In the 21st century, transitioning to renewable energy sources is imperative, with fossil fuel reserves depleting rapidly and recognizing critical environmental issues such as climate change, air ...pollution, water pollution, and habitat destruction. Embracing renewable energy is not only an environmental necessity but also a strategic move with multiple benefits. By shifting to renewable energy sources and supporting their production through the acquisition of renewable energy certificates, we foster innovation and drive economic growth in the renewable energy sector. This, in turn, reduces greenhouse gas emissions, aligning with global efforts to mitigate climate change. Additionally, renewable energy certificates ensure compliance with regulations that mandate the use of renewable energy, enhancing legal adherence while promoting transparency and trust in energy sourcing. To monitor the uptake of renewable energy, governments have implemented Renewable Energy Certificates (RECs) as a tracking mechanism for the production and consumption of renewable energy. They certify the generation of a specific amount of electricity from renewable sources, allowing for accurate tracking of renewable energy contributions to the overall energy mix. However, there are two main challenges to the existing REC schema: (1) The RECs have not been globally adopted due to inconsistent design; (2) The consumer privacy has not been well incorporated in the design of blockchain. In this study, we investigate the trading of RECs between suppliers and consumers using the directed acyclic graph (DAG) blockchain system and introduce a trading schema to help protect consumer information. The DAG system reduces the intense calculation of typical blockchains for scalability and lowers mining fees by eliminating the mining of blocks. The proposed approach allows renewable energy suppliers and consumers to trade RECs globally and to take advantage of secure trading transactions. Our results demonstrate lower transaction time by 41% and energy consumption by 65% compared to proof-of-stake.
•Challenges and issues associated with Renewable Energy Certificate (REC) trading.•Privacy in Renewable Energy Certificate transactions and measures to ensure the security of personal data.•Insights into Renewable Energy Certificate trading, promoting wider adoption of renewable energy while emphasizing transparency in transaction.
•A novel distribution network planning method using the Monte Carlo tree search-based reinforcement learning is proposed.•The updating of the planning scheme is modeled as a Markov decision-making ...process instead of the optimization model.•A deep neural network is trained and applied to update the planning scheme to meet the desired performances.•Results show that the desired performances can be guaranteed at a lower investment cost compared to other cases.
Active distribution network planning is of importance for utility companies in terms of distributed generation investment, reliability assessment, optimal reactive power planning, substation evaluation, and feeder reconfiguration. However, it is challenging for current model-based optimization problems to guarantee the performances of active distribution network planning, due to an empirically pre-defined solution space. To overcome this issue, this paper proposes a performance-oriented method for the active distribution network planning. The solution space of the planning model is dynamically updated through using deep neural networks which are trained by the Monte Carlo tree search-based reinforcement learning until the desired performances are satisfied. Simulation results based on the standard IEEE 33-bus test system demonstrate that the proposed method can successfully improve the performances of the active distribution network planning to a desired level at a lower investment cost compared to other cases.
The emerging role of energy prosumers (both producers and consumers) enables a more flexible and localised structure of energy markets. However, it leads to challenges for the energy scheduling of ...individual prosumers in terms of identifying idiosyncratic pricing patterns, cost-effectively predicting power profiles, and scheduling various scales of generation and consumption sources. To overcome these three challenges, this study proposes a novel data-driven energy scheduling model for an individual prosumer. The pricing patterns of a prosumer are represented by three types of dynamic price elasticities, i.e., the price elasticities of the generation, consumption, and carbon emissions. To improve the computational efficiency and scalability, the heuristic algorithms used to solve the optimisation problems is replaced by the convolutional neural networks which map the pricing patterns to scheduling decisions of a prosumer. The variations of uncertainties caused by the intermittency of renewable energy sources, flexible demand, and dynamic prices are predicted by the developed real-time scenarios selection approach, in which each variation is defined as a scenario. Case studies under various IEEE test distribution systems and uncertain scenarios demonstrate the effectiveness of our proposed energy scheduling model in terms of predicting scheduling decisions in microseconds with high accuracy.
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•A novel data-driven prosumer-centric energy scheduling model to improve the computational efficiency and scalability.•Convolutional neural networks map pricing patterns to potential scheduling decisions.•Real-time scenarios selection to adapt uncertain scenarios with dynamic operations of prosumers.•Case studies validate the efficiency under various IEEE test distribution systems and uncertain scenarios.
Residential solar photovoltaic (PV) system installations are expected to continue increasing due to their growing cost competitiveness and supportive government policies. However, excessive ...installations of unknown behind-the-meter solar panels present a challenge for accurate load prediction and reliable operations of power networks. To address such growing concerns of distribution network operators (DNOs), this research proposes a novel model for distributed PV system capacity estimations. Innovative extracted features from 24-hour substation net load curves were fed into a deep neural network to estimate the PV capacity linked to the substation feeder. A comprehensive study into the sensitivity of the model’s accuracy to specific temporal scales of data collection, number of households served by a substation, and proportion of PV-equipped properties was conducted. This study revealed that a model developed to be used exclusively in summer achieved a 18.1% decrease in estimation root mean squared error (RMSE) compared to an all-year model, whilst using only a third of the training data amount. Similarly, compared to an all-year model, RMSE decreased by 26.9% when only data from Mondays to Thursdays were used to train and test the model. Also, for the all-year model, the most accurate estimations occur when 20% to 80% of households have PV systems installed and estimation percentage error tend to remain constant at around 10% when more than 20% of households have PV systems installed. A machine learning-ready dataset of substations with known PV capacity and experiment results are both useful to inform DNOs on the potential of the proposed method in reducing grid operation costs.
The non‐biodegradability of Ethylene‐Propylene Side‐by‐Side (ES) fibers has led to significant environmental pollution from waste sanitary products, thereby posing a severe challenge to the ...environment. Replacing traditional non‐biodegradable materials with biodegradable polymeric materials is the most effective method to achieve a green environment. In this study, core‐sheath fibers composed of biodegradable poly (butylene adipate‐co‐terephthalate) (PBAT) as the sheath layer and polylactic acid (PLA) as the core layer were fabricated. The effects of different viscosity ratios and the composite ratios of sheath and core on the structure and performance of the resultant core‐sheath fibers were investigated in detail. The results showed that when the PBAT/PLA composite ratio is 50:50 and the viscosity ratio range from 0.8 to 1.43, the PBAT/PLA core‐sheath composite fibers exhibit good spinnability and a complete core‐sheath structure, with their tensile properties comparable to those of PP/PE core‐sheath composite fibers. Further research found that when the viscosity ratio was 1.00 and the PLA component content in the PBAT/PLA composite fibers increased from 40% to 60%, the fibers still maintained good spinnability and a complete core‐sheath structure. In addition, when the PBAT/PLA composite ratio was 60:40, after 120 days of biodegradation, its strength retention rate was only 35.2%.
The influence of viscosity ratio and composite ratio on the spinnability of PBAT/PLA sheath‐core fibers.
Bisphenol A (BPA) is a ubiquitous endocrine-disrupting chemical in the environment that exerts potential harm to plants. Phytohormones play important roles both in regulating multiple aspects of ...plant growth and in plants’ responses to environmental stresses. But how BPA affects plant growth by regulating endogenous hormones remains poorly understood. Here, we found that treatment with 1.5 mg L
−1
BPA improved the growth of soybean seedlings, companied by increases in the contents of indole-3-acetic acid (IAA) and zeatin (ZT), and decreases in the ratios of abscisic acid (ABA)/IAA, ABA/gibberellic acid (GA), ABA/ZT, ethylene (ETH)/GA, ETH/IAA, and ETH/ZT. Treatment with higher concentrations of BPA (from 3 to 96 mg L
−1
) inhibited the growth of soybean seedlings, meanwhile, decreased the contents of IAA, GA, ZT, and ETH, and increased the content of ABA and the ratios of ABA/IAA, ABA/GA, ABA/ZT, ETH/GA, ETH/IAA, and ETH/ZT. The increases in the ratios of growth and stress hormones were correlated with the increase in the BPA content of the roots. Thus, BPA could affect plant growth through changing the levels of single endogenous hormone and the ratios of growth and stress hormones in the roots because of BPA absorption by the roots.
Wind turbines (WT) are built to a particular standard based on past or current climate conditions. With climate change, the frequency of exposure of a WT to extreme weather events (EWE) will be more ...extreme than those for which they were designed, which may lead to decreasing operational performance and physical damage to the turbine structure. Given this, there is a need to investigate the location of future planned offshore wind farms (OWF) to ensure resilience to future wind extremes. The research presented covers the UK exclusive economic zone (EEZ) and uses the 2. 2km UK Climate Projection 2018 (UKCP18) hourly maximum wind gust datasets. Two future scenarios corresponding to 2021-2040 and 2061-2080 are considered for future planning analysis. The statistical characterization of extreme wind gusts is assessed using multiple distributions, and a Beta distribution is found to adequately predict the hourly wind gust data. Changes in extreme wind loading on wind turbine structures (50-year return period, hereafter U_{50}) are investigated. Both risk ratio (RR) and relative change (RC) calculations have been used to set the recommended site from the achieved wind gust threshold corresponding to a U_{50}. As a result, regions in the North have a 99.6% increase in desired OWF siting locations in the 2061-2080 scenario. The East region has a 71.4% increase in the 2021-2040 scenario. In both scenarios, the South region has a decreased number of suitable future locations to site OWF.
In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon ...mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions.
Spring precipitation is the predominant factor that controls meteorological drought in Inner Mongolia (IM), China. This study used the anomaly percentage of spring precipitation (PAP) as a drought ...index to measure spring drought. A scheme for forecasting seasonal drought was designed based on evidence of spring drought occurrence and speculative reasoning methods introduced in computer artificial intelligence theory. Forecast signals with sufficient lead-time for predictions of spring drought were extracted from eight crucial areas of oceans and 500-hPa geopotential height. Using standardized values, these signals were synthesized into three examples of spring drought evidence (SDE) depending on their primary effects on three major atmospheric circulation components of spring precipitation in IM: the western Pacific subtropical high, North Polar vortex, and East Asian trough. Thresholds for the SDE were determined following numerical analyses of the influential factors. Furthermore, five logical reasoning rules for distinguishing the occurrence of SDE were designed after examining all possible combined cases. The degree of confidence in the rules was determined based on estimations of their prior probabilities. Then, an optimized logical reasoning scheme was identified for judging the possibility of spring drought. The scheme was successful in hindcast predictions of 11 of the 16 (accuracy: 68.8%) spring droughts that have occurred during 1960–2009. Moreover, the accuracy ratio for the same period was 82.0% for drought (PAP ≤ −20%) or not (PAP > −20%). Predictions for the recent 6-year period (2010–2015) demonstrated successful outcomes.
