Iet Generation Transmission & Distribution最新文献

The need to transmit energy in the most effective way arises in parallel with the demand for energy in developing countries. As the amount of transmitted power increases, transmission lines can become overloaded, causing a blackout. This study aims to increase the transmission capacity without experiencing right-of-way problems by converting Turkey's existing high voltage AC systems to hybrid AC/DC systems. With such a design, it is aimed at providing more efficient and continuous transmission by using the high-voltage DC system's low-loss feature at long distances. Using the 400 kV double circuit line structure, the existing electricity transmission system is modelled using real data in the DigSILENT PowerFactory. The electromagnetic fields that will occur due to the use of AC and DC circuits on the same tower in such a transformation situation are analysed with FEMM 4.2, based on the finite element method. The compliance of the electromagnetic fields that will occur with the use of hybrid 400 kV AC and 500 kV DC was determined in the study. In addition, according to the simulation results, it is predicted that in the case of hybrid AC/DC transmission, the current transmission capacity would increase and the losses would decrease.

Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR-implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario-based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed-integer non-linear programming (MINLP). A time-series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario-based planning method.

Combined hydrogen, heat, and power (CHHP) is the cogeneration unit simultaneously meeting hydrogen, heat, and power demands. Due to the low efficiency of the electrolyser, the main grid or other generation units supply its input power. Herein, the photovoltaic farm's (PV farm) output power is used as the input power of the electrolyser. Optimal economic and emission scheduling of CHHP-based microgrid (CHHP_MG) is defined as an objective function. To improve system efficiency, the scheduling problem is done in two cases, reconfigured SP-PV farm and conventional SP-PV farm. To optimal management of both demand and generation sides, a time of use (TOU) based demand response program is employed in the proposed short-term study. To have a comprehensive study, the optimum solar panel tilt angle is calculated, and its impacts on the main function are analysed. Multi-objective genetic algorithm is used to solve optimization problems. By solving the objective function, the obtained values for profit and pollution amount are 620$ and 69,245.78 gr, respectively. By applying the load response program, the mentioned values are changed to 715.132$ and 6872.5 gr. Since the input power is supplied through solar panels, the placement angle of the panels is very important and with the optimal setting of the placement angle, the values change to 780$ and 6825.3 gr.

This article introduces a Wasserstein distance-based distributionally robust optimization model to address the transmission expansion planning considering renewable-based microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both long-term uncertainty and short-term variability over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG-based generating units among profit-driven private investors. The Wasserstein distance uncertainty set is used to characterize the long-term uncertainty associated with future load demand. Short-term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender's method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6-bus and IEEE 118-bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.

By increasing the number of electric vehicles (EVs) to achieve a less carbon environment, not only they consume power from the grid to be charged, but also do they deliver power to the grid, and this can play a significant role in load frequency control. However, EVs take part in frequency regulation based on their state of charge (SoC) which will cause uncertainties. In order to manage these uncertainties, EV aggregator (EVA) concept has been introduced. The EV owner participates in the demand response program provided by the EVA arbitrarily considering her/his requirements. Accordingly, EVA calculates the up and down power reserve for the power system operator. Following any frequency deviation, EVA sends the proper commands to each EV to consume or to inject power to the system. There are also communication delays between different parts, uncertainties, and non-linearities that existed in the power system. To overcome these issues, this study proposes a new robust load frequency controller based on feedback theory and artificial neural network which is designed through the non-linear multi-machine power system. Simulation results on IEEE 39-bus power system show that the proposed controller regulates frequency more desirably in comparison with other methods.

In recent years, the “asset management” or “managing assets” technique has been expected to support the rationalization of the maintenance and operation of electric power equipment, especially as a countermeasure for aging equipment. For this purpose, the development of diagnostic methods for aging, remaining life, and faults is necessary. Furthermore, it is important to accumulate case studies applying such diagnostic methods to real equipment and the analysing benefits of the applications of the diagnostic methods. In this report, frequency response analysis and a partial discharge measurement are applied to a shunt reactor suspected of occurrence of partial discharge. Results of the diagnostic methods suggest the breakage of the earth bar connecting the iron-core blocks and the occurrence of the partial discharge at the iron core. From these diagnostic findings, the shunt-reactor operator decided to postpone the replacement for 2 years, compared with the case where no diagnostic methods were applied. In this report, the benefits of deciding the postponement is also evaluated.

The movement towards electrification is entailing deep changes in power systems. On the demand side, the adoption of electric heating (EH) has grown rapidly in recent years. To eliminate the voltage fluctuations caused by the random switching-on/-off behaviors of EHs in some application scenes with special-power-line in low voltage distribution networks (LVDNs), a swarm-intelligence-based coordinated control approach of EHs for voltage stabilization with zero communication burden while guaranteeing the users’ thermal comforts is researched. A novel bird-perch-on-branch (BPB) -based swarm intelligence theory is proposed, which reveals the mapping relation between local observations and global states. On this theoretical basis, a multi-agent reinforcement learning (MARL) framework is developed for the coordinated dispatch of EHs, where the deep-Q-network (DQN) algorithm is adopted to learn and generate the optimal control policy for each EH. The implementation of the proposed MADQN-BPB approach is described based on the principle of centralized-training and decentralized-execution. Comparative control performances of MADQN-BPB with a commercially available approach, and the global optimal solution are evaluated. Simulation results verify the capability of MADQN-BPB in voltage stabilization with zero communication burden. Its control performance is close to that of the global optimal solution, and is scalable to the variations of environment.

This study proposes a frequency coupling suppression strategy for voltage source converters (VSCs) with DC-link dynamics to address the challenges posed by the asymmetric system structures for system modelling and control design. The strategy introduces a reshaping outer loop into the q-axis, which eliminates the conjugate voltages and currents induced by the DC-link dynamics, to achieve decoupling of the DC-link voltage control (DVC). Therefore, the VSC is modelled as a single-input single-output (SISO) admittance model, which provides clear physical insights for the analysis of admittance characteristics. Compared with the admittance matrix, the SISO model reveals the dominance of each control dynamics on VSC admittance in different frequency bands. This insight facilitates the exploration of optimized control strategies to improve the system stability margin. Based on the SISO model, a virtual admittance for compensating the negative resistive effect of the PLL is designed to illustrate the advantages of the proposed method in providing design-oriented analysis. Experimental results verify the effectiveness of the proposed control strategy.

Electric vehicles (EVs) and small photovoltaic (PV) installations advance residential power grids by lowering charging costs and fostering eco-friendly operations. Yet, the variable nature of EV charging presents challenges to grid reliability. This research introduces a Monte Carlo-based simulation for predicting EV charging loads and a systematic charging method that integrates a ‘green electricity’ pricing scheme with a joint optimization model for PV and EV management. By applying an improved ant lion optimizer (IALO) algorithm enriched with differential evolution features, an optimization strategy that markedly enhances grid performance is devised. In a park scenario, this ‘green electricity’ model reduced the mean square error of EV charging load by 11.82%, smoothed the power load curve, and improved grid stability. When compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms, the IALO algorithm boosted overall revenue by 16.8% and 12.8%, increased PV utilization by 162.3% and 37.1%, and significantly cut carbon emissions by 159.6% and 31.6%, respectively. These outcomes affirm the financial, environmental, and functional benefits of our proposed approach.

The partial exposure of submarine cable may cause damage to the metal sheath, which will affect its safe and stable operation. Existing detection methods for burial depth of submarine cables are difficult to implement and have a long detection period. In this contribution, a method is proposed to calculate the burial depth of alternating current (AC) submarine cable based on surface optical fiber monitoring temperature. According to the functional relationship between the transient temperature rise of submarine cable outer sheath above the ambient temperature and the burial depth (in the IEC-60853 standard), the burial depth of submarine cables is calculated. The optical fiber temperature, electric load and meteorological temperature data are used for 30 consecutive days in September 2020 to calculate the change of burial depth of AC 500 kV oil-filled submarine cable. The calculation results are basically consistent with the detection conclusion of side-scan sonar, which shows that the method can accurately calculate the burial depth of submarine cables and locate the exposed position. This provides a theoretical basis for the intelligent operation and maintenance of AC submarine cables.