Smart Energy Hub Research Articles

Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub

The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. And transitioning to renewable energy sources is essential for achieving sustainability and combating climate change. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, considering the minimization of operational running costs, and the maximization of profit. In this paper, two optimization scenarios were studied to compare the results. In the first scenario, the minimization of carbon emissions was achieved. In the second scenario, the minimization of running costs and the maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then, the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly ones via the Kalman filter rather than by applying the classic polynomial interpolation methods.

An interactive framework for strategic participation of a price-maker energy hub in the local gas and power markets based on the MPEC method

Power-to-gas (P2G) technologies within smart energy hubs (SEH) offer unprecedented operational flexibility to distribution networks. This is achieved by integrating various energy carriers both regionally and locally, particularly in the presence of renewable energy sources (RES). According to this subject, the present work outlines an interactive risk-based model and meticulous mathematical solution to justifiably incorporate the P2G units into the SEH to participate in the local electricity and natural gas markets strategically. As a price-maker prosumer, the SEH utilizes cross-product arbitrage strategies to optimize its proposals for power and gas exchanges in the local markets. The participation model for SEH is developed as a bi-level framework. At the top level, the goal is to maximize SEH's profit while accounting for the risks posed by RES. At the bottom level, the aim is to increase social welfare under network constraints through local market settlements. Finally, a mathematical program with equilibrium constraints (MPEC) is offered as a solution to solve the bi-level model. Using integer disjunctions on the complementarity and slackness criteria and the strong duality theory on the bilinear product terms, a mixed-integer linear programming (MILP) has been derived to approximate the suggested MPEC formulation. It can be concluded from the results that the integration of P2G technologies and the use of mutual product arbitrage in the local electricity and natural gas markets, not only increases the profit of the SEH, but also reduces operating costs and improves social welfare as well as cost-recovery of the entire network.

Balancing Possibilist-probabilistic risk assessment for smart energy hubs: Enabling secure peer-to-peer energy sharing with CCUS technology and cyber-security

This paper introduces the Possibilistic-Probabilistic Risk-based Smart Energy Hub (PPR-SEH) scheduling model, addressing cybersecurity challenges in the transition to advanced energy systems characterized by decarbonization, decentralization, and digitalization. The model integrates cutting-edge technologies like Carbon Capture Utilization and Storage (CCUS) and demand response programs. It addresses uncertainties from renewable energy sources, demand variability, fuel supply, and energy price fluctuations using a Z-number-based approach. Covering various energy types—electricity, heat, cooling, gas, and water—the PPR-SEH model offers a comprehensive energy management solution. Employing a mixed-integer linear programming (MILP) framework optimized with the CPLEX solver in GAMS software, it uses an epsilon constraint method and a fuzzy satisfying approach for solution selection. The model also evaluates the economic and environmental impacts of peer-to-peer energy-sharing markets linked with carbon emission trading, enhancing the efficiency and sustainability of energy distribution. The findings reveal that incorporating robust cybersecurity measures and integrating demand response and CCUS significantly influence operational efficiency and sustainability, evidenced by an increase in costs and pollution by approximately 11.43 % and 1.8 %, respectively, compared to deterministic approaches. This study underscores the importance of robust planning and the beneficial impact of a carbon system on load curve management and economic returns in smart energy systems.

Enhancing smart city operation management: Integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study.

Novel AMI in Zigbee Satellite Network Based on Heterogeneous Wireless Sensor Network for Global Machine-to-Machine Connectivity

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses.

Optimal resource allocation and operation for smart energy hubs considering hydrogen storage systems and electric vehicles

Energy hubs (EHs) have substantially paved the way for the coordinated operation of various energy carriers, converters, and storage. However, the establishment of optimal planning and operation of the EH include several challenges, e.g., the stochastic nature of non-dispatchable generation assets, obtaining a satisfactory performance from the storage assets with reasonable energy and power density costs, and long-term load growth. Accordingly, this paper proposes a two-stage stochastic optimal resource allocation and operation approach for multiple smart energy hubs-based microgrids. The first stage optimizes the location of each EH and different assets' capacities. On the other hand, the second stage optimizes the charging/discharging rates of each asset and parking lot power. These microgrids combine renewable energy sources. Moreover, for research gap and unlike existing research, the cooperative operation of hybrid storage systems (i.e., solar-powered compressed air energy storage, hydrogen storage systems, battery energy storage systems, thermal energy storage systems) along with plug-in electric vehicles (PEV) has been investigated. Furthermore, annual load growth and price-based demand response (PBDR) are employed in this study. The simulation results demonstrate that the controlled charging/discharging of the PEVs reduces the total annualized cost by 5.49%, while the operation and emission costs are reduced by 21.3% and 19.86%, respectively. Furthermore, employing PBDR has led to a reduction of 10.1% in capital and operational costs.

A market framework to exploit the multi-energy operating reserve of smart energy hubs in the integrated electricity-gas systems

Faced with increasing uncertainties in the integrated electricity and gas system (IEGS), the multi-energy operating reserve(MOR), i.e., extra electric and gas reserve capacity, is becoming vitally important to guarantee the reliable operation of the system. Smart energy hubs(SEHs) located at the demand side of the IEGS can adjust their electricity and gas consumption through energy substitution, which has great potential to provide the MOR. Current studies mainly concentrate on the energy trading mechanism among different SEHs in the energy-only market, which may not effectively incentivize SEHs to provide MOR for system operation. Besides, due to the limited energy capacity, SEHs cannot directly participate in the wholesale reserve market along with other large-capacity resources, e.g., power plants and gas wells. To address these, a two-stage market framework is proposed for exploiting the multi-energy operating reserve of SEHs. In the first stage, the wholesale market is cleared considering the participation of large-capacity resources to determine the clearing results of MOR. Based on the results, an energy-loss-cost calculation model is developed to measure the value of the specific MOR level. By altering the reserve requirements, the values of different MOR levels can be accordingly calculated to formulate the multi-energy operating reserve demand surface(MOR-DS), which can quantify varying MOR values with different levels of the reserve. With the MOR-DS describing the price elasticity of the MOR, a distributed MOR clearing mechanism is developed in the second stage to determine the quantity and price of the reserve from SEHs. The clearing mechanism is constructed as a distributed optimization model considering optimal collaboration among different SEHs. Case studies verify that the proposed market framework can effectively stimulate SEHs to provide MOP through economic incentives, which can realize the reliability improvement of IEGS and the revenue increase of SEHs.

An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWhth in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities.

Energy scheduling in a smart energy hub system with hydrogen storage systems and electrical demand management

The energy price uncertainty in energy markets is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a smart energy hub (SEH) under the uncertainty of electricity prices in the day-ahead. The proposed optimization approach is implemented by a two-layer interval approach for minimizing energy generation cost. In first layer optimization, demand side management (DSM) for electrical demand subject to the optimal level of electrical consumption is implemented. In second-layer optimization, the interval approach is used for modeling uncertainty. The energy generation cost is converted to bi-objective functions including average and deviation costs with conflicting nature than each other. Also, the multi-performance of the hydrogen storage system based on electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the case studies to verify obtained results. The participation of the DSM and the hydrogen storage systems leads to minimizing the average and deviation costs by 3.03 % and 3.16 % in comparison with non-participation.

Optimal operation of smart energy hub considering high-temperature heat and power storage

Due to the impacts of high intermittent renewable resources, increasing their penetration in the generation sector of energy systems is still challenging. In this regard, the integration of different energy networks can play a key role in dealing with this challenge. This paper focuses on the analysis and optimization of multi-generation energy storage (MGES) system’s performance and investigates the role of this type of energy storage in the operation of future smart energy systems by considering 100% green energy goals. Hence, this paper first introduces high-temperature heat and power storage (HTHPS) system, as a novel MGES unit, for a local integrated energy system (IES) with different energy carriers. Then, a novel optimal energy scheduling scheme for this system is presented by considering the energy hub concept. The proposed IES is introduced as a Smart Energy Hub (SEH) which supplies local energy demands through local renewable resources generation, as well as upstream energy networks, by considering a competitive energy market. Finally, the performance of the proposed SEH is investigated by simulating different case studies and its effectiveness is proved in increasing the renewable generation penetration.

Possibilistic–Probabilistic Risk-Based Smart Energy Hub scheduling considering cyber security in advanced metering infrastructures

Future energy systems will be more efficient because of the energy transition of which, decarbonization, decentralization, and digitalization are important aspects. In addition, cyber security becomes more important in the digitalized energy system as cyber-attacks increase. Since the Smart Energy Hub (SEH) is well known as an efficient type of smart digitalized energy system, cyber security is of great importance in SEH operation. In this paper, a novel possibilistic–probabilistic​ SEH model is proposed to evaluate the effect of cyber-attacks on the demand side of the energy system. The presented model is called Possibilistic–Probabilistic Risk-based Smart Energy Hub scheduling model considering Cyber Security in the Advanced Metering Infrastructure (PPRSEHAMICS). It contains different uncertainties regarding renewable generations, demand response programs, gas fuel, wind turbines, and energy prices. The uncertainty of demand response programs is represented using the Z-number as a possibilistic–probabilistic method. Other uncertainties are represented using a scenario-based hybrid approach and Monte Carlo Simulation. Also, the downside risk constraint (DRC) approach is used to model the behavior of the SEH operator in the presence of uncertainties, so the operation and risk costs are simultaneously optimized. Furthermore, a novel Monte Carlo-based FDI detection/correction method is proposed in order to determine and mitigate the effects of cyber-attacks. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem and the GAMS environment is used to solve it. In this paper, different scenarios are considered to evaluate the effects of demand response (DR) programs, SEH operator behavior, false data injection, and correction on the operation cost and risk-in-cost of smart energy hub systems. In the first stage, the obtained results demonstrate that using DR accompanied by DRC yields the optimum operation strategy where the operation cost and risk are maintained at an acceptable point. Further, simulation results suggest that FDI attacks can deviate the operation cost and risk by a substantial amount, and the proposed Monte Carlo-based detection/correction method mitigates almost 79 percent of the false data while keeping the convergence speed in an acceptable range. Moreover, modeling the participation rate of the demand response program by the Z- number approach increases the validity of the results and the applicability of the proposed model.

A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty

A local integrated electricity-heat market design among multi Smart Energy Hubs with renewable energy generation uncertainty

Rethinking Ports as Multienergy Hubs: Managing cold ironing and hydrogen supply/bunkering

Modern ports are expected to play a key role in the transportation chain, being transformed into smart grids and smart energy hubs where electric energy needs predominate. Cold ironing (CI), also known as onshore power supply (OPS) or alternate marine power, is indeed one the most energy-demanding applications. The power demand of a berthed ship depends on its characteristics and may vary from hundreds of kW, for container ships, to tens of MW, in the case of cruise ships.

Toward Smart Green Seaports: What should be done to transform seaports into intelligent and environment-friendly energy systems?

As maritime transport rapidly grows, seaports are becoming larger, more complex, and energy intensive. Seaports are facing the challenges of increasing their energy efficiency, adopting and producing technological innovations, limiting their carbon footprint, and complying with legislation that aims to reduce the environmental impact of marine industry activities. To this end, they aim to resynthesize their master plan, according to which they are transformed into smart energy hubs where extensive electrification plays a key role. The electric power consumption of seaports is continuously increasing because of operational, regulatory, and environmental factors, making intelligent energy networks a very promising solution.

An Improved Max-Min Game Theory Control of Fuel Cell and Battery Hybrid Energy System Against System Uncertainty

In this article, a new energy management strategy (EMS) for a smart building with fuel cell and battery hybrid energy storage (FBHES) is proposed based on an improved game theory (GT) with higher system efficiency and extending lifetime for both battery and fuel cell (FC). The building working as a smart energy hub is regarded as one of the key factors for energy-saving and long-term emissions reduction. EMS that determines the optimal power flow for each energy sector is of great importance for smart buildings subject to the increasing renewable generations, various energy storage, and flexible loads. The energy storage system plays a key role to unlock the management regions and control constraints of the EMS; however, the lifetime of the energy storage device is generally less than expected because of the power uncertainty brought by new energy sectors. Therefore, in this article, the FBHES and the uncertainty of the demand power are considered as two aspects in the game, aiming at decoupling the randomness of electricity demand and hybrid energy storage performance. The EMS is developed with an improved max-min GT, where the compensation effect of the battery is developed to keep power balance in the smart building system. Simulation results show that the degradation of the FC obtained by the proposed method, the improved GT, which is 12.64% lower than the traditional GT. Meanwhile, the lifetime of the battery under the proposed method is increased by 23.16% and 82.81% compared with mixed-integer linear programming EMS and rule-based strategy.

Management and real-time monitoring of interconnected energy hubs using digital twin: Machine learning based approach

Getting equipped by highly new smart technologies, Energy Hubs (EHs) and Smart Grids (SGs) are gaining interest these days. Energy management will advance over time as a result of the interaction impact among power and natural gas grids, and the use of smart technology for communications. The present study proposes a novel approach entitled Smart EH (SEH) for modeling multi-carrier energy systems in SG environments. Furthermore, this paper determines the optimum management and sizing of combined heat and power, auxiliary boiler, absorption chiller, as well as transformer unit as the essential components of an SEH. It is difficult to address the requirements of SGs with most conventional load scheduling algorithms because they lack robustness and performance in complex environments. An evaluation of the benefits and costs of optimizing such parameters was carried out in this paper and the Reinforcement Learning (RL) algorithm is applied to solve the optimization problem. An individual user in a dynamic electrical market was examined as an SEH in support of the suggested approach. According to simulation outcomes, the suggested method is effective regarding time efficiencies and load variations.

Virtual smart energy Hub: A powerful tool for integrated multi energy systems operation

The present work introduces a novel unit, named virtual smart energy hub (VSEH). VSEH consists of a combination of smart energy grids, smart energy hubs (SEHs), and virtual power plants (VPPs) and simultaneously benefits from the advantages of SEHs and VPPs on a smart-energy-grid platform. A comprehensive two-stage model was proposed to solve the VSEH optimal operation problem in order to trading in day-ahead and intraday heat and power market. The proposed stochastic programming model is risk based and the conditional value at risk (CVaR) index was employed to represent the risk in the scheduling model. On the first stage of the proposed model, VSEH schedules transactions amounts by considering energy prices and SEHs constraints in order to maximize profits from participation in day-ahead heat and power markets. In the second stage, VSEH, through the more accurate prediction of uncertainties before operation time and with the aim of maximizing profits and correcting deviations relative to the settled transactions in the first stage, re-schedules the SEHs in intraday heat and power markets. Non-arbitrage constraints were established between day-ahead and intraday energy markets in the proposed model. Moreover, the presented structure was modeled and comprehensively defined in mathematical terms as a mixed-integer linear programming (MILP) model with a minimum number of real variables. The performance of this model was evaluated by implementing it on a coalition with various SEHs, as a result of which the profitability of the coalition and the increase in the risk tolerance level of the SEHs group was confirmed. The results indicate that formation of the VSEH can act as means of risk management in addition to reducing risk-associated costs regardless of the circumstances on the day of operation and the acceptable level risk tolerance of the SEHs. In other words, the integration of SEHs will result in a lower risk aversion cost.

DIGITAL TWIN SOLUTION IMPLEMENTED ON ENERGY HUB TO FOSTER SUSTAINABLE SMART ENERGY CITY, CASE STUDY OF SUSTAINABLE SMART ENERGY HUB

Abstract. The scarcity of energy is one of humanity's most pressing issues in the twenty-first century. These problems can be found in various areas, including energy supply, exchange, and consumption. Population growth, rising global energy demand, natural resource shortages, and environmental concerns contribute to energy scarcity. Furthermore, energy shortage needs the expansion of renewable energies and energy efficiency, a major concern for all governments and organisations. Building energy efficiency is a crucial concept to consider when it comes to smart cities. Buildings are the greatest energy consumers, accounting for 40% of total energy consumption. Recent advances in machine learning within a big data context have paved the way for more efficient building energy management. Building energy consumption may be successfully managed by a real-time measurement procedure, allowing the economy to shift from a linear to a circular consumption model. This will tackle the issue of late notice of failed energy-saving initiatives and allow for immediate correction to restore the energy management system to ultimate performance.The energy hub might be as small as a single home energy system or as large as an energy system for the city. This paper will present a case study on developing a smart energy hub called Hubgrade 4.0 that relies on connected products using digital twin as a significant enabler for energy-saving initiatives. Hubgrade 4.0 provides an innovative approach to successfully implementing energy efficiency improvement using artificial intelligence and real-time data. Hubgrade 4.0 is the name of Enova by Veolia smart energy hub; Enova is the regional leader in integrated energy and multi-technical services, delivering performance-based energy and facilities management solutions. The energy hub digital twin is a link between a physical platform that administers the energy hub's IoT and a virtual platform that can derive services that are valid for the energy hub. Successful enterprises are using several new technologies to achieve the goals of Industry 4.0: efficiency, speed, agility, and customer-centricity. This paper concluded and highlighted lessons learned from the successful implementation of innovative energy management, which relied on a dedicated organisation, effective adoption of digital technologies, and embracing new business models, resulting in power savings of 254 million kW and water savings of around 3 million cubic meters, as well as financial savings of about 138 million AED in 5 years since Hubgrade 4.0 started operations its first energy-saving contract in 2017.

A sustainable framework for long-term planning of the smart energy hub in the presence of renewable energy sources, energy storage systems and demand response program

Energy hub systems are known as the most important local energy systems and simultaneously meet the needs of their customers for different energies. Optimal design of energy hub systems has become a very important issue, as these systems have many customers. So, this paper presents a sustainable framework for the energy hub long-term planning in the presence of renewable energy sources (RESs). The hub is capable of supplying electrical, cooling and heating loads and is also equipped with three storage systems, electrical, thermal and cooling storage units. The uncertainties of different loads as well as photovoltaic (PV) panel output power are considered and the planning problem is dynamically modeled for a 15-year horizon. In addition, a Price-Based Demand Response (PBDR) program is included in the proposed model so that hub consumers can participate in it, leading to a modification of the demand curve and a reduction in operating cost. The planning and operation problem is modeled as a mixed-integer linear programming (MILP) problem and solved by the CPLEX solver in GAMS software. The effects of PBDR program, PV panels, uncertainties and energy storage systems on the sizing of equipment and operating costs have been studied in detail. The results show that the use of PBDR program reduces the installed capacity of the electrical storage system (EES) and thus leads to a reduction of about 5% of investment costs. In addition, the results show that the absence of PV panels in the model increases the operation costs by about 9%.

A novel energy management framework incorporating multi‐carrier energy hub for smart city

The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi-carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy.