Energy Load Research Articles

Evaluation of a Peer-To-Peer Smart Grid Using Digital Twins: A Case Study of a Remote European Island

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

Punching Shear Capacity of RC Flat Slabs with Steel and Superelastic Ni-Ti Shape Memory Shear Reinforcement under Repeated Loads

Flat slab systems are a popular choice among engineers because of their numerous advantages, such as reduced building height, flexibility in spatial layout by eliminating the need for beams, and cost savings. However, Brittle slab failure under punching shear presents a considerable challenge. The primary objective of this study was to examine how flat slabs respond to PS and conduct an experiment using steel and superelastic Ni-Ti shape memory alloy (SMA) bars as shear reinforcement to assess their effectiveness in improving the PS capacity of flat slab-column connections under vertical Repeated load. Nine half-scale samples were tested, consisting of 1100 × 1100 × 120 mm slabs with a protruding column from one side of 150 × 150 mm and 400 mm height. The factors considered in this research were the type of materials, spacing between stirrups, stabilization method, and inner RFT. After that, finite element analysis by using ABAQUS 6.14 was developed to simulate all the tested specimens. The finite element analysis showed identical results compared to the experimental ones concerning energy absorption, load capacity, absorbed energy, and model stiffness. According to the experimental and finite element analysis findings, the flat slab reinforced with inclined stirrups exhibited a significant increase in punching strength, ranging from 28.8% to 51.3% compared with the control specimen. The stiffness increases by 8.5% when the distance between stirrups is reduced to 0.5d, and inclined-shaped memory alloy stirrups are employed.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

A mixed-method approach to study the impacts of energy micro-generation combined with appliance-level feedback on everyday practices

Energy feedback is recognized in energy literature as vital for altering and curbing energy usage. However, recent studies highlight the limitation of energy feedback, questioning its real capacity to promote energy-saving behaviours. Meanwhile, micro-generation, such as photovoltaic panels (PVs), is deemed more effective in reshaping daily practices by encouraging time-shifting of activities, as a way to reduce the load of energy consumed in the grid, while increasing the one from renewable resources. This study explores the impacts of appliance-level consumption feedback combined with micro-generation on everyday practices; it adopts a practice theoretical perspective, viewing households as practitioners rather than mere rational agents. Qualitative and quantitative data were collected between 2022 and 2023 in households with PVs on two islands located off the west coast of Ireland. Smart plugs were installed and connected to different home appliances to collect energy data and provide feedback on their energy usage through a webapp. Our analysis focuses on pre- and post- plugs’ installation and feedback delivery periods, to assess households’ responses both in relation to their practices and to PV usage. While appliance-level feedback showed potential in enhancing PV use and practice changes, heightened awareness does not guarantee change, and households’ responses to feedback depend on various influencing factors deeply embedded in everyday practices – including the (non) negotiability of practices, app design, household dynamics and previous experience with the PV system. Our findings suggest opportunities for energy research to develop more tailored strategies for cutting energy consumption, taking into consideration people’s practices.

Urban heat islands’ effects on the thermo-energy performance of buildings according to their socio-economic factors

Urban areas experience the urban heat island (UHI) effect, which affects the thermal comfort and energy consumption of buildings. These consequences could vary depending on the socio-economic status of the neighbourhoods. Few studies have investigated how UHI affects socio-economically contrasting districts in thermal comfort and energy performance. Therefore, the primary goal of this research is to evaluate and compare the energy efficiency and thermal comfort conditions of residential buildings in the same city (Temuco, Chile) but located in socio-economically contrasting neighbourhoods. Urban weather files were first modelled in four urban zones using UWG software. Also, EnergyPlus building simulations were conducted to evaluate discomfort hours in adaptive comfort models and energy performance. The results showed annual average UHI intensities between 1.5 and 2.5 K. Urban–rural cooling energy load differences ranged between 12.47% and 38.92%, while heating energy load differences ranged between -20.47% and -81.95%. These distinctions depended on the urban zone, residence model analysed, or energy building standard applied. Similarly, urban-rural differences in thermal comfort times varied from 0.5% to 100%. Results illustrate that the risk of overheating could increase in socio-economically vulnerable areas. This issue could worsen if urban segregation continues to generate poor urban design in low-income districts.

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.

Directional enhancement of underwater impact and bubble loads in neighbor two-phase fluid domains charge

The loading characteristics of underwater explosions and the dynamic behavior of bubbles are directly related to the charge structure. This study proposes a unique charge structure in a gas–liquid two-phase fluid domain. Numerical methods are used to investigate the effects of fluid layer thickness and gas–liquid ratio on underwater explosion shock wave load, bubble dynamics, and bubble pulsation load. The results show that the two-phase fluid layer significantly enhances the directional release of shock wave energy and bubble pulsation load. During the shock wave phase, a lagging wave effect appears in the liquid layer direction, causing a secondary high-energy shock, significantly increasing the specific impulse. The gas layer direction may form a pressure relief channel effect, enhancing the shock wave peak pressure. For the bubble motion phase, differences in the physical properties of the fluid layer medium lead to irregular bubble boundary movements, promoting bubble tearing and rupture. The gaseous medium converts the accumulated shock wave energy into the internal energy of the bubble, increasing its volume potential. Although this characteristic reduces the pulsation frequency, it significantly increases the specific impulse. Altering the fluid layer medium can control explosion loads and bubble movement, offering new insights for ocean engineering applications.

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm

AbstractAn islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.

Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes considering load management

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

Optimization of renewable energy-based integrated energy systems: A three-stage stochastic robust model

The integration of renewable energy into an integrated energy system (REIES) represents a promising approach to achieving clean and low-carbon energy consumption. However, the inherent uncertainty and variability of renewable energy sources and load demands present significant challenges to the operational efficiency and stability of REIES. To address these challenges, we propose a three-stage stochastic robust optimization (TRSRO) model. This model accounts for market electricity prices, source-load scenario probabilities, and output uncertainties to optimize system configurations in REIES. It also includes the optimization of power exchanges with the regional grid and strategic operational scheduling. Initial scenarios of uncertainty variables are generated using the spectral normalized conditional Wasserstein generative adversarial network (SNCWGAN), forming polyhedral uncertainty sets. Comprehensive norm constraints are then applied to capture probability distribution uncertainties within these sets. To improve the efficiency of solving the model, we introduce a two-layer column constraint generation algorithm, considering variable alternate iterations (TLCCG-VAI). The results demonstrate that the proposed method can achieve a 12.16 % reduction in total cost compared to the baseline method. Furthermore, the TLCCG-VAI algorithm reduces runtime by 82.97 % while maintaining the same level of solution accuracy.

Optimal scheduling of battery energy storage system operations under load uncertainty

This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Building Energy Savings and Power Output Augmentation of Roof Mounted PV Using Co-Located Rooftop Reflectors

Abstract Photovoltaic panels (PVs) installed on building rooftops yield a positive influence on the thermal performance of the building due to the shading of the PV panels, decreasing cooling loads while causing a smaller increase in heating loads. Additionally, the electrical power output of PV panels has been shown to be increased by including reflectors between PV rows, concentrating the solar flux onto the active portion of the panels. When implemented into the spaces between the rows of a roof-mounted PV array, reflectors might further improve the positive thermal effects of rooftop installed PV arrays. This work focuses on predicting rooftop heat flux and temperature for a building rooftop equipped with PV panels and reflectors. The Saved Energy Load (SEL), Additional Energy Load (AEL), PV power output, rooftop heat flux and the utility factor (ratio of positive building energy impacts to negative building energy impacts) are reported parametrically for variations in the rooftop absorptivity and reflector area for three United States locations. Utility factors of 375, 140 and 160 are found for Phoenix, Boise and Dayton, respectively, for a reflector covering the full area between panels with a roof having a minimal absorptivity. A building in Phoenix exhibits a 15% increase in the utility factor of the PV-building system when reflectors are incorporated compared against a PV-building system without reflectors, while a building in Dayton showed a 22% increase in utility factor when reflectors are included between the rows of a roof-mounted PV array.

Optimal operation of CCHP system with duality operation strategy considering hydrogen trading and carbon capture

The combined cooling, heating, and power (CCHP) system, known for its outstanding compatibility performance, has been widely integrated with renewable energy sources such as hydrogen, wind, and photovoltaics, as well as decarbonization technologies in the energy field. However, the increased complexity of CCHP scheduling due to the high proportion of renewable energy sources and load fluctuations leads to negative returns if renewable energy sources are not scheduled reasonably and decarbonization technologies are not utilized. To address this challenge, this study introduced solid oxide electrolyzer cell (SOEC) and carbon capture system (CCS) into the CCHP system, and constructed a novel CCHP model considering hydrogen trading and decarbonization technologies. First, for the scheduling of SOEC and CCS, a game model was presented based on hydrogen sales and energy storage benefits. Second, a nudge and compel theory-based scheduling strategy and a duality operation strategy (DOS) considering sources-load fluctuation were proposed. Third, for the optimal energy scheduling problem of CCHP under new strategies and technologies, a novel multi-objective PID-based search algorithm with dynamic disturbance response was introduced. Finally, the proposed new strategies, methods, and models were verified through actual case studies on multiple typical days. The results revealed that, compared with the following electrical load strategy and following thermal load strategies, the DOS reduced costs by 3.03 % and 6.99 %, and emissions by 7.84 % and 1.39 %, respectively. The obtained outcomes contribute to the application and development of clean energy and decarbonization techniques.

Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware

To achieve optimization operation between the energy hub operator and the load aggregators, this paper proposes a collaborative model for the energy hub and load aggregator considering the carbon intensity-driven and uncertainty. Firstly, a time-coupled carbon emission flow model for energy storage is proposed to cope with the change in carbon intensity at the injection node. Meanwhile, a carbon intensity-based incentive price is developed for interruptible loads with diverse carbon intensity. Secondly, the Wasserstein metric-based distributionally robust optimization method is leveraged to height against the energy hub uncertainty, in which the infinite-dimensional distributionally robust optimization model is reformulated into a tractable linear programming. Thirdly, considering the multi-entity characteristics of energy hub and load aggregators, a collaborative optimization method based on the alternating direction method of multipliers algorithm is constructed. Finally, to meet the convergence requirements of the alternating direction method of multipliers algorithm, the iterative convexification algorithm is applied to transform the nonconvex bi-level energy hub-users model into an iterative convex model by avoiding binary variables and big-M parameters. Consequently, the energy hub and load aggregator can operate cooperatively and protect privacy in uncertain conditions. Simulation results verify the effectiveness of the proposed model and method. Simulation results demonstrate the effectiveness of the proposed model and method. Specifically, the iterative convexification algorithm achieved a profit of ¥1653.82 for the load aggregator, which is 24.4 % higher than the profit obtained by CONOPT (¥1329.73) and 20.3 % higher than that obtained by IPOPT (¥1374.69), thus providing substantial advantages over traditional nonlinear solvers.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

U-Net architecture for high-resolution thermal image segmentation: Enhancing CFD models in smart HVAC design

This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.

Three-dimensional analysis of multiple inclined borehole heat exchangers

This research develops and applies a numerical modelling method for inclined borehole heat exchangers (BHE) in ground-source heat pump systems. Inclined BHEs have the advantage of reducing the ground-level footprint of the overall BHE field. Computational modelling of the ground heat transfer using the commercial code COMSOL Multiphysics has revealed that inclined BHEs offer additional performance benefits compared to vertical BHE fields. When heat is injected constantly, both types of fields perform similarly at first, but over a longer period of time, the inclined BHE fields show significant benefits due to the increased ground volume in the deeper portion of the field. Similar conclusions are reached when transient loading conditions are considered. Notably, when cooling dominates, inclined BHE fields outperform vertical ones for a simulated period of 10 years. This indicates that inclined BHE fields are better equipped to handle imbalanced energy loads. To summarize, inclined BHE fields offer advantages in ground-volume utilization and resilience to energy load imbalances compared to vertical fields.