Hybrid Energy Supply Research Articles

Predicting Energy Consumption for Hybrid Energy Systems toward Sustainable Manufacturing: A Physics-Informed Approach Using Pi-MMoE

Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. However, conventional studies often focus solely on predicting single types of energy consumption and overlook the integration of physical laws and information, which are essential for a comprehensive understanding of energy dynamics. In this context, this paper introduces a multi-task physics-informed multi-gate mixture-of-experts (pi-MMoE) model that not only considers multiple forms of energy consumption but also incorporates physical principles through the integration of physical information and multi-task modeling. Specifically, a detailed analysis of manufacturing processes and energy patterns is first conducted to study various energy types and extract relevant physical laws. Next, using industry insights and thermodynamic principles, key equations for energy balance and conversion are derived to create a physics-based loss function for model training. Finally, the pi-MMoE model framework is constructed, featuring multi-expert networks and gating mechanisms to balance cross-task knowledge sharing and expert learning. In a case study of a textile factory, the pi-MMoE model reduced electricity and steam prediction errors by 14.28% and 27.27%, respectively, outperforming traditional deep learning methods. This demonstrates that the model can improve prediction performance, providing a novel approach to intelligent energy management and promoting sustainable development in manufacturing.

Dynamic computation scheduling for hybrid energy mobile edge computing networks

The rapid expansion of Mobile Edge Computing (MEC) is driven by the growing demand for resource-intensive applications within the Internet of Things. Computation offloading allows these applications to be executed at the network edge, but it leads to significant electricity expenses for network operators. To mitigate these costs, one promising solution is to power base stations (BSs) with a hybrid energy supply that combines unpredictable harvested energy with stable energy from the smart grid. This paper investigates joint computation scheduling for mobile devices (MDs) and resource allocation in a MEC network incorporating hybrid energy sources. Our objective is to maximize long-term time-averaged service utility by optimizing parameters such as BS battery supply, harvestable energy, CPU frequency, transmission power, task-partition factor, and MD-BS associations. To tackle this complex problem, we exploit the Lyapunov optimization framework to decompose it into deterministic subproblems for each time slot and propose an online network service utility maximization scheduling (NSUMS) algorithm. Experimental results show that our algorithm outperforms benchmark schemes in service utility and energy expenditure, improving the completion ratio by 32%, reducing the failure rate by 80%, and decreasing MD energy consumption by 28%.

Optimal electric bus scheduling method under hybrid energy supply mode of photovoltaic-energy storage system-power grid

Currently, the charging energy of electric buses (EBs) primarily relies on the power grid (PG), and the production of the electricity for the power grid still results in carbon emissions. In recent years, a remarkable development has been observed in the photovoltaic (PV) technology. If EBs can be charged using electricity generated from PV, it has great potential to significantly reduce carbon emissions for EB systems at the source. Considering the inherent output power fluctuations from PV source, we propose a hybrid electricity supply mode named “Photovoltaic-Energy Storage System-Power Grid” (PV-ESS-PG). Firstly, considering the characteristics of different electricity supply modes, we introduce charging strategies tailored to different scenarios and formulate a cooperative optimization model for EB dispatching and charging plans. Secondly, we decompose this model into two sub-problems: bus dispatching and charging scheduling. To solve these two sub-problems, we employ the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to obtain the optimization results of bus dispatching plan, charging mode, charging start time, and charging duration. Finally, we validate the proposed method using real-world data of EB operation and PV output power. We further analyze the influences of weather conditions, ESS capacity, and EB rated battery capacity on the optimization results. We find that, compared to the conventional unitary power grid electricity supply mode, the proposed method reduces daily charging costs by 25.48% and carbon emissions by 68.71% of the whole bus route.

A hybrid energy supply system based on metamaterial antenna integrated solar cells for IoT nodes

In this paper, a hybrid energy supply system based on solar cells integrated metamaterial (MTM) antenna is presented for use in Internet of Things (IoT) nodes. The work develops an energy supply system having dimensions of 40×40 mm2, that is qualified for storing and releasing energy. To realize miniaturization technique and gain enhancing, the design of antenna employs complementary split-ring resonator (CSRR) and a 4×1 array. Meanwhile, this work is miniaturized by integrating solar cells on the substrate. The result is a very compact integrated antenna with overall dimensions of 82.5×33 mm2 and the ability to harvest radio frequency (RF) energy and solar energy, which covers frequency band from 5.2 to 6.2 GHz. Different antenna structures were built to analyze S11 spectra, voltage standing wave ratio (VSWR), radiation patterns and surface current distribution, respectively. For further verification, the design was fabricated and compared to the simulated one. Moreover, practical experiments were conducted with four kinds of environment setup. The fabricated antenna provides maximum voltage of 1.63 V, indicating a good applicability to RF energy harvesting. The energy supply system provides a stable output voltage with 2.9 V in different conditions. The results highlight the feasibility of energy supply system in energy harvesting and powering for various IoT nodes.

Energy Consumption Optimization in Cell-Free Massive MIMO Systems With Hybrid Energy Supply

Energy Consumption Optimization in Cell-Free Massive MIMO Systems With Hybrid Energy Supply

A sustainable multi-objective optimization model for the design of hybrid power supply networks under uncertainty

In today's industrialized world, the scarcity of fossil fuels and the adverse biological effects resulting from fossil fuel consumption, on one hand, and the increasing population and energy demands, on the other hand, have posed significant challenges to the sustainable development of societies. This research has developed an optimization model for designing a hybrid energy supply network that simultaneously addresses economic, environmental, and social objectives. The economic goal of the model is to maximize after-tax company profits, while the environmental objective is to minimize greenhouse gas emissions, and the social objective is to maximize social profit. The optimal solution for individual goals reveals that the gas field is active and beneficial for economic objectives, renewable plants are active for environmental goals, and for social objectives, the gas field, combined heat and power (CHP), and wind power are active. The model is solved using the augmented ε-constraint method, and the resulting Pareto optimal solutions are provided to decision-makers. To account for real-world complexities, uncertainties in key model parameters have been considered. Validation for this study has been conducted using data from Iran, an energy exporter to Pakistan.

Self-Adaptive PDLC Control Strategy With Smart Light Intensity Adjustment Using Photovoltaic-Thermoelectric Hybrid Energy Supply Technology

As an electronically controlled glass whose transmittance varies with voltage, polymer dispersed liquid crystal (PDLC) has the advantages of good privacy, high transmittance, fast response speed, and good heat shielding performance. It can be applied to electronically controlled building windows. However, PDLC needs to consume extra power, and electronically controlled PDLC has not yet been integrated with intelligent home environment control. In this paper, we proposed an adaptive variable voltage step-size fast control algorithm in Field Programmable Gate Array (FPGA)-based home environment control system, which is powered by photovoltaic-thermoelectric (PV-TEG) hybrid sources. The power supply voltage of the PDLC can be dynamically changed through the adaptive Pulse width modulation (PWM) duty cycle adjustment technology to realize intelligent transmittance control, thereby improving the indoor lighting environment. Experimental results show that by applying the proposed algorithm, the indoor temperature is reduced by 2°C, and the adaptive and stable regulation of the optimal indoor illumination of 500 Lux can be completed in less than 2.5 seconds. This paper provides a feasible solution for the low-energy intelligent integrated control and monitoring of the modern home environment.

Communication-efficient distributed electricity and carbon allowance management for community microgrids with hybrid energy supply stations

With the rapid development of plug-in electric vehicles and hydrogen fuel vehicles, the introduction of hybrid energy supply stations into modern energy systems has attracted much attention in recent years. The hybrid energy supply stations are promising energy facilities that can provide charging services for plug-in electric vehicles and refilling services for hydrogen fuel vehicles. This paper studies electricity and carbon allowance sharing among community microgrids integrated with hybrid energy supply stations from a new perspective. The main objective of this work is to reduce the communication overheads when implementing distributed energy and carbon allowance management for microgrids, rather than merely protecting the privacy of microgrids as other studies have done. Thus, a communication-censored alternating direction method of multipliers-based communication-efficient distributed algorithm is proposed for solving the electricity and carbon allowance sharing problem in a distributed manner. Moreover, an adaptive penalty coefficient selection method is introduced and integrated into the communication-censored alternating direction method of multipliers for improving the convergence performance of the distributed algorithm. Simulation results demonstrate that the proposed mechanisms for sharing electricity and carbon allowance among microgrids can result in a 50.18% reduction in the total operation cost and a 13.70% reduction in the total carbon emissions, compared to the scenario where such sharing is not implemented. In comparison to the alternating direction method of multipliers, the proposed distributed algorithm can reduce the total communication overhead by 46.47%. Furthermore, the adaptive penalty coefficient selection method can improve the convergence performance of the communication-censored alternating direction method of multipliers by 21.26%.

Energy, exergy, and economic analysis of a solar photovoltaic and photothermal hybrid energy supply system for residential buildings

In this study, a dynamic simulation model is developed based on the mathematical model of a solar photovoltaic and photothermal hybrid energy supply system (PV-PTHS) employing the TRNSYS simulation software. The corresponding operational control strategy for the PV-PTHS is proposed. The impact of different PV and PT areas on the performance and the energy and exergy flows of the PV-PTHS throughout the year are analyzed using simulations. In addition, the dynamic performance of the PV-PTHS on typical days is reported during the heating period, transition period, and cooling period. Also, the monthly exergy performance of the system is comprehensively analyzed. Finally, the cost-effectiveness and energy savings of the PV-PTHS are reported and compared against the single PT and single PV systems. The results show that the energy loss of the PV cells is the highest, followed by the solar collector. However, the exergy efficiency of the collector is lower than that of the PV cell in the PV-PTHS, and the low exergy efficiency of the collector results in a lower exergy efficiency of the PT subsystem (PTS). Considering similar initial investments, the PV power supply ratio of the PV-PTHS is the highest in areas with a large heating load, such as Nagqu and Yinchuan. The heat supply ratio and the cost-effectiveness of the signal PT system are the best. Overall, the proposed PV-PTHS yields the highest primary energy saving ratio (PESR) and the best cost-effectiveness for areas with a small heating load, such as Wuhan and Fuzhou.

Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

An optimal algorithm for energy harvesting in optical networks

Optical fiber cables, which make up the majority of the world's telecommunications infrastructure, now carry the vast majority of digital data. However, because to the Internet's exponential expansion and a slew of bandwidth-hungry new services like online gaming, 3D or high-definition television, and cloud computing, this infrastructure will soon run out of capacity. The growing need for faster transmission speeds by clients has led to the development of heterogeneous optical networks. High throughput, portability, and future information interconnects may all be accommodated by such a system. Delivering such high-capacity connections to a mobile client should need heterogeneous optical networks. Different multiple access user technologies have been suggested to meet the needs of a group of future heterogeneous optical network users. In order to address the high energy consumption, poor QoS and low resource utilization in cloud radio access network, this paper proposed a novel algorithm. The main idea is to allocate virtual resources with energy-awareness and hybrid energy supply. Then, the energy arrival and consumption models were established based on different network devices. Simulation results show that the proposed algorithm effectively improved the energy consumption performance and QoS as compared with existing methods.

Hybrid Optimization Assisted Green Power Allocation Model for QoS-Driven Energy-Efficiency in 5G Networks

5G networks are progressing to offer services with high Quality of Service (QoS) features like high user mobility skills, higher data rates, high reliable communiqué, and lower latency. For this, numerous novel settings like integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) should be implemented in the network system. Here, this paper plans to design an optimization-based power allocation model to increase the Effective Power Efficiency (EPE) that offers guaranteed QoS over “Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)-channel oriented 5 G networks”. Moreover, the statistical QoS-based green power allotment system was also analyzed to increase the EPE over other MIMO networks. Particularly, this paper makes an attempt to make the power allocation optimal, which directly ensures the 5 G network energy efficiency when guaranteeing QoS. This is considered to be the optimization issue, which is solved by MS-DA. Our suggested approach can be applied to hybrid energy supply networks with various network topologies, such as 5 G networks, which are now the most extensively used, and is capable of achieving a good resolution through iterative convergence.

Two Time-Scale Energy and Spectrum Allocation for MEC Networks with Hybrid Energy Supplies

Integrating power grid and renewable energy to charge mobile-edge computing (MEC) server is a promising solution for green networks, which can reduce the energy consumption of power grid and carbon emission. Intelligent management of energy and bandwidth resources in the two time-scale framework can increase the energy efficiency and network revenue in MEC networks with heterogeneous energy supplies and multiple mobile users (MUs). In this paper, we investigate the joint energy management (for workloads offloading and execution) and bandwidth allocation (for workloads transmission) problem that maximizes the network revenue in the MEC network with hybrid energy supplies. We formulate it into a challenging nonlinear optimization problem because of the data randomness and the temporal coupling effect. Based on the Lyapunov optimization approach with quick convergence speed and low complexity, we propose an online algorithm to overcome the obstacles by relaxing the battery constraints, allowing us to achieve close-to-optimal maximum network revenue with the stability of battery level and different throughput requirements of MUs. Extensive numeral simulations verify the theoretical analysis and demonstrate the superior performance of the proposed algorithm in different algorithms and scenarios.

Resource Allocation in Cell-Free Massive MIMO Networks With Hybrid Energy Supplies

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks were proposed as a novel architecture for radio access networks, which consists of a large number of access points (APs) over a vast area that simultaneously offer services to user equipment (UEs) in a single frequency band. However, powering a large number of APs along with impossibility or high cost of accessing the electrical grid in some scenarios remains a key challenge in unleashing the full potential of CF-mMIMO networks. One remedy is to resort to various forms of renewable energy sources and come up with AP designs equipped with energy harvesting capabilities. Regarding the unpredictable nature of renewable energy sources, energy-efficient resource allocation techniques need to be developed so that APs remain operational at all times. In this paper, we envision a CF-mMIMO system in which different APs get their power from an electrical grid, a solar energy source, or a hybrid design. We aim at designing an AP-to-UE power allocation scheme with the objective of minimizing overall energy consumption from the electrical grid while satisfying the spectral efficiency (SE) constraints. Moreover, we come up with a green energy budgeting mechanism to prevent energy deficiency when solar energy is scarce. We first propose an idealistic offline formulation of the problem, and transform it into a second-order cone programming (SOCP) problem. Then, we come up with a suite of lightweight model-free myopic heuristic algorithms, and compare their performance against offline benchmark.

Проблемы и перспективы развития зеленой энергетики в условиях перехода к устойчивому развитию

Subject/topic. This article explores the features of green energy development, which is a crucial condition for achieving low-carbon economic development, thereby reducing the impact on the planet's climate system. Goals/Objectives. The aim of this research is to analyze changes in greenhouse gas emissions by countries and economic sectors, and to determine the trajectory of green energy development. Methodology. In the process of working on the article, various methods were employed, including analysis, synthesis, systematization, economic and logical analysis, content analysis, as well as comparisons and groupings. Results. Over the past few decades, there has been significant growth in the use of green energy in many countries. The expansion of green energy is closely linked to reduced costs for renewable energy, increased energy efficiency, ongoing technological progress, informed decision-making at the business level, global collaboration among countries, and effective national policies. To meet the growing energy demand in the near future while ensuring sustainable societal development, the application of hybrid energy supply models is necessary, with renewable energy sources as their foundation. Conclusions/Significance. Green energy will contribute to addressing Earth's environmental issues by reducing greenhouse gas emissions and will help avoid energy crises. The use of renewable energy sources requires a balanced assessment of resources, appropriate technologies, and systems that can support sustainable and low-carbon economic development.

Techno-economic study of a 100-MW-class multi-energy vehicle charging/refueling station: Using 100% renewable, liquid hydrogen, and superconductor technologies

Renewable energies such as the wind energy and solar energy generate low-carbon electricity, which can directly charge battery electric vehicles (BEVs). Meanwhile, the surplus electricity can be used to produce the “green hydrogen”, which provides zero-emission hydrogen fuels to those fuel cell electric vehicles (FCEVs). In order to charge/refuel multi-energy vehicles, we propose a novel scheme of hybrid hydrogen/electricity supply using cryogenic and superconducting technologies. In this scheme, the green hydrogen is further liquefied into the high-density and low-pressure liquid hydrogen (LH2) for bulk energy storage and transmission. Taking the advantage of the cryogenic environment of LH2 (20 K), it can also be used as the cryogen to cool down superconducting cables to realize the virtually zero-loss power transmission from 100 % renewable sources to vehicle charging stations. This hybrid LH2/electricity energy pipeline can realize long-distance, large-capacity, and high-efficiency clean energy transmission, to fulfil the hybrid energy supply demand for BEVs and FCEVs. For the case of a 100 MW-class hybrid hydrogen/electricity supply station, the system principle and energy management strategy are analyzed through 9 different operating sub-modes. The corresponding static and dynamic economic modeling are performed, and the economic feasibility of the hybrid hydrogen/electricity supply is verified using life-cycle analysis. Taking an example of wind power capacity 1898 MWh and solar power capacity 1619 MWh per day, the dynamic payback period is 15.06 years, the profitability index is 1.17, the internal rate of return is 7.956 %, and the accumulative NPV is 187.92 M$. The system design and techno-economic analysis can potentially offer a technically/economically superior solution for future multi-energy vehicle charging/refueling systems.

Shore Power Optimal Scheduling Based on Gridding of Hybrid Energy Supply System

In order to reduce the environmental pollution near the port and save the cost of power supply, it is necessary to use shore power technology to power the ships that dock. This paper studies a power distribution strategy based on hybrid energy supply system. Through the establishment of wind power generation subsystem, photovoltaic power generation subsystem, and then combined with the national grid system to form a hybrid energy onshore power supply system, using the hybrid energy power supply system to power the ship. Without considering the power connection device, the whole shore power system was gridding processing. The objective function is established with the lowest cost of power supply system, and the grid node coefficient is calculated with different optimization algorithms to realize power distribution of port shore power supply system. The results showed that the power supply cost of the hybrid power supply system obtained by genetic algorithm (GA) and particle swarm optimization algorithm (PSO) is lower than the traditional power supply cost, and the power distribution is realized according to the distribution node coefficient. It provides a theoretical basis and application reference for the optimization scheme of energy management combined with port power and distributed power supply and the construction and management of new shore power.

Simulation of a Hybrid Solar Power Plant with a Hydrogen Generator in MATLAB/Simulink Environment

Ensuring reliable electricity supply to consumers in isolated and distributed energy systems can be achieved through grid connection, the use of energy storage systems for generation, or creating conditions for consumption control, including the installation of energy storage for consumers. This article explores various methods to enhance the reliability of electricity supply to consumers from power plants based on renewable energy sources (RES). The paper presents a mathematical model and optimization algorithm for the operation of a hybrid energy complex, implemented in the MATLAB/Simulink environment. The model takes into account the influence of various factors on its operational modes and has been tested through a series of calculations, confirming its efficiency and adequacy. The proposed methods and results can be applied in designing energy supply systems for remote and inaccessible re- gions, as well as in distributed energy system nodes. The paper also discusses the use of a hydrogen generator as an alternative energy source and provides insights into the properties of hydrogen, methods of production, prospects, and challenges of hydrogen energy. It offers an overview of the most common methods of hydrogen and other substance extraction, with a primary focus on water electrolysis. The simulation model also considers the performance of the hydrogen generator, a vital component of the hybrid energy supply system. The hydrogen generator produces hydrogen from water through electrolysis, which can be optimized based on input energy and water supply. The energy storage system, including battery banks, is also modeled to monitor energy storage and delivery processes during periods of high and low demand. The conclusions of this article encompass a mathematical model and optimization algorithm for the operational modes of a hybrid power plant based on renewable energy sources with a hydrogen generator and an energy storage system. The proposed methods and results can be valuable in designing energy supply systems for consumers in remote and distributed energy systems.

Two-Timescale Resource Allocation for Automated Networks in IIoT

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.

Collaborative task offloading and resource allocation with hybrid energy supply for UAV-assisted multi-clouds

Cloud computing has emerged as a promising paradigm for meeting the growing resource demands of Internet of Things (IoT) devices. Meanwhile, with the popularity of mobile aerial base stations, Unmanned Aerial Vehicle (UAV) assisted cloud computing is essential for providing diversified service at areas without available infrastructure. However, it is difficult to meet the requirements of a number of IoT devices which distribute a large area through one single UAV cloud server, and thus multi-clouds have been applied in large-scale IoT environments. Due to the limited battery capacity of UAV, hybrid energy supply has been considered as an effective approach. How to allocate the computing resources and offload the tasks to the UAV-assisted clouds is a challenging task. In this paper, we study the trade-off between the energy consumption and system performance in a UAV-assisted multi-clouds system. Considering the transmission and execution cost, a dynamic optimization problem with the objective of minimizing the power consumption of UAVs with the constraint of queue stability is formulated, which is further decomposed into three sub-problems using stochastic optimization techniques. A collaborative task offloading and resources allocation algorithm (CTORAA) based on artificial intelligent (AI) technique is proposed to jointly determine task offloading and energy harvesting. We provide corresponding mathematical analysis showing that CTORAA can reach the arbitrary profit-stability trade-off. Finally, we conduct simulation experiments to validate the efficacy of our algorithm.