When an author under the pseudonym Satoshi Nakamoto published the paper "Bitcoin: A Peer-to-Peer Electronic Cash System" in 2008, the first cryptocurrency using the new blockchain technology was introduced. Over the last decade, more than 1,000 different cryptocurrencies, such as Ethereum, Ripple, and Litecoin were developed and Bitcoin's currency had almost reached an equivalent value of 20,000 $/BTC. After recognizing the disrupting momentum that the blockchain technology generated, scientists started to develop blockchain use cases for the energy sector. However, the scientific literature so far offers only rough and incomplete estimations when questions about the current and future energy consumption of the Bitcoin network are raised. This paper introduces a new scenario model to estimate the mining power demand of the Bitcoin and Ethereum network. Six scenarios are developed on the basis of mining hardware efficiency and network parameter data. The results show that an increase of the mining hardware efficiency will only have a limited impact on the overall power demand of blockchain networks. Furthermore, the current power demand of the Ethereum network is in the range from 0.6 to 3 GW and therefore, is similar to the one of Bitcoin. In case of linear growth of the block difficulty and sigmoidal increase of the hardware efficiency until the year of 2025, the mining power demand for the Bitcoin blockchain will be approximately 8 GW. Furthermore, the model and the scenarios are adaptable to other cryptocurrencies that use the proof-of-work consensus algorithm to create scenarios for their future power demand.
The continuous decentralisation of the energy system due to the expansion of renewable energies requires new coordination mechanisms such as Local Energy Markets (LEMs) that are capable of integrating millions of prosumers as active participants. Since the end of the 2010s, the blockchain technology has been discussed as a potential infrastructure for LEMs and as a potential game-changer in the energy industry. In this work, the authors introduce LEM specific technology-independent infrastructure requirements, present a Solidity and Python toolbox that allows to compute a comparative performance analysis between a blockchain-based and a central LEM and evaluate the added value of a blockchain-based implementation compared to a conventional reference implementation. Simulations of a LEM with a periodic double auction and settlement showed that a blockchain-based LEM operation requires more than 140 times the computation time compared to a centralised implementation and cannot fulfil data security requirements. Thus, the authors find that blockchain technology in its current state of development does not add significant value to LEMs. All implemented programmes are published in the open-source project lemlab as part of the research project RegHEE.
The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs.
The impact of renewable energies on the power grid is continuously increasing. Besides the emission-free power generation, the renewable energies often are the cause for congested grids, component failure and costly interventions by the distribution system operators (DSO) and transmission system operators (TSO) in order to maintain grid stability. The scientific community discusses in recent years the usability of distributed energy resources (DER) as flexible devices. However, no approach can be found that actually quantifies the potential flexibility and sets a price to it. The model presented in this paper optimizes the charging operation of an electric vehicle (EV) according to a price signal with a state of the art exhaustive search algorithm. Furthermore, this model offers all possible deviations from the optimal operation as flexibility to a corresponding market platform and sets a price to each offer, which is dependent on the future price level of the energy. With this model, it is possible to offer positive and negative prices for flexibility. The proposed model shows that an exhaustive enumeration algorithm is feasible to calculate flexibility offers, prices and applicable on currently discussed platform models. The example of an EV charging schedule is successfully modelled and described in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.