Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

A Review on Solar Energy Policy and Current Status: Top 5 Countries and Kazakhstan

The article describes the world's experience in developing the solar industry. It discusses the mechanisms of state support for developing renewable energy sources in the cases of five countries that are the most successful in this area—China, the United States, Japan, India, and Germany. Furthermore, it contains a brief review of state policy in producing electricity by renewable energy facilities in Kazakhstan. This paper uses statistical information from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), British Petroleum (BP), and the Renewable Energy Network (REN21), and peer-reviewed sources. The research methodology includes analytical research and evaluation methods to examine the current state of solar energy policy, its motivators and incentives, as well as the prospects for its development in Kazakhstan and in the world. Research shows that solar energy has a huge development potential worldwide and is sure to take its place in gross electricity production. This paper focuses on the selected economic policies of the top five countries and Kazakhstan, in what may be considered a specific research limitation. Future research suggestions for the expansion of Renewable Energy (RE) in Kazakhstan could include analysing the impact of introducing dedicated policies and incentives for solar systems and exploring the benefits and challenges of implementing large RE zones with government–business collaboration.

Meeting the Hospital Oxygen Demand with a Decentralized Autonomous PV System: Effect of PV Tracking Systems

When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

Integration of neural network and distance relay to improve the fault localization on transmission lines

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still not perfect with nonzero errors of the fault locations. This paper will present a new approach using the neural networks in addition to a distance relays to correct the fault location estimation of the relay. The solution will be based only on the voltage and current signals measured at the beginning of the lines. The training samples' signals of the transient states on the lines are generated using ATP/EMTP, and then regenerated into the relay tester Omicron CMC-356 to test with the real Siemens 7SA522 relay to improve its fault location results. The numerical results will show that the solution had helped to reduce the average fault location error from 0.92% to 0.42% for 4 types of shortage faults on the lines. [ FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Challenges in the construction of a tunnel under crossing a channel in Singapore

To increase the conveyance capacity to Western Singapore and to meet long-term water needs in a more cost-effective manner, four new transmission pipelines consisting of 2 numbers of 2200 mm diameter and 2 numbers of 1200mm diameter water pipes will be needed by 2024 to convey water from a Water Reclamation Plant to existing networks in the western region of Singapore. Out of the several possible routes studied, the most cost-effective and technically feasible route was selected by laying the proposed 1.6km-long pipelines that under crosses a channel via a 6m diameter subsea tunnel. This paper outlines the challenges the team faced throughout the project thus far. It also examines the difficulties such as the construction of a 56m-deep launching shaft near a highly sensitive 700mm diameter Gas Transmission Pipeline (GTP) and at a location with high groundwater;and manpower and supply disruptions caused by the COVID-19 pandemic situation. © 2023 The Author(s).

Projecting the Electricity Demand of the Residential Sector in Thailand During and After the COVID-19 Pandemic

The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand's rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand's residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term. © 2023 World Scientific Publishing Co.

Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Experimental Analysis of Power Quality in a Power Substation Used in a Residential and Educational Sector

The article describes the experimental measurements made at a low-voltage residential and educational power substation, in a point of common coupling. Two groups of experiments were carried out, in normal conditions and during the COVID-19 pandemic. Measurements were made using a power quality analyzer and include phase RMS voltages and line currents, total harmonic distortion and unbalance of phase voltages and line currents, neutral current, active, reactive and apparent powers, power factors and displacement power factors, Fresnel diagrams, and harmonic spectra. Measurements indicate significant differences of power quality indicators between the two measurement groups. © 2023 IEEE.

A Modified Bottom Up Approach to Estimate Expected Demand Reduction During a Rare Event

Indian power system witnessed its largest very short-time demand ramping during light off event conducted to express solidarity with COVID-19 volunteers. 32 GW demand ramping was observed within 25 minutes and recorded as the highest ramping event across the globe. System operator has taken precautions and successfully handled the event with the help of hydro generation. However, system experienced severe frequency and voltage deviations due to unexpected consumer behaviour. A systematic study and an in-depth analysis of such a severe event would help system operators and planners to prepare for similar events. This paper presents a critical analysis of the activity and conducted a survey to understand consumer response during that event. It also proposes a modified Bottom-Up Approach to estimate Expected Demand Reduction (EDR) for such critical events. Proposed model is validated using data collected from the conducted survey. Proposed EDR estimation model offers better results than the Top Down and Bottom-up approach models used by system operator. © 2022 IEEE.

Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd

Occupational Health Hazard Identification and Risk Assessment with Introduction of Biomass in Coal Based Captive Power Plant

Introduction: Occupational Health should aim at the Promotion and Maintenance of the highest degree of physical, mental, and social well-being of all the employees. A pilot project was taken up due to acute shortages of coal during the COVID Pandemic, on industrial level, mixing of biomass with coal at a ratio of 20:80 respectively was considered as a good raw material. With introduction of biomass, workers were exposed to different organic substances either directly through dermal route or respirable dust with risk of becoming victims to Occupational diseases. Objective(s): The objective of the study is to identify and mitigate occupational health hazard of various nature prevailing at workplace after introduction of new raw materials;to safeguard the workforce from discomfort and occupational illness and to provide healthy working environment at RIL-Hazira. Method(s): Walk through survey was initiated by team of industrial hygienist and medical officer along with the process engineer. Subsequent workplace evaluation was done according to ACGIH screening criteria for respirable dust & VOC monitoring. To measure airborne respirable contaminants, we have considered housekeeping staff, operator, field executive, Boiler operation engineer which were found more likely to be at the risk of airborne contaminant exposure. To identify the concentration of contaminants, personal air sampler (SKC Make) was used for collection of respirable dust samples for different job category of workers. NIOSH 600 method was used for exposure assessment and samples were collected by using PVC filter used at the flow rate of 2.5 lpm. The composition of biomass pellets was received from biomass team & chemical analysis of biomass was done at our laboratory. Occupational Diseases known to be caused by organic agricultural compounds used as fuel were taken into account such as Bagasossis, farmer's lung & other hypersensitivity pneumonias, non-tubercular mycobacterial infections, infections caused by various fungi & bacteria. Prevention & Control measures were taken during the project such as modification of process, local exhaust ventilation, worker education on different diseases, personal hygiene, use of PPE, good housekeeping. Result(s): Through effective Risk assessment, Hazard Identification and measures taken to mitigate Occupational health hazards, no occupational health disease was reported after implementation of the change in process in a total of 55 identified workers. Moving forward these workers will be periodically monitored. The amount of total respirable dust was reduced by approx. 10- 25% at different location of the plant after control measures taken. This project also brought huge monetary benefits to the plant. Leading forward as the pilot project for introduction of biomass was a great success it has been planned to be scaled up to 40% mixture of biomass.

Successful Implementation of Gas Driven Electric Power Grid Project for Wells and Facilities Electrification, Yielding GHG Emissions and Cost Benefits

Objectives/Scope: Kuwait Energy is exploring, developing, and operating four concessions located in the Western Desert and the Gulf of Suez in Egypt;the company implemented many projects that had a significant impact on saving operating expenses and reducing greenhouse emissions to preserve the environment. One of these recent executed projects was replacing scattered diesel generators with a Central gas-driven electric power grid in Al- Jahraa field in East Abu-Sennan concession. In this , we present the challenges we faced during the planning phase and execution strategy applied to overcome these challenges. Methods: Al-Jahraa Field includes 13 running wells, a waterflood station, and a main oil and gas production plant. The field electricity is supplied by 15 scattered diesel generators for wells and facilities, consuming 100,000 liters of diesel per month. During the feasibility study phase of the project, many challenges were faced which had a negative impact on the project's economical assessment and that would result in cancelling the project, the challenges were summarized as following;the existence of wells at long distances from the site of the proposed main power station, which would require extending long lengths of electric power cables at a high cost, also the expected delay in the implementation and commissioning of the project resulting from the long delivery time of materials, especially copper cables and main switchgear during the COVID-19 pandemic. Several scenarios were studied for connecting the wells to the power station: The first scenario was to connect all wells and field facilities directly to the main power station. In this case, the estimated power cable lengths required to be extended were 25,000 Mt, in addition to using two 1 MW generators, one in service and the other would be a standby generator to provide backup power during a repair or maintenance service. This option economic model showed negative NPV due to the high cost of cables and extended execution time. Therefore, this option was cancelled. The second alternative was to connect each group of wells to three power stations to be operated using three diesel generators of 500 kVA for each station, with three backup generators. But the implementation of this option would lead to saving the cost of copper cables by 50%, but the cost of purchasing generators would increase due to the increase in the number of stations accordingly, in addition to the increase in operating expenses resulting from the increase in fuel consumption and maintenance cost compared to the first option. The third alternative, in which the economics of the project proved to be the best, is to divide the wells into three groups. Each of the two remote groups of wells are connected to an electric distribution panel, and then the two panels are connected by a main cable to the main power station. Moreover, the project cost was reduced by 50% due to the implementation of the following innovative optimization approach: • Re-using ESP cables instead of copper cables optimized both cost and delivery time as these materials are pulled from ESP wells. • These cables are designed for harsh downhole conditions increases its durability and extends its lifetime. • Using step-up and step-down transformers enabled us to reduce cable sizing, which also reflected on the lower cost of the project and, accordingly, increased its feasibility to be constructed. • An Incremental development approach, was followed in the management and implementation of the project, led to the speed of project delivery, and reduced the project risks and uncertainties. Results: The project was completed and commissioned within the allocated budget and time frame, leading to: ◦ 100% reduction of diesel fuel consumption levels. ◦ +68% reduction in total emissions;emissions are reduced by 2.5tons per year on average. ◦ reduced operational costs for each kilowatt hour generated due to using associated gas as fuel and releasing 13 rental generators. ◦ With the replacement of 1 rental generators with just one, the amount of maintenance waste, such as batteries, used oil, oil filters, fuel filters, and so on, is significantly reduced. ◦ These projects showed positive economic indicators (+NPV), with less than 1 years of payback. Conclusion: From this project's planning, execution, and results, we can claim that if risk assessments, detailed scope of work, good resource and time management, and cost-effective choices were addressed carefully, shall result in outstanding performance. The design of a high-efficiency electrical power supply system and use of associated gas in power generation reduces levels of fuel consumption, GHG emissions, and operational costs. Power generation project is a repeated case performed in one of our own assets in Egypt due to positive results and are easily transferable to sister IOCs & NOCs. Copyright © 2023, Society of Petroleum Engineers.

Determination of control area and design of fuzzy rule-tuned PID controller for LFC of multimachine power system

In load frequency control (LFC) study of a large power system, the key concept is control area, which is the segment of the system consisting of strongly interconnected buses, generator buses thereof working in unison. For accurate linearization of load frequency control problem, proper determination of control area is important. In the present work, a novel deterministic method is proposed and formulated to calculate the sharing of load changes by the generators to determine the control areas for LFC study of multimachine systems. This method is applied on a weakly interconnected two-area system and then on the 10-Machine New England Test System for area segmentation of each of the two systems. Furthermore, LFC studies are carried out with proposed Fuzzy Rule-tuned PID controllers (FRT-PID Controllers) for both the systems incorporated with Dish-Stirling Solar thermal system (DSTS) in each area. The scaling factors and the controller gains are optimized using Coronavirus Herd Immunity Optimizer Algorithm (CHIOA). Performance of the proposed FRT-PID controllers is compared with that of the Conventional PID controllers for the LFC studies of the systems. To test effectiveness of the FRT-PID controllers, effect of random step load perturbation (SLP) in load buses located in different areas are considered. © 2023 Elsevier B.V.

Residential Load Forecasting based on Deep Neural Network

This paper presents residential load forecasting using multivariate multi-step Deep Neural Networks (DNN) such as LSTM, CNN, Stacked LSTM, and Hybrid CNN-LSTM. A preliminary Exploratory Data Analysis (EDA) is conducted, and the decision variables are identified. An elbowing method is used to determine the number of clusters. Data is categorized based on weekdays, weekends, vacations, and Covid-Lockdown. Dimensionality-reduction using principal component analysis (PCA) is conducted. Seasonality-based clustering is found to improve the DNN model prediction accuracy further. A comparative analysis employs error metrics such as RMSE, MSE, MAPE, and MAE. The multivariate LSTM model with feedback is found to be the best fit model with the better performance indices. © 2023 IEEE.

Design of Auto-Tuning Nonlinear PID Tracking Speed Control for Electric Vehicle with Uncertainty Consideration

This study presents a new auto-tuning nonlinear PID controller for a nonlinear electric vehicle (EV) model. The purpose of the proposed control was to achieve two aims. The first aim was to enhance the dynamic performance of the EV regarding internal and external disturbances. The second aim was to minimize the power consumption of the EV. To ensure that these aims were achieved, two famous controllers were implemented. The first was the PID controller based on the COVID-19 optimization. The second was the nonlinear PID (NPID) optimized controller, also using the COVID-19 optimization. Several driving cycles were executed to compare their dynamic performance and the power consumption. The results showed that the auto-tuning NPID had a smooth dynamic response, with a minimum rise and settling time compared to other control techniques (PID and NPID controllers). Moreover, it achieved low continuous power consumption throughout the driving cycles. © 2023 by the author.

A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day.

Challenge of Supplying Power with Renewable Energy Due to the Impact of COVID-19 on Power Demands in the Lao PDR: Analysis Using Metaheuristic Optimization

Human activities have been limited by coronavirus disease 2019 (COVID-19), and the normal conditions of our lifestyles have changed, particularly in terms of electricity usage. The aim of this study was to investigate the impact of COVID-19 on the power sector in the Lao PDR in 2020, as well as the challenge of using solar energy to supply power to the network using an optimal approach. The returns on investment of network extension and the purchase of solar energy were also evaluated. Furthermore, load conditions caused by the country's lockdown policy were analyzed. We analyzed the optimal sizing and location of solar energy using a particle swarm optimization method based on the main objective functions, with the system's power loss decreasing and its reliability improved. The results demonstrated that the suddenly reduced load from industry and commercial business did not have a large impact on its operations;however, revenue was reduced. The optimal method for connecting solar energy to a network can reduce power loss and improve system reliability. In addition, we discovered that the location and capacity of solar generation can reduce the investment costs of extensions for new lines, with the surplus power being exported.

Impacts of COVID-19 Pandemic on Italian Electricity Demand and Markets

In this paper, the short-, medium-, and long-term effects of the COVID-19 pandemic on the Italian power system, particularly electricity consumption behavior and electricity market prices, are investigated by defining various metrics. The investigation reveals that COVID-19 lockdown caused a drop in load consumption and, consequently, a decrement in day-ahead market prices and an increase in ancillary service prices. © 2015 CSEE.

Impact of the UK's COVID-19 lockdown on the residential electricity consumption

Extreme disruptive scenarios such as pandemic lockdown force people to alter regular daily routines, impacting their energy consumption pattern. The implication of such a disruptive scenario for a more extended period on energy consumption is uncertain. This study aimed to investigate the impact of COVID-19 lockdown on residential electricity consumption in 100 houses from the southwestern UK. For the study, we analysed highly granular (1-minutely) electricity consumption data for April-September 2020 compared to the same months in 2019 for the same houses. Our study showed statistically significant differences during the lockdown period (the analysed six months) in energy demand. The minutely average electricity demand was 1.4-10% lower during April-September 2020 than in 2019. Our analysis showed that not all houses had similar type of changes during the lockdown. Some houses demonstrated a 38% increase in electricity demand, whereas some houses showed a 54% reduction during the lockdown period compared to 2019. Some houses showed significantly higher electricity use during the morning and afternoon than in 2019, which might be due to working and schooling from homes during the lockdown. © International Building Performance Simulation Association, 2022