Extra Generation Research Articles

Balancing the great British electricity system — Bulk dispatch optimisation

The Balancing Mechanism, managed by the British electricity system operator, National Grid ESO, is designed to maintain a balance between electricity generation and demand. It achieves this by purchasing extra generation (or demand reduction) through accepted offers and extra demand (or generation reduction) through accepted bids from Balancing Mechanism Units (BMUs) in real-time. The current manual approach for instructing BMUs becomes increasingly challenging as access to the market widens and the number of BMUs grows. This paper introduces a proof-of-concept optimisation model to assist Control Room engineers in making optimal decisions for a large number of BMUs. We outline the requirements for instructing BMUs, provide their mathematical formulation, and illustrate this using simple examples. Computational performance results based on test cases with up to 500 units are presented.

Aerodynamics and aeroacoustics of ducted propellers: A study on the design and geometry effects

Ducted propellers present broad applicability in urban air mobility vehicles due to their enhanced operational safety, improved aerodynamic performance, and potential to mitigate noise emissions. This study proposes a numerical approach for designing adequate duct geometries, focusing on the duct's lip profile, expansion ratio, and tip clearance, aiming to provide valuable design guidance for ducted propellers. The simulations are validated through experimental data, showing reasonable agreement in terms of thrust generation and far-field noise. The mean flow and generated thrust are characterized with a parametric study using steady simulations, while delayed detached eddy simulations are employed to capture transient flow characteristics and investigate noise generation. The noise levels were computed using the integral solution of the Ffowcs-Williams and Hawkings equation. The lip geometry impacts the flow distribution and generated thrust, modifying the tonal noise. Furthermore, slightly divergent ducts can increase the total thrust by minimizing flow separation on the duct wall while increasing the suction on the duct lip. The primary noise sources are identified at the propeller's leading edge and tip. The results reveal that divergent ducts effectively reduce tonal noise at all observer angles but increase broadband noise, attributed to the noise sources at the leading edge of the propeller and the interaction with the duct lip. Additionally, reducing the tip clearance from 2 to 1 mm enhances the total thrust by more than 20% without causing extra noise generation.

Two-stage decision-dependent demand response driven by TCLs for distribution system resilience enhancement

Service restoration is a critical function of the self-healing distribution system to cope with extreme events. Cold load pickup (CLPU) caused by thermostatically controlled loads (TCLs) requires extra generation power to retore loads, slowing down the distribution system restoration process. Meanwhile, increasing renewable energy brings the supply-demand imbalance because of the power variability and may cause voltage or current violations. Accordingly, a two-stage decision-dependent demand response (DR) methodology is proposed to address the two barriers to distribution system resilience enhancement. Specifically, two load control strategies are developed in the first-stage DR to regulate TCLs for CLPU mitigation, which reduces the additional power requirement when re-energized. In the second-stage DR, the restored TCLs that can be controlled to provide flexibility are characterized as virtual energy storage (VES) for managing renewable energy variability. In this way, the required power generation is reduced, and the power fluctuation problem caused by renewable energy can be well addressed. It helps to speed up the load restoration and keep the distribution system operating securely. Moreover, the load profiles with CLPU mitigation control, VES parameters and VES availability time, depend on the re-energization time. In other words, load restoration decisions determine the two-stage DR. This is the most important finding of this paper and explicitly formulated in the load restoration model for accurate load characterization. The load restoration model is described by a mixed integer second-order cone programming (MISOCP) problem and solved using MATLAB 2021 with Gurobi solver. Case studies show that the proposed methodology can significantly mitigate CLPU and restore more loads. The decision-dependent behaviors of the two-stage DR are also validated.

Solution-processed organic-inorganic hybrid material based on P3HT and SnS2 nanoparticles layers: Synthesis and properties

Sn-based nanocomposites attract extensive attention as a new generation of photoelectric hybrid materials due to their high photoelectric conversion efficiency. In this work we synthesized SnS2 nanoparticles by a hydrothermal method employing citric acid (capping agent) and performed a capping agent exchange by pyridine to improve their solution processing and final properties. We fabricated layers with pyridine-capped nanoparticles and poly(3-hexylthiophene-2,5-diyl) (P3HT), employing the classical partial film sequence of a solar cell (ITO/PEDOT:PSS/P3HT:Nps). We found a “coupling" between the nanoparticles and the polymer and highly ordered structures within the layers. The addition of nanoparticles improved the UV–vis absorption of P3HT and allowed the exploitation of the photoluminescence emission to promote extra charge carrier generation. Moreover, we found that SnS2_py nanoparticles form a nanoscopic mixture with the P3HT. Our pyridine-capped SnS2 nanoparticles are good candidates for fabricating hybrid materials, employing P3HT as the organic compound for application in polymeric-inorganic solar cells.

Design and control of a stand-alone microgrid with renewable energy sources

This study describes the design and operation of an autonomous microgrid that includes photovoltaic (PV) panels, wind turbines, a diesel engine, and a lithium-ion battery. The objective is to evaluate the efficiency and performance of a central control system that prioritizes renewable energy sources and, when necessary, uses a diesel generator and battery as backup power sources. Additionally, MATLAB is utilized to model and simulate the microgrid system. The needs for the microgrid, including the capacities of each energy source and the load demand, are first laid out in the study. In this study, the power production from the PV panels and wind turbines is estimated based on solar radiation and random wind speeds, respectively. The energy output from each source is measured and shown. After that, a centralized control system is established to oversee the operation of the microgrid. The control algorithm ensures that a considerable portion of the load need is satisfied via renewable energy sources. The extra generation that is stored as energy for later use charges the battery. In the case of a load deficit, however, the battery and diesel generator are employed to provide the demand. The battery's energy reserve and charge status are monitored and displayed. The research results, the suggested centralized control system efficiently controls the microgrid operation by maximizing the use of renewable energy sources and reducing dependency on the diesel generator. The findings highlight the need to use effective control techniques to maximize the integration of renewable energy sources.

Analysis and hypothesis testing of redundant energy of solar home systems without net-metering

Solar home systems (SHS) are increasing being deployed as sustainable energy supply for the residential sector to meet the Sustainable Development Goal 7 target by 2030, especially for countries in sub-Saharan Africa (SSA) where national grid electricity supply is inadequate or weak. For SHS in SSA, however, a unique challenge exists as many of the households do not have access to net-metering system that allows extra PV energy generation during the daytime to be exported to the grid. This leads to waste energy generation (redundant energy) when the household energy demand is lower than the PV energy generation. In this study, analysis has been conducted to determine the magnitude of redundant energy of 3 SHS. Hypothesis testing of the existence of redundant energy from the SHS is also conducted. Our study has revealed that generally, there is redundant energy generation in the hours of 10 a.m. to 3 p.m. for the households, with hourly values ranging from 0.37 kWh to 1.55 kWh. The redundant energy represents 29.6 %–56.3 % of the households' monthly PV energy generation. The findings of this study give insights into the potential of harnessing redundant energy of SHS for planning smart energy cities if net-metering systems were available.

Invasions and Local Outbreaks of Four Species of Plague Locusts in South Africa: A Historical Review of Outbreak Dynamics and Patterns.

The current paper provides a detailed review of the historical outbreaks of each of the four plague locust species found in South Africa, namely the brown locust, the African migratory locust, the red locust, and the southern African desert locust. The history and dynamics of the plague infestations and the major local outbreaks are summarized. The typical patterns of the outbreaks of the different species are described, and the threat of these locusts to agriculture in South Africa is defined. The brown locust produces regular outbreaks in the semi-arid Karoo, with large-scale eruptions of plague proportions occurring about once per decade. Patterns of outbreaks often repeat themselves, but the sheer size of the plague outbreaks is almost impossible to stop, and the brown locust has the potential to threaten food security throughout southern Africa. The African migratory locust produces outbreaks in some of the main maize and wheat cropping areas where it is difficult to control. This locust has taken advantage of the man-made crop environment to produce an extra generation per year that was not previously possible in the original grasslands. The coastal area of KwaZulu Natal Province in South Africa was a prime reception and breeding area for plague invasions of the red locust in the past, and the country, therefore, relies on the successful control of outbreaks in east and central Africa to prevent the recurrence of the plague invasions. The southern African desert locust occurs in the Kalahari Desert area, and outbreaks requiring chemical control are rare.

Performance evaluation of large-scale photovoltaic power plant in Saharan climate of Algeria based on real data

In this study we evaluate a large-scale, grid-connected photovoltaic power plant (LS-PVPP) in a hot climate in Adrar, Algeria. The plant's performance was evaluated using both real-world data from 2018 and simulations conducted with PVsyst in terms of various parameters, such as reference yield, capacity factor, performance ratio, statistical indicators, and carbon emission savings. The results indicate a significant reduction in carbon emissions by the LS-PVPP, with a final yield of 4.98 kWh/kWp/day, a performance ratio of 71.67 %, a capacity factor of 20.72 %, and a global system efficiency of 10.66 % and save 19,388 metric tons of carbon in 2018. The industrial approach analysis revealed a target performance ratio (PR) of 71.17 %. The difference of 0.5 % led to extra energy generation of 252.67 MWh and additional revenue of 27,957.42USD. The simulated data predicted a final yield of 5.36 kWh/kWp/day, a performance ratio of 77 %, a capacity factor of 22.38 %, a global system efficiency of 11.55 %. The aim of this research is to offer data on the performance of a (LS-PVPP) in a hot climate. Researchers, photovoltaic project developers, and stakeholders can utilize the findings to assess project viability, optimize performance, and maximize the environmental and economic benefits.

Enhancing resilience and sustainability of distribution networks by emergency operation of a truck-mounted mobile battery energy storage fleet

Distribution networks have always been exposed to outages due to various internal and external causes. During emergencies, the damaged areas are disconnected and as a result, a portion of the connected loads remained unmet until ending required repairs. Accordingly, utilizing the total renewable potential in smaller islands may be unmanageable and result in curtailment. In this paper, a method is proposed to overcome these challenges by utilizing a fleet of mobile battery storage (MBS) systems. The MBSs will overcome renewable curtailment and unmet load simultaneously during emergencies via a shift in the produced energy. The excess energy produced in the islands with extra generation will be stored and then used at another location and time in the islands without production adequacy. To do this, a multi-stage and spatio-temporal operation model for a fleet of truck-mounted MBS systems based on road transport is proposed. Both time and cost of transportation are considered and modeled by linear formula. The battery lifetime and full four-quadrant power factor of the integrated inverter are also considered. The proposed model aids to decrease computational burden by decomposing the transportation model from the distribution network. Besides, it ensures the minimum cost operation while maximizing renewable resources utilization trapped on small islands. The model is linear and capable of handling real-life and large-scale systems when integrated into commercial distribution software. The model is validated by implementation on the 33-bus distribution grid coupled with a 15-node transportation network and results demonstrate increased resilience besides enhanced renewable resources utilization.

Showcasing New Tourism Destination by Using Gis: a Study of Sikkim

Purpose: The main purpose of this research is to find out new tourist destinations and suggest developmental strategies through scientific ways for solving overtourism-related problems. Theoretical framework: In this research paper, the researchers emphasized finding new tourist spots and analyzing their potential both in touristic and infrastructural value was assessed by the researcher with the help of a most modern tool like Geographic Information System. The researchers have explained a scientific process of exploring new tourist destinations or tourist spots by using GIS and have shown how to develop proper infrastructure around those spots. Design/methodology/approach: A “Multicriteria Spatial Decision Support System modeling” was used to find out and validate the existing and new tourist spots. In the pair-wise comparison method, a criterion versus criteria was created to compare each pair of criteria and assign relative ratings using the scale of pair-wise comparison. In this research, the Simple Additive Weighting (SAW) method is used because it gives the “most acceptable results for majority of single-dimensional problems”. The researcher has created this map to support the ‘Destination Fetching Model’, and it can also be used for promoting sustainable tourism in the area. If the entire land cover is visible to the planners with an aerial view, they can plan for tourism infrastructure and superstructures from anywhere. GIS-based layer data has created an "all covered" aerial map with the help of advanced GIS technology, which not helped the planners to understand the current land condition, but also showed the open area which can be utilized for new tourism set-up. Manmade and natural resources are also classified through this LULC model. Findings: These new tourist spots will help planners and stakeholders to distribute excessive crowds to comparatively new destinations. It is observed that most of the Indian tourism destinations suffer from “over Tourism” in peak season. This research will help spread the flow of tourists to the nearest newly added destinations. Research, Practical & Social implications: Scientific tourism planning through GIS will also help tourism service providers and planners to add infrastructure to the new destinations, and would also help local people to generate income through tourism. Originality/value: This research will solve the “over tourism” problem of any destination or state, and help generate more visitor’s day at the destination which is proportionate to extra revenue generation for the local, regional and national tourism stakeholders.

Van der Waals Nonlinear Photodetector with Quadratic Photoresponse.

With unique electronic and optical attributes and dangling-bond-free surface, two-dimensional (2D) materials have broadened the functionalities of photodetectors. Here, we report a quadratically nonlinear photodetector (QNPD) composed of a van der Waals (vdW) stacked GaSe/InSe heterostructure. Compared with the reported 2D material-based photodetectors, the extra second-harmonic generation (SHG) process in GaSe/InSe leads to the quadratically nonlinear function between photocurrent and optical intensity, extending the photodetection wavelength from 900 to 1750 nm. The QNPD is highly sensitive to the variation of optical intensity with improved spatial resolution. With the light-light interaction in SHG converted into electrical signal directly, we also demonstrate the QNPD as an autocorrelator for measuring ultrafast pulse widths and an optoelectronic mixer of two modulated pulses for signal processings. The simultaneous involvement of light-light interaction and photoelectric conversion in the vdW stacked QNPD promises its potential to simplify the optoelectronic systems.

Theoretical modelling and optimization of a geothermal cooling system for solar photovoltaics

Solar cells lose efficiency as the operating temperature increases under normal operating conditions, resulting in a very significant power output reduction. This issue is most relevant in areas with the highest photovoltaic energy harvesting potential, where irradiance and ambient temperature are particularly high. A novel cooling system for commercial photovoltaic modules based on low-enthalpy geothermal cooling is proposed in this paper. Excess heat is evacuated from the solar module in a clean and efficient manner by a single-phase and close-loop cooling circuit that uses the underground as a natural heatsink. In addition, a thermoelectrical theoretical model is presented, that allows the optimization of the flowrate and the maximization of the extra power generation of the cooled module. A prototype was manufactured and tested in outdoor conditions in September 2021 in Spain. Test results are in good agreement with the theoretical model, which has been used for prediction of the performance of the system for a typical meteorological year in the region. A peak efficiency improvement of 10.7% is calculated for the month of July, with an optimal flowrate of 1.8 l/min per square meter of solar module. A yearly weighted average power gain of up to 4.9% is also calculated.

Optimal control strategies-based maximum power point tracking for photovoltaic systems under variable environmental conditions

To increase the efficiency of photovoltaic (PV) array output under variable environmental conditions, maximum power point tracking (MPPT) of the solar arrays is needed. This paper proposes fuzzy logic controller (FLC)-based MPPT, artificial neural network (ANN)-based MPPT, neuro-fuzzy (NF)-based MPPT, particle swarm optimisation (PSO)-based MPPT, and cuckoo search (CS) algorithm-based MPPT to combine an adaptive controller and an optimisation, to guarantee global stability and a constant settling time for all operation conditions. This combination enables an increase in the power generated in comparison with conventional MPPT techniques. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions, and achieve good performance. It is also noticed that operating the photovoltaic array based on maximum power point tracking conditions gives about 43% extra power generation than in the case of normal operation.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of the efficient working of power grid stations. Accurate forecasts help to detect the fault and enhance grid reliability for organizing sufficient energy transactions. STLF ranges from an hour ahead prediction to a day ahead prediction. Various electric load forecasting methods have been used in literature for electricity generation planning to meet future load demand. A perfect balance regarding generation and utilization is still lacking to avoid extra generation and misusage of electric load. Therefore, this paper utilizes Levenberg–Marquardt (LM) based Artificial Neural Network (ANN) technique to forecast the short-term electricity load for smart grids in a much better, more precise, and more accurate manner. For proper load forecasting, we take the most critical weather parameters along with historical load data in the form of time series grouped into seasons, i.e., winter and summer. Further, the presented model deals with each season’s load data by splitting it into weekdays and weekends. The historical load data of three years have been used to forecast week-ahead and day-ahead load demand after every thirty minutes making load forecast for a very short period. The proposed model is optimized using the Levenberg-Marquardt backpropagation algorithm to achieve results with comparable statistics. Mean Absolute Percent Error (MAPE), Root Mean Squared Error (RMSE), R2, and R are used to evaluate the model. Compared with other recent machine learning-based mechanisms, our model presents the best experimental results with MAPE and R2 scores of 1.3 and 0.99, respectively. The results prove that the proposed LM-based ANN model performs much better in accuracy and has the lowest error rates as compared to existing work.

Style Uncertainty Based Self-Paced Meta Learning for Generalizable Person Re-Identification.

Domain generalizable person re-identification (DG ReID) is a challenging problem, because the trained model is often not generalizable to unseen target domains with different distribution from the source training domains. Data augmentation has been verified to be beneficial for better exploiting the source data to improve the model generalization. However, existing approaches primarily rely on pixel-level image generation that requires designing and training an extra generation network, which is extremely complex and provides limited diversity of augmented data. In this paper, we propose a simple yet effective feature based augmentation technique, named Style-uncertainty Augmentation (SuA). The main idea of SuA is to randomize the style of training data by perturbing the instance style with Gaussian noise during training process to increase the training domain diversity. And to better generalize knowledge across these augmented domains, we propose a progressive learning to learn strategy named Self-paced Meta Learning (SpML) that extends the conventional one-stage meta learning to multi-stage training process. The rationality is to gradually improve the model generalization ability to unseen target domains by simulating the mechanism of human learning. Furthermore, conventional person Re-ID loss functions are unable to leverage the valuable domain information to improve the model generalization. So we further propose a distance-graph alignment loss that aligns the feature relationship distribution among domains to facilitate the network to explore domain-invariant representations of images. Extensive experiments on four large-scale benchmarks demonstrate that our SuA-SpML achieves state-of-the-art generalization to unseen domains for person ReID.

RETRACTED: Renewable source uncertainties effects in multi-carrier microgrids based on an intelligent algorithm

High volatility and unpredictable behavior of renewable energy sources provide various challenges in the operation of power systems with maintaining the system's reliability. To solve the challenges of grid-connected renewable energy sources, microgrids (MGs) can play an essential role due to their tuning capability and flexibility. The fast growth of power-to-gas technology causes to support renewable energy integration and transforms the extra renewable generation to combined normal gas at a suitable time. The optimal type, size, and commissioning year of the accessible distributed energy resources through the economic application of nominated resources are evaluated to be applied in the suggested multi-carrier microgrid. This study focuses on the simultaneous operation of multi-carrier MGs and their equipment as an optimization problem. Small-scale energy sources and load demand in MGs are modeled to reduce costs of exchange between the main grid and MGs, as well as the load balance between MGs and load demand. Given that it is difficult to solve the desired problem considering various constraints, the developed meta-heuristic whale optimization algorithm (WOA) is used along with adaptive weighting coefficients and a local search operator. Moreover, this model investigates the unstable behavior of loads, renewables, and electricity prices using the probabilistic load flow method (PLF), considering multiple carriers simultaneously to match the real world. In this paper, MGs with multiple-carriers exchange energy with the upstream grid. The energy exchange between MGs is also considered. The results of simulations performed on the grid consisting of three interconnected multi-carrier MGs confirmed the effectiveness of the suggested model. Obtained numerical results prove the validity of the suggested multi-carrier microgrid expansion planning issue by economic, environmental, and technical points.

Evaluating the impact of productive uses of electricity on mini-grid bankability

Mini-grids are discussed as a cost-effective and sustainable solution to provide electricity to populations lacking access, of which the majority reside in sub-Saharan Africa. Projects struggle to be fiscally solvent, however, in part because electricity demand from rural households is low relative to installed mini-grid generation and distribution capacity, making it difficult to recoup investment costs. This paper explores how productive uses of electricity may increase electricity demand and evaluates impacts of increased electrical load to the technical and economic metrics of mini-grid projects. A full cashflow analysis is completed to evaluate business model efficacy for mini-grid developers and investors for a case study of a township in Sierra Leone. Productive uses of electricity are shown to have minimal improvement to the financial viability of residential mini-grid projects, and subsidies are required to reach financial metrics suitable to attract private investment. Of the productive uses investigated, year-round loads had the most positive impact to payback periods. Seasonal loads offer only small improvements to payback period and internal rate of return because they require extra generation assets to meet peak demand in the high season that are left idle or heavily under-loaded during the low season. Overall, the study finds the most effective way to improve financial outcomes through productive uses is to increase the mini-grid load factor with productive uses of electricity that operate year-round.

Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm

Thermal load is an important type of demand response (DR) resources to maintain the power balance of regional electricity-heating energy systems. However, the diversity of power-consuming patterns and time series coupling of production processes arise challenges for precisely tracking the scheduled command of industrial and domestic thermal loads. This paper proposes a feasible solution to achieving collaborative scheduling of diversified thermal loads meanwhile optimizing the tracking performance of scheduled power, via a mixed integer linear programming (MILP) based decomposition algorithm. The power-temporal granularity is firstly introduced and modeled to characterize the available regulated capacity of diversified thermal loads in each power regulation event. Then, the time series coupling model among multiple thermal devices is established to describe the coupling relationship in production processes. On this basis, the diversified thermal loads are co-regulated in the considered system to accomplish the decomposition objectives and without taking up extra generation and storage regulation resources. Case studies based on real data of a regional thermal loads are conducted, which demonstrates the feasibility and effectiveness of proposed method in improving comprehensive benefits and reducing power deviation.

Induction of ROS mediated genomic instability, apoptosis and G0/G1 cell cycle arrest by erbium oxide nanoparticles in human hepatic Hep-G2 cancer cells

The remarkable physical and chemical characteristics of noble metal nanoparticles, such as high surface-to-volume ratio, broad optical properties, ease of assembly, surfactant and functional chemistry, have increased scientific interest in using erbium oxide nanoparticles (Er2O3-NPs) and other noble metal nanostructures in cancer treatment. However, the therapeutic effect of Er2O3-NPs on hepatic cancer cells has not been studied. Therefore, the current study was conducted to estimate the therapeutic potential of Er2O3-NPs on human hepatocellular carcinoma (Hep-G2) cells. Exposure to Er2O3-NPs for 72 h inhibited growth and caused death of Hep-G2 cells in a concentration dependent manner. High DNA damage and extra-production of intracellular reactive oxygen species (ROS) were induced by Er2O3-NPs in Hep-G2 cells. As determined by flow cytometry, Er2O3-NPs arrested Hep-G2 cell cycle at the G0/G1 phase and markedly increased the number of Hep-G2 cells in the apoptotic and necrotic phases. Moreover, Er2O3-NPs caused simultaneous marked increases in expression levels of apoptotic (p53 and Bax) genes and decreased level of anti-apoptotic Bcl2 gene expression level in Hep-G2 cells. Thus it is concluded that Er2O3-NPs inhibit proliferation and trigger apoptosis of Hep-G2 cells through the extra ROS generation causing high DNA damage induction and alterations of apoptotic genes. Thus it is recommended that further in vitro and in vivo studies be carried out to study the possibility of using Er2O3-NPs in the treatment of cancer.

Modeling Horizontal Single Axis Solar Tracker Upon Sun-Earth Geometric Relationships

Many investments and commitments have recently been set to use renewable energy source, overcome energy crisis and align with climate target. Solar power development and deployment make investment in power generation sustainability. The goal of this study is harvesting energy by rotating solar panel toward the sun direction. Astronomical formula is derivate to calculate the sun altitude and azimuth depending on given latitude, longitude coordination. The photovoltaic (PV) panels rotate horizontally and track the sun direction in 9 positions regarding to their actual time and calculated azimuth angle. Partial shaded effectiveness that produces between the adjacent panels due to PV panel’s inclination is calculate accordingly. The total increment of power production from fix to tracked panel structure is 17.3% per day. The extra power generation is distributed over the period between solar noon times.