Energy Conversion and Economics最新文献

Power system instability primarily results from the deviation of the frequency from its predefined rated value. This deviation causes voltage collapse, which further leads to sudden blackouts of the power system network. It is often triggered by a lack of reactive capacity. The solution to the reactive capacity problem can be obtained in two stages. In the first stage, the vulnerable buses, also known as ‘weak buses’, where voltage failure might occur are identified, and the Var compensating devices are mounted at those locations. The proposed approach utilizes three simple vulnerable bus detection methods: the fast voltage stability index, line stability index, and voltage collapse proximity index (VCPI). In the second stage, various optimization algorithms are implemented to determine the optimal setting of Var sources, such as particle swarm optimization, differential evolution, the whale optimization algorithm, the grasshopper optimization algorithm, the salp swarm algorithm, grey wolf optimization, and oppositional grey wolf optimization (OGWO). The results indicate that the best approach to poor bus recognition is the VCPI, and the OGWO technique provides a much less expensive system than other optimization strategies used for problems of optimal reactive power planning.

As a critical technology for clean and sustainable energy transition, Internet of Things (IoT) is becoming increasingly popular for its use in extending connectivity into multiple energy resources. Based on the heterogeneous networking integration of devices, IoT has the potential of achieve seamless management of various facilities, thus enabling real-time optimisation of supply chains and dynamic response to energy system dispatch. In addition, IoT can help improve the visibility and controllability of distributed energy resources and leverage flexible loads via extensive connections among numerous devices. This special issue has received wide attention from the research community. The five papers selected for publication in this issue are briefly introduced as follows.

In ‘Architecture and function analysis of integrated energy service stations considering cyber–physical integration’, Liu et al. proposed integrated energy service stations (IESSs), which comprise substations, integrated multi-energy conversion stations, data centres, communication base stations and other functional units. Two feasible schemes were then designed to realise the construction of IESSs, including entity IESSs which require refined planning and construction, and virtual IESSs which involve transformation based on existing substation resources. The motivation and practical implementation for constructing IESSs are discussed. Finally, future research interests regarding IESSs are summarised.

In ‘A survey on policies, modelling and security of cyber–physical systems in smart grids’, Wang et al. provided an overview of the policy drivers for and barriers to the implementation of cyber–physical systems (CPSs). With the widespread deployment of behind-the-metre distributed energy resources (DERs), there is an increasing demand to model hardware, software and their interactions in a smart grid environment. This paper reviewed the modelling and applications of an intelligent CPS for a decentralised energy system. The integration of DERs and the supportive infrastructure can cause a modern power system to become more vulnerable to external threats such as terrorist attacks and therefore less reliable as a secure system. The latest progress in CPS implementation was summarised considering critical infrastructure identification and protection as well as risk assessment and methods for mitigating cyber threats and attacks.

In ‘Strategic PMU placement to alleviate power system vulnerability against cyber attacks’, Khare et al. presented a strategic phasor measurement unit (PMU) placement scheme to reduce cyber vulnerability of power systems to cyber attacks. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. "The authors also proposed an index to quantify the vulnerability of

Phasor measurement units (PMUs) have been widely deployed in power grids, while the bad PMU data problem threatens power system monitoring and control. This paper first gives the objective of the bad PMU data detection and gives an illustrative bad data instance. Then, the time-series PMU data of neighbouring buses are cast as a two-dimensional diagram, of which the spatio-temporal correlation analysis is performed to design the normal and outlier data detection problem. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM) are ensembled for bad PMU data detection. Moreover, the statistical analysis and bound modification of data clustering are developed to further improve the detection accuracy. Finally, the procedure of the two-stage bad PMU data detection is given, which consists of ensemble learning and modification. The proposed hybrid clustering-based bad data detection is unsupervised and is applied to online bad PMU data detection with a short computation time. Visible and numerical case study results validate the outperformance of the proposed method.

To perform a fast faulted-phase selection under the power swing in a series compensated line with a thyristor-controlled series capacitor (TCSC) is an extreme challenge facing the transmission line protection schemes. Thus, this paper proposes a new scheme for recognizing the faulted-phase in TCSC-compensated transmission lines during the power swing. Primarily, the fault feature is extracted by using a modified Interclass correlation coefficient. The scheme utilizes the system-current samples during the fault period and system-current samples during the health state as two variables for obtaining the modified interclass correlation coefficient. Then a cumulative approach is used to enlarge the fault feature. The proposed scheme has been subjected to a wide variety of tests through different faults circumstances under different compensation levels. The experimental results have shown good performance against the high impedance/resistance fault under different TCSC-compensation levels during the power swing. Also, the results showed a distinction in terms of time response due to its simple computation process.

Power electronics load is considered to be a strengthening factor that leads the system operators more concerned about power quality issues. Poor power quality can lead to distinctive operation of electrical components and devices, which may cause heavy economic loss to the customers and power network operators. An application of fractional order PI^λD^μ (FOPI^λD^μ) controller for power quality improvement in smart power distribution systems is presented here. Analytical design steps are presented for the FOPI^λD^μ controller. A genetic algorithm is used for tuning the control parameters of the FOPI^λD^μ controller optimally. The FOPI^λD^μ controller is employed and evaluated for shunt active power filter (APF) to filter out harmonics and improve reactive power compensation for non-stationary, non-linear load conditions for a smart power distribution system. Effectiveness of FOPI^λD^μ controller for instantaneous reactive power theory based PQ control algorithm applied to shunt APF is validated using a small-scale laboratory experimental setup. The experimental results confirm the excellent performance of the FOPI^λD^μ controller in terms of its superior transient response, significant reduction in harmonic distortion, and improved reactive power compensation in a smart grid environment.

A more reliable, efficient, and resilient smart grid depends on the applications of advanced information and communication technologies to support new functions and controls. The critical infrastructure of a smart grid consists of some major components such as monitoring, controls, communication protocol and software. The cyber-physical system (CPS), which integrates these components, is an important enabler for the expected transition of the energy system driven by decarbonization, digitalization and decentralization. This paper provides an overview of the policy drivers and barriers for the implementation of CPS in power systems. With the vast deployment of distributed energy resources (DERs), there is increasing demand to model the hardware, software and their interactions in the smart grid environment. This paper reviews the modelling and applications of intelligent CPS for decentralized energy systems. The integration of DERs and the supportive infrastructure make modern power systems more vulnerable and less reliable to external threats such as terrorist intrusion. There are growing concerns over the risk of cyber-attacks in mart grids. This paper surveys the latest progress on critical infrastructure identification and protection, as well as risk assessment and mitigation methods for cyber-attacks. Finally, some advanced issues in cyber-physical energy systems are addressed.

In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy. Residential households and commercial buildings with DERs have become prosumers in local grids because they can sell surplus power to others. Research has been initiated to integrate and utilize DERs through better control and communication strategies. With the advances in the Internet of Things (IoT) technology, unprecedented coordination among DERs can be achieved to facilitate energy trading and transactive energy management. However, preventing leakage of user information during the optimization process remains a challenge for researchers, which drives them to develop privacy-preserving energy management systems. In this study, a fully decentralized transactive energy management method using a consensus-based algorithm is developed. Specifically, a virtual pool is designed for prosumers to trade energy and exchange information with the support of IoT technologies. The consensus-based algorithm enables prosumers to obtain an optimal energy schedule independently in a coordinated manner without revealing any personal data. Practical data was used to perform simulations and validate the proposed algorithm. The results showed that the authors' consensus-based decentralized transactive energy management strategy is feasible and can significantly reduce the overall system cost.

With the continuing development of the energy internet (EI), the high complexity of multi-energy coupling and the dramatic increase in cyber-physical integration impose stricter requirements on the planning and construction of multi-energy systems. The construction of a new energy hub through the intensive use of existing power substation resources is one of the feasible approaches for satisfying these requirements. Integrated energy service stations (IESSs), which comprise substations, multi-energy conversion stations, data centres, communication base stations, and other functional units, constitute the emerging generation of energy and information control centres. IESSs are capable of all-in-one monitoring and energy optimization, and can effectively manage regional energy services involving various energy requirements. This study initially proposes two feasible schemes to realize IESSs: entity IESSs, which require replanning and construction, and virtual IESSs, which involve transformation through existing substation resources. Thereafter, based on the requirements of three types of users, that is high-energy-consumption users, highreliability users, and high-comfort users, the necessity of IESSs in the construction of the EI is analysed in depth. Furthermore, the feasibility of constructing an IESS based on existing power grid resources and the direction for the future development are discussed.

A multi-energy microgrid (MMG) aims to integrate multiple energy carriers in the form of electricity, heating, and cooling, as well as gas in a microgrid architecture. To achieve higher energy generation and utilisation efficiency, MMGs can be implemented in distribution networks, smart buildings, smart homes, smart factories, and mobile microgrids such as ship power systems. In these systems, multiple energies can be simultaneously generated, transmitted, stored, and consumed through the seamless coordination of heterogeneous generation units, energy storage systems, and flexible loads. The key research challenges for MMG include accurate modelling of the multi-energy carrier units considering their diverse characteristics, optimally sizing and deploying the units in the MMG, flexibly dispatching and controlling them for MMG operation, and guiding effective trading on the generation and demand sides. This special issue has received wide attention from the research community, and five papers have been finally accepted which cover the topics of planning, operation, control, as well as the power quality and reliability of the MMG. A brief introduction of these five papers is given below.

In ‘Holistic Data-Driven Method for Optimal Sizing and Operation of an Urban Islanded Microgrid’, Feng and Tseng. presented a holistic data-driven method for the optimal sizing and operation of a building-level islanded microgrid with renewable energy resources in an urban setting. First, various meters were integrated on an energy-monitoring platform where field data were collected. A randomised learning-based forecasting model was designed for supply/demand prediction in a microgrid. Based on the forecasting results, data-driven uncertainty modelling was used to characterise the uncertainties associated with renewable energy supplies and demands. An optimal sizing approach was then proposed to determine the optimal sizes for energy storage systems and distributed generators with the overall aim of minimising the investment and maintenance costs. Based on the optimal sizing and uncertainty scenarios, a two-stage coordinated energy management method was proposed to minimise the operating cost under uncertainties.

In ‘Capacity Configuration Optimisation of Standalone Multi-energy Hub Considering Electricity, Heat and Hydrogen Uncertainty’, Liu et al. proposed a novel multi-objective capacity configuration model for standalone multi-energy hub considering electricity, heat and hydrogen energy uncertainty. First, a standalone multi-energy hub model with electricity, heat, and hydrogen energy was established. It considered photovoltaic generators, wind generation, combined heat and power units, power to gas, gas boiler, and hydrogen storage tank to meet electrical, thermal, and hydrogen energy demands. Meanwhile, to solve the influence of uncertainties on hub capacity configuration, typical source-load scenarios were established considering the uncertain

Multilevel converters (MLCs) are extensively used in solar photovoltaic (SPV) applications owing to their advantages such as low total harmonic distortion (THD) in the converter voltage, reduction in device stress, and switching losses. A suitable modulation technique is important for the efficient closed-loop control of megawatt (MW)-scale solar photovoltaic plants. This work utilises different modulation techniques, such as phase-shifted (PS) multicarrier pulse width modulation (PWM), selected harmonic elimination (SHE), and nearest level modulation (NLM), for switching of cascaded H-bridge (CHB) converter-based large-scale SPV systems. The investigation on improving power quality is presented with a suitable fast Fourier transform (FFT) analysis and comparative graphs. The presented control and modulation enhance the power quality of the output current being fed to the grid in the dynamic solar profile. Moreover, the low switching frequency employed in this photovoltaic converter at a high power rating increases the system efficiency. Graphical illustrations of losses with fundamental and PWM switching were analysed for the MW-rated system. The obtained results show that SHE-PWM provides the best performance for large-scale solar power plants. Furthermore, the IEEE-519 standard was met for both grid voltages and currents. The system was modelled and simulated in MATLAB/Simulink and validated in a real-time environment.