The integrated use of electricity and natural gas has captured great attention over recent years, mainly due to the high efficiency and economic considerations. According to the energy hub design and ...operation, which allows using different energy carriers, it has turned into a critical topic. This paper develops a two-stage stochastic model for energy hub planning and operation. The uncertainties of the problem have arisen from the electric, heating, and cooling load demand forecasts, besides the intermittent output of the solar photovoltaic (PV) system. The scenarios of the uncertain parameters are generated using the Monte-Carlo simulation (MCS), and the backward scenario reduction technique is used to alleviate the number of generated scenarios. Furthermore, this paper investigates the effectiveness of demand response programs (DRPs). The presented model includes two stages, where at the first stage, the optimal energy hub design is carried out utilizing the particle swarm optimization (PSO) algorithm. In this respect, the capacity of the candidate assets has been considered continuous, enabling the planning entity to precisely design the energy hub. The problem of the optimal energy hub operation is introduced at the second stage of the model formulated as mixed-integer non-linear programming (MINLP). The proposed framework is simulated using a typical energy hub to verify its effectiveness and efficiency.
•Proposing an MINLP model for the optimal energy hub design and operation.•Modeling the seasonal variations of loads demand for each season.•Evaluating the impact of DRPs on the sizing of the energy hub assets.•Investigating the effect of uncertainties on the optimal energy hub design.•Investigating a comprehensive energy flow model for serving the loads.
With the increasingly fierce global energy competition, the Arctic, a "treasure land" of energy, has gradually become the focus of competition among countries. Based on the life cycle theory and the ...characteristics of the world oil market, this paper analyzed the theoretical basis of constructing the oil production equation of Arctic countries, and selected the multi-cycle Hubbert model and scenario analysis method to construct the oil production equation of Arctic countries. Moreover, based on the actual situation of theory and reality, different development scenarios were reasonably set up in order to analyze the impact of oil resources in the Arctic on the oil production of Arctic countries, and then studied the development potential of oil resources in the Arctic. The results showed that once the oil resources in the Arctic were developed, the peak oil of Arctic countries could be postponed for 2~4 years. The key development areas in the Arctic (West Siberia Basin, North Slope Basin of Alaska, Northwest I
Flooding hazard is an important and dangerous natural phenomenon that leads to significant material losses. It should be studied and scenario to prevent significant losses. The studies should ...consider the impact of many factors such as human, infrastructure, economic,..etc. The main objective of the research is the risk management procedure. The study was conducted in Baghdad, Iraq. The materials for completing this research were prepared by gathering a satellite image and Digital Elevation Model (DEM) via the USGS website, then processed, analyzed, and converted into a different flood region concerning the probability of rising water levels where the normal height of Baghdad city is 28m over sea level. This scenario defines 3m, 4m, and 5m heights over the Tigris river cliff, which are the possible heights at which the water level may rise above the normal height. The range of the elevations becomes either 31m, 32m, or 33m over sea level. Many software’s were conducted in this research, such as MatLab, ArcGIS, and ENVI.
This two-part paper investigates the application of artificial intelligence (AI) and, in particular, machine learning (ML) to the study of wireless propagation channels. In Part I of this article, we ...introduced AI and ML and provided a comprehensive survey on ML-enabled channel characterization and antenna-channel optimization, and in this part (Part II), we review the state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state of the art, the future challenges of AI-/ML-based channel data processing techniques are given as well.
Curbside parking in a residential area may be induced by the presence of a public transport stop. Travelers may park their car near the stop and continue their trip to the city center by other means. ...This is called informal park-and-ride. The magnitude of the phenomenon is estimated by simulation. Parking demand is derived from the history of parking time (tickets) sold at vending machines. For each ticket, an activity location is determined by stochastic sampling from a buildings (facilities) database based on the position of the vending machine. The activity timing is derived from the parking duration specified by the ticket. Suitable parking spots for an activity are determined for the cases (i) drive-park-walk and (ii) drive-park-publicTransport-walk respectively. The generalized cost (based on money and travel duration) is determined for both options. The decision is sampled by means of a behavioural model. Several scenarios are considered and the results allow to evaluate the complaints issued by residents of a study area because microsimulation enables the generation of probability densities for parking occupation in such area in each scenario. This paper reports how the method is applied to a study area in Amsterdam.
In this paper we analyze the problem of data-driven university management. We propose a concept of an intelligent strategic decision support system (ISDSS) to ensure that informed decisions are made ...in the management of higher education institutions. We show that the underlying task of various decision support tasks in university management is the analysis and forecasting of scientific and technological trends. We propose an approach to solving this task which includes determining promising emerging and existing technological areas, forecasting the further development of each of the selected areas, generating possible scenarios of their development and preparing suggestions for decision makers.
While many transition scenarios describe potential low-carbon systems, few link these system-level outcomes to the microlevel stakeholder decision-making needed to actualise them, resulting in a ...‘planning gap’. Closing this gap requires that insights from modelling-based transition scenarios on what must happen to achieve climate targets are linked to those on how to make it happen from stakeholder-focused transition scenarios. This link requires a different understanding of decision-making rationality from that of a representative agent with rational expectations, as employed in much climate-change modelling currently. Rationality conceived as ‘frame-sensitive reasoning’ can better account for heterogenous stakeholders' alternative preferences, the actions they take in pursuit of them, and the effect of these actions on low-carbon transitions. This paper augments the Intuitive Logics (IL) stakeholder-focused scenario approach to enable frame-sensitive reasoning and provide modelling-based transition scenarios with realistic innovation-diffusion assumptions. In so doing, the paper assists in closing the planning gap.
China's paper industry development is rapid, but the recycling rate of China's waste paper has been low all the time. Meanwhile, material flow analysis can help determine the flow of waste paper, and ...life cycle assessment (LCA) is the methodological framework for quantifying greenhouse gas emissions. Therefore, present study integrates these two methods into the model construction of China's waste paper recycling decision system. Present study constructs a benchmark model of China's waste paper recycling decision system in 2017, focusing on the impact of nonstandard waste paper recycling on the economic and environmental benefits of China's domestic waste paper recycling system. This model construction is followed by sensitivity analysis of the relevant parameters affecting the efficiency of the waste paper recycling system. Finally, present study forecasts the system's economic benefits and greenhouse gas (GHG) emissions in the context of integrating and regulating nonstandard recycling vendors. The results show that the economic benefit of China's waste paper recycling in 2017 is approximately 458.3 yuan/t and that the GHG emissions are 901.1 kgCO2eq. The standard recovery rate and nonstandard recovery acceptance rate will both have a significant impact on the system's economic benefits and improve the GHG emissions structure. In the context of integrating nonstandard recycling enterprises and individual recycling vendors, the economic benefits will rise to 3312.5 yuan/t in 2030, while GHG emissions will rise to 942.9 kgCO2eq. Present study can play a certain guiding role for policy makers in formulating waste paper recycling industry specifications and formulating relevant policies.
•Present study constructs the life cycle framework of Chinese paper and paperboard by the combination of MFA and LCA.•Quantitative analysis of the economic benefits and GHG emissions generated by China's waste paper recycling system.•Sensitivity analysis on the main parameters of the economic and environmental benefits of the waste paper recycling system.•Benefits of China’s waste paper recycling from 2018 to 2030 are evaluated by integrating nostandard and standard recycling.
•This paper integrates system dynamics and technology roadmapping.•From a conceptual view, a systematic way of building scenarios is suggested.•Technology roadmapping is conducted to link the ...scenario to the planning process.•System dynamics simulation is carried out to evaluate each scenario.•As a case study, three scenarios of car-sharing business are analyzed.
Due to the volatile market environment, the use of scenario approach comes to the forefront in business strategy. As a means of scenario planning, several approaches have been proposed and conducted. However, previous research, mainly having resorted to the expert judgment for planning and evaluation, still remains conceptual and lacks a systematic link to the planning process. In response, this paper provides an integrative approach to the technology roadmap and system dynamics to support scenario planning. The proposed approach consists of three parts: scenario building, technology roadmapping, and system dynamics simulation. The first step is to construct the scenarios which are used as inputs for the scenario planning. Second, technology roadmap is developed, incorporating the scenarios built in the first step. The technology roadmap works as a strategic framework to realize the hypothetical scenarios, linking the external and hypothetical business and internal strategies. Finally, the strategic model for technology roadmap is transferred to the operational viewpoint using system dynamics. When the simulation ends, the result of each scenario is reflected to the technology roadmapping, making the multi-path technology roadmapping. As an illustrative example, three scenarios of car-sharing business are developed and analyzed.
The COVID-19 pandemic broke the balance of oil supply and demand. Meeting these oil market challenges induced by the pandemic required a more accurate assessment of the impact of the pandemic on oil ...consumption. The existing measurement of the impact of the pandemic on oil consumption was based on year-over-year calculation. In this work, a new measurement approach based on a comparison of simulated and actual oil consumption was proposed. In this proposed measurement model, the actual oil consumption was from the official statistics, whereas the simulated oil demand came from business-as-usual (without pandemic) scenario simulation. In order to reduce the simulation error, three hybrid simulation approaches were developed by combining the simulation technique and machine learning technique. The mean relative errors of the proposed simulation approaches were between 1.08% and 2.51%, within the high precision level. An empirical research on the US oil consumption was conducted by running the proposed measurement model. Through analyzing the difference between the simulated and real US oil consumption, we found the impact of the epidemic on U.S. oil consumption was obvious in April–May 2020 and January–February 2021. At its worst, the oil decline in the United States reached 973 trillion British thermal units, compared to the state without the epidemic. During the entire survey period (January 2020–March 2021), the US oil consumption under the epidemic was about 18.14% lower than that under the normal epidemic-free situation, which was 5% higher than the 13% inter-annual decline rate reported. This work contributed to understand the impact of the pandemic on oil consumption more comprehensively, and also provided a new approach for analyzing the impact of the pandemic on energy consumption.
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•The impact of COVID-19 on U.S. petroleum consumption.•Difference between pandemic-free scenario and real scenario.•Average relative error of ARIMA-BP method is as low as 1.08%.•The decline in U.S. oil is positively correlated with new confirmed cases.•The epidemic has reduced US oil by 18.14% instead of 13% in statistics.
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