In an integrated energy distribution system (IEDS), an energy hub has been introduced and deemed to be a suitable tool for managing and integrating multi-party energy forms. Due to different energies having diverse characteristics and being coupled with each other, it is difficult for an energy hub to implement the optimal scheduling of multiple energy sources. Therefore, an energy optimization management model is proposed based on the Stackelberg game, which considers the exergy conversion of multi-party energy sources in different operation modes. The problem is solved by the two-layer distributed optimization algorithm, in which the energy hub acts as the leader and is followed by the users. Furthermore, in order to alleviate the deception, malicious tampering, subpeption, and other secure risks in energy trading, blockchain is introduced into the energy hub and the concept of exergy coin (EC) is proposed. A credit-based blockchain framework and concurrent block building consensus process is explored to reduce the calculation cost and promote the exergy trading efficiency. Finally, the case study shows how the proposed method can effectively optimize energy scheduling and configure a more reasonable energy solution.
Blockchain has the potential to accelerate the deployment of emissions trading systems (ETS) worldwide and improve upon the efficiency of existing systems. In this paper, we present a model for a permissioned blockchain implementation based on the successful European Union (EU) ETS and discuss its potential advantages over existing technology. We propose an ETS model that is both backwards compatible and future-proof, characterised by interconnectedness, transparency, tamper-resistance and high liquidity. Further, we identify key challenges to implementation of a blockchain ETS, as well as areas of future work required to enable a fully-decentralised blockchain ETS.
J. S. Sudarsan, W. Jyesta, Dr Manish Kumar Jaisal, Pola Vamshi · 5 authors
The different types of human exercises particularly those recognized with development, signifies outflow of ozone depleting substances, greenhouse emissions that outcome into an Earth-wide temperature boost and environmental change. This affects nature and leads to imbalance in the environment and ecosystem. Henceforth it has turned out to be important to reduce dangerous atmospheric changes by some sustainable mitigation processes. In all the greenhouse gases, CO2 is the main compound that adds to a worldwide temperature alteration. Thus it is fundamental responsibility for every person to control the carbon outflow by understanding the concept of sustainable development. Carbon footprint estimation for an institutional and residential building was done separately for each component of structure or as a whole organization. The reason for this investigation includes distinguishing proof and examination of different emission inventories and the measure of carbon transmitted by them regarding an institutional structure and the residential building materials also assessed. Furthermore this study examines the current methodologies and approaches to estimate carbon footprint in an institutional building and residential building in Karnataka. To ascertain the status of carbon emission in the residential building materials a study was carried out in a proposed residential structure. It was concluded that the existing institutional building is carbon positive and also the amount of carbon emission by building materials used in the residential building also contribute to carbon footprint results to carbon positive. So as to lessen the emission and to convert the institutional structure and the residential building materials from carbon positive to carbon negative, general mitigation measures are recommended and by implementing the same how much carbon emissions also reduced was discussed .This research study also helps in creating awareness about the impact of amount carbon emissions happening in an institutional building due to different activities among the students, faculties and other public/Stake holders.
We explore the impact of investors’ beliefs on cryptocurrency demand and prices using three new individual-level surveys. We find that younger individuals with lower income and education are more optimistic about the future value of cryptocurrencies, as are late investors. We then estimate the cryptocurrency demand functions using a structural model with rich heterogeneity in investors’ beliefs and preferences. To identify the model, we combine observable beliefs with an instrumental variable strategy that exploits variation in the amount of energy required for the production of the different cryptocurrencies. We find that beliefs explain a large fraction of the cross-sectional variance of returns. A counterfactual exercise shows that banning entry of late investors leads to a decrease in the price of Bitcoin by about $3,500, or approximately 30% of the price during the boom in January 2018. Late investors’ optimism alone can explain about a third of the decline.
Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome.
This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.
This paper evinces the ability of gold to avoid risks during periods with great fluctuations in the Bitcoin market. We apply bootstrap full- and subsample rolling-window Granger causality tests to explore the causal relationship between Bitcoin price (BCP) and gold price (GP). The empirical results show that an increase in BCP can cause GP to decrease, indicating that the prosperity of the Bitcoin market undermines the hedging ability of gold. However, a decrease in BCP causes GP to increase, and it also emphasizes that the ability of gold to avoid risks persists. Hence, the status of gold will not be completely threatened by Bitcoin, and they are complementary to each other instead of in competition. In turn, both positive and negative influences of GP on BCP suggest that fluctuations in BCP can be predicted through the gold market. In situations of severe global uncertainty and complicated investment environments, investors can benefit from complementary markets to optimize their asset allocation. Additionally, countries can grasp the trends in Bitcoin and gold prices to prevent large fluctuations in both markets and to reduce the uncertainty of the financial system.
Bu çalışma, Bitcoin’in fiyat davranışını otoregresif birim kökü olan iki rejimli bir TAR modeli kullanarak araştırmaktadır. Çalışmada, durağan dışılığı ve doğrusal olmamayı eş zamanlı olarak sınayan Caner ve Hansen (2001) tarafından geliştirilen yöntem kullanılmıştır. Bu amaçla, 16.07.2010 – 27.11.2018 dönemi için (3.056 adet günlük gözlem) Bitcoin kapanış fiyatlarına ait veri seti oluşturularak Bitcoin fiyatlarının etkin olup olmadığı incelenmiştir. Elde edilen bulgular, Bitcoin fiyatlarının tüm dönem dikkate alındığında zayıf formda etkin piyasalar hipotezini desteklemektedir. Ancak rejimler arası geçiş dikkate alındığında Bitcoin fiyat serisinde iki rejim olduğu sonucuna ulaşılmıştır. Birinci rejimde zayıf forma etkin piyasalar hipotezinin geçerli olduğu, ancak ikinci rejimde geçerli olmadığı tespit edilmiştir.
The stability and economy of the electronic vehicle distribution network system is increasingly important as the number of electric vehicles in use continues to rise. An electric vehicle (EV) and Internet of things (IoT) charge scheduling method is proposed in this paper which uses smart contract in the distribution network (DN) with uncertain renewable energy output. Based on user charging demand and power grid load level, this paper explores peak load shifting, guiding EV charging options by electricity price to change the demand response of each node, thereby regulating the DN power quality. A smart contract is created between the user and the charging station to realize the electricity price renewal in the power flow calculation cycle. This enhances the rationality of electricity price formulation and reduces deviation between the forecast load and the actual load, ensuring the validity of the method to a certain extent. According to the achievement of the smart contracts signed with the charging station, users are given rewards or fines, which reduces the default rate of the user. This decentralized transaction process improves the security and completeness of the transaction. The feasibility of utilizing this method for the distributed power grid is verified through simulation on a 34-node test system.
The study investigates the bitcoin-altcoin price synchronization hypothesis using cointegrating test and VEC Granger Causality/Block Exogeneity Test approaches on the daily data of bitcoin and ten selected alternative coins (altcoins) between August 8, 2015 and December 31, 2018. The data is structurally divided into three distinct periods which are; August 8, 2015 to December 31, 2016; January 1, 2017 to December 31, 2017 and January 1, 2018 to December 31, 2018. The study establishes pure price separation between the altcoin and bitcoin in 2015-2016, price synchronization between bitcoin and each of the selected altcoin in 2017 and dominant altcoin-to-bitcoin price formation in 2018. The study concludes that cryptocurrency buyers are more sensitive in 2018 to the features and quality of project each coin promotes, unlike the indiscriminating choices which dominated cryptocurrency world during the 2017 boom.
The ongoing, in-depth transformation of the electricity sector towards increased use of alternative, renewable energy sources extends beyond a simple decentralisation drive in the electricity market. The transformation process is characterised by the interplay of old and new technologies from the energy sector as well as structural coupling with other sectors, such as the information and communications technology (ICT), enabling the technology transfer as well as market entry by information technology (IT) actors. Blockchain-based technologies have the potential to play a key role in this transition by offering decentralised interfaces and systems as well as an alternative approach to the current organisation form of the energy market. This paper discusses the applicability and prospects for blockchain-based technologies in the energy sector, which are described using the term “blockchain energy”. For the purposes of this study, blockchain energy encompasses all socio-technical and organisational configurations in the energy sector based on the utilisation of the blockchain principle for energy trading, information storage, and/or increased transparency of energy flows and energy services. In the following chapters, the authors present and discuss the current transformation in the electricity market, followed by a review of the different utilisation possibilities for blockchain technologies in the energy sector and a discussion of the barriers and potential for blockchain energy using a transition studies perspective. Finally, the opportunities and risks of blockchain energy are discussed.
High Resolution Image Download MS PowerPoint Slide This study estimates the environmental impact of mining Bitcoin, the most well-known blockchain-based cryptocurrency, and contributes to the discussion on the technology’s supposedly large energy consumption and carbon footprint. The lack of a robust methodological framework and of accurate data on key factors determining Bitcoin’s impact have so far been the main obstacles in such an assessment. This study applied the well-established Life Cycle Assessment methodology to an in-depth analysis of drivers of past and future environmental impacts of the Bitcoin mining network. It was found that, in 2018, the Bitcoin network consumed 31.29 TWh with a carbon footprint of 17.29 MtCO 2 -eq, an estimate that is in the lower end of the range of results from previous studies. The main drivers of such impact were found to be the geographical distribution of miners and the efficiency of the mining equipment. In contrast to previous studies, it was found that the service life, production, and end-of-life of such equipment had only a minor contribution to the total impact, and that while the overall hashrate is expected to increase, the energy consumption and environmental footprint per TH mined is expected to decrease.
Yasin Akın, Caner Dikkollu, Bekir Baran Kaplan, Uğur Yayan · 5 authors
The following topics are dealt with: learning (artificial intelligence); data mining; pattern classification; feature extraction; social networking (online); neural nets; security of data; convolutional neural nets; Big Data; evolutionary computation.
Eric Masanet, Arman Shehabi, Nuoa Lei, Harald Vranken · 6 authors
Bitcoin mining is becoming an increasingly energy-intensive process whose future implications for energy use and CO2 emissions remain poorly understood. This is in part because—like many IT systems—its computational efficiencies and service demands have been evolving rapidly. Therefore, scenario analyses that explore these implications can fill pressing knowledge gaps, but they must be approached with care. History has shown that poorly constructed scenarios of future IT energy use—often due to overly-simplistic extrapolations of early rapid growth trends—can do more harm than good by spreading misinformation and driving ill-informed decisions. Indeed, the utility of an energy demand scenario is directly proportional to its credibility, which is typically demonstrated through careful attention to technology characteristics and evolution, analytical rigor and transparency, and designing scenarios that align with plausible future outcomes.