IET Renewable Power Generation最新文献

This article investigates multi-objective size optimization of a hybrid energy system (HES) composed of biomass, photovoltaic (PV) and diesel generator considering technical, economic and environmental aspects. For this aim, two multi-objective frameworks (techno-economic and techno-enviro-economic) are developed where in the techno-economic framework, total net present cost (TNPC) and loss of power supply probability (LPSP) are considered as the conflicting objectives. LPSP value is used as an index to measure the system reliability (by decrease of LPSP value, reliability will increase). In the techno-enviro-economic framework, net present cost of CO₂ emission is also included in TNPC value. Furthermore, to determine the impact of biomass and diesel generator fuel costs on the Pareto front, a sensitivity analysis is conducted. Over the case study, simulated results show that in techno-economic framework, at small values of LPSP, it is cost-effective to use PV-diesel rather than PV-biomass system. In techno-enviro-economic framework, though at LPSP = 0, it is cost-effective to use PV-diesel system, in other levels of LPSP, it is desired to use PV-biomass system.

Real-time acquisition of microgrid (MG) operation data and remote control play a crucial role in the safe and stable operation of MG. A design scheme of monitoring system is proposed for the wind/photovoltaic/energy storage islanded direct current MG. The core controllers used in direct current MG system are programmable logic controller and digital signal processor. The monitoring system mainly adopts Ethernet communication, together with serial port communication mode Recommend Standard 232 (RS232) and Recommended Standard 485 (RS485) communication modes. A heterogeneous networking is built with controllers and data display instruments. Different monitoring interfaces are designed, the important information can be real-time detected, collected and displayed. The multi-energy and multi-load dispatching operation control are realized according to scheduling operation plan. The parameters of the inverter can be adjusted, and the output voltage waveform and sinusoidal pulse width modulation waveform of the inverter can be measured. The communication of the proposed monitoring system is reliable, flexible and expandable, and easy to realize remote operation control. Moreover, the detection and control functions can be extended further. Through experiments, the effective control and monitoring of the designed monitoring system is verified, and the intelligent management is realized.

To reduce phenomenon of abandoning wind and photovoltaic power, improve the limitations of traditional methods in dealing with uncertainty of wind and photovoltaic power and system planning, and improve the optimal configuration of resources, an optimal capacity configuration of joint system considering uncertainty of wind and photovoltaic power and demand response is proposed. Firstly, using probability distribution information of wind and photovoltaic power output, the distance between actual probability distribution and forecast probability distribution is constrained based on the 1-norm and ∞-norm. A fuzzy set considering uncertainty probability distribution is constructed, and a two-stage distributed robust planning model is established. The first stage involves optimizing joint system capacity for scenarios with the lowest probability of wind and photovoltaic power; the second stage builds on capacity optimization scheme from the first stage and aims to minimize operating costs through simulation optimization. Secondly, column and constraint generation is used to solve the model. Finally, constructing an example based on actual data from a power grid in Northeast China for simulation and analysis, the results show that the method achieves a balanced optimization of robustness and economy, effectively reduces carbon emissions and improves ability of the system to consume wind and photovoltaic power.

The EU project PROMETEO has the scope of testing a 25 kW solid oxide electrolysis system integrated with a concentrated solar power plant via thermal energy storage in a relevant environment. Given the plant layout and the hydrogen demand characteristics, this work aims to identify how to operate the system effectively when renewable electricity is unavailable and how to modulate the load during hydrogen generation, thus defining the system's operation modes and control strategy. A 5 kW_e stack has been tested at the FBK facility. The hot standby tests show that feeding a reducing gas at the negative electrode and air at the positive electrode, without polarizing the stack, effectively keeps the stack hot at 750°C and prevents degradation. Conversely, the electric protection approach leads to significant stack degradation (15% voltage drop in 200 h for one cluster). Regarding modulation of hydrogen generation, with low steam flowrates, the stack current and the flow rate of produced hydrogen mainly depend on the steam flow rate, while it is not affected by the stack temperature; conversely, with high steam flowrates, the current depends only on the stack temperature (from 25 A at 670°C to 65 A at 760°C). Based on the results, two hot standby modes and two load control strategies to be implemented and tested in the PROMETEO prototype are proposed.

The integrated energy microgrid (IEM) has good potential for both active and reactive power regulation, which are gradually becoming critical resources for participating in power system ancillary services. This paper constructs a distributed optimization model for the IEMs participating in voltage regulation ancillary services by considering multiple physical network constraints. To reduce computational complexity, the DistFlow power flow model and the second-order cone relaxation method are introduced to transform the optimization model into a second-order cone programming problem. Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. Finally, the proposed optimization model and solution method are tested in the modified IEEE-33 node test case to verify their effectiveness and feasibility.

This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non-standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS-PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier-processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs.

The existing methods for determining the module arrangement in photovoltaic (PV) farms are considered insufficient as they are generally limited to the environment of flat ground without considering both physical and electrical factors. The orientations of PV modules may be very diverse when installed in places with complex topography, e.g. mountains and abandoned mine sites. Thus, the received irradiance by the modules is inconsistent directly results in current differences and hence leads to significant mismatch loss in the PV arrays. This paper proposes a solution to determine the most appropriate combination of tilts and orientations of PV modules as well as the arrangement of PV arrays. The complex topographies are fully considered to minimize the mismatch loss phenomenon, and hence the power generation degradation. The solution adopts a set of models, i.e. the irradiation model for the calculation of optimal module tilts and orientations, the shadow model for shadow effect analysis, and the mismatch model for mismatch condition analysis. A two-layer multi-objective optimization is implemented for the optimal arrangement. The proposed solution is assessed through the case study of a 30 MW PV farm. The result confirms the effectiveness of the proposed solution for the optimal spatial module placement.

The study utilizes the RBF neural network model to evaluate the green development of the Pearl River Delta urban agglomeration, revealing a consistent increase in development levels from 2005 to 2020 despite notable inter-city variations. The model adeptly handles complex interactions within urban green development, offering insights into comprehensive impacts and aiding in policy evaluation and optimization. It provides urban planners and decision-makers with data-driven support, enhancing the scientific basis and stability of policy-making.

Wind energy being a free source of energy is becoming popular over the past decades and is being studied extensively. Integration of wind turbines is now being expanded to urban and offshore settings in contrast to the conventional wind farms in relatively open areas. The direct installation of wind turbines poses a potential risk, as it may result in financial losses in scenarios characterized by inadequate wind resource availability. Therefore, wind energy availability analysis in such urban environments is a necessity. This research paper presents an in-depth investigation conducted to predict the exploitable wind energy at four distinct locations within Nottingham, United Kingdom. Subsequently, the most suitable location, Clifton Campus at Nottingham Trent University, is identified where a comprehensive comparative analysis of power generation from eleven different wind turbine models is performed. The findings derived from this analysis suggest that the QR6 wind turbine emerges as the optimal choice for subsequent experimental investigations to be conducted in partnership with Nottingham Trent University. Furthermore, this study explores the selection of an appropriate probability density function for assessing wind potential considering seven different distributions namely, Gamma, Weibull, Rayleigh, Log-normal, Genextreme, Gumbel, and Normal. Ultimately, the Weibull probability distribution is selected, and various methodologies are employed to estimate its parameters, which are then ranked using statistical assessments.

The cover image is based on the article A novel prediction method for low wind output processes under very few samples based on improved W-DCGAN by Shihua Liu et al., https://doi.org/10.1049/rpg2.13073.