefits of real-time control of smart storage systems identified were:</p><ul><li>Significant peak flow reductions ranging from 85% (for a larger storage size of 80% of storm volume) to 35% (for small storages size of 15% of runoff volume).</li> <li>Importantly, real-time control of storages significantly outperforms storages without real-time control, with additional peak flow reduction of between 35% to 50%.</li> </ul><p>These results highlight the potential for using distributed storages for mitigating urban flooding, even for extreme events. The potential benefits of smart storages in more realistic cases studies (uncertain rainfall forecasts and larger scales) are also discussed.</p>

Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor

Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms of the past are incapable of obtaining a correct sorted sequence because data to be sorted cannot be totally stored in the data storage. This paper proposes a new sorting algorithm called streaming data sort for streaming data on a uniprocessor constrained by a limited storage size and the correctness of the sorted order. Data continuously flow into the storage as consecutive chunks with chunk sizes less than the storage size. A theoretical analysis of the space bound and the time complexity is provided. The sorting time complexity is O (n), where n is the number of incoming data. The space complexity is O (M), where M is the storage size. The experimental results show that streaming data sort can handle a million permuted data by using a storage whose size is set as low as 35% of the data size. This proposed concept can be practically applied to various applications in different fields where the data always overflow the working storage and sorting process is needed.

Content-Based Transaction Access From Distributed Ledger of Blockchain Using Average Hash Technique

There are many critical applications working with blockchain-based technology including the financial sector, healthcare, and supply chain management. The fundamental application of blockchain is Bitcoin, which was primarily designed for the financial value transfer. Owing to the feature of decentralized storage structure, immutability, integrity, availability, and reliability of transactions, the blockchain has become the need of the current industry like VANET. However, presently, not much work has been done in order to mitigate the redundancy in the distributed ledger. Hence, the authors arrive at the intelligible conclusion to detect a similar transaction that can mitigate the redundancy of transaction in a distributed ledger. In this chapter, they are addressing two main challenges in blockchain technology: firstly, how to minimize the storage size of blockchain distributed ledger and, secondly, detecting the similar transaction in the distributed ledger to mitigate the redundancy. To detect similar transaction from the distributed ledger they have applied the average hash technique.

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.