Scenario Analysis Method Research Articles

Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

The configuration of energy storage in the integrated energy system (IES) can effectively improve the consumption rate of renewable energy and the flexibility of system operation. Due to the high cost and long cycle of the physical energy storage construction, the configuration of energy storage is limited. The dynamic characteristics of the heating network and the demand-side response (DR) can realize the space-time transfer of energy. Although there is no actual energy storage equipment construction, it plays a similar role to physical energy storage and can be considered as virtual energy storage in IES planning. In this paper, a multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of the heating network and DR is proposed. To make full use of the energy storage potential of the proposed model, the virtual energy storage features of the dynamic heating characteristics of the heating network and DR are analyzed at first. Next, aiming at the uncertainty of wind turbine (WT) and photovoltaic (PV) output, the scenario analysis method is used to describe the wind and photovoltaic power output with different probabilities. Finally, an electrothermal IES with an IEEE 33-node network and a 26-node heating network serves as an example to verify the effectiveness of the proposed model. The case study shows that the proposed model effectively reduces the physical energy storage configuration and achieves the economic trade-off between the investment cost and the operation cost.

Research on optimization of ecological service function and planning control of land resources planning based on ecological protection and restoration

With the rapid development of Chinese society, cities have begun to expand, and more and more rural areas and towns are moving closer to cities, but land is limited. In the process of urban economic development, it is bound to be related to land planning and agricultural land. Contradictions arise between. In order to reasonably plan the distribution of land resources, rationally divide the construction land, provide sufficient land resources for urban development, and combine actual needs to provide reasonable suggestions for land use and urbanization development. In this process, we need to fully understand the purpose of the land and make a reasonable division of the land, not only to enable the smooth progress of the urbanization process, but also to ensure that there is enough agricultural land to achieve the harmonious development of urban construction. The protection of the ecosystem is also a step that cannot be ignored in the development process. In cities, the artificial urban green space ecosystem is the direct carrier of providing ecological services for human beings. Therefore, if you want to maintain the stability of the urban ecosystem, you must do a good job in urban greening. . Now China’s urbanization is developing rapidly. Due to problems such as insufficient planning, there are serious problems in the utilization of ecological resources and land resources in China, which cannot meet the needs of social development and humanity. Scenario analysis is a method of analyzing and transforming uncertain information into future development scenarios. The purpose of this article is to introduce scenario analysis methods into the planning of land resources. In this article, first introduced the basic concepts and principles of situation analysis methods, and studied the basic application methods of situation analysis in practice based on examples. Then, in accordance with the characteristics of situation analysis, we further plan the application of situation analysis in land and resources planning to improve the adaptability of situation analysis to supplement the technical framework of planning management. • With the rapid development of Chinese society, cities have begun to expand, and more and more rural areas and towns are moving closer to cities, but land is limited. • In order to reasonably plan the distribution of land resources, rationally divide the construction land, provide sufficient land resources for urban development, and combine actual needs to provide reasonable suggestions for land use and urbanization development. • In cities, the artificial urban green space ecosystem is the direct carrier of providing ecological services for human beings. Therefore, if you want to maintain the stability of the urban ecosystem, you must do a good job in urban greening. • The purpose of this article is to introduce scenario analysis methods into the planning of land resources.

Impact of the collaboration mechanism of PPP projects based on consumer participation: A system dynamics model of tripartite evolutionary game.

Developing countries need a large number of social infrastructure projects (e.g. schools, medical care, nursing homes). But the government's finance to invest in these projects is limited. By using the public-private partnership (PPP) mode to attract social capital to invest in PPP projects, it can relieve the financial pressure and improve the operation efficiency. The cooperation between government and consumer can ensure the sustainable development of the project operation. A system dynamics model of tripartite evolutionary game is developed to analyze the interaction of participant's strategies and simulate the corresponding evolution process. We employ the scenario analysis method to investigate the impact of the key parameters in relation with PPP projects based on realistic scenario assumptions. The results reveal the effect of some policies including reverse effect, blocking effect and over-reliance effect. Specifically, the results show that high penalty can prevent social capital from providing low-quality services, the low cost of government regulation can promote social capital to provide high-quality services, compensation to consumer can increase the enthusiasm of consumer participating in supervision, appropriate difference between price and cost of high-quality service as social capital's profit can encourage social capital to provide high-quality service. These policy suggestions will contribute to the sustainable development of social infrastructures in PPP mode.

Predicting the elderly's quality of life based on dynamic neighborhood environment under diverse scenarios: An integrated approach of ANN, scenario analysis and Monte Carlo method

There is an increasing global population of older adults in recent years, and the trend will be more acute in the following decades. Owing to low mobility and physical impairment, the elderly are sensitive to their nearby neighborhood environment. However, it is challenging to accurately judge changes of the elderly’ quality of life (QoL) before conducting improvement strategies of neighborhood environment due to complicated environmental impacts. This study proposes a QoL prediction approach by integrating artificial neural network (ANN) model, scenario analysis and Monte Carlo experiment. The QoL of the elderly is measured from four domains, and the neighborhood environment is measured by 16 key indicators. Based on the measurement data collected from Nanjing, the ANN model is trained to fit the influence relationship between neighborhood environment and the elderly's QoL. Scenario analysis sets up potential scenarios for neighborhood environment under natural progressions and human interventions. Finally, Monte Carlo experiment is conducted to predict the probability distribution of the elderly's QoL values under potential scenarios by using the trained ANN model as functions. The predictive QoL values of the elderly show the change pattern of the elderly's QoL with dynamic neighborhood environment, reveal the independent and compound effects of natural progressions and human interventions, and confirm the mutual promotions between human interventions. Furthermore, the integrated prediction approach can be implemented in other cities and regions to forecast the local elderly's QoL under possible scenarios, and offer concise evidence for deciding improvement strategies of neighborhood environment to support aging-in-place.

Scenario analysis and assessment of China’s nuclear power policy based on the Paris Agreement: A dynamic CGE model

In the 2016 Paris Agreement, which takes effect in 2020, China pledged to slow the growth of its carbon emissions and increase the share of nonfossil energy in its primary energy consumption. To comprehensively evaluate and compare the effects of different nuclear policies under the same background and compensate for the lack of policy comparisons in the field, this paper innovatively develops the traditional computable general equilibrium (CGE) model of nuclear power and China’s economy into a dynamic general equilibrium model. We simulate and compare three different policies using the scenario analysis method: improving the level of technology (TC), implementing price subsidies (PS), and levying energy taxes (ET). These scenarios achieve different effects, and the following points can be drawn from the results. First, China should have strong confidence in achieving the goals of the Paris Agreement and the nuclear energy industry has a strong competitive advantage as new energy that can be promoted for development. Second, improving the level of science and technology to make the conversion efficiency reach 40% has the largest impact on the macro economy and a less impact on improving nuclear power generation in these scenarios. Third, government subsidy of 0.4 yuan/kWh have the largest impact on nuclear power generation while a less impact on the macro economy in these scenarios. Fourth, energy taxes have the largest impact on carbon dioxide emission reduction, while the least impact on increasing the share of nuclear power in these scenarios. Finally, we combine different nuclear power policies to meet the different development goals of the government.

Scenario Analyses of Exhaust Emissions Reduction through the Introduction of Electric Vehicles into the City

In recent years, policymakers of urban agglomerations in various regions of the world have been striving to reduce environmental pollution from harmful exhaust and noise emissions. Restrictions on conventional vehicles entering the inner city are being introduced and the introduction of low-emission measures, including electric ones, is being promoted. This paper presents a method for scenario analysis applied to study the reduction of exhaust emissions by introducing electric vehicles in a selected city. The original scenario analyses relating to real problems faced by contemporary metropolitan areas are based on the VISUM tool (PTV Headquarters for Europe: PTV Planung Transport Verkehr AG, 76131 Karlsruhe, Germany). For the case study, the transport model of the city of Bielsko-Biala (Poland) was used to conduct experiments with different forms of participation of electric vehicles on the one hand and traffic restrictions for high emission vehicles on the other hand. Scenario analyses were conducted for various constraint options including inbound, outbound, and through traffic. Travel time for specific transport relations and the volume of harmful emissions were used as criteria for evaluating scenarios of limited accessibility to city zones for selected types of vehicles. The comparative analyses carried out showed that the introduction of electric vehicles in the inner city resulted in a significant reduction in the emission of harmful exhaust compounds and, consequently, in an increase in the area of clean air in the city. The case study and its results provide some valuable insights and may guide decision-makers in their actions to introduce both driving ban restrictions for high-emission vehicles and incentives for the use of electric vehicles for city residents.

Cybersecurity Risk Assessment in Smart City Infrastructures

The article is devoted to cybersecurity risk assessment of the dynamic device-to-device networks of a smart city. Analysis of the modern security threats at the IoT/IIoT, VANET, and WSN inter-device infrastructures demonstrates that the main concern is a set of network security threats targeted at the functional sustainability of smart urban infrastructure, the most common use case of smart networks. As a result of our study, systematization of the existing cybersecurity risk assessment methods has been provided. Expert-based risk assessment and active human participation cannot be provided for the huge, complex, and permanently changing digital environment of the smart city. The methods of scenario analysis and functional analysis are specific to industrial risk management and are hardly adaptable to solving cybersecurity tasks. The statistical risk evaluation methods force us to collect statistical data for the calculation of the security indicators for the self-organizing networks, and the accuracy of this method depends on the number of calculating iterations. In our work, we have proposed a new approach for cybersecurity risk management based on object typing, data mining, and quantitative risk assessment for the smart city infrastructure. The experimental study has shown us that the artificial neural network allows us to automatically, unambiguously, and reasonably assess the cyber risk for various object types in the dynamic digital infrastructures of the smart city.

Carbon emission of energy consumption of the electric vehicle development scenario.

To explore the impact of promotion of electric vehicles on carbon emissions in China, this paper used the principal component analysis (PCA)-logistic regression model to predict the demand for traditional vehicles, and used the scenario analysis method to analyze the proportion of electric vehicles in traditional vehicles qualitatively. Then this paper calculated the carbon emissions during the power generation process based on the existing power structure. In addition, the IPCC carbon emission calculation method is used to compare the CO2 emissions produced by electric vehicles and fuel vehicles with similar vehicle quality while driving the same distance and consuming different energy sources. The results showed that the CO2 emissions of electric vehicles accounted for only 37.05% of fuel vehicles. By 2050, the annual electricity demand of electric vehicles will be 828.7, 776.9, and 752.1 billion kWh under the radical scenario, the reference scenario, and the negative scenario, respectively. According to the current power structure, the carbon emissions will be 1.2, 1.1, and 1 billion tons, respectively. The rapid growth of electric vehicles has a substantial impact on the grid load. Studying the changes in CO2 emissions from energy substitution is significant to formulate the development strategy of the automobile industry and adjust energy structure policies.

Operational Scenario Analysis Method for Civil Aircraft Based on Requirement Capture

Scenario analysis is an accepted method in civil aircraft requirements capture and validation.This paper introduces the operation scenario analysis method for requirement capture, including stakeholder identification, interaction activity analysis, design requirement capture, etc.Based on SysML language, the modeling and analysis of operation scenarios were carried out.Taking the push/tow scenario of civil aircraft as an example, the structure diagram, use case diagram, activity diagram and requirement diagram are formed by modeling. The design requirements in this scenario are captured through the interaction analysis between the different stakeholders and the aircraft.

Analysis of ATO System Operation Scenarios Based on UPPAAL and the Operational Design Domain

With the gradual maturity of the automatic train operation (ATO) system in subways, its application scope has also expanded to the high-speed railway field. Considering that the ATO system is still in the early stages of operation, it will take time to fully mature, and definite specifications of the requirements for system operation have not yet been formed. This paper presents the operational design domain (ODD) of the high-speed railway ATO system and proposes a scenario analysis method based on the operational design domain to obtain the input conditions of the system requirements. The article models and verifies the scenario of the linkage control of the door and platform door based on the UPPAAL tools and extracts the input and expected output of the system requirements of the vehicle ATO system. Combined with the input conditions of the system requirements, the system requirements of the vehicle ATO in this scenario are finally obtained, which provides a reference for future functional specification generation and test case generation.

Multi-objective optimization for green-grey infrastructures in response to external uncertainties

The optimized green-grey infrastructures are promising solutions to combat the urban flood and water quality problems which have been severe owe to the increasing urbanization and climate change. However, the focusses in existing researches have been either on finding the best strategy by scenario analysis method or optimal design of LID practices under the hypothesis of unchanged grey infrastructures. Little is known regarding the synergistic effect of synchronous optimization design of both green and grey infrastructures. In the study, we conduct green-grey infrastructures synchronous optimization by modifying the decision variables of traditional simulation-optimization frameworks and investigate how external uncertainties will affect their performance. The methodology was applied to a case study in Suzhou, China. The results showed that although the cost of green-grey synchronous optimized scenarios is lower than that of green optimized only scenarios by 1.69–4.19 thousand USD per km2, the runoff/pollutants reductions of green-grey synchronous optimized scenarios are 0.11%–5.24% higher than that of green optimized only scenarios. In the green-grey synchronous optimized scenarios, green infrastructures can contribute to runoff/pollutants control by 50%–63%/62%–70%, while grey infrastructures can contribute to the remaining part by 37%–50%/30%–38%.

Analysis of the palladium response relationship of a receiving water body under multiple scenario changes in rainfall-runoff pollution.

Urban rainfall-runoff pollution is one of the main reasons for the deterioration of the receiving water quality. In this study, the lower reaches of the Meishe River on Hainan Island, China were adopted as the research area, and palladium (Pd) was selected as the target pollutant. The purpose of this study was to construct an input response model and to examine the Pd response relationship of receiving water bodies under multiple scenario changes of rainfall-runoff pollution combined with scenario analysis methods. The results showed that the mean absolute percent error (MAPE) and relative mean square error (RMSE) of the input response model were within 15%, which demonstrated the reliability of the model when applied to the simulation of the response of Pd in receiving water bodies to rainfall runoff. The dissolved Pd concentration in the receiving water body decreased in the following order: the moderate rain scenario > rainstorm scenario > the heavy rain scenario. The suspended Pd concentration in the receiving water body first increased and then decreased, and its decay rate was closely related to rainfall intensity and duration. Under the heavy rain and rainstorm scenarios, within 20 m downstream from the outfall, the occurrence time of the maximum suspended Pd concentration in the receiving water body was inversely proportional to the distance. The number of previous clear days was inversely proportional to the dissolved Pd concentration in the receiving water body and proportional to the suspended Pd concentration in the receiving water body. Under the short period of previous clear day scenario, the maximum suspended Pd concentration in each section of the receiving water body appeared earlier than that under the moderate and long periods of previous clear day scenarios.

Climate and land use change impacts on water yield ecosystem service in the Yellow River Basin, China

The Yellow River Basin is an important water conservation and ecological barrier in China. Studying its water supply services is of great significance for the development of the Yellow River Basin and conservation of its ecosystems. This study is based on the InVEST model and scenario analysis method. We used data including land use cover, meteorology and soil type as inputs to analyze the spatial and temporal pattern of water yield in the Yellow River Basin from 1995 to 2018 and the impact of precipitation and land use change on water yield in the basin. The results show that from 1995 to 2018, the water yield in the Yellow River basin increased by 20,106 million m3, the high-value areas of water yield distributed in the southwest and southeast, the low-value areas distributed in the northwest region, and the spatial pattern of water yield changed insignificantly. From 1995 to 2005, the contribution rates of land use change and climate change to water production were 3.32% and 96.68%, respectively, and the contribution rates of land use change and climate change to water production from 1995 to 2018 were − 0.48% and 100.48%, respectively. In conclusion, the influence of precipitation on water production is more significant, and the effect of land use change on water production is smaller. This study reveals the temporal and spatial variation characteristics of water yield and its controlling factors in the Yellow River Basin, which is of great significance to the protection of ecological environment and the maintenance of ecological balance of the whole basin.

Analysis of the development scenarios and greenhouse gas (GHG) emissions in China’s aluminum industry till 2030

Abstract The rapid economic growth and industrialization in China have induced a large aluminum demand since 1990. Faced with the complex international economic situation recently, China proposed a new national development pattern of “with the big domestic cycle as the main body, the domestic and international double cycles promote each other”. Under the guidance of this kind of macro-control thinking, China’s aluminum industry (CAI) may also need to make corresponding adjustments to future development policies. This paper used dynamic material flow analysis (MFA), regression analysis, and normal life distribution to estimate the domestic consumption, scrap generation, and in-use stock of aluminum from 1990 to 2030 in China. The results show that the amount of domestic consumption, scrap generation, in-use stock has been increasing from 1990 and are expected to reach 54, 19, and 640 Tg in 2030, respectively. Then, the scenario analysis method was adopted to discuss the impact of reducing external circulation (export volume) of aluminum on China’s aluminum production and related GHG emissions. The results of the scenario analysis indicate that effective measures to reduce the export of aluminum products could lead to significant mitigation in China’s primary aluminum production and GHG emissions. Besides, the amount of recycled aluminum will gradually increase in the future, and the proportion of it will reach about 30% by 2030 (22% in 2017) even under the most unfavorable scenario. Relying on the growth of recycled aluminum and the decline in exports of aluminum products, CAI is expected to reach the peak of GHG emissions around 2030, on the premise of meeting the domestic strong aluminum consumption.

Optimal scheduling of regional integrated energy system considering multiple uncertainties

Integrated energy system (IES) is an effective way to realize the efficient utilization of energy. Under the deregulated electricity market, IES operator gains profits by providing customers with energy service, including electricity, heat or cooling energy. With the deepening of market reform, higher penetration rate of renewable energy, economic risks embed in the IES. Based on this, an optimal scheduling model of regional IES considering uncertainties is proposed, aiming at maximizing the profits. Scenario analysis method has been adopted to model the uncertainties: Markov-Chain-Monte-Carlo (MCMC) sampling method, which has a better performance in fitting the probability distribution, is utilized to generate scenarios; K-means clustering method is applied to narrow down the sampling sets. By replacing the parameters in the deterministic model with the sampling sets, a series of optimal results can be achieved. The case study shows that the cooling storage tank can improve the economic benefits about 4.97% by converting electricity to cooling energy at lower price period and releasing energy at peak hours. Besides, through the proposed optimization model, operators can have a straight understanding of the venture brought by the uncertainties and a more reliable scheduling result is formed for reference.

What Goes Around Comes Around: How Large Are Spillbacks From US Monetary Policy?

We quantify spillbacks from US monetary policy based on structural scenario analysis and minimum relative entropy methods applied in a Bayesian proxy structural vector-autoregressive model for the time period from 1990 to 2019. We find that spillbacks account for up to half of the overall slowdown in domestic real activity in response to a contractionary US monetary policy shock. Moreover, spillbacks materialise as stock market wealth effects impinge on US consumption, and as Tobin's q effects impinge on US investment. In particular, a contractionary US monetary policy shock depresses global equity prices, weighing on the value of US households' portfolios; and it depresses earnings of US firms through declines in foreign sales inducing them to cut back investment. Net trade does not contribute to spillbacks because US monetary policy shocks affect exports and imports similarly. Finally, spillbacks materialise through advanced rather than through emerging market economies, consistent with their relative importance in US foreign equity holdings and US firms' foreign demand.

What Goes Around Comes Around: How Large are Spillbacks from US Monetary Policy?

We quantify spillbacks from US monetary policy based on structural scenario analysis and minimum relative entropy methods applied in a Bayesian proxy structural vector-autoregressive model estimated on data for the time period from 1990 to 2019. We find that spillbacks account for a non-trivial share of the overall slowdown in domestic real activity in response to a contractionary US monetary policy shock. Our analysis suggests that spillbacks materialise as Tobin’s q/cash flow and stock market wealth effects impinge on US investment and consumption. Contractionary US monetary policy depresses foreign sales of US firms, which reduces their valuations/cash flows and thereby induces cutbacks in investment. Similarly, as contractionary US monetary policy depresses US and foreign equity prices, the value of US households’ portfolios is reduced, which triggers a drop in consumption. Net trade does not contribute to spillbacks because US monetary policy affects exports and imports similarly. Finally, spillbacks materialise through advanced rather than emerging market economies, consistent with their relative importance in US firms’ foreign demand and US foreign equity holdings.

Quantification of heat mitigation by urban green spaces using InVEST model—a scenario analysis of Nagpur City, India

Urban green spaces (UGS) are known for providing a cooling effect by evapotranspiration, shade, and by altering the albedo. Heat mitigation by UGS reduces the space cooling demand, provides comfort, and enhances productivity. Rapid urbanization in developing countries has resulted in dwindling green spaces and their protective role is often neglected. We have quantified the heat mitigation by UGS using the InVEST model (Integrated Valuation of Ecosystem Services and Trade-offs) for present and future scenarios of Nagpur City which is situated in a heatwave-affected zone. Four future plausible scenarios were generated with a combination of drivers—economic development and commitment to promoting UGS, using the two-axis scenario analysis method. The simulated UGS in each future scenario (by allowing 10% variation in the land use) is utilized for quantification of heat mitigation and energy conserved. In comparison with the present situation, 21–29% less space cooling energy is conserved in scenarios driven by economic development (least commitment to UGS), whereas 17% more energy is conserved when UGS are promoted. In similar lines, the average temperature is increased by 0.5–0.7 °C when UGS are neglected, while the temperature dropped by 0.5 °C when UGS are promoted in Nagpur City. The methodology presents an integration of scenario analysis with heat mitigation modeling which can enable urban planners and researchers to improve their understanding of the ecological structure of urban centers and can aid in appreciating the potential of UGS in heat mitigation for human wellbeing.

Method for Calculating Safety Features of Railway Automation Devices

The problem of quantitative analysis of safety of microelectronic and microprocessor systems of railway automation and telemechanics is considered. The problem remains relevant, since the subject of safety analysis is rarely occurring, but extremely dangerous events. The risk and significance of failure are selected as the main safety features of these systems. The way to identify a failure was chosen according to MILSTD‑1629A standard, as the most adequate.Calculated expressions for significance of a failure are proposed. The probability of a dangerous failure is calculated by the method of model analysis. It is proposed to calculate the probability of a failure further developing into an accident using scenario analysis methods by constructing event trees. Calculated ratios for ratings of violations are suggested, allowing to compare dangerous failures and emergency sequences developed from a failure. The risk assessment of operation of railway automation systems was selected not related to economic categories, and thus convenient for rationing. It is based on probabilistic concepts of the nature of risk and is calculated using the methods of probability theory. The developed design ratios and models make it possible to analyze performance of the functions of train traffic safety systems by methods common to control systems, at the same time reflecting the features of operation of railway automation.

FORECASTING OF FINANCING OF ENTERPRISE CONVERSION

Purpose of the study. The task set by the President of Russia to increase the output of civilian products at the enterprises of the defense complex to 50% in 2030 requires the study of various ne-cessary resources, as well as models and methods of managing them. In the context of the econom-ic crisis caused by the coronavirus in 2020, the relevance of studying the possibilities of achieving this significant task is increasing. In this work, the goal is to develop a mathematical model, conduct modeling and assess the need for financing the growth of civilian output at enterprises in the re-search and development industry. Methods. A simulation model for multi-period forecasting of the financial state of the enterprise is used. Methods of processing big data are used to obtain the in-itial information. The means of regression analysis are used to determine the dependencies of the fi-nancial statements. Scenario analysis methods allow us to consider and highlight the significant fac-tors influencing the research goal. Results. A multi-period model of forecasting the financial and economic state of an enterprise has been developed to assess the consequences of an increase in ci-vilian production at enterprises of scientific research and development. The initial data of financial statements based on open data from Russian Agency for Statistics (Rosstat) were obtained, and the aggregation of reporting indicators was carried out. Simulation calculations were carried out in the scenarios of inertial growth, growth with an increase in efficiency, and replacement growth. The task of increasing the share of civilian products to 50% by 2030 at scientific and technical en-terprises in Russia will require significant additional funding, an estimate of about 1,300 billion ru-bles in an inertial scenario. The development and implementation of a program to improve the effi-ciency of enterprises significantly reduces the need for additional financing, the estimate is about 1,100 billion rubles with a slight improvement in efficiency parameters. Conclusion. The results of this study can be used to develop industrial development programs both at the industry level and at the level of individual enterprises. Increasing the output of civilian products will require significant funding. Development and implementation of operational efficiency programs of enterprises can significant-ly reduce the need for additional funding.