In this paper, a framework is proposed for integration of peer-to-peer (P2P) trading-based local energy market (LEM) with the blockchain technology. The proposed LEM model allows prosumers and consumers to trade electricity among each other ensuring the presence of the retailer and network utility – who are also essential parts of a P2P network. The P2P contracts settled between various prosumers and consumers are governed by mutually agreed upon smart contracts – which are then written in an Ethereum blockchain to record and store bidding history, P2P transactions, and settlements. An effective formulation is also presented to capture P2P trading quantities and prices among participating prosumers and consumers in a decentralised fashion with an appropriate analysis of financial viability. Finally, a case study is conducted in a real Australian context; in which the engagement of both prosumers and consumers are taken into account, and the performance of the proposed blockchain-enabled LEM is compared with business-as-usual (BAU) to demonstrate the model's superiority.
Cloud computing is already a part of the daily lives of both people and companies and as such, there is a concern about how it impacts the environment. The union of these two realities provides an opportunity for the emergence of Green Cloud Computing with new proposals, approaches, and metrics to make data centers more efficient, mainly in terms of energy, as well as to reduce CO2 emissions and its environmental impact. Knowing the metrics that can be used to measure the energy cost and environmental impact of data centers are fundamental in Green Computing, this article studies the relationship between energy consumption and execution times, architecture, and costs of a cloud environment. Using simulators it was possible to see an improvement in energy efficiency above 44%, as well as to get a cost reduction of at least 17%, and a reduction of of 55% in the environmental impact from the emission of CO2. It is important to note that there was no impact on processing times, by using the adoption of algorithms for complex problems, in the scheduling of Virtual Machines.
Halted business from grid outages leads to financial damage suffered by the business. Photovoltaic and battery energy storage system paired microgrids are presenting an attractive opportunity for investors, by enabling them to generate and store their own energy and reducing their electricity cost for operations. A microgrid can, during grid outage, operate in islanded mode in which the loads at the microgrid remain energized by the BESS locally. In this paper models for the energy and financial transactions between microgrid and grid are formulated. A simulation is programmed from these models in which multiple grid reliability and microgrid configurations are simulated. The capability to island along with battery arbitrage, outages, and cost of electricity transactions are captured in a case study. It is shown that as grid reliability decreases, through increase in SAIFI, the optimal BESS size also increases. Finally, the lowest cost for electricity is found with small-storage + large-capacity PV systems because systems in this range show higher resilience against grid outages and benefit from lower component costs.
The adoption of blockchain in various industries is gaining more popularity, especially in the energy industry. With the increase of distributed energy resources (DER), energy users can generate, store, and trade their resources with others. Utility companies or energy users are influenced by blockchain-based peer-to-peer (P2P) energy trading markets. Blockchain adds transparency and immutability to the involved transactions. Smart contracts in blockchain automatically execute when the conditions are met without any third-party intervention. Motivated by these benefits, in this paper an energy trading framework is developed using Ethereum smart contracts. Energy users can trade their excess energy or buy energy using the smart contract functions. Smart contract written in solidity is compiled and deployed using remix with injected metamask provider. Ganache is used to create accounts and these accounts are imported to metamask for signing transactions. We also discuss alternative methods for smart contract deployment. Computational cost analysis is performed by evaluating the gas consumption analysis for the smart contract functions.
Switch mode power supplies present light weight power conversion solutions, but degradation affecting sub-components have been known to transition the converter to move to unstable dynamic operations. This paper presents a method to build interpretive prognostics for switch mode power supplies with electromagnetic input filters by modeling sub-component degradation trajectories and using discrete event simulation to generate lifecycle data associated with the system impedances to use as inputs into machine learning based prognostics to make interpretable remaining useful life predictions. As a usage case, a buck-boost switch mode power supply with parasitic elements for all components and time-dependent degradation concerning the input and output filtering capacitor is analyzed.
