Load Management Research Articles

Optimization of Fuel Consumption by Controlling the Load Distribution between Engines in an LNG Ship Electric Propulsion Plant

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

Assessment of the Technical Impacts of Electric Vehicle Penetration in Distribution Networks: A Focus on System Management Strategies Integrating Sustainable Local Energy Communities

Aligned with the objectives of the energy transition, the increased penetration levels of electric vehicles as part of the electrification of economy, especially within the framework of local energy communities and distributed energy resources, are crucial in shaping sustainable and decentralized energy systems. This work aims to assess the impact of escalating electric vehicles’ deployment on sustainable local energy community-based low-voltage distribution networks. Through comparative analyses across various levels of electric vehicle integration, employing different charging strategies and system management approaches, the research highlights the critical role of active system management instruments such as smart grid monitoring and active network management tools, which significantly enhance the proactive management capabilities of distribution system operators. The findings demonstrate that increased electric vehicle penetration rates intensify load violations, which strategic electric vehicle charging management can significantly mitigate, underscoring the necessity of load management strategies in alleviating grid stress in the context assessed. This study highlights the enhanced outcomes derived from active system management strategies which foster collaboration among distribution system operators, demand aggregators, and local energy communities’ managers within a local flexibility market framework. The results of the analysis illustrate that this proactive and cooperative approach boosts system flexibility and effectively averts severe grid events, which otherwise would likely occur. The findings reveal the need for an evolution towards more predictive and proactive system management in electricity distribution, emphasizing the significant benefits of fostering robust partnerships among actors to ensure grid stability amid rising electric vehicle integration.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Optimizing hybrid energy storage: A multi-objective approach for hydrogen-natural gas systems with carbon-emission management

This article addresses the challenges of load curve variability in integrated energy systems (IES) and emphasizes carbon neutralization in response to global environmental concerns. It introduces a game-theoretic approach to understand the collaborative dynamics within an integrated energy-hydrogen system (HGESS). The study develops a collaborative game model for the IES-HGESS partnership, focusing on carbon neutralization considerations. Using game theory, it investigates the determinants shaping this partnership and extends to create a hybrid model for day-ahead and real-time markets. To foster enduring cooperation, the study introduces a cooperation profit coefficient and devises an income distribution strategy for HGESS-IES interactions. A load management program is incorporated to reduce costs and enhance profitability. Addressing complexity and constraints, the article introduces an advanced multi-objective particle swarm optimization method. Tested on a sample system under various conditions, the results demonstrate that the alliance between IES and HGESS can significantly enhance net profits, achieving mutual benefit and a win-win outcome while advancing sustainability in energy management.

Two-Stage Robust Optimization for Large Logistics Parks to Participate in Grid Peak Shaving

As new energy integration increases, power grid load curves become steeper. Large logistics parks, with their substantial cooling load, show great peak shaving potential. Leveraging this load while maintaining staff comfort, product quality, and operational costs is a major challenge. This paper proposes a two-stage robust optimization method for large logistics parks to participate in grid peak shaving. First, a Cooling Load’s Economic Contribution (CLEC) index is introduced, integrating the Predicted Mean Vote (PMV) and Sales Pressure Index (SPI). Then, an optimization model is established, accounting for renewable energy uncertainties and maximizing large logistics parks’ participation in peak shaving. Results illustrate that the proposed method leads to a reduction in the peak shaving pressure on the distribution network. Specifically, under the scenario tolerating the maximum potential uncertainty in renewable energy output, the absolute peak-to-valley difference and fluctuation variance of the park’s net load are decreased by 45.82% and 54.59%, respectively. Furthermore, the PMV and the SPI indexes are reduced by 39.12% and 26.36%, respectively. In comparison with the determined optimization method, despite a slight cost increase of 20.06%, the proposed method significantly reduces EDR load shedding by 98.1%.

Thermally modified sepiolite integrated with aluminum chloride for phosphorus loading management of heavily polluted urban waters

Phosphorus (P) is one of the characteristic pollutants in urban heavily polluted river waters. How to control P enrichment and manage the P loading by a green and efficient adsorbent is an important challenge to control polluted waters. Modified sepiolite is often utilized for the regulation of phosphate in contaminated urban aquatic environments due to its excellent adsorption capacity. In the research, the modification of natural sepiolite with chloride at high temperature (C/Al-Sep) was conducted to enhance its P adsorption capacity and effectively manage the P loading in polluted waters. Batch adsorption experiments demonstrated that the capacity of phosphate adsorbed by C/Al-Sep was significantly improved after composite modification. Adsorption isotherm results indicated that the maximum phosphate adsorption capacity was approximately 13.75 mg/g. Furthermore, the modified sepiolite maintained a stable adsorption capacity over a wide pH range, except CO32- and HCO3-. The adsorption capacity of these common anions in water was almost unaffected. The characterization and adsorption experiments showed that the phosphate adsorption by C/Al-Sep was primarily driven by chemisorption. Additionally, the modification treatment increased the specific surface area of the sepiolite. The main mechanism of phosphate adsorption by the modified sepiolite was the formation of calcium-phosphate precipitates with the inner layer of Al-OP complexes. The results of the research indicate that the use of thermally modified sepiolite in combination with aluminum chloride provides an all-purpose, environmentally friendly material for the subsequent in-situ coverage of contaminated substrates and for the remediation of phosphates in heavily polluted urban waters.

Explorations of Integrated Multi-Energy Strategy under Energy Simulation by DeST 3.0: A Case Study of College Dining Hall

Buildings characterized by high energy consumption necessitate the implementation of efficient multi-energy complementary systems to achieve energy conservation and emission reduction objectives. College dining halls use a lot more electricity than typical residential buildings, despite their relatively small size. The dining hall at the Dongshan Campus of Shanxi University is employed as a representative case study in this research. By utilizing DeST 3.0 software, a comprehensive dynamic load analysis is conducted to estimate the annual energy consumption of the dining hall, with the ultimate goal of an energy-saving system being proposed based on the analysis results. Leveraging DeST 3.0 software, dynamic load characteristics were assessed, revealing an annual energy consumption of 2.39 × 106 kWh for the dining hall. Cooling accounted for 0.91 × 106 kWh, while heating requirements amounted to 0.24 × 106 kWh. These findings illustrate peak power consumption trends, seasonal variations, and potential avenues for energy conservation. To satisfy the heating, cooling, and electricity demands of the dining hall, an integrated energy system incorporating solar and wind energy, as well as utilizing restaurant kitchen garbage as a biomass source, was proposed. This study compares two solar energy utilization systems: photothermal and photovoltaic, with total capacities of 2.375 × 106 kWh and 2.52 × 106 kWh, respectively. The research outcomes underscore that Strategy 2, which integrates a photovoltaic system with wind and biomass energy, emerges as the optimal approach for load management. Ultimately, this investigation demonstrates the feasibility and promise of constructing a hybrid renewable energy system within a college dining hall setting, aligning with sustainability objectives and global trends toward environmentally responsible energy solutions.

Investigation of the utilization of forest woodchips in a commercial small-scale open-top gasifier

The present work investigates the gasification of forest woodchips, which also contain needles, branches and cones, in an 85-kW open-top gasifier with 60 %–100 % load. This lower quality fuel has higher contents of ash, bark and fines compared to typical requirements of commercial small-scale gasifiers. The woodchips were utilized in unsieved and sieved (>10 mm) fraction. By using gas analysis and temperature measurements at a high local and temporal resolution, three main novel results were found: 1) The results of this work confirm that preferential paths in the fuel bed, caused by the fines content, led to a worse gas quality. The positive effects of a load reduction (i.e., increasing hydrogen and decreasing tars contents) were not found with the unsieved fuel. 2) Opposite trends for the methane concentration with the load were found with unsieved and sieved fuel. The preferential paths probably prevented the complete decomposition of longer-chain hydrocarbons and increased the intermediate product methane, while the non-methane hydrocarbon content reduced at a similar rate with both fuel fractions. 3) When using unsieved fuel, reducing the load massively decreased the fluctuations of the air mass flow and the total hydrocarbon content. With sieved fuel, however, a satisfying process stability was already achieved in all loads. Therefore, an appropriate load management was identified to enable the utilization of fuels with higher fines contents. With these new findings, forest chips in unsieved and sieved form can be successfully used in small-scale open-top gasification.

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.

An integrated price- and incentive-based demand response program for smart residential buildings: A robust multi-objective model

Residential buildings consume a significant amount of energy, emphasizing the importance of optimizing energy usage. Demand-side management (DSM) helps consumers and producers manage energy consumption through incentives and pricing. This study develops a new mathematical model to manage DSM in smart residential buildings. Extant literature commonly considers only a single objective function, ignores uncertainties, and applies only one price- or incentive-based program to load management in smart residential buildings. This study develops a multi-objective mixed-integer linear programming (MILP) model that applies both price- and incentive-based programs and considers uncertainties. The objectives are cost reduction, peak load minimization, user comfort improvement, and load factor maximization. This model can manage optimal schedules for household appliances and power exchange within buildings. The study shows that participating in the incentive-based program in a four-household residential complex yielded a 2 % decrease in electricity costs and a 1 % reduction in peak load while upholding comfort and load factor levels compared to non-participation. When extended to an eight-household complex, potential benefits include an 8.3 % decrease in electricity cost and a 2.6 % reduction in peak load, highlighting the program’s effectiveness in residential energy management strategies.

Modern developments and analysis of household electricity utilization by applying smart meter and its findings

The efficient utilization of household electricity is pivotal in the context of rising energy demands and environmental concerns. This study addresses the critical issue of electricity wastage in Chinese households by applying advanced smart meter technology. Aiming to analyze and optimize electricity consumption patterns, the research utilizes the Smart Energy Utilization Model (SEUM) over a period from January 2020 to December 2022, incorporating methodologies such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Least Squares Support Vector Regression (LS-SVR), and deep learning. The findings reveal that households equipped with smart meters demonstrated a 15 % reduction in overall energy consumption, a 10 % decrease in peak-hour usage, and a notable improvement in energy-saving behaviors among users. Additionally, the integration of smart meters facilitated better load management and contributed to reducing the carbon footprint. The study also found that smart meter data analytics helped in identifying and rectifying inefficient appliances, resulting in a 12 % reduction in energy waste. Moreover, the increased awareness and real-time feedback provided by smart meters led to a 20 % increase in user engagement with energy-saving initiatives. These findings underscore the potential of smart meter technology, enhanced by advanced analytical methods, to enhance energy efficiency and support sustainable development policies.

Optimization model of combined peak shaving of virtual power grid and thermal power based on power IoT

Optimization model of combined peak shaving of virtual power grid and thermal power based on power IoT

Assessing hydropower capability for accommodating variable renewable energy considering peak shaving of multiple power grids

Hydropower can play a transformative role in supporting the substantial increase in variable renewable energy (VRE) via flexible regulation ability, but the capability of cascade hydropower to buffer VRE is indistinct. This study proposes a systematic framework to assess hydropower capability for integrating VRE considering the peak shaving demands of multiple power grids. A chance-constrained multi-time aggregate model for the integrated hydro–wind–solar operation is established to determine the optimal wind and solar capacity and coordinated operational strategies. An independent hydropower peak shaving operation model is proposed for comparison. The proposed models are recast as mixed integer linear programming formulations based on the proposed solution method. Comparative situation experiments divided by different influencing factors are implemented to analyze hydropower capability for integrating VRE. The results revealed the following: (1) Integrating VRE would reduce the peak shaving effectiveness of hydropower — the lowest load rate decreases to 0.899. (2) Hydropower capability for integrating wind and solar power is affected by hydrological conditions. Hydropower capability is limited under fixed hydrological conditions, and it prioritizes expanding wind power and restricting solar power. (3) Expanding the transmission capacity will increase hydropower capability from 13300 to 17000 MW and enhance peak shaving performance through the increased hydropower contribution.

Renewable, Flexible, and Storage Capacities: Friends or Foes?

Problem definition: More than 99% of the new power generation capacity to be installed in the United States from 2023 to 2050 will be powered by wind, solar, and natural gas. Additionally, large-scale battery systems are planned to support power systems. It is paramount for policymakers and electric utilities to deepen the understanding of the operational and investment relations among renewable, flexible (natural gas-powered), and storage capacities. In this paper, we optimize both the joint operations and investment mix of these three types of resources, examining whether they act as investment substitutes or complements. Methodology/results: Using stochastic control theory, we identify and prove the structure of the optimal storage control policy, from which we determine various pairs of charging and discharging operations. We find that whether storage complements or substitutes other resources hinges on the operational pairs involved and whether executing these pairs is constrained by charging or discharging. Through extensive numerical analysis using data from a Florida utility, government agencies, and industry reports, we demonstrate how storage operations drive the investment relations among renewable, flexible, and storage capacities. Managerial implications: Storage and renewables substitute each other in meeting peak demand; storage complements renewables by storing surplus renewable output; renewables complement storage by compressing peak periods, facilitating peak shaving and displacement of flexible capacity. These substitution and complementary effects often coexist, and the dominant effect can alternate as costs change. A thorough understanding of these relations at both operational and investment levels empowers decision makers to optimize energy infrastructure investments and operations, thereby unlocking their full potential. Funding: This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. This research was also supported by Kelley School of Business, Indiana University, and Haslam College of Business, University of Tennessee. O. Q. Wu thanks Grant Thornton for their generous support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0068 .

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.

Demonstration Project: 1.86 MWH Battery Energy Storage System and 540 KVA Inverter Integration

This research focused on the implementation of state-of-the-art system integration, involving a three-phase 540 KVA bidirectional inverter and a lithium-ion battery energy storage system with a capacity of 1.86 MWh, at the Florida Solar Energy Center (FSEC). The system was firstly put into use at the FSEC for load shifting. The second step was to add photovoltaic (PV) panels for peak shaving. The third step was to take the FSEC completely off-grid. The goal of this research was to provide a case study by implementing a power management algorithm, estimating the PV panel size, and presenting the test results and an analysis. Based on the power management algorithm, load shifting was achieved by using 180 kWh energy storage. To acquire peak shaving and the uniformity of power consumption, an 80 kW PV farm was sufficient. The optimal PV panel size was 500 kW for the off-grid scenario.

Based on the Security Control of Pumped Storage Hydropower under Extreme Fault Conditions after Grid Connection

Abstract As an important part of the new energy grid connection, pumped storage hydropower plays an important role in black start, frequency modulation and phase modulation, peak shaving and valley filling, energy storage and accident reserve. In order to ensure the security and stability of the power system under extreme fault conditions after the pumped storage hydropower is connected to the grid, it is particularly important to configure reasonable security and stability control measures. Based on the development status and operation characteristics of pumped storage hydropower stations at home and abroad, considering the need of system security and stability control, this paper takes a pumped storage hydropower station in Southwest China as an example to simulate and analyze the transient characteristics of the pumped storage hydropower after grid connection. In view of the possible extreme fault conditions, a regional security control system is designed, and the configuration, control measures and strategies of the security control device on the pumping side are studied.

Subjective and Objective Monitoring Markers: Are They Related to Game Performance Indicators in Elite Female Volleyball Players?

This cohort study aimed to investigate the relationship between subjective (wellness and internal training load [ITL]) and objective (neuromuscular fatigue) monitoring markers and performance aspects (reception quality [RQ] and attack efficiency [AE]) in professional female volleyball players. The study was conducted over an 8-week period during the final mesocycle of the competitive phase. A total of 24 training sessions and 10 matches were included in the analysis. Subjective measures of wellness and ITL were assessed, and neuromuscular fatigue was evaluated using countermovement-jump (CMJ) height. RQ and AE were determined based on game statistics. The study found a positive relationship between wellness and RQ, particularly affecting outside hitters and liberos. ITL showed a positive association with AE, primarily impacting outside hitters, opposite hitters, and middle blockers. Additionally, ITL demonstrated a negative correlation with RQ, mainly affecting outside hitters and liberos. CMJ performance was associated with AE, where a decrease in CMJ height was linked to reduced AE. The findings highlight the importance of considering players' wellness scores in training and match strategies for different positions. Careful management of training loads, considering both physical and technical demands, is crucial for optimizing performance outcomes. Monitoring neuromuscular fatigue, as indicated by CMJ performance, is particularly relevant for outside hitters, opposite hitters, and middle blockers involved in attack actions. Coaches, trainers, and sports practitioners can use these insights to develop position-specific training protocols and implement effective strategies for maintaining or improving performance metrics under various stressors.