Consumption Curve Research Articles

Optimal operation of diesel generator and battery energy storage system for total fuel cost minimization in hybrid power system

This study addresses the challenge of optimizing the operation of the diesel generator (DG) and battery energy storage system (BESS) to minimize the total fuel cost in a hybrid tugboat application, using the Whale Optimization Algorithm (WOA), a metaheuristic optimization algorithm. A specific fuel consumption (SFC) curve and an optimal control algorithm are developed to enhance the performance of the hybrid tugboat. Various case studies examine the impacts of different tugboat mode durations on the optimal operation of DG and BESS. The effectiveness of WOA is validated with other optimization algorithms. From the developed SFC curve and optimal control algorithm, the optimal DG operating conditions to improve fuel efficiency are determined. As a result, through the optimized operation of DG and BESS suggested by WOA, the total fuel cost of the hybrid tugboat is reduced by 9.23 % compared to the conventional tugboat. In contrast to other algorithms, WOA performed exceptionally well in achieving the optimal operation of DG and BESS, with the highest total fuel cost savings and the smallest BESS operating capacity. The findings highlight that DG should be utilized mostly during the operating mode with the longest duration and higher load demand to minimize the total fuel cost.

Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value

Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users.

Optimizing solar energy integration in Tallinn's district heating and cooling systems

Using solar energy is one way to integrate sustainable, clean and non-combustional energy to energy mix. In electricity sector, the share of solar energy has significantly grown over the last decade because of increased public awareness, declining costs and government incentives. However, the adoption of solar energy in heating and cooling sector is relatively new. There is a visible relation between solar energy production curves and cooling energy consumption curves which indicates that using solar energy in cooling sector would be efficient way to use solar energy. Still, the utilisation of solar heat for district cooling remains a grey area. In district heating and cooling sector, the use of solar energy in Estonia has been modest so far, although there is a significant solar energy potential. Hence, Tallinn district heating and cooling system has been chosen as a case study to investigate how solar energy can be used most beneficially and efficiently. In this regard, three main integration scenarios with respect to the different technical configurations and energy transformations are analysed. It was observed that the proposed solar park could generate 27.58 GWh thermal energy per annum. The share of useful solar energy (or solar fraction) reached more than 98.5 %, when TES integration is considered. From the analysed scenarios, it can be concluded that integration of TES is highly important to tap solar heat to the fullest. The seasonal match between load and generation contributed to higher share of solar energy for district cooling than district heating. This study is expected to be useful reference material for project developers, investors and policy makers.

Impact resistance study of lime slag–stabilised steel slag mixes

ABSTRACT Given the transient nature of impact loading, exploring the microscopic damage characteristics and damage mechanisms of materials through indoor tests is difficult. In this study, the damage morphology and energy dissipation characteristics of lime slag–stabilised steel slag (LSSS) mixes under vertical impact loading is explored using ABAQUS software, a finite element model of LSSS for falling hammer impact test is established, the validity of finite element calculation is verified by comparing the results of indoor tests, and the strain characteristics, deflection changes, and energy transformation process of LSSS under vertical impact loading are analysed. Results show that the LSSS tensile and compressive strains under impact loading are roughly divided into low strain rate, precracking, and cracking phases with a rapid increase in strain. Under the same impact energy conditions, the LSSS bottom tensile strain, span deflection, drop hammer kinetic energy conversion time, and LSSS plastic energy consumption curve rising slope are sensitive to drop hammer mass. Thus, improvement in drop hammer mass will prolong the impact loading of the drop hammer–LSSS system internal energy conversion time, and the LSSS strain peak range to the central concentration, enhancing span deflection and bottom deformation.

Aging Retardation of Lead-Acid Batteries by Adjusting Charge Controller Threshold Values in Off-grid Photovoltaic System

Hybrid systems or coupled power plants with energy storage are becoming more and more popular due to the rising need for energy. Researchers have been conducting several experiments to assure longer-lasting battery use due to the increasing widespread use of energy storage and the depletion of resources such as silicon and precious metals. In this study, a simulation study is carried out in PVSyst software on lead-acid batteries, which have a low cycle and a very traditional electrochemical structure. The simulation is realized by scaling Spain’s consumption curve in 2023, taken from the European Network of Transmission System Operators for Electricity (ENTSO-E), together with lead acid batteries and off-grid photovoltaic (PV) modules. After the off-grid system design is created and consumption data is imported, it is aimed to extend battery life by changing the charge-discharge threshold values of the charge controllers. Threshold values are in two categories, high and low, and seventeen different cases are simulated. According to the results of the simulation, the state of charge (SOC) levels of the batteries decreases significantly due to radiation and cloudiness in the winter months and remain high in the spring and summer months. When charge controllers are set to high thresholds levels, they cause gassing currents that rise to an average of 20 amps, while battery life extends up to 5.3 years. When low threshold values are used, negligible levels of oxygen and hydrogen gasses are formed in the battery, but battery life decreases to 4.1 years.

Derivation of one- and two-factor experience curves for electrolysis technologies

Green hydrogen is seen as a promising energy carrier contributing to the decarbonization of the energy system. The competitiveness of green hydrogen compared to conventional produced (grey) hydrogen strongly depends on the investment cost of electrolysers and the costs of required green electricity. Accordingly, the expected investment costs and the efficiency of the production process for electrolysis technologies will play a decisive role. Experience curves can help estimate these crucial key parameters better. This paper applies experience curves to electrolysis technologies to enhance the understanding of these developments, specifically for alkaline, proton exchange membranes and solid oxide electrolysis. Experience rates are estimated using one- and two-factor experience curves for CAPEX and one-factor experience curves for electricity consumption. An independent and extensive database of CAPEX, electrical consumption and cumulative installed capacity was developed. Additionally, two different time frames and different data handling techniques are applied to understand the impact on the experience rates and the fit of the experience curve to the data provided. Based on the extensive database developed, CAPEX experience rates for the technologies range from 7%–21%, depending on the technology. In almost all cases, a two-factor experience curve analysis reduces the experience rate based on the cumulative installed capacity with the experience rate based on the size ranging between 7.4-10.2 % for all technologies. Lastly, the experience curve of the electrical consumption expects a reduction of around 1-3.5 % with each doubling of the capacity.

An optimal demand side management for microgrid cost minimization considering renewables

AbstractIn an ordinary microgrid configuration, the required load changes from hour to hour. The power system firms determine the cost of energy at different times of day by considering the highest and lowest points of the consumption curve. This is referred to as time‐of‐use (TOU) pricing for power. The hourly basis load demand is divided into flexible and inflexible categories. Demand side management (DSM) lowers peak demand while rewarding customers for their participation based on load lowering. His rebuilds the whole load model on the pillars of demand cost movement. The research recommends a DSM methodology based on a combined intellect method to lower the total cost of employing loads in a microgrid (MG) structure while considering carbon tax as an unavoidable constraint to lower the emission of pollutants. This is because 40% of microgrid customers are willing to participate in the DSM scheme. The results obtained in each illustration demonstrate that the suggested DSM technique is suitable in terms of cost reduction. The generating cost was decreased from $15,488 to $15,354 when 0%–40% of clients engaged in the DSM programme. With just a 3% compromised increase in generation costs, a carbon price combined with economic emission dispatch reduced the pollutants emitted by up to 78%, from 70 to 15 tons.

PROPIEDADES DINÁMICAS DE IMPACTO DE COMPUESTOS DE LODOS DE PAPEL DOPADOS CON OXICLORURO DE MAGNESIO

To address the heavy pollution caused by paper sludge, this study investigates a novel approach by incorporating paper sludge into magnesium oxychloride cement to develop a new type of concrete composite material. To achieve waste utilisation, cost savings and pollution reduction, the impact dynamics properties of the concrete specimens were conducted using an electromagnetically driven Hopkinson pressure bar, while a high-speed camera recorded the specimen destruction process, stress-strain curve, destruction morphology, deformation field, and evolution of energy consumption. Results show that as the paper sludge doping increases, the peak stress of the stress-strain curve and the energy consumption gradually increase. Increasing the voltage leads to a gradual rise in peak stress and energy consumption of the specimen. During the impact test, the transverse fissures gradually are developed in the test block, and as the impact stress increases, the fissures are widened until the specimen eventually being fractured. Moreover, the higher impact voltage and paper sludge content result in the increased specimen displacement and strain. It is expected that nearly 50% of paper sludge will be recycled every year by this method, which will save 10%-20% of raw materials. The conclusions obtained in this study provide a reference for the engineering application of the paper sludge concrete composites. Keywords: Paper sludge, Magnesium oxychloride, Digital image correlation, Energy consumption, Impact dynamic properties.

Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

The effect of bilateral ultrasound-guided erector spinae plane block on postoperative pain control in idiopathic scoliosis patients undergoing posterior spine fusion surgery: study protocol of a randomized controlled trial

BackgroundPosterior spinal fusion (PSF) for the correction of idiopathic scoliosis is associated with severe postoperative pain. Erector spinae plane block (ESPB) has been proposed to provide analgesia and reduce opioid consumption. We aimed to investigate the effect of bilateral ultrasound-guided single-shot ESPB on postoperative analgesia in pediatric patients undergoing PSF.MethodsThis double-blinded, randomized controlled trial will enroll 74 AIS patients undergoing elective PSF. Participants will be assigned to the ESPB group or control group at a 1:1 ratio. Patients in the ESPB group will receive ultrasound-guided bilateral ESPB preoperatively, and patients in the control group received sham ESPB using normal saline. The primary joint endpoints are the area under the curve (AUC) of numerical rating scale (NRS) score and opioid consumption in postoperative 24 h. The secondary endpoints are numerical rating scale (NRS) score and opioid consumption at postoperative 0.5, 3, 6, 9, 12, 24, 36, and 48 h, rescue analgesia, recovery outcomes, and adverse events.DiscussionAt present, studies investigating the effect of ESPB on pediatric patients are still needed. This study focuses on the effect of ESPB on pediatric patients undergoing PSF on postoperative pain control and intends to provide a new strategy of multimodal analgesia management for major spine surgery.Trial registrationChinese Clinical Trial Registry ChiCTR2300074505. Registered on August 8, 2023.

Análisis de mecanismos bioquímico-fisiológicos involucrados en las respuestas a sequía en soja

Water deficit limits soybean crop productivity and reduces yield stability. Many parameters have been associated with tolerance to water deficit, where the control of leaf water loss is by far the most important. Since most of the water loss occurs through the stomata, the objective of this thesis was to deepen the understanding of the response to water deficit in soybeans through the control of stomatal closure and to determine how this can affect the water consumption of soybeans and its tolerance to water deficit. To achieve this, we delved into the study of stomatal closure control. We focused on the role of nitric oxide (•NO) as a key molecule in the control of stomatal opening/closing. •NO kinetic accumulation was analyzed from the establishment of the hydric deficit, and its association with stomatal closure was assessed. Our work showed an endogenous •NO accumulation at the guard cell level before stomatal closure, which allowed us to postulate that in the early perception of stress there is an accumulation of •NO that inhibits stomatal closure, potentially through blocking responses to ABA. This initial fine control of the stomatal closure by •NO would be decisive to define the final responses of water loss/consumption during the establishment of the water deficit. Furthermore, we developed an empirical mathematical model in soybean that describes the kinetics of water consumption under water deficit conditions of a controlled plant-pot-substrate growth system. The model defined two parameters that determine the water consumption curve, t0.5 (time at which the system lost half of the maximum amount of potentially evapotranspirable water) and Gw(t0.5) (stomatal conductance at t0.5). A correspondence analysis between t0.5 and Gw(t0.5), and the genetic structure of two soybean populations, indicated the existence of genetic associations with consumption phenotypes based on these parameters. On the other hand, using data from the drought susceptibility index (DSI) in the field, generated by previous work on one of the populations of this thesis, and through a correspondence analysis, a grouping between the model parameter t0.5 and DSI is observed. This would indicate that the developed phenotyping strategy could also be useful in the characterization of genotypes at the field level.

Methodology for the Generation of Hourly Residential Drinking Water Consumption Curves and Their Relationship with the Consumption of Socioeconomic Strata

Methodology for the Generation of Hourly Residential Drinking Water Consumption Curves and Their Relationship with the Consumption of Socioeconomic Strata

Algorithm for managing the consumption curve of a consumer with self-consumption photovoltaic generation

Algorithm for managing the consumption curve of a consumer with self-consumption photovoltaic generation

The impact of photovoltaic self-consumption on the daily electricity demand in Spain: Definition of a model to estimate it

IntroductionA radical shift in energy production is underway worldwide, replacing fossil fuels with renewable sources and causing structural changes in power generation systems. Problem statementPhotovoltaic installations for self-consumption have experienced a steep increase in recent years. They have reached a significant installed capacity to cause a noticeable reduction in consumption from the national grid, which can cause serious management problems. ObjectivesIn this work, the evolution of the Spanish demand in the last years is analyzed to identify the influence of self-consumption in the overall demand. In addition, a mathematical model is defined to estimate this influence. MethodologyThe demand curves of equivalent days in years with high and low installed self-consumption photovoltaic systems have been compared. Then, an estimation of the electricity generated with this source is proposed, with a mathematical model that takes into account data on solar radiation, installed photovoltaic power for self-consumption and other relevant factors. ResultsThe analysis of the demand has shown a significant reduction of the electricity demand in daylight hours when the number of self-consumption photovoltaic systems increases. Moreover, the proposed model has been able to provide an estimation of the electricity generated with this source. The addition of these estimates to the actual consumption curves of years with a high number of self-consumption installations gives profiles close to those obtained when self-consumption was low. RecommendationNew storage systems need to be implemented and grid management need to be improved to take advantage of the surpluses produced by photovoltaic systems.

Reinforcement learning-based heuristic planning for optimized energy management in power-split hybrid electric heavy duty vehicles

In this work, we systematically integrate relevant expertise, specifically on the optimal brake-specific fuel consumption (BSFC) curve, battery characteristics and terrain information into the development of an energy management plan for heavy-duty power-split hybrid electric vehicles. We utilize deep deterministic policy gradient (DDPG) algorithm as one of the most sophisticated reinforcement learning technique. Initially, we begin by explaining the vehicle configuration's system modeling. Subsequently, we then present an energy management approach based on deep Q-learning concepts. A novel algorithm, Deep Deterministic Policy Gradient, for energy management control, have been created to combat the “curse of dimensionality” in reinforcement learning. The new AMSGrad optimization technique is used by DDPG algorithm to update the weight of neural networks. We robustly train the proposed control system in realistic driving environment. The Knowledge Incorporation (KI) based DDPG based system is compared systematically to the conventional DDDPG methodology and the benchmark Dynamic Programming (DP) method, the latter of which usually uses the RMSProp Optimizer in its formulation. The results show that as compared to the typical DQL policy, deep reinforcement learning approaches, notably expert Knowledge incorporation KI-DDPG and terrain information with the AMSGrad optimizer, achieve faster training speeds and reduced fuel consumption while maintaining the terminal state of charge (SOC).

The Hourly Peak Coefficient of Single-Family and Multi-Family Buildings in Poland: Support for the Selection of Water Meters and the Construction of a Water Distribution System Model

Taking care of water resources and minimizing water losses in water supply networks requires a broad approach to identifying and neutralizing operational problems. The correct selection of water meters to minimize apparent losses requires knowledge of the characteristic flows that may occur in the facility to which water is supplied. The research aimed to develop tools in the form of mathematical models and water consumption curves along with hourly water consumption coefficients to facilitate the process of selecting water meters for engineers and creating computer models of water supply systems. The research involved monitoring the flow of 76 single-family and multi-family buildings in four towns in Poland, followed by data analysis and development of tools supporting the selection of water meters and the construction of computer models of water distribution networks. High correlation coefficients of the studied variables indicate the results’ usefulness. Four models were developed to determine the maximum flow values in multi-family buildings (three models) and single-family buildings (one model) in the range of water meter diameters DN15-DN40. Characteristics of the average hourly peak coefficient (HPC) values were also developed, along with the range of changes in HPC values for single-family and multi-family buildings.

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Facial Expression Realization of Humanoid Robot Head and Strain-Based Anthropomorphic Evaluation of Robot Facial Expressions.

The facial expressions of humanoid robots play a crucial role in human-computer information interactions. However, there is a lack of quantitative evaluation methods for the anthropomorphism of robot facial expressions. In this study, we designed and manufactured a humanoid robot head that was capable of successfully realizing six basic facial expressions. The driving force behind the mechanism was efficiently transmitted to the silicone skin through a rigid linkage drive and snap button connection, which improves both the driving efficiency and the lifespan of the silicone skin. We used human facial expressions as a basis for simulating and acquiring the movement parameters. Subsequently, we designed a control system for the humanoid robot head in order to achieve these facial expressions. Moreover, we used a flexible vertical graphene sensor to measure strain on both the human face and the silicone skin of the humanoid robot head. We then proposed a method to evaluate the anthropomorphic degree of the robot's facial expressions by using the difference rate of strain. The feasibility of this method was confirmed through experiments in facial expression recognition. The evaluation results indicated a high degree of anthropomorphism for the six basic facial expressions which were achieved by the humanoid robot head. Moreover, this study also investigates factors affecting the reproduction of expressions. Finally, the impulse was calculated based on the strain curves of the energy consumption of the humanoid robot head to complete different facial expressions. This offers a reference for fellow researchers when designing humanoid robot heads, based on energy consumption ratios. To conclude, this paper offers data references for optimizing the mechanisms and selecting the drive components of the humanoid robot head. This was realized by considering the anthropomorphic degree and energy consumption of each part. Additionally, a new method for evaluating robot facial expressions is proposed.

Challenges and Opportunities in Electric Vehicle Charging: Harnessing Solar Photovoltaic Surpluses for Demand-Side Management

The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.

Why do drinkers earn more? Job characteristics as a possible link.

After some initial controversy, an inverted U-shape relationship between the consumption of alcohol and earnings seems to be an established result, at least in North America. It has been dubbed a "drinking premium", at least in the lower portion of the consumption curve. It is still unclear, perhaps even counter-intuitive, why such a drinking premium exists and the literature suggests it is not causal but results rather from selection effects. We suggest here that part of the premium is linked to occupation: some occupations pay better, controlling for the usual human capital determinants, and also attract drinkers or induce workers to drink more. Using a sample of full-time employed or self-employed individuals aged 25-64 and not in poor health from the 2015-16 Canadian Community Health Survey (CCHS), we confirm the existence of a drinking premium and a positive return to the quantity or frequency of drinking up to high levels of consumption. Using information on jobs held by respondents, linked to a data set of job characteristics, we find that controlling for job characteristics reduces the premium or return to drinking by approximately 30% overall, and up to 50% for female workers.