• Energy Research
• 2021-2025close
• BE close
• Energies close

In modern inverter applications, multilevel converters are used thanks to their capability of reducing the passive fi lter volumes, improving the overall THD, sharing the power sources across the several converter cells, and providing a reconfi guration ability in case of failure. However, increasing the number of cells makes more complex the control of the converter. This article proposes a decentralized control technique based on the use of elementary modular controllers, associated with each converter cell and communicating with their close neighbors to obtain the balancing of the powers they supplied. Each modular controller can be dynamically removed or added to allow reconfi guration of the converter for functional safety purpose. This method is applied to a 240V/3A 5-cells Cascaded Full-Bridge Multilevel Converter. The response of the system to load transients and cell voltage disturbances demonstrates the robustness of the proposed decentralized control method. Thanks to the use of modular controllers, the number of levels of the converter can be extended to the order N without adding complexity to its control.

The establishment of a long-term, cross-border market in which forward market coupling and bilateral contracts are developed in an integrated approach is instrumental for the European internal electricity market. We propose the joint energy and transmission right auction (JETRA) mechanism, developed by O’Neill et al., as a solution for long-term cross-border markets in Europe. The main contribution of this research lies in its examination of the underlying market structures for effective JETRA implementation. We compare the institutional setting, market rules, and grid modeling under nodal and zonal pricing systems, adapting JETRA to the flow-based market coupling (FBMC) mechanism that is currently implemented in the European day-ahead market. This adaptation reveals the inherent limitations of FBMC in supporting JETRA, in particular in the long-term auction. We also identify constraints posed by existing European market rules, particularly those that affect the application of multi-settlement rules and the effective timeframe of hedging instruments. In conclusion, our research suggests that transitioning from zonal to nodal pricing is essential for JETRA’s effective implementation. Furthermore, a comprehensive market reform is required to seamlessly integrate long- and short-term markets.

High-resolution wind datasets are crucial for ultra-short-term wind forecasting. Penetration of WT installations near urban areas that are constantly changing will motivate researchers to understand how to adapt their models to terrain changes to reduce forecasting errors. Although CFD modelling is not widely used for ultra-short-term forecasting purposes, it can overcome such difficulties. In this research, we will spatially extrapolate vertical profile LIDAR wind measurements into a 3D wind velocity field over a large and relatively complex terrain with the use of stationary CFD simulations. The extrapolated field is validated with measurements at a hub height of three WTs located in the area. The accuracy of the model increases with height because of the terrain anomalies and turbulence effects. The maximum MAE of wind velocity at WT hub height is 0.81 m/s, and MAPE is 7.98%. Our model remains accurate even with great simplifications and scarce measurements for the complex terrain conditions of our case study. The models’ performance under such circumstances establishes it as a promising tool for the evolution of ultra-short-term forecasting as well as for the evaluation of new WT installations by providing valuable data for all models.

The energy transition to a decarbonized energy scenario leads toward distributed energy resources in which end users can both generate and consume renewable electricity. As a result, several challenges arise in terms of decentralized energy resource management and grid reliability. With microgrids, the cooperation of distributed energy resources is improved, and with peer-to-peer energy exchange and demand response programs, better energy allocation and flexible management of consumption loads according to the needs of supply systems are achieved. However, effective peer-to-peer energy allocation and flexible demand management in microgrids require the development of market structures, pricing mechanisms, and demand response strategies enabled by a reliable communication system. In this field, blockchain offers a decentralized communication tool for energy transactions that can provide transparency, security, and immutability. Therefore, this paper provides a comprehensive review of key factors for peer-to-peer energy trading and flexible energy demand management in blockchain-enabled microgrids. The goal is to provide guidelines on the basic components that are useful in ensuring efficient operation of microgrids. Finally, using a holistic view of technology adoption as a tool for peer-to-peer communication in microgrids, this paper reviews projects aimed at implementing blockchain in energy trading and flexible demand management.

PV-Battery systems are commonly sized based on the lowest solar radiation of the site of implementation. This implies that during days of high solar radiation excess energy is available. This study investigated the potential of excess energy for thermal storage from PV systems for a case of a rural health center. The system components of a typical PV installation in a rural health center in Tigray, Ethiopia, were considered. The electricity load profile of the health center and solar radiation data available from Mekelle city were used as inputs to a TRNSYS model. Analysis of excess energy in the system at hourly and ten-minute time intervals was conducted. The analysis results indicate that during the months from September to May excess energy was available that could be thermally stored and utilized. During these months, the excess peak power ranged from 737 to 841 W and daily average excess energy ranged from 2070 to 2959 Wh. In contrast, in the months from June to August, no excess energy was available due to low solar radiation.

In this paper, a numerical model of a capillary tube is developed. The considered expansion device is placed against the suction line at the inlet of the compressor. Wrapping the capillary tube around the suction line allows heat to be recovered by superheating the refrigerant leaving the evaporator. This increases the degree to which the fluid is superheated, preventing liquid droplets from entering the compressor and causing damage. The open-source software PYTHON is used for modelling the non adiabatic capillary tube, and the results are validated by comparing them with experimental tests. This study demonstrates that an accurate contact of the capillary tube with the suction line affects the superheating of the compressor inlet fluid by increasing its temperature by up to 5 degrees and produces an increase in COP of 3–4%. On the other hand, the length of the capillary tube affects the flow rate of the refrigerant circulating in the cycle; in particular, it is noted that a 300% increase in the capillary tube length leads to a decrease in the refrigerant flow rate of up to 50–60%.

Data-driven models, either simplified or detailed, have been extensively used in the literature for energy assessment in buildings and districts. However, the uncertainty of the estimated parameters, especially of thermal masses in resistance–capacitance (RC) models, still remains a significant challenge, given the wide variety of buildings functionalities, typologies, structures and geometries. Therefore, the sensitivity analysis of the estimated parameters in RC models with respect to different geometric characteristics is necessary to examine the accuracy of identified models. In this work, heavy- and light-structured buildings are simulated in Transient System Simulation Tool (TRNSYS) to analyze the effects of four main geometric characteristics on the total heat demand, maximum heat power and the estimated parameters of an RC model (4R3C), namely net-floor area, windows-to-floor ratio, aspect ratio, and orientation angle. Executing more than 700 simulations in TRNSYS and comparing the outcomes with their corresponding 4R3C model shows that the thermal resistances of 4-facade building structures are estimated with good accuracy regardless of their geometric features, while the insulation level has the highest impact on the estimated parameters. Importantly, the results obtained also indicate that the 4R3C model can estimate the indoor temperature with a mean square error of less than 0.5 °C for all cases.

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.