Low-cost Generation Research Articles

Short term multi-steps wind speed forecasting for carbon neutral microgrid by decomposition based hybrid model

Combining the benefits of an empirical mode decomposition (EMD), a convolutional neural network (CNN), and a long-short term memory (LSTM) neural network, this research proposes a hybrid model for forecasting short-term multistep ahead wind speed that can assist microgrids in achieving their low generation cost and carbon neutrality goals. Prior to EMD, historical wind speed time series were pre-processed, and then the resulting time series of wind speeds were decomposed into intrinsic mode functions (IMFs). Following that, a CNN-LSTM model that had been subjected to optimization was employed to carry out the training and subsequent evaluation (validation and implementation of the model). While CNN is able to directly extract the time series' intrinsic features, LSTM networks can fully leverage the data for improved prediction. In order to demonstrate the superiority of the proposed model, its results were compared to those of six other models (including hybrids and standalones), and the findings of the experiments provide empirical evidence that the hybrid EMD-CLSTM model proposed is superior. The strategy that has been proposed has also been utilized in industrial settings, where it has been found to be successful. The green energy management system (GEMS) of Leonics Co. Ltd. has incorporated the proposed model into their short-term decision-making and scheduling procedures in order to achieve their goals of a better energy mix, low-cost power generation, and carbon neutrality. Last but not least, the energy management system of the modified GEMS microgrid was upgraded to enable a greater penetration of wind energy sources without decreasing the microgrid's stability or reliability, making it possible for the system to eventually become carbon neutral.

Role of hydrogen in China's energy transition towards carbon neutrality target: IPAC analysis

China announced its intention to achieve carbon neutrality by 2060 in 2020, and there is an urgent need to understand the viability of emission pathways to reach this goal. This study presents the IPAC modelling team's scenario analysis on China's hydrogen utilisation as a key option for carbon neutrality pathways. In contrast to other studies, this study examines the demand for hydrogen as a feedstock and process material in the industrial sector and as a source of energy in the transportation sector in relation to China's energy system transition. The process of manufacturing hydrogen from carbon-free power generation was also analysed. The finding indicates that the demand for hydrogen could reach 52.4 Mt by 2050 and that the hydrogen will come from renewable power generation and nuclear energy, increasing the demand for electricity by 1884.8 TW h. There are several regions in China with abundant renewable energy and low power generation costs, which will make the hydrogen-based industry competitive in these regions after 2035.

Command Value Determination Considering Generation Cost and Control Performance Using Model Predictive Control in Load Frequency Control

This paper deals with load frequency control (LFC) problem for power system. The control is conducted to retain power demand supply balance by controlling output of generators in power network. In order to realize LFC with low power generation cost while maintaining control performance, this paper proposes frequency control method using Moving Horizon Estimation (MHE) and Model Predictive Control (MPC). First, MHE is used to estimate the state of the system. Next, optimization calculation is performed using MPC to determine the command values for each generator. Finally, simulation of load frequency control is carried out to verify the effectiveness of the proposed method.

Quantum dots and their applications in television display technologies

Quantum dots (QDs) are the best emissive materials ever made something that may revolutionize the display industry and lead to a new generation of low cost and high-performance displays. Due to the low absorption cross-section, conventional phosphor colour conversion cannot support high-resolution displays. This gap will be filled by QDs materials because of their remarkable photoluminescence, narrow bandwidth emission, color tunability, high quantum yield and nanoscale size providing a powerful full-colour solution for display technology. QDs based display technology to position itself at the forefront of next-generation display technology competition. The purpose of this paper is to present an overview of QDs based display technology's research progress and application prospects in overseas.

La–Sr–Co oxide catalysts for oxygen evolution reaction in anion exchange membrane water electrolyzer: The role of electrode fabrication on performance and durability

Anion exchange membrane water electrolysis is an attractive technology for low-cost generation of “green” hydrogen by combining the use of noble metal-free catalysts with pure water feed. By thus addressing main drawbacks of the liquid alkaline electrolysis and proton exchange membrane water electrolysis, anion exchange membrane water electrolysis stands an excellent chance of replacing the two technologies. The development of active and stable platinum group metal (PGM)-free catalysts for oxygen evolution reaction (OER) is crucial for making anion exchange membrane water electrolyzers (AEMWEs) practical. Here, we synthesized, characterized and tested two La–Sr–Co oxide-based OER catalysts. First, we characterized the catalysts by XRD, SEM, and N2 physisorption and assessed their OER activity in a three-electrode cell. Next, we focused on electrode fabrication, demonstrating the importance of catalyst-ink application to the porous transport layers (PTLs) and a key role of adding a binder to the catalyst ink to prevent the catalyst detachment from the PTL in pure water. We tested three membrane electrode assemblies prepared using different formulations of the anode catalyst ink. The results show that the optimum ink formulation is essential for the performance on pure-water feed by maximizing OH− conductivity of the catalyst layer and catalyst-membrane interface.

Multi-band orbital angular momentum mode-division multiplexing by a compact set of microstrip ring-shaped resonator antenna.

Optical beams carrying orbital angular momentum (OAM) have received much attention due to the prospects of their use in terahertz communications, biomedical engineering, and imaging. Here we propose an antenna design for the generation of multiple beams carrying OAM with different topological states at the same frequency. The proposed OAM generator is based on a compact set of microstrip ring-shaped resonators. An analytical solution for the radiated field of a single circular ring resonator antenna is derived involving the cavity model and the magnetic current approach. To verify our theoretical description, the numerical full-wave simulation is performed for an actual size OAM generator with the use of the ANSYS HFSS electromagnetic solver, and an antenna prototype operating in the microwave band is fabricated and tested. Conditions of the antenna operation in the combined OAM and mode-division multiplexing (OAM-MDM) regimes are discussed. Obtained results prove that the proposed antenna can be used as a compact and low-cost generator of multiple beams with different OAM states.

Fast tuning of transmission power flow routers for transient stability constrained optimal power flow under renewable uncertainties

This paper proposes a robust transient stability constrained optimal power flow (OPF) problem that addresses the impact of renewable uncertainties by the coordination of power flow router (PFR) tuning and generation re-dispatch. PFR refers to a general type of network-side controller that enlarges the feasible region of OPF problem. The coordination between network-side and generator-side control in the proposed model is more general than the traditional methods which focus on generation dispatch only. Applying semidefinite programming (SDP) relaxation and scenario approach allows us to simplify the original robust optimization problem into a scenario-based SDP problem. Then, an offline–online solution framework is developed to solve the problem efficiently. Under this framework, the time-consuming transient stability analysis and scenario reduction are carried out at the offline stage. At the online stage, we only need to solve a lower-dimensional deterministic problem based on the information obtained offline, which achieves fast implementation of PFR parameter tuning and generation re-dispatch. The proposed method is verified on a modified New England 39-bus system. Numerical results demonstrate that the proposed method is efficient and achieves lower generation cost without compromising the robustness of transient stability under renewable uncertainties. • A robust TSC-OPF is proposed with power flow routers and renewable uncertainties • The model introduces a coordination of network control and node power control • An offline–online solution framework is designed to efficiently solve the problem • The model achieves lower cost without compromising the stability robustness

Mitigation of transmission line jamming by price intrusion technique in competitive electricity market

ABSTRACT In the past years of the electric market, very big corporation companies enjoyed domination or monopoly and takes complete responsibility for generating the electric power. In the new-age market electricity grid faces several techno-commercial challenges like power outage, conductor snag, equipment rundown, transmission congestion, financial constraint, price settlement issue, etc, so this cause transfer of generated electricity from generating station to load becomes a challenge. Also, transmission jamming or congestion is an issue that leads to catastrophe if not mitigated. Therefore to avoid such issues different techno-financial methods are adopted in a particular electricity zone with a large network. In this paper, we adopted an innovative technique to control the electricity transfer limit. Here an analytic tool based on the price-sensitive algorithm is developed to determine a suitable combination of Financial Transmission Rights (FTR) and Location-based low prices with curtailments in a competitive electricity market. The significant objective of this technique is to minimise the dispatch of low-cost generators and overall operational expenses thereby mitigating the traffic issue in a particular load node or zone. Finally, the transmission congestion mitigation is discussed with price sensitivity technique and with a proper financial mechanism like FTR with location pricing in a particular zone.

Modeling and optimization of a novel Oxy-fuel/Solar/Wind/Battery power generation system

• The optimized combined system can perfectly meet the needs of electricity grid. • The cold energy of LNG is used to condense CO 2 and nearly zero CO 2 emissions is achieved. • The optimal model is obtained by considering various wind-solar ratios and scales of battery storage capacity. • The LCOE of the combined system is reduced by 3% to 6% with the employment of solar-wind energy. It is proposed and built a novel oxy-fuel/solar/wind/battery power generation system. It consists of a solar-wind renewable system (SWRS) with battery bank and a liquefied natural gas-fueled oxy-fuel combined cycle (OFCC). The work is unique in that it uses SWRS to power OFCC’s main energy-consuming equipment, such as the recycled CO 2 compressor (RCC) and air separation unit (ASU). SWRS power is consumed locally, avoiding the negative effects of SWRS intermittency and variability on the electricity grid. CO 2 is compressed, cooled and then condensed by evaporating the cold LNG. MATLAB/Simulink and Aspen Plus were used to create the SWRS and OFCC, respectively. In order to find the optimal model of the combined system in terms of economic factors, various wind-solar ratios, scales of SWRS and OFCC, and battery storage capacity have been considered and combined. The results show that the optimized combined system can perfectly meet the needs of the electricity grid while having a high renewable energy utilization efficiency, no negative impact on power grid stability, low power generation costs, and low CO 2 emissions. This work will serve as a guide for the combined power system’s power supply planning.

Adopting electrocoagulation technology for removing arsenic from contaminated water

The increasing demand for drinkable water has speeded the expansion of groundwater use. Unfortunately, many geogenic contaminants like arsenic significantly decrease the quality of groundwater. Arsenic in water has harmed at least 0.3 billion people globally. Researchers used various types of treatment technologies to treat arsenic from synthetic wastewater, such as reverse osmoses, adsorption, and electrocoagulation. The electrocoagulation technique has gained popularity due to its increased removal efficiency compared to traditional treatments, low cost, and low sludge generation. This research aims to analyse arsenic treatment and examine the effect of technique variables on the removal performance to determine the best process variables for water purification. The outcome highlighted that arsenic removal improved with higher current densities needing less operating time. As a result, the removal effectiveness of arsenic from water using the electrocoagulation approach went from 88% to 96% in an alkaline environment, which is preferable. The best working settings for arsenic removal include a current density of 6 mA/m2 and a treating time of 30 minutes to reach 81% arsenic removal effectiveness. The best pH level for treating arsenic-contaminated water has been identified as 9.

Beyond tree cover: Characterizing southern China's forests using deep learning

AbstractMapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time‐series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF‐1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high‐resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency.

Experimental Investigation and Comparison of the Net Energy Yield Using Control-Based Solar Tracking Systems

As the world trend is going towards renewable energy, solar photovoltaic (PV) energy shines the most due to the low generation cost especially by using the latest PV cell technologies and materials, although the conventional silicon cell has an efficiency in the range of 15-16%. A PV tracking system (PVTS) could be a considerable method to increase electrical PV efficiency. In this study, the performance enhancement of the daily output power and energy has been experimentally investigated using single- and dual-axis tracking mechanisms for flat-plate conventional PV panels under the insolation conditions of the Eastern province of Saudi Arabia (SA). In the current study, the active PVTS has been designed and implemented with PID controllers and controlled in real-time with an embedded system. The fixed-tilt (FTPV), single-axis solar tracking system (SAST), and dual-axis solar tracking system (DAST) were tested simultaneously under clear-sky conditions. The PV electrical energy balance has revealed that the energy consumed by the electrical controllers of the SAST and DAST is 3.4% and 3.9%, respectively. In addition, the energy losses due to the actuators of the SAST and DAST are 7.8% and 13.0%, respectively, where they contributed mainly to the energy losses. However, the PV energy production by the DAST was high enough to compensate for the higher actuating mechanism losing rate compared to the SAST and performed incremental net energy output of around 8.64%. On other hand, the DAST and SAST were more efficient than the FTPV recording higher net energy increase by 28.98% and 18.73%, respectively.

Fuzzy vector reinforcement learning algorithm for generation control of power systems considering flywheel energy storage

To reduce carbon emissions, the proportion of renewable energy in power systems is increased. However, the volatility and uncertainty of renewable energy cause power deviations. To reduce frequency deviations caused by power deviations, a fuzzy vector reinforcement learning (FVRL) is developed for the generation control of power systems considering flywheel energy storage systems (FESSs). The FVRL algorithm consists of two independent fuzzy controls, two independent Q-learnings (QLs), and vector operation. Area control error and control performance standard 1 index are the inputs of one QL; the input of the other QL is the output power of the FESS. Fuzzy control fuzzifies the inputs of QL and provides the row number of the Q-table. Vector operation is acted for the output values of two QLs; the amplitude of the obtained vector is the final power regulation command of automatic generation control. The FVRL, proportional–integral, five reinforcement learnings, and deep Q-network are compared in two cases. Case studies show that the proposed FVRL obtains the lowest frequency deviation, the lowest area control error, the lowest generation cost, and the highest control performance standard 1 index.

Evaluating efficacy on tumor biopsy tissue ex vivo before treatment for equitable cancer care.

e13502 Background: Nearly half the world will be diagnosed with cancer. Each year, > 1.7 million new cancers are diagnosed in the US. Worse, systemic therapy turns out to be ineffective in 70% of patients because those drugs do not match the patient’s tumor, imposing a substantial physical, emotional, and financial burden. Each tumor’s distinct composition and heterogeneity can result in diverse responses to the same treatment. There is a critical and urgent need to develop ex vivo personalized cancer models that match patients with the right treatment – before treatment. Here we present a low-cost ex vivo personalized cancer model to rapidly evaluate effectiveness of various therapeutics on intact core biopsy tissue obtained from a patient’s tumor. Objective: The goal of this study is to deliver multiple isolated fluid streams to spatially distinct regions (SDRs) of intact core biopsy specimens for evaluating efficacy of multiple anticancer agents simultaneously. Methods: Core biopsies were generated from xenograft and resected human tumor tissue using 18-gauge and 20-gauge spring-loaded biopsy systems. Xenografts were established using cell lines with known sensitivity and resistance profiles. Fluid streams with anticancer agents, nucleic acid stains, and mock drugs were delivered to spatially distinct regions (SDRs) on intact core biopsy specimens in our patented Personalized Oncology Efficacy Test (POET) lab-chip bioreactors. High content imaging and custom image processing algorithms were used to measure differential activity. Results: No lateral fluid surface conduction, and only minimal lateral diffusion, was observed up to 200 μm deep in tissue. Extent of observed lateral diffusion was within operational parameters of the bioreactor. Integrity of spatially distinct regions on intact core tissue was maintained. Differential activity from anticancer agents compared to mock drugs was observed and quantitatively measured using custom image processing algorithms. Conclusions: The NCI warns that “current methods to assess potential cancer treatments are cumbersome, expensive, and often inaccurate.” A personalized medicine approach, based on efficacy observed on tissue directly from the patient’s own tumor, is fundamentally better than the current approach. Low-cost next generation ex vivo cancer models that can rapidly match treatments to tumors, such as POET, could transform cancer treatment and make a significant impact on the lives of patients.

Direct Sourcing from Renewable Energy under Cost Uncertainty: A Bargaining Approach

Sourcing electricity from renewable energy suppliers becomes critical to improving renewable energy penetration. However, the common problem of bilateral bargaining between an electricity retailer and renewable energy supplier has rarely been studied in the literature. Considering the uncertainty of renewable generation cost, we introduce the incentive contract theory and signalling game to examine how the electricity procurement contract is designed in a Rubinstein bargaining framework. We derive the corresponding equilibrium outcomes depending on the renewable supplier’s generation cost uncertainty. The results show that if the possibility of a high generation cost is large, the retailer provides contracts for the renewable supplier of both high and low generation costs to achieve the simultaneous separating equilibrium; otherwise, he only proposes the contract for supplier with a low generation cost to achieve the sequential separating bargaining. Our work demonstrates that the proposed incentive contracts urge the renewable supplier to reveal the private information of generation cost and prevent the retailer’s profit deviation due to the adverse selection.

Increasing Flexibility of Combined Heat and Power Systems Through Optimal Dispatch With Variable Mass Flow

In combined heat and power systems, varying mass flow can better make use of the heating system’s inertia to increase the flexibility of electric power systems. This is challenging, however, due to integer variables and bilinear constraints in existing optimal dispatch models. In this paper, we incorporate an improved heat pipeline model to eliminate integer variables without compromising on accuracy. Subsequently, a novel decomposition method is proposed to solve the resulting optimization model with bilinear constraints. Namely, the original optimization model is decomposed into a lower-level convex sub-problem with the fixed mass flow and a simple upper-level problem to search for the optimal mass flow. Numerical tests show that the proposed method can achieve lower generation costs than existing variable mass flow dispatch methods with outstanding computational efficiency.

Bootstrap aggregating approach to short-term load forecasting using meteorological parameters for demand side management in the North-Eastern Region of India

Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland’s load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.

Energy and economic evaluation of MSW incineration and gasification in Brazil

Ever-increasing waste generation from the world population, along with the demand for renewable energy sources, are two topics gaining prominence in global conversations. Energy recovery of municipal solid wastes stands out as a possible solution for both problems. Thus, the present study aims to analyze the use of gasifiers and incinerators as technology for municipal solid waste treatment and energy generation from economic and energetic perspectives. The study was carried out in three municipalities in different regions of Brazil with distinct characteristics: Itajubá, Campinas and Campo Grande. The calculations were performed considering each city's waste production values and gravimetric composition. For each, two different waste flow scenarios were considered: the first adopted constant power and waste treatment values, while the second used values that varied year to year. The results obtained showed that the second scenario had greater energy potential than the first; however, its generation costs are higher, due to the greater flow of waste. Regarding the types of technology, gasification resulted in lower generation costs in Itajubá, while incineration was more attractive for Campinas and Campo Grande. This distinction can be explained by the different levels of organic matter in the cities involved. The use of both types of technology did not prove to be economically viable for any of the cities and conditions analyzed, thus demonstrating the need for government incentives for their viability.

Electric Truck Hydropower, a flexible solution to hydropower in mountainous regions

The world is undergoing a transition to a more sustainable energy sector dominated by renewable energy sources. This paper proposes an innovative solution that consists of catching water from streams at high altitudes to fill storage containers and transport them down a mountain, converting the potential energy of water into electricity with the regenerative braking systems of electric trucks and storing it in the truck's battery. The energy stored in the electric truck can be sold to the grid or used by the truck to transport other goods. Results show that the levelized cost of the electricity truck hydropower is 30–100 USD/MWh, which is cheap when compared with conventional hydropower 50–200 USD/MWh. The electricity generation world potential for the technology is estimated to be 1.2 PWh per year, which is equivalent to around 4% of the global energy consumption in 2019. Apart from being a low cost and impact electricity generation technology, electric truck hydropower can operate in combination with solar and wind resources and provide energy storage services to the grid.

Study on automatic ultrasound scanning of lumbar spine and visualization system for path planning in lumbar puncture surgery.

In lumbar puncture surgery, compared with the conventional methodologies like computed tomography and magnetic resonance imaging, ultrasound imaging offers the advantages of being low cost, no radiation and real-time image generation. However, the use of ultrasound equipment in lumbar puncture involves a cumbersome and time-consuming process for the subjective imaging of the overall structure of the lumbar spine in order to determine the exact puncture point and path. Meanwhile, the robotic arm puncture system has the advantages of high precision, good stability and simple and efficient operation. As a result, robotic-assisted ultrasound scanning is valuable for the assessment of a puncture path in spinal tap surgery. In this pursuit, based on the official URSDK development package for a robot arm and the Transmission Control Protocol/Internet Protocol, the system proposed in the present study involves a program to control the robot arm to clamp down onto an ultrasonic probe to enable automatic scanning and acquisition of images. A three-dimensional reconstruction program based on the visualization toolkit was designed, and a lumbar spine experiment was conducted with this system. A total of 136 two-dimensional ultrasound images were collected in the lumbar spine model experiment by enhancing contrast of and denoising the original ultrasound images, and a linear interpolation algorithm was used to perform the three-dimensional reconstruction of the lumbar spine model. The reconstructed structure was defective, but the location of the spinous process gap was determined with the sagittal and coronal images. The feasibility of the system was verified by the reconstruction results, which can provide a reference for determining the puncture point and path planning in the lumbar puncture surgery.