Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.

Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

In the era of an energy revolution,grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level.Microgrids are considered a driving component for accelerating grid decentralization.To optimally utilize the available resources and address potential challenges,there is a need to have an intelligent and reliable energy management system(EMS)for the microgrid.The artificial intelligence field has the potential to address the problems in EMS and can provide resilient,efficient,reliable,and scalable solutions.This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids.We analyze EMS methods for centralized,decentralized,and distributed microgrids separately.Then,we summarize machine learning techniques such as ANNs,federated learning,LSTMs,RNNs,and reinforcement learning for EMS objectives such as economic dispatch,optimal power flow,and scheduling.With the incorporation of AI,microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources.However,challenges such as data privacy,security,scalability,explainability,etc.,need to be addressed.To conclude,the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.

Energy Management and Capacity Optimization of Photovoltaic, Energy Storage System, Flexible Building Power System Considering Combined Benefit

Building structures themselves are one of the key areas of urban energy consumption,therefore,are a major source of greenhouse gas emissions.With this understood,the carbon trading market is gradually expanding to the building sector to control greenhouse gas emissions.Hence,to balance the interests of the environment and the building users,this paper proposes an optimal operation scheme for the photovoltaic,energy storage system,and flexible building power system(PEFB),considering the combined benefit of building.Based on the model of conventional photovoltaic(PV)and energy storage system(ESS),the mathematical optimization model of the system is proposed by taking the combined benefit of the building to the economy,society,and environment as the optimization objective,taking the near-zero energy consumption and carbon emission limitation of the building as the main constraints.The optimized operation strategy in this paper can give optimal results by making a trade-off between the users’costs and the combined benefits of the building.The efficiency and effectiveness of the proposed methods are verified by simulated experiments.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems(BEMS)are computer-based systems that aid in managing,controlling,and monitoring the building technical services and energy consumption by equipment used in the building.The effectiveness of BEMS is dependent upon numerous factors,among which the operational characteristics of the building and the BEMS control parameters also play an essential role.This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS.The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions.The simulation is developed using Visual Basic Application(VBA)in Microsoft Excel,based on discrete-event Monte Carlo Simulation(MCS).The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model.Further,during the second simulation,the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case.The model compares the energy consumption from the first simulation and the second simulation.Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5(1 being poor effectiveness and 5 being excellent effectiveness of BEMS).This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

Enhanced properties of stone coal-based composite phase change materials for thermal energy storage

Phase change materials (PCMs) can be incorporated with low-cost minerals to synthesize composites for thermal energy storage in building applications.Stone coal (SC) after vanadium extraction treatment shows potential for secondary utilization in composite preparation.We prepared SC-based composite PCMs with SC as a matrix,stearic acid (SA) as a PCM,and expanded graphite (EG) as an additive.The combined roasting and acid leaching treatment of raw SC was conducted to understand the effect of vanadium extraction on promoting loading capacity.Results showed that the combined treatment of roasting at 900℃ and leaching increased the SC loading of the composite by 6.2%by improving the specific surface area.The loading capacity and thermal conductivity of the composite obviously increased by 127%and 48.19%,respectively,due to the contribution of 3wt% EG.These data were supported by the high load of 66.69%and thermal conductivity of 0.59 W·m^(-1)·K-1of the designed composite.The obtained composite exhibited a phase change temperature of 52.17℃,melting latent heat of 121.5 J·g^(-1),and good chemical compatibility.The SC-based composite has prospects in building applications exploiting the secondary utilization of minerals.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Numerical modeling on strain energy evolution in rock system interaction with energy-absorbing prop and rock bolt

The interaction mechanism between coal and rock masses with supporting materials is significant in roadway control, especially in deep underground mining situations where dynamic hazards frequently happened due to high geo-stress and strong disturbed effects. This paper is to investigate the strain energy evolution in the interaction between coal and rock masses with self-designed energy-absorbing props and rock bolts by numerical modeling with the finite difference method. The interaction between rock and rock bolt/prop is accomplished by the cables element and the interface between the inner and outer props. Roadway excavation and coal extraction conditions in deep mining are numerically employed to investigate deformation, plastic zone ranges, strain energy input, accumulation, dissipation,and release. The effect on strain energy input, accumulation, dissipation, and release with rock deformation, and the plastic zone is addressed. A ratio of strain energy accumulation, dissipation, and release with energy input a, β, γ is to assess the dynamic hazards. The effects on roadway excavation and coal extraction steps of a, β, γ are discussed. The results show that:(1) In deep high geo-stress roadways, the energyabsorbing support system plays a dual role in resisting deformation and reducing the scope of plastic zones in surrounding rock, as well as absorbing energy release in the surrounding rock, especially in the coal extraction state to mitigate disturbed effects.(2) The strain energy input, accumulation is dependent on roadway deformation, the strain energy dissipation is relied on plastic zone area and disturbed effects, and strain energy release density is the difference among the three. The function of energyabsorbing rock bolts and props play a key role to mitigate strain energy release density and amount, especially in coal extraction condition, with a peak density value from 4×10^(4) to 1×10^(4)J/m^(3), and amount value from 3.57×10^(8) to 1.90×10^(6)J.(3) When mining is advanced in small steps, the strain energy accumulation is dominated. While in a large step, the released energy is dominant, thus a more dynamic hazards proneness. The energy-absorbing rock bolt and prop can reduce three times strain energy release amount, thus reducing the dynamic hazards. The results suggest that energy-absorbing props and rock bolts can effectively reduce the strain energy in the coal and rock masses, and prevent rock bursts and other hazards.The numerical model developed in this study can also be used to optimize the design of energyabsorbing props and rock bolts for specific mining conditions.

Sustainable Development of Energy Systems and Climate Systems:Key Issues and Perspectives

Climate change and energy security issues are prominent challenges in current energy system management,which should be governed synergistically due to the feedback relationships between them.The“Energy Systems Management and Climate Change”Special Collection Issue in the journal of Energy Engineering provide insights into the field of energy systems management and climate change.From an extended perspective,this study discusses the key issues,research methods and models for energy system management and climate change research.Comprehensive and accurate prediction of energy supply and demand,the evaluation on the energy system resilience to climate change and the coupling methodology application of both nature and social science field maybe the frontier topics around achieving sustainable development goals of energy systems.

A hybrid decision support system with golden cut and bipolar q‑ROFSs for evaluating the risk‑based strategic priorities of fintech lending for clean energy projects

In the last decade,the risk evaluation and the investment decision are among the most prominent issues of efficient project management.Especially,the innovative financial sources could have some specific risk appetite due to the increasing return of invest-ment.Hence,it is important to uncover the risk factors of fintech investments and investigate the possible impacts with an integrated approach to the strategic priorities of fintech lending.Accordingly,this study aims to analyze a unique risk set and the stra-tegic priorities of fintech lending for clean energy projects.The most important contri-butions to the literature can be listed as to construct an impact-direction map of risk-based strategic priorities for fintech lending in clean energy projects and to measure the possible influences by using a hybrid decision making system with golden cut and bipolar q-rung orthopair fuzzy sets.The extension of multi stepwise weight assessment ratio analysis(M-SWARA)is applied for weighting the risk factors of fintech lending.The extension of elimination and choice translating reality(ELECTRE)is employed for con-structing and ranking the risk-based strategic priorities for clean energy projects.In this process,data is obtained with the evaluation of three different decision makers.The main superiority of the proposed model by comparing with the previous models in the literature is that significant improvements are made to the classical SWARA method so that a new technique is created with the name of M-SWARA.Hence,the causality analysis between the criteria can also be performed in this proposed model.The find-ings demonstrate that security is the most critical risk factor for fintech lending system.Moreover,volume is found as the most critical risk-based strategy for fintech lending.In this context,fintech companies need to take some precautions to effectively manage the security risk.For this purpose,the main risks to information technologies need to be clearly identified.Next,control steps should be put for these risks to be managed properly.Furthermore,it has been determined that the most appropriate strategy to increase the success of the fintech lending system is to increase the number of financi-ers integrated into the system.Within this framework,the platform should be secure and profitable to persuade financiers.

Critical Success Factors Influencing the Implementation of Sustainable Energy System in Uganda: A Case of Inter-University Council of East Africa Energy Project at the Head Quarters in Kampala, Uganda

The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail to sustain energy systems. The council is only connected to the national grid electricity supply system, with diesel generators as the only alternative, which is unhealthy and unsafe. Surprisingly, even with such alternatives, power shortages have persisted despite government efforts to provide a solution to the shortages by installing numerous off-grid systems. Due to such a situation, the council would construct a sustainable energy system as a remedy. Thus, the purpose of this study was to establish critical success factors influencing the implementation of a sustainable energy system at the Inter-University Council of East Africa (IUCEA) Head Quarters, Kampala-Uganda. A cross-sectional survey design was used;a sample size of 84 participants was selected. Questionnaire survey and interview methods were utilized. The study found that the most significant (p < 0.05) critical factors in the implementation of sustainable energy in institutions are;the use of innovative technologies and infrastructure, the use of efficient zero emissions for heating and cooling, integration of renewable energy use in the existing buildings, building and renovating in an energy-efficient way, integrating regional energy systems, improving energy efficiency in the buildings, enhanced zero emission power technologies, energy efficient equipment in place and stakeholder empowerment in energy management. This study concludes that institutions like;the Inter-University Council of East Africa (IUCEA) need to clearly state policies and actions of energy management. The roles and responsibilities of each member have to be clearly stated while capturing the activities involved in energy conservation, energy security and energy efficiency.

Study on creep deformation and energy development of underground surrounding rock under four‐dimensional support

There is an urgent need to develop optimal solutions for deformation control of deep high‐stress roadways,one of the critical problems in underground engineering.The previously proposed four‐dimensional support(hereinafter 4D support),as a new support technology,can set the roadway surrounding rock under three‐dimensional pressure in the new balanced structure,and prevent instability of surrounding rock in underground engineering.However,the influence of roadway depth and creep deformation on the surrounding rock supported by 4D support is still unknown.This study investigated the influence of roadway depth and creep deformation time on the instability of surrounding rock by analyzing the energy development.The elastic strain energy was analyzed using the program redeveloped in FLAC3D.The numerical simulation results indicate that the combined support mode of 4D roof supports and conventional side supports is highly applicable to the stability control of surrounding rock with a roadway depth exceeding 520 m.With the increase of roadway depth,4D support can effectively restrain the area and depth of plastic deformation in the surrounding rock.Further,4D support limits the accumulation range and rate of elastic strain energy as the creep deformation time increases.4D support can effectively reduce the plastic deformation of roadway surrounding rock and maintain the stability for a long deformation period of 6 months.As confirmed by in situ monitoring results,4D support is more effective for the long‐term stability control of surrounding rock than conventional support.

Energy Management Control StraEnergy Management Control Strategy for Renewable Energy System Based on Spotted Hyena Optimizertegy for Renewable Energy System Based on Spotted Hyena Optimizer

Hydrocarbons,carbon monoxide and other pollutants from the transportation sector harm human health in many ways.Fuel cell(FC)has been evolving rapidly over the past two decades due to its efficient mechanism to transform the chemical energy in hydrogen-rich compounds into electrical energy.The main drawback of the standalone FC is its slow dynamic response and its inability to supply rapid variations in the load demand.Therefore,adding energy storage systems is necessary.However,to manage and distribute the power-sharing among the hybrid proton exchange membrane(PEM)fuel cell(FC),battery storage(BS),and supercapacitor(SC),an energy management strategy(EMS)is essential.In this research work,an optimal EMS based on a spotted hyena optimizer(SHO)for hybrid PEM fuel cell/BS/SC is proposed.The main goal of an EMS is to improve the performance of hybrid FC/BS/SC and to reduce the amount of hydrogen consumption.To prove the superiority of the SHO method,the obtained results are compared with the chimp optimizer(CO),the artificial ecosystem-based optimizer(AEO),the seagull optimization algorithm(SOA),the sooty tern optimization algorithm(STOA),and the coyote optimization algorithm(COA).Two main metrics are used as a benchmark for the comparison:the minimum consumed hydrogen and the efficiency of the system.The main findings confirm that the minimum amount of hydrogen consumption and maximum efficiency are achieved by the proposed SHO based EMS.