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Oil-related events have increased the uncertainty and complexity of the worldwide oil market. This paper investigates the effects of four types of oil-related events on world oil prices, using an event study methodology and an AR-GARCH model. The Internet information concerning these events, which is derived from search query volumes in Google, is introduced in an analytical framework to identify the magnitude and significance of the market response to oil-related events. The results indicate that world oil prices responding to different oil-related events display obvious differentiation. The cumulative abnormal returns, which reflect the influence of the global financial crisis, tend to drop first and then reverse and rise, while the cumulative abnormal returns induced by other oil-related events present a stronger persistent effect. The impact of the global financial crisis on oil price returns is significantly negative, while the impact of the Libyan war and hurricanes is significantly positive. However, the reactions of oil price returns to different OPEC production announcements are inconsistent. [ABSTRACT FROM AUTHOR]

Highlights: [•] The impact of market concern derived from the Web on oil volatility is analysed. [•] It has an equilibrium relationship between oil prices and long-run market concern. [•] The short-run market concerns have an asymmetric influence on oil price volatility. [•] The Internet can exaggerate the impact of information shocks on oil price. [ABSTRACT FROM AUTHOR]

It has been proved by recent works that reputation can help to improve the efficiency of blockchain and enhance the fairness of energy trading markets. However, the application of distributed reputation in the energy field has not been fully studied yet. In this paper, we design a distributed reputation system to simulate real-world trust in blockchain-based peer-to-peer energy trading. It is a comprehensive reputation system in the sense that reputation scores are decided by the behavior of participants as consensus nodes, energy buyer, and energy sellers. Its implementation relies on blockchain, especially the smart contract technology, to achieve distributed and automatic reputation management. The distributed reputation system in turn helps to implement a delegated consensus algorithm for blockchain and a reputation-based k -double auction matchmaking scheme for peer-to-peer energy trading. In addition, we define a fairness indicator to capture the reputation-based average benefits and costs when considering reputation as the contribution to the peer-to-peer energy trading market. By simulating the comprehensive system, the numerical results demonstrate the effects of distributed reputation in improving the efficiency of blockchain and balancing fairness indicators between sellers and buyers during peer-to-peer energy trading. As far as we know, this paper is one of the few works to provide a formal method of evaluating the fairness of the peer-to-peer energy trading market. • A distributed reputation system is designed based on the blockchain technology. • Reputation is used to improve blockchain consensus and peer-to-peer energy trading fairness. • A formal definition of fairness of peer-to-peer energy trading is proposed. [ABSTRACT FROM AUTHOR]

• We propose a new CCS project investment model, which helps to design the CCS development roadmap for achieving the next 2050. • Reductions in the cost of technologies as a result of learning-by-doing is considered to enrich the model. • We transform the original continuous problem into a nonlinear integer programming problem. • We are trying to answer questions about when to adopt CCS technology and what are their cost. • Flexible installation levels can be considered to improve efficiency and reduce costs. In the medium to long term, China must conduct deep emission reduction actions to transform the country's economy and meet the conditions of the Paris Agreement. As an advanced emission reduction technology, carbon capture and storage (CCS) is undoubtedly an important means of achieving this goal. China must consider how to retrofit existing thermal power plants to install CCS technology. Thus, the expected future roadmap for power plants with CO 2 capture is of significant interest. To achieve this aim, we propose a new CCS project investment model, which helps design the CCS development roadmap for achieving the emission targets for 2050. Reductions in the cost of technologies as a result of learning-by-doing is considered to enrich the model to describe the reality more sensibly. Through some mathematical skills, we transform the original continuous problem into a nonlinear integer programming problem. By solving the model, we are trying to answer questions about when to adopt CCS technology and the cost. The results reveal that early large-scale CCS demonstrations are not necessary. The peak investment period of CCS is around 2035. Operating costs account for 80% of the overall cost of CCS, and thus, policymakers must fully motivate the development and investment of operating technologies, especially capture technologies. The results also show that when the scale of CCS technology is promoted, flexible installation levels can be considered to improve efficiency and reduce costs. [ABSTRACT FROM AUTHOR]

• A methodology is proposed for dynamic probability assessment of natural gas pipeline for third-party damage. • Failure factors are identified into two aspects of intentional factors and unintentional factors. • The probabilities of third-party damage failure causations are estimated by Fuzzy set theory and Dempster-Shafer evidence theory. • Fault tree analysis and event sequence diagram are coupled and transformed into dynamic Bayesian network model to eliminate uncertainty. • A real-field case study is used to prove the practicality and advantages of the proposed method. Failure and leakage of natural gas pipelines can lead to serious ecological losses and casualties. Third-party damage has become an important cause of pipeline failure and leakage, which urgently needs an accurate risk assessment method to assess the risk. Conventional qualitative risk analysis methods can only point out the critical events of failure accidents but fails to predict the failure probability. This paper proposes a dynamic risk probability analysis method based on Dynamic Bayesian network (DBN), which is validated by a third-party damage case under uncertainty. First, human factors are taken as the main analysis object in the risk analysis, by which two subcategories of intentional and unintentional factors are classified. A complete risk factor analysis is performed by combining expert recommendations with the fault tree analysis method and developing a coupled model with the event sequence diagram. Second, in order to deal with the uncertainty of risk factors, the coupled model is mapped to a DBN model. The prior probabilities of the input DBN model are obtained by database, fuzzy set theory, and Dempster-Shafer evidence theory. Weibull distribution is applied to construct the probability transfer process between time segments, which better fits the characteristics of third-party disruptive factors in onshore pipelines. Finally, the practicality and advantages of the proposed method are demonstrated by a real case study, which identifies 6 critical events and predicts the probabilistic information in different time slices. Furthermore, the method predicts the probability of failure events and potential consequences by processing the time series information, and it is found that the probability of structural damage and explosion is higher than other consequences. In this way, some risk management countermeasures are proposed in a targeted manner. The results show that compared with the conventional BN model which only performs probabilistic inference once, the DBN model can perform temporal dynamic inference to achieve the prediction of failure probability, and it can effectively achieve the numerical prediction of risk failure probability. [ABSTRACT FROM AUTHOR]

• The improved brute-force search transforms the energy-saving operation problem into optimal turning point solution. • Considering the speed limit, a more reasonable energy-saving turning point is obtained by adding a constant speed section. • The energy-saving effect of improved brute-force search is better than GA and PSO algorithms in Guangzhou Metro Line 7. Rail train operation energy consumption mainly focuses on train traction energy consumption. Reducing train traction energy consumption in rail transit operation is significant to developing a green and low-carbon economy and reducing operation costs. The rail train operation energy-saving optimization framework is developed considering the utilization of regenerative braking energy. Firstly, three objectives of punctual arrival, fixed-point parking and minimum energy consumption are provided by train operation strategy analysis. Secondly, the improved brute-force search is developed to solve train operation energy-saving multi-objective problems. The running time, speed, distance, power, and energy consumption of operation intervals are calculated. Finally, Guangzhou Metro Line 7 is taken as an example to verify the effectiveness of the developed optimization model. The results show that the improved brute-force search method effectively finds a more energy-saving turning point under constant interval operation time and has a better energy-saving effect than two other heuristic algorithms. [ABSTRACT FROM AUTHOR]

Spatial correlation is a critical characteristic of adjacent wind farms, but its importance is always ignored in wind power modeling, further resulting in modeling bias. In order to investigate the necessity of involving spatial correlation and validate whether it has effects on power system, this paper proposes a method to construct the spatial correlation model and applies it into an IEEE test system. Specifically, a 7-dimensional t -copula method is used to capture the spatial dependence by forming a multi-variable distribution function, from which different scenarios are generated. Subsequently, a k -means clustering method is used for scenario reduction. As for the case study, a modified IEEE 39-bus test system with 7 wind farms deriving from the AEMO dataset is utilized to conduct optimal power flow under the circumstance with and without considering spatial dependence. Based on this, the cost of generation, including traditional fuel cost and levelized cost of wind power is obtained. Through comparing the cost results and analyzing the gap, it can be concluded that the spatial correlation has significant effects that when levelized cost of wind power exceeds a particular value, the generation cost can be greatly underestimated if ignoring spatial correlation with a high penetration of wind power. • An extensive comparative study for spatial correlation of renewable is presented. • A high-dimensional spatial correlation model is established. • Spatial correlation of wind is represented by reduced scenarios. • Optimal power flow is reformulated with high renewable and LCOE. • Sensitivity analysis of LCOE is implemented with different wind penetration levels. [ABSTRACT FROM AUTHOR]