Energy Conversion and Economics最新文献

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.

With the ever-growing demand for electricity, fast development of intermittent renewable energy generation (IREG), and evolving electricity pricing mechanisms, different network tariff schemes are implemented in various countries to address emerging challenges in power system planning and operation as well as electricity market evolution. Given this background, a survey of current practices on dynamic network tariffs in some representative countries is first presented. Subsequently, key issues of dynamic network tariffs including prerequisite, implementation and effects are described. Finally, from the perspective of electricity consumers, distribution system operators (DSOs) and regulatory authorities, the challenges associated with the implementation of dynamic network tariffs are discussed.

Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large-scale energy storage technique, the advanced adiabatic compressed air energy storage (AA-CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA-CAES has immense potential in multi-energy supply systems, CCHP dispatch with AA-CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA-CAES system equipped with WPG is addressed. The AA-CAES system is comprehensively modelled by considering its thermal characteristics, air-temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA-CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA-CAES system is minimised. The numerical results demonstrate that the participation of AA-CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA-CAES are also discussed.

Restructuring the power system with higher penetration of distributed energy resources (DERs) and intelligent devices offers the potential for more efficient, reliable, and better resource utilisation of power systems through the transactive energy framework (TEF). This article provides a general overview of the mathematical models and formulations of the TEF reported in the literature. TEF concepts can be applied to various levels of the power system. Here, the TEF-related literature is divided into individual DER-, building-, microgrid-, and macrogrid-level TEF. The mathematical models of transactive agents corresponding to each level and power system network models are presented. Furthermore, TEF models for energy management and trading of integrated multi-energy systems are analysed. Finally, the potential challenges and future research directions for transactive energy are discussed.

The real-time carbon emission estimation of generators helps quantify the carbon emission costs and reduce power system emissions of power generation. Accurate estimation relies on the accuracy of the carbon emission factors (EFs) and power generation measurements. The dynamic carbon emission factor (DEF) of generators was proposed recently as a linear function of the output power. However, there is a significant deviation between the modelled DEFs and the actual measurement EFs, especially when the output power is low. This paper first presents the general definition of the DEF to characterize the emissions and focuses on the unit-level DEF (UDEF). The piecewise non-linear UDEF (P-UDEF) model is then proposed, which can better represent the unit emission characteristics. Then an accurate piecewise linear cost approximation method is proposed considering the segment points and extreme points of both P-UDEF and generation costs function. Last, the system carbon emissions and costs estimation are estimated by combined economic emission dispatch (CEED), and the reduction potential is evaluated. Case studies on an IEEE 30-bus system with piecewise linear cost functions show that the proposed P-UDEF can realize real-time emission and cost estimation as well as reduce the total system emissions by considering the incomplete combustion cost of generating units.