Energy Conversion and Economics最新文献

Prolonged exposure of power transmission lines to extreme ice disasters in the atmosphere disrupts transmission. First, this study establishes a comprehensive failure probability model for the impact of extreme ice disasters on the transmission lines to better understand and improve the transmission lines' ability to withstand such disasters. It predicts the line failure probability based on the initial design data of the lines. Second, the system's weak points are identified, and the fault scenario set is established using the Monte Carlo state sampling method. Next, the system's state is calculated using the resilience assessment index and the DC optimal load reduction model. Finally, an optimal decision-making method for the transmission line maintenance sequence is proposed from the post-disaster maintenance scheduling perspective. Considering the travel time and maintenance effect, this method can effectively restore the system state in response to extreme ice disasters. The IEEE 30-bus power system is taken as an example of simulation verification. The results show that this method can effectively complete the system load state restoration and improve the transmission system's resilience. It also has certain practicality and good practical application values.

Smart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end-users. However, the impact of end-users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end-users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human-centred approach for future development and validation of socio-technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human-centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far-reaching implications for power systems, energy markets, community usage, and energy strategies.

Distribution system planning is a multifaceted topic involving financial, regulatory, and system level analysis. The wide nature of the topic warrants a holistic study considering all aspects of analysis. The distribution utility is a natural monopoly that is subjected to utility regulation. The regulator can impact customer experience by strategically influencing the planning decisions of the utility. Hence, this paper reviews the existing utility regulation methods in the context of the distribution system and their efficacy in improving certain reliability and efficiency objectives. A two-bus system is used to demonstrate the impact of classical models in alleviating reliability and efficiency issues through demand response. Further, a review is conducted on distribution system planning models without a regulatory regime, and suitable models for holistic analysis are identified. A two-person complete information regulator and utility game with a comprehensive distribution system model at the lower level is proposed. A framework based on the Mixed Integer Bilevel Linear Program (MIBLP) is discussed to find the equilibrium point of the proposed game.

Nowadays, transactive energy markets (TEMs) are emerging as interesting frameworks in deregulated power markets to control the balance of supply and demand in the entire electrical network. Due to wide deployment of renewable energy resources, grid connected micro-grids, and open access transmission and distribution networks, the planning and operation of TEMs become complex. So, an efficient optimal dispatch model for TEMs should be developed to achieve the objectives of TEMs, such as feasible sizes of transactions and optimal dispatch of these transactions with minimal operating costs. The transactive dispatch problem is similar to the resource allocation/matching problem. Recently, optimal transport (OT) has received significant attention in various fields including optimization theory and resource matching problems due to its potency and relevance in modeling and optimization. An OT-based approach is proposed here for optimal dispatch of transactions in energy markets while minimizing the cost of transactions considering the operating constraints of the system. The proposed approach can efficiently determine the feasible sizes of transactions without any security issues. The optimal solutions of the OT-based approach are obtained using a Sinkhorn iterative technique. Also, the load uncertainties are considered in this work to analyse the impacts of load uncertainties on the optimal dispatch of transactions. The numerical simulation results on the modified IEEE 9-bus system, modified IEEE 57-bus system, modified IEEE 118-bus system, and Indian Northern Regional Power Grid (NRPG) system illustrate the efficacy of the proposed OT-based framework.

With the increase in inverter-based renewable energy resources, the complexity and uncertainty of low-carbon power systems have increased significantly. Deep reinforcement learning (DRL)–based approaches have been extensively studied for frequency control to overcome the limitations of traditional model-based approaches. The goal of DRL-based methods for primary frequency control is to minimise load shedding while satisfying frequency safety requirements, thereby reducing control costs. However, the vulnerabilities of DRL models pose new security threats to power systems. These threats have not been identified and addressed in the existing literature. Therefore, in this paper, a series of vulnerability assessment methods are proposed for DRL-based frequency control with a focus on the under-frequency load shedding (UFLS) problem. Three adversarial sample production methods are designed with different optimisation directions: Q-value-based FGSM (Q-FGSM), action-based JSMA (A-JSMA), and state-action-based CW (SA-CW). Furthermore, combining the advantages of the above three attack methods, a hybrid adversarial attack algorithm is designed, Q-value-state-action-based mix (QSA-MIX), to significantly affect the decision process of the DRL model. In case studies of the IEEE39 bus system, the proposed attack methods had a severe impact on system operation and control. In particular, the high attack transferability of the proposed attack algorithms in a black-box setting provides further evidence that the vulnerability of current DRL-based control schemes is prevalent.

Power disturbances, defined as the waveform distortion of a power system under normal or abnormal conditions, contain considerable system and equipment state information. Obtaining equipment and system state information from power disturbance is very important to ensure the safety of power grids. To adapt to the development of power electronics, informatisation and digitisation of power systems, several applications with waveform-recording devices have obtained large amounts of disturbance waveform data, laying an important foundation for the analysis and application of power disturbance waveform data. First, typical disturbance waveform monitoring devices and a disturbance trigger detection algorithm are introduced. Then, disturbances are classified as switching, fault, or abnormal operations, according to the cause. The characteristics of various typical disturbance waveform data were analysed by combining the simulation and measured data. This paper summarises the application analysis of power disturbance waveform data at both the system and equipment levels. Finally, the construction scheme of a power disturbance waveform data monitoring and analysis platform for two different application scenarios was proposed for commutation failure monitoring and medium-voltage distribution network fault warning. The research conducted here is expected to support the construction of a power disturbance waveform analysis platform.

Cryptocurrencies, as a way of extraction, are categorized into two main groups of non-mineable and mineable. Bitcoin (BTC), the most famous mineable digital currency, utilizes the Proof of Work (PoW) algorithm for maintaining network solidarity. Its mining process is done by devices known as application specific integrated circuits (ASICs), which consume electricity and turn it into heat. They shall operate within an accepted temperature range of around 25°C; otherwise, they will face an efficiency drop; consequently, all mining sites require a proper cooling system. This study aims to investigate the problem of high energy consumption for cooling in digital currency mining sites and to present a solution for that via optimizing the capacity and operation of the chiller and ice thermal storage system (ITS). The results show the utilization of the ITS system reduces the operational costs by 10%, and the ITS system provides 1320 kWh of cooling energy during peak hours. Finally, the sensitivity analysis results considering the impact of the electricity tariff on the size and operation of the system have confirmed that with an increase in peak time tariff, the role of the ITS system in cost reduction increases.

The new emerging technologies utilize various sensors, deployed in an ad-hoc manner to reduce energy consumption in data communication. The data collected from these sensors is huge and have a high possibility of being polluted by outliers. Therefore, researchers are trying to develop better and faster outlier detection techniques that can handle large amount of data. In this paper, the research works from the year 2000 to 2022 have been reviewed. Several fundamental and latest outlier detection methods are discussed and categorized on the basis of statistical properties, density, distance, and clustering. The other methods discussed in this paper are ensemble methods and learning-based methods. The definitions, causes of outliers, and different methods of outlier detection are discussed. Further, one of the efficient methods from each category of the method is implemented on synthetic data of the IEEE 13-bus distribution system. The IEEE 13-bus system is assumed to have a Multi-Function Meter (MFM) at each line in the system. The data captured is injected with a fixed number of outliers at a given instant. Thereafter, the performance of all the methods is tested based on the number of outliers being detected.

With an increasing frequency of natural disasters and security attacks, the safe and stable operation of smart grid has been challenged unprecedently. To reduce the economic loss and social impact caused by power outage accidents, it is urgent to develop and improve the smart grid technology, and strengthen the disaster resistance and recovery capability of smart grids when faced with extreme events. As an efficient and flexible secondary energy source, hydrogen is crucial in improving the resilience of smart grid and supporting energy security. To further promote the deep integration of hydrogen systems and smart grid and improve the energy system resilience, the resilience of smart grids supported by hydrogen is assessed in this study. First, a technical framework of hydrogen-powered smart grid resilience is established, and the value of hydrogen-powered smart grid resilience is analysed considering different time frames (before, during and after an extreme event) of smart grids facing extreme events. Then, hydrogen-powered smart grid resilience is investigated from perspectives of pre-prevention regulation, in-process correction regulation, and post-recovery regulation. Finally, future direction for hydrogen-powered smart grid resilience is investigated and related policy suggestions are provided.

Renewable energy sources (RES) and electric vehicles (EVs) pose ‘energy-risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer-to-peer transactive energy market (P2P TEM) are improbable. This paper proposes a two-stage master–slave portfolio optimization approach for combining energy-risk of RES and EVs, and welfare-risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind-solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail-risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade-off Particle Swarm Optimization (SWT-PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare.