Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

A critical survey of proton exchange membrane fuel cell system control: Summaries, advances, and perspectives

Poly (isatin biphenylene) polymer containing ferrocenium derivatives for anion exchange membrane fuel cell

Maximum power point tracking (MPPT) is an effective method to improve the power generation efficiency and power supply quality of a proton exchange membrane fuel cell (PEMFC). Due to the inherent nonlinear characteristics of PEMFC, conventional MPPT methods are often difficult to achieve a satisfactory control effect. Considering this, artificial bee colony algorithm combining fuzzy control (ABC-fuzzy) was proposed to construct a MPPT control scheme for PEMFC. The global optimization ability of ABC algorithm was used to approach the maximum power point of PEMFC and solve the problem of falling into local optimization, and fuzzy control was used to eliminate the problems of large overshoot and slow convergence speed of ABC algorithm. The testing results show that compared with perturb & observe algorithm, conductance increment and ABC methods, ABC-fuzzy method can make PEMFC obtain greater output power, faster regulation speed, smaller steady-state error, less oscillation and stronger anti-interference ability. The MPPT scheme based on ABC-fuzzy can effectively realize the maximum power output of PEMFC, and plays an important role in improving the service life and power supply efficiency of PEMFC. 

https://www.nature.com/articles/s41598-022-08327-5.pdf

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state-of-the-art survey

Recent advances and summarization of fault diagnosis techniques for proton exchange membrane fuel cell systems: A critical overview

For decades, solar energy has taken an increasingly important part, which will continue to rise, driven by carbon peaking and carbon neutrality strategic goals, in the energy consumption of China (Yang et al., 2021a; Mahidin et al., 2021). Due to the intermittence and volatility of sunlight, photovoltaic (PV) power generation is more erratic than conventional power which results in some problems of the grid: frequency instability (Liu et al., 2020; Murty and Kumar, 2020), dispatch difficulty (Peng et al., 2020; Tummala, 2020), and voltage and current surges (Bozorg et al., 2020; Yang et al., 2021b). Hence, accurately forecasting the power generation of the PV system is one of the major issues of PV system’s engineering practice to settle the aforementioned problems (Huang et al., 2021a; Yang et al., 2021c). According to the modeling means of prediction, the prevailing PV power prediction methods are broadly divided into three categories, namely, physical, statistical, and artificial intelligence (AI) forecasting technologies (Yang et al., 2021d). Furthermore, the applicable ranges of different forecasting technologies are given in Figure 1. Moreover, these PV power forecasting technologies face different challenges. First, it is difficult for physical forecasting technology to obtain accurate future weather forecast information and determine output characteristic model parameters. Second, statistical forecasting technology is not demanding for geographical location and other information of PV systems but requires masses of historical data to deduce statistics laws. As for AI forecasting technology, it is easy to trap in the local optimum because of internal defects of the AI algorithm. This work aims to clarify aforementioned problems and give some perspectives on various PV power prediction methods.

/pdf/state-of-the-art-solar-energy-forecasting-approaches-1295hxe0.pdf

State-Of-The-Art Solar Energy Forecasting Approaches: Critical Potentials and Challenges

With the swift development of economy, traditional energy resources have been exposed to problems such as decreasing reserves, environmental pollution, and health hazards of human beings (Yang et al., 2020a; Yang et al., 2021a). Nowadays, renewable energy resources (RESs) have been given great importance by the public and achieved significant development under the support of government policies (Sun and Yang, 2020; Mahidin et al., 2021). Among a variety of RESs, wind energy resources are abundant, cheap to develop, have high conversion efficiency, and are environmentally friendly (Guchhait and Banerjee, 2020; Li et al., 2020). As a result, wind farms have sprung up all over the country. However, with the gradually increasing proportion of wind turbine commitment in the grid, the severe effects of randomness and intermittency of wind farms on the grid cannot be ignored (Xi et al., 2016). In order to settle the above issues, wind forecasting technology is proposed (Sachdeva and Verma, 2008), whose contribution is mainly reflected in the following three aspects: 1) optimize the scheduling of grid and provide conditions for wind power bidding on-grid; 2) easy maintenance and overhaul of wind turbines; and 3) reduce reserve capacity and operating cost. In general, the classification of wind speed and power prediction is mainly carried out from two views, as shown in Table 1. Wind speed and power forecasting technologies are classified as long-term forecasting, medium-term forecasting, short-term forecasting, and ultra-short-term forecasting on the basis of the time scale. Based on the prediction model, these research techniques are classified as physical approaches, statistical approaches, and learning approaches.

Wind Speed and Power Prediction Approaches: Classifications, Methodologies, and Comments

Copyright © 2022 Ye, Yang, Han and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Approved by: Frontiers Editorial Office, Frontiers Media SA, Switzerland

https://www.frontiersin.org/articles/10.3389/fenrg.2022.924118/pdf

Haoyin Ye

Papers

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state-of-the-art survey

Recent advances and summarization of fault diagnosis techniques for proton exchange membrane fuel cell systems: A critical overview

State-Of-The-Art Solar Energy Forecasting Approaches: Critical Potentials and Challenges

Wind Speed and Power Prediction Approaches: Classifications, Methodologies, and Comments

Corrigendum: State-Of-The-Art Solar Energy Forecasting Approaches: Critical Potentials and Challenges