This paper introduces a new contract-theoretic framework to tackle this challenge by designing optimal contracts for ESs. A dynamic pricing scheme is developed that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in local electricity market

In this article, a new method is proposed to identify the health condition of composite insulators through investigating the leakage current (LC) along with the electric potential (EP) and electric field stress (EFS) profiles. In this regard, variations of the LC harmonics and the distribution profiles of the EP and EFS are investigated through a series of experimental and simulation analyses conducted on composite insulators under various environmental and pollution conditions. Fast Fourier transform (FFT) and a COMSOL-multiphysics environment are utilized to analyze this variation. The results reveal a strong correlation between the LC harmonics and the insulator health condition. A new diagnostic criticality index based on the third to the fifth harmonic ratios of the insulator LC is proposed to quantify the composite insulator criticality and predict the likelihood of flashover occurrence. The EP and EFS analyses can be used to provide a complementary diagnosis of the insulator's health state under severe pollution and humidity conditions. The proposed analysis enables the transmission line network operator to get an insight into the insulators' functional status, thus improving the network reliability through avoiding insulator failure and adopting a proper condition-based maintenance scheme.

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies.

Mining sectors require a continuous and reliable power supply; however, reliance on traditional grid utilities results in high costs and disruptions and increases extreme carbon emission. The Merredin WA sector seeks to resolve critical energy challenges affecting mining operations in Western Australia. Thus, this research proposes an optimal solar PV system with battery storage and backup generation for the mining sector to ensure a stable and cost-effective power supply that reduces harmful environmental effect. A hybrid data-driven long short-term memory (LSTM)-classical optimization framework is designed here, thereby optimizing PV-battery storage operational cost savings and energy usage. The optimization results indicate that approximately 57% of load demand can be fulfilled by the proposed optimal PV system with future cost savings of USD $8627.53 per annum. The optimization method also resulted in the lowest computation time of 1.153 s and highest accuracy 99.247% when compared with other existing algorithms. Furthermore, the integration of renewable energy (RE) technologies within mining operations substantially reduces carbon emissions by 67%, thus contributing to broader global sustainability purposes. The study presents a sustainable and economically viable energy solution for mining operations, setting a precedent for RE adoption in remote and energy-intensive industries.