Iet Generation Transmission & Distribution最新文献

This work introduces an efficient operational strategy for electric vehicles (EVs) to optimize economic outcomes in a wind-integrated hybrid power system. The proposed method enhances the profitability of a combined wind-thermal-EV-fuel cell system while maintaining grid frequency stability and managing the energy states of EV storage. Accurate wind speed forecasts are crucial, as wind farms must provide projected generation data to the market controller for coordinated scheduling with thermal units. Due to wind speed variability, discrepancies between actual and predicted values can lead to mismatches in wind power output, causing financial penalties from divergence prices. To address this, the optimal deployment of the EV storage system is designed to mitigate these financial impacts. By coordinating EV, wind, and thermal operations, the approach effectively reduces wind power unpredictability and ensures economic efficiency, a necessity in competitive power markets. Four distinct energy states of the EV battery- maximum, optimal, low, and minimum, are proposed to enhance cost efficiency. The EV storage mode is dynamically adjusted based on real-time grid frequency and wind speed data. Additionally, a fuel cell is incorporated to boost economic returns further. The effectiveness of the strategy is validated using an IEEE 30-bus test system, employing sequential quadratic programming and demonstrating notable improvements over existing methods.

The reliability of power electronic converters is one of the essential issues in designing of electric vehicles. This paper estimates the lifetime of the boost converter switch by Semikron and Coffin-Manson models for two common failure mechanisms of Bond wire and Base plate solder, respectively. Four mission profiles based on the Artemis standard are applied to hybrid electrical vehicle model to determine unidirectional output power. Kharitonov's theory is used to design a robust controller to handle the uncertainty raised by different power cycles of the electric vehicle and the parameters of the converter during simulation. Stability of converter is achieved during simulation by identifying simple proportional integral controller coefficients with Kharitonov's theorem. A prototype 230 W boost converter is designed and utilized to validate average model and switch loss calculation relationships. The lifetime results indicate that the number of cycles, and the average and the maximum junction temperature have a more impact than the duration of the drive cycle on the lifetime of the converter. A mixed mission profile is considered to investigate the effect of sudden change in driving modes and speed on total consumed life and lifetime to enhance the study's applicability. Lifetime of switch is decreased significantly in mixed mode in comparison with other mission profiles in the same driving time. Furthermore, the motorway mission profile has 53%, 39.6%, and 160% less total consumed life in comparison with the urban, rural and mixed mission profiles, respectively. In addition, the effect of ambient temperature changes on IGBT lifetime has been investigated for four mission profiles. While motorway had the least total consumed life in 25°C, the urban had better performance in comparison with other mission profiles from 25 to 55°C.

The depleting oil reserves, air pollution and increasing energy demand, have overturned the focus of the scientific community to renewable energy sources. Among which the photovoltaic (PV) systems occupy more than half of the market share and are generally installed at the distribution level. The volatile and uncertain nature of these PV productions necessitates flexible resources in energy systems. To this end, the district heating systems have an outstanding flexibility on account of their high thermal inertia. This study investigates the optimal unit commitment scheduling for gas-fired and non-gas-fired distributed generation units (NGU) in an integrated energy distribution system (IEDS) within the physical constraints of the electrical, natural gas and thermal energy distribution networks. Moreover, a planning-based optimization framework is proposed to investigate the investment of battery storage systems in the electric distribution network under the high penetration of PV systems with the aim of enhancing flexibility and reducing the operating costs of the IEDS. In this framework, the information gap decision theory is deployed under risk-averse and risk-seeker strategies to deal with uncertain PV energy production. Additionally, the environmental emissions are considered in a multi-objective approach. The IEDS is embodied through IEEE 33-bus EDS, 20-node natural gas network and an 8-node district heating systems. Eventually, The proposed approach makes a noteworthy contribution to the advancement of solar energy systems in IEDS.

This study proposes a direct $�Z_{\mathrm{BUS}}$ three-phase four-wire power flow method to accurately analyse the neutral line and multiple grounding characteristics. In particular, the proposed grounding impedance building-based solution method was used to analyse the neutral grounding impedance in power flow studies based on the slack bus grounding impedance. The accuracy of the proposed method was verified using a neutral-to-earth voltage test system. IEEE 13-bus and 123-bus test systems were used to compare the advantages and disadvantages of the proposed method. Compared to the current injection full Newton and forward–backward sweep methods, the proposed method achieves a significant reduction in iteration numbers of up to 76.92% and 77.78%, respectively. For different grounding scenarios, stable convergence characteristics were exhibited by the proposed method after six to seven iterations.

Wildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning-based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning-based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real-world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature.

The integration of super large-scale offshore wind clusters into the onshore power grid brings challenges for the flexibility of the transmission network. Therefore, this paper proposes a two-stage collaborative planning of points of common coupling siting and transmission network expansion for super large-scale offshore wind clusters, based on a multi-voltage-level stratified integration mode which enables offshore wind power to transmit to larger regions for accommodation. At first, the transmission network flexibility indexes are established. Then, a two-stage collaborative planning model for points of common couplings siting and transmission network expansion is proposed, which comprises a collaborative planning model and an optimal power flow operation model. The proposed model reduces wind curtailment and load shedding by optimizing the annual total cost and flexibility transmission capability of the network. Finally, the effectiveness of the proposed method is verified using a modified IEEE 30-bus system and a 65-bus system.

Recently, distributed generators (DGs) have been widely integrated into distribution network, so that the distribution network is gradually transforming into an active distribution network (ADN). Due to the influence of meteorological conditions, the output of DGs has high uncertainty. At the same time, considering the increasing variety of loads in ADNs, the uncertainty of load demand of user side is also increasing. In order to fully consider the uncertainty of measurement and quantitatively evaluate the operational status, this paper proposes a steady-state analysis method for ADNs under uncertain conditions. Firstly, this paper proposes a steady-state analysis method including power flow analysis model and evaluation indicators for the operation status from the perspectives of node and network. Secondly, the uncertainty factors are elaborated from three aspects: sources, impact on evaluation index and impact on scheduling. The evaluation indicators considering uncertain conditions, the impact on system security and scheduling of network are further discussed. Finally, through the simulation analysis of the modified IEEE 33-node test system, the effectiveness of the proposed method is verified.

The grid-connection of high-permeability new energy through voltage-source converters (VSCs) brings new oscillation risks to the power system, which seriously threatens the stable operation of the system. Therefore, an evaluating method based on modal analysis is proposed to investigate the power oscillation characteristics in a multi-VSC grid-connected system. Initially, based on the analogy method, the output admittance model of voltage-source controlled VSCs (VC-VSCs) and current-source controlled VSCs (CC-VSCs) is established. Subsequently, the output admittance model of a multi-VSC grid-connected system is constructed. Then, the modal analysis method is introduced to investigate the power oscillation characteristics of VC-VSCs and CC-VSCs, and the influence factors of power oscillation and its variation law are analysed. Next, a sensitivity analysis is also employed, which helps to identify the origin of each oscillation mode and determine the contribution of each component to the power oscillation. Finally, simulation and experiment are carried out to verify the effectiveness of the above analysis method.

Large-scale wildfires can significantly reduce the air gap insulation resistance of high-voltage transmission lines and cause chain tripping incidents. To assess the resilience of the power transmission system during wildfire, this paper proposes a resilience assessment framework for transmission system that considers the entire process of wildfire disaster. Firstly, a wildfire spread model, considering multiple influencing factors, is developed based on the cellular automaton. Based on the air gap breakdown mechanism during wildfires, the trip-out probability of transmission lines is calculated, and various failure scenarios are obtained by using the Monte Carlo sampling. Secondly, considering the geographical location of failures, maintenance personnel schedules and restoration time, a power transmission system restoration model is established. Thus, a resilience assessment method for power transmission system during wildfire disasters is proposed. Finally, IEEE RTS-79 transmission system is taken as an example to demonstrate the effectiveness of the proposed resilience assessment method. The results show that the proposed method can effectively calculate the wildfire spread tendency and transmission line's trip-out probability. Furthermore, three typical resilience improvement measures are quantitatively analysed, which provides a quantifiable reference for the power sector to formulate prevention and recovery strategies for extreme wildfire disasters.

With the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two-stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two-stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two-stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two-stage stochastic energy and reserve market clearing model. Within the two-stage framework, the first day-ahead stage serves for energy and reserve market clearing, while the second real-time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two-stage stochastic market clearing model is formulated as a mixed-integer non-linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE-118 test system.