Energy Load Research Articles

Research on Low-Carbon Design and Energy Efficiency by Harnessing Indigenous Resources through BIM-Ecotect Analysis in Hot Climates

In the face of contemporary challenges, such as economic instability, environmental degradation, and the urgent global warming crisis, the imperative of sustainability and energy efficiency has reached unparalleled significance. Sustainability encompasses not only the natural environment, but also extends to our immediate surroundings, including the built structures and the communities they serve. Embracing this comprehensive perspective, we embarked on a mission to conceive and construct a model house that harnesses state-of-the-art energy-efficient technologies. Our goal was to seamlessly integrate these features not only to meet our sustainability objectives, but also to mitigate environmental threats.This model embodies a harmonious fusion of indigenous resources, employing locally sourced stone and employing traditional construction techniques. Through this approach, we achieved significant reductions in carbon emissions and established a framework for passive cooling and heating systems. Moreover, the design is intrinsically attuned to its contextual surroundings, preserving the diverse tapestry of regional architectural styles. This study stands as a testament to the potential of innovative design and technology in shaping a sustainable future. The research employs a multi-dimensional approach, encompassing strategies of architectural design with a traditional planning approach, sustainable material selection, energy efficiency, and life cycle assessment across a diverse set of case studies. Building energy analysis is conducted through the application of BIM (Ecotect), providing insights into how BIM can adapt and thrive in various environments. Key findings underscore that thermal performance, minimizing energy loads, and reducing carbon emissions are pivotal aspects in designating a building as both green and energy efficient.

Robust optimization dispatch for PV rich power systems considering demand response and energy storage systems

In recent years, the ever-rising penetration of distributed photovoltaics (PV) power has presented substantial challenges in power system dispatch due to its inherent randomness and unpredictability. To bridge this gap, this paper proposes a two-stage robust optimization method for power system security dispatch considering traditional generators as well as flexible resources, such as load demand response and energy storage systems. Specifically, a price-based demand response model is established to optimize the system’s load curve during a day. On this basis, a two-stage optimization problem for day-ahead and intra-day power system dispatch model is proposed. The dispatch objective is to minimize the overall cost in worst-case scenarios through properly scheduling unit commitment (UC) as well as flexible resources in each dispatch interval. Column and constraint generation (C&CG) algorithm is adopted for problem solving. The effectiveness of the proposed method is validated by case studies based on a modified 6-node system and a 24-node system. Simulation results indicate that through appropriately scheduling the energy storage system and load demand response, the proposed dispatch method can significantly reduce the total operation cost of a PV rich power system, which in turn facilitates the integration of PV power.

Extended Energy Conversion and Electron Acceleration Behind Dipolarization Front

AbstractEnergy transfer and transport in the terrestrial magnetotails are primarily driven by dipolarization fronts (DFs) embedded inside plasma jets. The DF‐driven energy transfer has hitherto been believed to occur locally at the fronts. Different from the traditional knowledge, here we present the first observation of persistent energy conversion extended far behind a DF. The persistent energy conversion, which was dominated by energy loads and mainly contributed by electron currents, developed inside a turbulent, decaying flux pileup region (FPR), nearly 10 dDF (DF’s thickness) behind the DF. The energy transfer chain may be initiated by interaction between the ion flow and ambient plasmas and closed by electron dynamics, leading to electron acceleration perpendicular to magnetic field. These results highlight that electron physics in turbulent FPRs plays a crucial role in the energy transport in the planetary magnetospheres.

An efficient hybrid technique for energy management system with renewable energy system and energy storage system in smart grid

This manuscript proposes a hybrid technique for an energy management system (EMS) with a renewable energy system and energy storage system using the smart micro grid. This work's novelty liesin the integration of Flying Foxes Optimization (FFO) algorithm and Deep Attention Dilated Residual Convolutional Neural Network (DADRCNN). Unlike existing methods, the FFO-DADRCNN method effectively leverages both optimization and predictive modelling capabilities to enhance grid stability, efficiency, and load prediction accuracy. The main goals of the proposed approach are to increase grid stability and efficiency, decrease micro grid system costs, and increase load prediction accuracy. The micro grid system incorporates PV, an energy storage system, wind turbine and load demand. To regulate the micro grid system and estimate load utilizing the DADRCNN most effectively, the proposed FFO technique is used. The proposed method's performance is evaluated using the MATLAB platform and compared with existing approaches, including Giza Pyramids Construction, Heap-based optimizer, and Nomadic People Optimizer. The proposed technique shows better outcomes when compared to other methods. Compared to other methods currently available, our proposed approach boasts an impressive 98 % efficiency rate and comes at a low operating cost of $1840.

Impact of uncertainty in building operation patterns and electric energy demand on the design and techno-economic performance of solar photovoltaic systems in different climates

The optimal design of renewable energy systems for buildings requires accurately estimating building electric energy loads. While this energy demand highly depends on how occupants and facility managers use building systems, operation-related uncertainty is rarely accounted for in the renewable energy design workflow. This paper presents a comprehensive framework to quantify the impact of uncertainty in building operation patterns and electric demand on the techno-economic performance of grid-connected photovoltaic (PV) systems (with and without battery storage). A hybrid methodology is proposed, combining and comparing two approaches to studying building performance under uncertainty: (i) physics-based building performance simulations coupled with parametric variations of occupant behavioral profiles (e.g., austere vs. wasteful), and (ii) data-driven modeling using a large national building dataset. The approach is illustrated and validated on small, medium, and large-sized office buildings in different climate zones (hot dry, mixed marine, and cold humid). Results show that different building operational patterns could lead up to 60% increase in building electric demand, which translated to similar increases in PV energy capacity and the corresponding capital costs of the systems. The findings emphasize the urgent need to better account for uncertainty in building operational patterns in the design workflows of buildings with PV generation and energy storage capabilities.

Improved Newton-Raphson method with simplified Jacobian matrix and optimized iteration rate for power flow calculation of power system

With high penetration of renewable energy and novel loads connected to the distribution network, the voltage fluctuation becomes more severe and frequent, which may cause over- and under-voltage. The distribution system operator should calculate the power flow and validate the state to optimize the distribution network. Power flow calculation is the solution to the multivariate nonlinear problem, and the Newton-Raphson method is an effective algorithm for solving nonlinear problems. However, calculating the Jacobian matrix is a crucial process of the Newton-Raphson method, which is time-consuming. Therefore, this paper proposed an improved Newton-Raphson method, which simplifies and decreases the iterations of the calculation process of the Jacobian matrix to improve the calculation rate. To verify the effectiveness of the proposed method, the power flow of the IEEE 33-node power distribution system is calculated by the improved Newton-Raphson method and the conventional Newton-Raphson method.

A Comprehensive Survey of Load Balancing Techniques in Multipath Energy-Consuming Routing Protocols for Wireless Ad hoc Networks in MANET

As technology progresses, networking undergoes continuous evolution to align with contemporary trends. Mobile Ad hoc Networks (MANETs) have emerged as a pivotal technology facilitating communication among mobile nodes sans a central administering node. MANETs find diverse applications spanning communication networks to battlefield scenarios. Nonetheless, MANETs encounter various challenges, including load balancing, energy efficiency, packet loss, and connection failures. Routing, among these challenges, assumes a pivotal role in defining network performance and connectivity capacity within MANETs. Moreover, the energy consumption of mobile nodes, often reliant on batteries with limited rechargeability, presents a significant concern in MANETs. This paper delves into diverse techniques devised to tackle the issues of energy efficiency and load balancing in MANETs. Additionally, it offers a comparative analysis to consolidate the survey findings. The outcomes of this study enrich our comprehension of energy-efficient and load-balanced routing protocols in MANETs, thereby facilitating the design and deployment of efficient networking solutions.

Optimal PV Design for Educational Facilities Considering Renewable Energy Installation Obligation and Load Matching

Optimal PV Design for Educational Facilities Considering Renewable Energy Installation Obligation and Load Matching

Operation strategy for community integrated energy system considering source–load characteristics based on Stackelberg game

Instability in supply and demand, coupled with conflicting market interests, significantly impacts the operational efficiency, reliability, cost-effectiveness, and environmental sustainability of integrated energy systems. To address this issue, an operation strategy for a community integrated energy system based on Stackelberg game theory is proposed, taking into account the characteristics of supply and load. Initially, a master–slave game model for an integrated energy system is established, with energy marketer acting as leader and energy supplier and load aggregator as followers. To manage the uncertainties of wind and photovoltaic power generation, robust optimization theory is employed to construct models for both the short-term and long-term output errors of wind and photovoltaic power generation. This approach allows for a more detailed analysis of the uncertainty associated with wind and photovoltaic power generation. Subsequently, the uniqueness of the equilibrium in the established Stackelberg game model is proven. Utilizing an improved adaptive differential evolutionary algorithm in conjunction with the CPLEX solver, not only is the optimal solution solved for, but also the efficiency of the solution scheme is significantly enhanced. The simulation results demonstrate that the strategy not only boosts the reliability of the integrated energy system but also achieves a cost saving of 1.11%, an increase in energy supplier profit of 5.16%, and a load aggregator consumer surplus of 19.68%. In scenarios considering errors in wind and photovoltaic predictions, the cost savings reach 16.95%, with energy seller and supplier profits improving by 9.34% and 32.47% respectively, and consumer surplus increasing by 18.81%.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Shared Energy Storage Capacity Configuration of a Distribution Network System with Multiple Microgrids Based on a Stackelberg Game

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy.

Hygrothermal Performance of Roofs with High Initial Construction Moisture Subjected to Hot Climate

Moisture accumulation in the building components/assemblies that form building envelopes can lead to material deterioration and moisture related issues such as mould growth. As a part of the building envelope, this study focusses on assessing the moisture performance and energy performance (i.e., hygrothermal performance) of roofing systems. As roofs can be built with high initial construction moisture, numerical simulations were conducted with and without high initial construction moisture in order to investigate: (a) the hygrothermal performance of cool and black roofs having material layer with high initial construction moisture content, (b) the time needed so that the moisture content reaches acceptable level as per the building code requirements, (c) whether moisture accumulation and mould growth occur in the roofs, and (d) the energy savings as a result of installing white/cool roof instead of black roof. An advanced numerical model is used to conduct the numerical simulations for black and cool roofs when they are subjected to hot climate. This model solves simultaneously the Heat, Air and Moisture (HAM) transport equations in all layers of the building assemblies. The model was extensively validated by comparing its predictions with the experimental data of different building components at various operating conditions. For the roofing systems investigated in this paper, the results showed that mould growth occurred in the black and cool roofs only for the case with high initial construction moisture. The mould has totally disappeared after 378.8 day for the black roof and 479.3 day for the cool roof. The temperatures of the cool roof were much lower than those for the black roof. The total yearly energy load with the black roof was 77% greater than that with the cool roof.

Progress and challenges of latent thermal energy storage through external field-dependent heat transfer enhancement methods

The development of Energy Storage technologies is critical to achieving a cleaner energy future. As one of the most widely used energy storage technologies, Latent Thermal Energy Storage (LTES) still suffers from poor charging and discharging performance subjected to the low thermal conductivity of Phase Change Materials (PCMs) and inefficient heat transfer process. Heat transfer enhancement techniques, such as using fins and adding highly conductive materials, have been widely developed and optimized to overcome these challenges. Recently, additional novel methods integrating adjustable external fields such as gravity, magnetic field, and electric field have been proposed to enhance the heat transfer performance of LTES, due to their advantages including easy adjustment of the field parameters according to the evolution of the heat storage process. Firstly, this work briefly summarizes the progress of conventional heat transfer enhancement methods. Secondly, the advancement of heat transfer enhancement by integrating adjustable external field effects has been analyzed and discussed. Finally, the potential of external fields to improve the heat storage performance of LTES under fluctuating thermal sources is discussed considering the wide existence of fluctuating energy supply and load in real applications. The main contributions from this review are: (a) The emerging heat transfer enhancement methods for LTES are comprehensively summarized and discussed for the first time; (b) The potential of different heat transfer enhancement techniques is demonstrated to overcome the poor heat storage performance under fluctuating thermal sources when considering real applications of LTES.

Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

Research on deep peaking cost allocation mechanism considering peaking demand subject and thermal power unit

The current peak-shaving auxiliary service cost allocation mechanism balances the revenue and expenditure of the power plant side, the peak-valley difference of the power grid is mainly caused by the peak-valley characteristics of the load and renewable energy. It is not reasonable that not reasonable the peak-shaving cost allocation mechanism does not consider user allocation. Therefore, this paper establishes a peak-shaving cost allocation mechanism that considers the peak-shaving demand subject, this article incorporates renewable energy power plants and loads into the peak-shaving demand subject. Firstly, this paper constructs an alternative scenario without peak-shaving demand through the mean line of load and available energy output and establishes an optimal scheduling model with deep peak-shaving and pumped storage power stations, this article takes the difference of system peak-shaving cost between two scenarios with and without peak-shaving demand as the marginal peak-shaving cost. Then, the Shapley value method in cooperative game is used to establish the peak-shaving cost allocation mechanism considering the main body of peak-shaving demand. In addition, It is used that the equal-kWh following load method constructed the equivalent output curve of renewable energy, following load method improves the existing allocation mechanism by comprehensive similarity. Finally, this paper analyzes and compares three different peaking cost allocation mechanisms. The example analysis shows that the peaking cost allocation mechanism considering peaking demand subjects. The example can reflect the peaking cost caused by different subjects and reduce the peaking pressure of thermal power units.

Thermal behaviour of a gypsum board incorporated with phase change materials

This study investigates the influence of a microencapsulated Phase Change Material (mPCM) on building systems in a subarctic climate which is not commonly studied for PCM applications. The mPCM is incorporated into gypsum to make a composite board with a volume fraction of 30 vt%. The fabricated composite board is then used to make a box model. This model along with a reference model built only with gypsum boards are placed inside a climate chamber where temperature is regulated to a summer day of a subarctic country, where large temperature variation exists between day and night. In addition, a Finite Element Method (FEM), is also used for the validation of the experimental data. The thermal-physical properties of the mPCM gypsum board including the specific heat capacity and thermal conductivity are measured. The microscopic features of the composite board are also studied. In addition, the temperature variation and the thermal energy storage of the boards of the two models have been studied. Results indicate that incorporation of mPCM into gypsum will change the thermal properties of the material. PCM can work as an additional insulation layer due to its low thermal conductivity. Further, the temperature fluctuation inside of the model with mPCM is reduced. In addition, the energy stored in the mPCM composite is around 3 times higher than that of gypsum board, making it promising for building energy improvement and load shifting.

Analytical Selection of Leakage Inductance for Single-Phase Dual Active Bridge Converters

The single-phase dual active bridge (DAB) DC/DC converter is a vital component in modern power electronics systems, facilitating efficient bidirectional power transfer between various energy sources and loads. One of the critical aspects in the design of such converters is the selection of an appropriate leakage inductance, as it significantly affects the circulating power within the converter. This study investigates the influence of inductance on the reactive power flow and introduces an analytical formula for calculating the necessary leakage inductance based on the specified rated power of the DAB converter. By employing the inductance values determined through the proposed formula, the developed power control-based modulation method, implemented in discrete time, successfully achieves zero reactive power caused by the first harmonic components under all load conditions. This selection of leakage inductance optimizes the performance of the converter ensuring efficient power delivery across various applications. The applicability of this method extends to various DAB applications, including electric vehicles, integration with asynchronous microgrids, and aerospace systems.

Ultra-short-term prediction of microgrid source load power considering weather characteristics and multivariate correlation

Multiple microgrids interconnect to form a microgrid cluster. To fully exploit the comprehensive benefits of the microgrid cluster, it is imperative to optimize dispatch based on the matching degree between the sources and loads of each microgrid. The power of distributed energy sources such as wind and photovoltaic systems and the sensitive loads in microgrids is related to the regional weather characteristics. Given the relatively small geographical scope of microgrid areas and the fact that distributed energy sources and loads within the grid share the same weather characteristics, simultaneous ultra-short-term forecasting of power for both sources and loads is essential in the same environmental context. Firstly, the introduction of the multi-variable uniform information coefficient (MV-UIC) is proposed for extracting the correlation between weather characteristics and the sequences of source and load power. Subsequently, the application of factor analysis (FA) is introduced to reduce the dimensionality of input feature variables. Drawing inspiration from the concept of combination forecasting models, a combined forecasting model based on Error Back Propagation Training (BP), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory Neural Network (BiLSTM) is constructed. This model is established on the MV-UIC-FA foundation for the joint ultra-short-term forecasting of source and load power in microgrids. Simulation is conducted using the DTU 7K 47-bus system as an example to analyze the accuracy, applicability, and effectiveness of the proposed joint forecasting method for sources and loads.