Simulation Model For Planning Research Articles

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system.

Strategic location model for oil spill response vessels (OSRVs) considering oil transportation and weather uncertainties

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Hospital reform in Germany: Population-based calculation and simulation model for planning and impact assessment

Background: Due to the increasing shortage of specialists, disproportionately rising costs and inadequate quality of care, Germany is planning a fundamental hospital reform. The government commission has developed the central principles for this reform. The aim is to achieve a centralization of hospital services in oversupplied metropolitan areas by planning according to groups of services based on defined requirements for structural quality. The economic pressure to increase the number of cases shall be reduced through the introduction of a reserve payment and by dividing hospitals into levels - at least by transparent information for the general public. So far, however, there has been no generic model to determine the significance of specific care providers considering population needs (reachability), capacity of a hospital location, and the number and severity of treated patients. Methods: We developed a generic model to determine the significance of hospital location-related care and the need for the various groups of services. The model may also be used to assess reserve financing. For the model, the groups of services were divided into four categories according to degree of specialization and urgency with limits of reachability of 30, 45, 90 and 180 minutes. Gravity models were used to simulate the population-based significance of care providers for each group of services. In the basic model, the allocation of the significance was based exclusively on the reachability for the population living within the respective reachability corridor. In extended models, the historical number of cases and their case severity were also taken into account in order to account for current care capacities and existing choices of the population regarding, among other things, the quality of care. The model was implemented on the basis of nationwide data provided in accordance with § 21 data (of the German law for hospital remuneration). We then determined the effects of weighting the three influencing variables (i) population, (ii) number of cases and (iii) case severity on the significance of the hospital sites. Using the example of "endoprosthetics of the knee" (service group 14.2 in North Rhine-Westphalia) in the federal state of Saxony, the effect of concentration on the distribution of the reserve budgets and on the accessibility of the population was illustrated. The importance of care determined in this way is contrasted with a key value that reflects the need for hospitals in terms of securing care in rural areas. Results: From the approximately 16.5 million somatic treatment cases in 2021, 98.5% could be assigned to one of the 60 somatic groups of services according to North Rhine-Westphalia model. The simulation models show a differentiated picture for the various service groups. For the majority of service groups, a moderate concentration of services does not lead to relevant restrictions in terms of reachability. Exclusively considering the population to be cared for (basic model) would lead to significant shifts in the financing of running costs, which are, however, very well mitigated by considering the historic number and severity of cases (extended models). As an example, we show the effects for the service group "endoprosthetics of the knee" in the federal state of Saxony compared to the extrapolation at location level. Discussion: The empirically based simulation model proposed here takes into account reachability, patient preference and hospital capacity and offers a scientific way of comparing the regional significance of care providers as well as the necessity of hospital locations for each service group against the background of the state's obligation to provide care and to make economical use of resources. The simulation model is meant to support hospital planning (here: the allocation of groups of services) in the federal states and to guide rational planning. It is also suitable for the management of financial resources in the context of hospital reform. It also enables an impact analysis. The model is not intended to automate planning or otherwise make rigid specifications. The calculations should be regarded as exemplary. The weights of all parameters can be varied. However, the reachability thresholds and the parameterization of the simulation model should be defined jointly by federal and state governments in terms of a common set of objectives.

Using a simulation model to optimize network planning using the critical path method

The article discusses the use of a network planning simulation model using the critical path method to optimize the planning process. The main goal of the study is to develop and apply an innovative approach to project management, which will optimize resources, reduce task completion time and increase planning efficiency. The paper presents the main stages of developing a simulation model, and also conducted experiments to evaluate the effectiveness of the proposed approach. The results obtained confirm the possibility of using simulation models and the critical path method in network planning to achieve optimal results.

Mivar-based route planning simulation model for obstacle-aware autonomous agricultural machinery

Autonomous robot navigation is increasingly becoming an important task that requires solutions. This paper explores the practical application of logical artificial intelligence to address the problem of route planning, using the example of a computer game. Within the scope of this work, a knowledge base model was created, and a new version of the mivar reasoner was developed for integration with Unity. This reasoner allows the activation of logical rules with linear complexity. As a result, a system was developed that processes user input for position and moves an autonomous agent in a virtual environment according to the rules. This work confirms the feasibility of using mivar technologies to improve control systems of autonomous robots in the area of agriculture. The study also emphasizes the adaptability of mivar networks in dynamic environments, demonstrating their ability to effectively process changes in real-time. This research shows enhanced decision-making capabilities and reliable navigation strategies for autonomous agents, setting a precedent for future developments in autonomous digital solutions for agriculture. The results obtained open new prospects for the advancement of technologies in the field of autonomous navigation.

EXTENDING SIMULATION-BASED ASSEMBLY PLANNING TO INCLUDE HUMAN LEARNING AND PREVIOUS EXPERIENCE: A SIMULATION STUDY

When using simulation-based assembly planning in the planning phase of designing modern assembly systems, the prospective system behavior should be predicted as reliably as possible by the simulation. For this purpose, personnel-related adjustment periods, such as those related to learning through task repetition should be considered in the simulation model, if employees are later to be involved in the assembly. The learning effect influences the overall performance of the system and can be described by learning curves. The aim of the approach presented in this paper is to increase the prediction quality of simulation models for assembly planning by taking into account the previous experience of the employees. For this purpose, a learning model is integrated into a discrete-event simulation and subsequently verified. The learning model includes the personnel-related learning curve as well as the previous experience of the employees as dynamic parameters. Simulation experiments with three forms of assembly organization were conducted to investigate the influence of learning and previous experience on the dynamic system behavior of an assembly system. The results indicate that assembly systems organized according to the One Piece Flow principle allow for broader, albeit slower, learning compared to row and group assembly.

Refined Urban Grid Simulation Planning Based on Low Carbon Goal Achievement

Abstract In this paper, a new grid planning combined with low-carbon operation simulation is proposed, a new grid planning and low-carbon operation simulation model is proposed, and the new grid planning model is investigated and illustrated from the four dimensions of constraints. For the new grid simulation planning model, the solution efficiency of the new grid planning model combined with low-carbon operation simulation is improved by the time series clustering method and the extreme scenario identification method based on the amount of lost load. Starting from the low-carbon benefit dynamics of the generation side and the consumption side, a refined urban new grid low-carbon benefit dynamic evaluation model is constructed, while the model parameters and table functions are set, and the low-carbon urban grid simulation planning based on low-carbon simulation is empirically analyzed by means of simulation analysis. The results show that in grid planning, when the carbon emission intensity is 1000gCO2/kWh, the S4 scenario only needs to add an extreme scenario in the iterative process to achieve a relative error accuracy of 0.1%, and the method of using the new grid planning and the low-carbon operation simulation model is sufficient to achieve the target of the loss of load rate and carbon emission intensity. In the evaluation of the low-carbon effect, with the increase in investment amount, the installed capacity of clean energy and the total CO2 emission reduction of the refined urban grid will show two relatively obvious upward trends.

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

A city’s transport and distribution system requires the effective organization of urban freight deliveries that take into consideration the economic, ecological, and social impact. Implementation of the concept of green logistics necessitates the use of environmentally friendly and energy-efficient modes of transport, which consider the existing infrastructure and the possibility of its development. The aim of this work was to facilitate the transfer to rail transport, using the existing tramway infrastructure, those shipments which previously entered the city center by road. The paper proposes a simulation model for small consignments delivered by freight trams in the city of Poznań, Poland. Operational planning is carried out in conditions of uncertainty and risk. The uncertainty is due to the stochastic nature of the demand for products. The risk is characterized by the probability that, due to technological and technical limitations, a given customer will not be fully served from the distribution center. The authors provide a decision support tool for evaluating the possible locations of tram depots and for route planning. The assignment of the routes, with the criterion of minimizing the costs for the carrier, and with a limitation on tram operating time, is based on the use of genetic algorithms, which makes it possible to obtain a solution that is close to optimal, within a reasonable time period. As a result of a series of simulation experiments and statistical data processing, the distribution laws and expected average values of the technological parameters were determined for the functioning of the city transport and distribution system under conditions of uncertainty and risk.

Simulating multiple scenarios of land use/cover change using a coupled model to capture ecological and economic effects

AbstractThe conflict between the demands of ecological protection and economic development is increasingly prominent. Land use change is sensitive to whether economic or ecological profits dominate, and prediction of future land use changes has become an important scientific issue. This article constructs a coupled model that comprises the grey model (GM), the fuzzy multiple objective linear programming model (FMOLP) and the future land use simulation model (FLUS). We used this model to calculate a macro‐scale quantitative forecast using the top‐down GM‐FMOLP model, and calculated micro‐scale spatial simulations of land use/cover change (LUCC) using the bottom‐up FLUS model under three dissimilar future scenarios, with different emphases on ecological and economic benefits. The scenarios were designed to meet different planning requirements in Chongqing: ecological benefits maximization, economic benefits maximization, and combined ecological and economic benefits maximization. The proposed model, FLUS, was applied to a LUCC simulation for Chongqing in 2015, beginning from verified historical land use data from 2010. The results show that the simulation agrees well with the actual land use in 2015. The overall accuracy and kappa coefficient are 85.92% and 0.76, respectively, which indicates the model performs well. The three land use types with the highest producer and user accuracies are forest, cropland and water areas. The GM‐FMOLP model was used to forecast future LUCC quantity demand, and the FLUS model was then used to calculate spatial predictions for the three scenarios for 2040. The prediction results for the six land use types were significantly different under the three different scenarios. The total areas of forest, urban land and water increased by different degrees under the three scenarios, and the unused land area dwindled. Cropland areas were largely converted into forest, urban land and water areas. The increases in forest and urban land areas generally represents internal gap‐filling between disparate areas and peripheral expansion of areas of the same land use type. The increase in water area results from increased surface runoff in mountainous valleys with severe terrain. Areas of unused land are fully transformed into other land use types. The outcomes from the designed scenarios demonstrate that the proposed models are reliable and effective for future LUCC simulation, and highlight key areas where land use changes differently according to the scenario. In summary, in the predictions for the scenario that combined ecological and economic benefits maximization, each land use type tried to maximize ecological and economic benefits under the constraints, taking into account ecological safety and economic growth, which is in line with the coordination, comprehensiveness and binding requirements in Chongqing's development plan. Therefore, this scenario becomes the optimal scenario for land use optimization in Chongqing in the future. This study provides an example of the application of simulation and forecast models in land use management, remediation and urban planning, and addresses the growing requirement for low‐cost and effective tools for prediction of dynamic land use succession patterns.

Simulation Research on Trajectory Planning of Double-arm Cooperative Live Working Robot for Transmission Lines

High-voltage transmission lines are the main artery for power transmission, dampers, insulators and drainage plates are typical hardware on transmission lines. The harsh service environment will damage transmission line hardware and seriously affect the safety and stable operation of transmission lines. The electrical power robots are used to carry out transmission line maintenance operations. It is of great significance to improve work safety and work efficiency. Through the analysis of typical hardware maintenance tasks, it can be seen that most of the operations require the coordinated movement of the double arms to complete. Based on this, this paper proposes a basic configuration of a double-arm collaborative robot. At the same time, based on the polynomial interpolation theory, the robot arm motion trajectory planning is carried out, and then the trajectory planning simulation model of the robot manipulator in the MATLAB environment has been established and the trajectory simulation research has been carried out on the robot manipulator to reconstruct the end tool and the manipulator to carry the end tool to the work point. The simulation results show that compared with the single-arm operation mode, the cooperative operation of the double arms can not only improve the operation efficiency, but also complete some tasks that cannot be completed by the single-arm operation, the research in this paper has important theoretical significance and practical application value for the intelligent operation and maintenance management of transmission network.

A long-term forecasting and simulation model for strategic planning of hospital bed capacity

Growing healthcare needs leverage the potential savings of using resources efficiently. To that end, ProMoBed is a comprehensive model that supports strategic planning of bed capacity in inpatient hospitals. The model consists of an extrapolation and simulation component, the former supplying input for the latter. The extrapolation model forecasts admission rates and the average Length of Stay for pathology groups, and corrects for demographic changes. Subsequently, the simulation model emulates the demand for bed capacity, and makes service-level based bed capacity suggestions. Additionally, the model uses the Shapley value principle to disaggregate the effects on demand for inpatient days due to different causes. Results from the extrapolation model are applied to regions in Belgium, showing expected divergence in inpatient day demand evolution.

Street Landscape Planning and Design Guided by Artificial Intelligence Interactive Experience.

As an important part of the city, urban streets have always played an active role in carrying public needs and optimizing public living spaces. They are also important to open public spaces in the city. In addition, with technology integration and life, the interactive experience of artificial intelligence (AI) in Street View is gradually increasing. A streetscape planning and design method based on AI interactive experience is proposed. CSiXRevit 2022 software builds street simulation models for better street planning and design. Street digital imaging technology is used to analyze the original streetscape. Landscape features are integrated with the street model. Based on intelligent technology, urban landscape design is studied, and AI interactive experience is analyzed. Analytic hierarchy process (AHP) and AI technology evaluate the effect of landscape design. Firstly, AHP is used to construct the evaluation index system of the rural landscape design effect, and the index weight is determined. Back propagation neural network (BPNN) is used to construct an evaluation model of landscape design effect. Three indexes of target, project, and indicator layer are used to evaluate the effect of rural landscape design. The results show that (1) the variety and richness of streetscapes in the survey area should be increased. Interactive buildings should be placed on the street. (2) The comprehensive evaluation score of the street landscape is 0.60, and the design effect is excellent. The comprehensive evaluation values of the three project layers are 0.65, 0.48, and 0.67, respectively. The data show that the carrying capacity index of the study area is the best, and the vitality index is the weakest. Therefore, when designing the street landscape, the economic development has been strengthened by attracting investment, introducing large-scale exhibition shops, increasing street income, strengthening the construction of street public facilities, and paying attention to the public street experience. This work can provide references for related streetscape designs.

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

AbstractSimulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

MA3: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains

Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to predict. Nevertheless, planning a safely traversable path through such an environment requires the ability to predict the outcomes of these interactions instead of avoiding them. One approach to doing this is to learn the interaction model offline based on collected data. Unfortunately, though, this requires large amounts of data and can often be brittle. Alternatively, models using physics-based simulators can generate large data and provide a reliable prediction. However, they are very slow to query online within the planning loop. This work proposes an algorithmic framework that utilizes the combination of a learned model and a physics-based simulation model for fast planning. Specifically, it uses the learned model as much as possible to accelerate planning while sparsely using the physics-based simulator to verify the feasibility of the planned path. We provide a theoretical analysis of the algorithm and its empirical evaluation showing a significant reduction in planning times.

Integrated Modelling of Decentralised Energy Supply in Combination with Electric Vehicle Charging in a Real-Life Case Study

Intelligent integration of decentralised energy resources, local storage and direct consumption are key factors in achieving the transformation of the energy system. In this study, we present a modular simulation concept that allows the planning of decentralised energy systems for buildings and building blocks. In comparison to related studies, we use a simulation model for energy planning with a high time-resolution from the perspective of the energy system planner. In this study, we address the challenges of the grid connection in combination with an increasing number of electric vehicles (EV) in the future. The here developed model is applied for an innovative building block in Germany with a photovoltaic (PV) system, a combined heat and power (CHP) unit, battery storage and electric vehicles. The results of the simulation are validated with real-life data to illustrate the practical relevance and show that our simulation model is able to support the planning of decentralised energy systems. We demonstrate that without anticipating future electric vehicle charging, the system configurations could be sub-optimal if complete self-sufficiency is the objective: in our case study, the rate of self-sufficiency of the net-zero energy building will be lowered from 100% to 91% if considering electric vehicles. Furthermore, our simulation shows that a peak minimising operation strategy with a battery can prevent grid overloads caused by EV charging in the future. Simulating different battery operation strategies can further help to implement the most useful strategy, without interruption of the current operation.

Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

Highway systems play a key role in providing mobility to society, especially during emergency situations, including earthquakes. Bridges in highway systems are susceptible to damage from earthquakes, causing traffic capacity loss leading to a serious impact on surrounding areas. To better prepare for such scenarios, it is important to estimate capacity loss and traffic disruptions from earthquakes. For this purpose, a traffic-capacity-analysisbased methodology was developed to model the performance of a transportation network immediately following an earthquake using a macroscopic multi-level urban traffic planning simulation model EMME4. This method employs the second order linear approximation (SOLA) traffic assignment and calculates total system travel time for various capacity loss scenarios due to bridge damage from earthquakes. It has been applied to Pohang City in Korea to evaluate the performance of traffic networks in various situations. The results indicate a significant increase in travel time and a decrease in travel speed as the intensity of an earthquake increases. However, the impact on traffic volume varies depending on the bridges. It is assumed that the location of the bridges and traffic routing patterns might be the main reason. The results are expected to help estimate the impact on transportation networks when earthquakes cause traffic capacity loss on bridges.

A Simulation Model for Capacity Planning of Nuclear Fuel Plants for Research Reactors

The demand for nuclear fuel for research reactors is increasing worldwide. However, some nuclear fuel factories have small production volumes. There is a gap in the literature regarding how to expand capacity of those facilities in a safe and reliable way. Thus, the purpose of this work is to propose and validate a conceptual model to increase the production capacity of these factories. We addressed factories that produce plate-type fuel elements loaded with LEU U3Si2-Al, which are typically used in nuclear research reactors. We collected data from a real nuclear fuel plant and applied the model to that data, i.e., we set up a case study. We developed two different strategies for the use of the model, thus generating several production scenarios. Each scenario is an experiment of the different possibilities to enlarge the capacity. We applied discrete events simulation in order to cover all production scenarios. Our tests indicated significant increases in productive capacity, thus showing that the model has fully achieved its proposed objective. One of the main conclusions that we point out is the model’s effectiveness. That effectiveness was demonstrated by using the model in two different strategies and obtaining increases in capacity with both of them.

Long-term budget planning in the context of globalization and its socio-economic significance

Research background: The process of globalization increases the possibility of a more equitable distribution of income and national wealth, which contributes to the solution of social problems and ensures the development of the material base of human potential. In these conditions, the issues of budget planning of mandatory budget expenditures for the social sphere, in particular for education. With the use of simulation modeling, it is possible not only to implement budget planning more effectively, adjust current social expenditures, but also to increase the horizon of long-term budget planning, which is especially important in conditions of high financial dependence. Purpose of the article: to study the use of simulation modeling for long-term budget planning of mandatory budget expenditures in the social sphere at the subnational level. Methods: we have researched based on the application of the “Any Logic” modeling method for long-term budget planning and adjustment of current social expenditures. Findings & Value added: the study conducted by the authors allows us to conclude that the use of “any logic” for long-term financial planning in the context of globalization makes it possible for States to model social expenditures in the current and future periods. Based on official statistics, the forecast model of mandatory budget expenditures for the social sphere takes into account various factors (changes in demography, the number of recipients of social services, etc.) and provides a forecast of socio-economic development. Long-term budget planning based on simulation models allows you to adjust current social programs, which will improve the quality of life in the context of globalization.

The Research on the Benefit Evaluation Methodology of the Global Energy Internet and Case Analysis

This research focuses on the impact of Global Energy Internet(GEI)on the power system, energy environment, economy and society, and builds the comprehensive benefit assessment system of GEI. The benefit evaluation model system that realizes the quantitative assessment of the comprehensive benefits of GEI from the perspectives of economy, society, energy, and environment is constructed, which is based on global power Planning and production simulation model, Global Trade Analysis project(GTAP) and Climate Change Assessment Model (MEGGIC).The results show that GEI is conducive to realize the complementary benefits of multiple energy sources across region, achieve a win-win between the main sending and receiving areas, promote the use of renewable energy resources, and help achieve the 2°C temperature control target.