Search

The district heating area in China is continuously increasing, which brings an increase in district heating load. In order to solve the shortage of heating power and realize the carbon neutral target, this study proposes two retrofit schemes for district heating system by integrating air source heat pump and water source heat pump, respectively. Mathematical models are established to study the performance of the integrated systems and a bilevel optimization model is proposed to optimize them. The results show that the air source heat pump combined district heating system has better performance compared to the conventional system, which reduces 50% energy consumption, 10.8% carbon emissions and achieves better economy. The dynamic coupling property of the district heating network and power grid are also considered, and the results indicate that the introduction of air source heat pump can effectively improve the efficiency and stability of power grid and reduce the seasonal fluctuation. The potential of large-scale application of air source heat pump combined system in Beijing is evaluated. The results reveal that retrofit scheme of integrating air source heat pumps into district heating system can cover 2930 MW heating load and bring 362 million Chinese Yuan profit by reducing 219,000 tons of carbon emissions and 539,000 tons of standard coal consumption in 2025. [ABSTRACT FROM AUTHOR]

The transportation industry has entered a new stage from quantity expanding to structure optimization, quality and efficiency improvement, and from respective governance to integrative development. This indicates that the traditional corridor mode allocation dominated by quantity equilibrium can no longer meet the requirements of the new stage. In this paper, we propose a multi-entity programming model based on the economic equilibrium between supply and demand. It not only ensures the economic equilibrium in the market, but also maximizes the social benefits of the whole system, thereby realizing the sustainable development of the transportation system. Also, the Globalsearch algorithm and intlinprog algorithm are designed to solve the problem. The actual case of Beijing-Shanghai corridor shows that the model and algorithms are effective, providing decision support for the optimal allocation of regional transport network resources. [ABSTRACT FROM AUTHOR]

This study investigates the carbon compensation mechanism as a tool to promote coordinated economic and environmental development in China's Jing-Jin-Ji region (Beijing, Tianjin, and Hebei). A novel approach for calculating carbon compensation cost that integrate emission responsibility as well as carbon abatement cost through a bi-level programming model is proposed. Shared producer and consumer responsibility is established for carbon emissions under the multi-regional input-output model. Marginal abatement costs of Beijing, Tianjin, and Hebei are further calculated through the input-output and linear programming method. Results highlight that the emission responsibility allocation method significantly affects the inter-regional carbon compensation amount. The production-based approach leads to a 77% reduction of carbon compensation amount from Beijing to Hebei, and a 170% overestimation from Tianjin to Hebei compared with the shared responsibility approach in 2060. The optimal carbon compensation unit cost ranges from 1539 to 6438 yuan/ton when the carbon reduction ratio ranges from 10% to 20%. Beijing would compensate Hebei 3.56 and 29.74 billion yuan for achieving 10% and 20% emission reduction, respectively. Similarly, Tianjin's compensation for Hebei would be 0.23 and 1.87 billion yuan in the two scenarios. Results further show that implementing the proposed carbon compensation mechanism can reduce total abatement cost by 43% and 84% in Jing-Jin-Ji under the two scenarios. The methods and results of this paper could help inform future carbon compensation implementation in China and other countries. • Develop a bi-level programming model to determine optimal carbon compensation cost. • Consider emission responsibility and carbon abatement cost in Jing-Jin-Ji. • Resulting mechanism reduces abatement cost by 43% for 10% emission reduction. • Production-based allocation underestimates carbon compensation amount by 62%. • Optimal carbon compensation unit cost changes with marginal abatement cost. [ABSTRACT FROM AUTHOR]

This study proposes a multiobjective mixed integer nonlinear programming model for a last-mile shuttle service to improve bus commuters' travel time reliability. The approach aims to assign the routes that pick up the transit passengers located at the different stops by shuttle service. A bilevel optimization model is established: the upper model of route design considers the tradeoff between time cost and fare cost when some of the passengers take the shuttles, and the lower model assigns the demand of transit passengers. The proposed model effectively captures the reliability of travel time because related parameters are estimated by a statistical fitting test with a large number of real-world bus geographic information system (GPS) data. Moreover, dynamic demand diverting from conventional transit to shuttle service and travel time reliability, including passenger in-vehicle time (IVT) and waiting time (WT), are fully considered in this model. Since the task is a nonlinear programming model, a two-stage algorithm combined with linearization processing is presented to find an optimal solution. Finally, from the case study of Zhongguancun Software Park zone in Beijing, it is indicated that when last-mile shuttle service is provided, bus passengers' travel time reliability of last-mile trips can be improved by 14%. The study can be an important reference for improving the low reliability widely existing in the current transit commuters' last-mile problem. [ABSTRACT FROM AUTHOR]

Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions. • The regional control strategy prevents the emergence of warning communities. • A bi-level model is developed to solve the traffic diversion of mixed traffic. • A variable neighborhood search algorithm is designed to solve the model. • The control strategies under different critical variables scenarios are analyzed. [ABSTRACT FROM AUTHOR]

The population of Beijing has already come to its loading capacity. The China central government plans to build an ideal city named Xiong'an nearby Beijing. The city is expected to work as a carrying hub for noncapital functions of Beijing. The central government does not rush to build before a deliberated urban planning is accomplished. For sustainable development, a difficulty faced by urban planners is that the maximum number of people can be migrated from Beijing to Xiong'an with constraint on level of transport service. This paper developed a specialized bilevel programming model where the upper level is to ensure a predetermined transport service level regarding to population migration, while the lower level is feedback equilibrium between trip generation and traffic assignment. To be more specific, trip is generated by the gravity model, and traffic is assigned by the user equilibrium model. It is well known that the bilevel programming problem is tough and challenging. A try-and-error algorithm is designed for the upper-level model, and a method of successive average (MSA) is developed for the lower-level model. The effectiveness of the model and algorithm is validated by an experimental study using the current transport network between Beijing and Xiong'an. It shows that the methods can be very useful to identify the maximum population migration subject to level of transport service. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles, the location problem of charging stations has been paid more and more attention. It is more efficient and scientific to select electric vehicle charging stations through intelligent algorithms. Aiming at the location selection of electric vehicle charging station based on time satisfaction, a bi-level planning model is constructed for electric vehicle charging station location, and introduces genetic algorithm into the model to scientifically calculate the location of charging station. The candidate data string is extracted by genetic algorithm, and the text candidate string and the image candidate string are obtained. The candidate string is used as the document attribute to construct the electric vehicle charging station location plan, and then the ideal charging station address is solved. Finally, the method is applied. It is used in the planning analysis of the area near Chaowai Street in Chaoyang District, Beijing. The research results show that the six charging points calculated by the method can meet the demand of the charging vehicles of the residents in the planned area, which is in line with the actual situation of the planned area. This also shows that the double-layer planning model is used for site selection. The research in this paper shows that the genetic algorithm can be effectively used in the location problem, which can improve the efficiency of work and the accuracy of site selection. The relevant conclusions can provide a theoretical reference for the development of site selection. [ABSTRACT FROM AUTHOR]

Layout scheme optimization of high-speed railway transfer hubs plays a significant role in guiding the compilation of train connection plan, improving the efficiency and service level of passenger transfer organization. Therefore, based on the analysis of influence factors, principles and objectives of layout scheme of transfer hubs, the optimization process is proposed. Then the bi-level programming model is formulated for the layout scheme of high-speed railway transfer hubs, where the upper-layer problem is to optimize passenger transfer hub layout scheme and the lower-layer problem is passenger flow assignment problem on railway physical network. After that, an iterative algorithm is developed between the selection of transfer hubs and passenger flow assignment, where the hybrid genetic algorithm is designed to solve passenger flow assignment problem. Finally, the performance of proposed model and algorithm is verified with Chinese Beijing-Shanghai High-speed railway related network in the year of 2016 on Matlab programming platform. [ABSTRACT FROM AUTHOR]

A bi-level fuzzy programming (BFLP) method was developed for energy systems planning (ESP) and carbon dioxide (CO 2 ) mitigation under uncertainty. BFLP could handle fuzzy information and leader-follower problem in decision-making processes. It could also address the tradeoffs among different decision makers in two decision-making levels through prioritizing the most important goal. Then, a BFLP-ESP model was formulated for planning energy system of Beijing, in which the upper-level objective is to minimize CO 2 emission and the lower-level objective is to minimize the system cost. Results provided a range of decision alternatives that corresponded to a tradeoff between system optimality and reliability under uncertainty. Compared to the single-level model with a target to minimize system cost, the amounts of pollutant/CO 2 emissions from BFLP-ESP were reduced since the study system would prefer more clean energies (i.e. natural gas, LPG and electricity) to replace coal fuel. Decision alternatives from BFLP were more beneficial for supporting Beijing to adjust its energy mix and enact its emission-abatement policy. Results also revealed that the low-carbon policy for power plants (e.g., shutting down all coal-fired power plants) could lead to a potentially increment of imported energy for Beijing, which would increase the risk of energy shortage. The findings could help decision makers analyze the interactions between different stakeholders in ESP and provide useful information for policy design under uncertainty. [ABSTRACT FROM AUTHOR]

The emerging customized bus (CB) can serve personalized trip requests in a more flexible and convenient way, especially for passengers who have multiple-trip requests in a short time period. Considering the CB service for passengers with multiple trips (CSPMT), a loading-state-oriented state–space–time network-based bi-level programming model is proposed to optimize the routing of CBs, with the objectives of maximizing the operational profit and minimizing the travel cost, while considering the characteristics of multiple trips, time windows, capacity and mixed loads. Besides, a nested algorithm combining the genetic algorithm (GA) and the augmented Lagrangian relaxation-based dynamic programming algorithm is proposed. Then, the proposed model and algorithm are verified and analyzed through a Sioux Falls network and a Beijing sketch network. It can be found from the results that the method can optimize a CB routing plan for passengers with multiple-trip requests and for different network scales. The proposed bi-level model and corresponding algorithm can better adapt to passengers' personalized trip requests, and promote a higher level of public transport service which will attract more residents from private cars to public transportation, ultimately reducing energy consumption and exhaust emissions, and promoting the sustainable development of modern metropolises. • Bi-level CB route optimization model for passengers with multiple trips. • Loading-state-oriented space–time network. • The GA with the service effectiveness identification procedure. • The augmented Lagrangian relaxation-based dynamic programming. [ABSTRACT FROM AUTHOR]

• A new bi-level planning model is proposed that integrates the interests of both charging station investors and EV users, where the upper model has the global economic return as the optimization objective, and the lower model has the EV user's satisfaction with the charging service (charging cost) as the optimization objective. The global economic cost is optimized while the service satisfaction of the user is improved. • The time cost, economic cost, and mileage anxiety are considered together to characterize the service satisfaction of EV users. Specifically, the charging queuing time, driving distance, desired charging volume, and actual charging volume are used to reflect the charging cost of EV users and to reflect the autonomy of EV users more accurately, and a combination of fast and slow charging posts are used for planning to meet the needs of different users and grid constraints, to improve the operational efficiency of charging stations and reduce the charging queuing time of EV users. • The conventional method and the bi-level planning method are solved separately for a region of Beijing, and the effectiveness of the proposed method is verified by comparing factors such as investors' returns, users' charging costs, and queuing times. The rapid development of electric vehicles (EV) has placed greater demands on the planning and construction of public electric vehicle charging stations (EVCS). As EV users are highly autonomous in their charging behavior, the interests of investors and EV users are mutually affected and challenging to balance. Therefore, this paper proposes a bi-level planning model to balance the interests of investors and EV users and optimize global economic costs while improving the service satisfaction of users. The upper-level model aims to optimize the economic cost. In contrast, the lower-level model aims to optimize the service satisfaction of EV users and characterizes the charging satisfaction of EV users through the costs of charging queuing time, distance traveled, desired to charge volume, and actual charging volume, to more accurately reflect the autonomy of EV users. A combination of fast and slow charging piles is also used for planning to meet the needs of different users and improve charging stations' operational efficiency. Finally, a case study is conducted in an area of Beijing to verify that the optimization model has the advantages of low global economic cost, short charging queuing time for users, and high service satisfaction. [ABSTRACT FROM AUTHOR]

• A bi-objective bi-level optimization for multi-modal evacuation scheduling and routing following a rail disruption • A hybrid NSGA-II and ant colony optimization algorithm is proposed to solve the model • A case study applies three transit-based strategies on a high-speed railway disruption on the Beijing-Hebei metropolitan corridor High-speed railway, the backbone for the multi-modal corridor of metropolitan areas, stimulates intercity commute and travel. With growing intercity railway demand, the risk of in-station congestion and passenger anxiety should be taken seriously after rail disruptions. This research defines an evacuation planning problem for stranded passengers along the interrupted line which is regarded to be in series and growing with time. The proposed multi-modal evacuation-based approach responds to the risk of in-station congestion and reflects the mitigation efficiency of investing in multi-modal schedules. The post-disruption transit system for evacuation is formulated by demand, operation, routing, and transfers, upon which the bi-objective bi-level optimization model for multi-modal scheduling and routing is based. A hybrid NSGA-II and ant colony optimization algorithm are proposed to solve the model. A case study applies three transit-based response strategies on a high-speed railway disruption on the Beijing-Hebei metropolitan corridor. Results show that proposed strategy provides a lower level of risk with acceptable cost and evacuation equilibrium points relative to other strategies, and differentiate transit passengers in groups resulting in less delay and left quantity. With the investment in capacity on high-speed modes, like helicopters and high-speed trains, the evacuation speed for both pick-up and travel is increased, and the evacuated quantity is improved. [ABSTRACT FROM AUTHOR]

The design of user-level integrated energy systems is challenged by a variety of combinations of energy converters, complex cascade utilization of multiple energy flows, as well as sequential matching between stochastic energy resources and periodic energy demands. These issues can be addressed by an optimal energy-hub based planning framework considering the synergistic effects under multiple uncertainties. The energy hub model is extended to analyze energy-level matching and source–load balance with time-varying coupling factors representing part-load characteristics. The optimization problem is formulated as a bi-level planning model with uncertainties evaluated by a two-stage global sensitivity analysis. The bi-level planning model determines the system structure and component sizing at the upper level and identifies the optimal operation strategy at the lower level by employing piecewise linearization of part-load characteristics of components involved. The global sensitivity analysis reduces model size with the elementary effect method, identifies the most influential uncertain parameters with a variance-based method. A case study in Beijing is demonstrated for the proposed methodology. The results show that the proposed method can effectively plan the integrated energy system considering sequential source–load matching with the rational scheduling strategy of components. The demand-side response influences the system configuration and renewable energy penetration. Integrating the components' part-load characteristics help avoid the mismatch between the component capacity and energy demand, reducing 6.8% cost compared to the scheme with constant energy efficiency. The three most influential factors identified among 551 uncertain parameters are natural gas price, valley electricity price and nominal efficiency of the gas turbine. • Energy planning framework considering synergistic effects under uncertainties. • Extending energy hub model for energy-level match and source–load balance. • Bi-level linear programming considering components' part-load characteristics. • Global sensitivity analysis performed to analyze multiple uncertainties. • Identifying the most influential factors among 551 uncertain parameters. [ABSTRACT FROM AUTHOR]

• A bi-level optimization model is proposed for charging and discharging battery-exchange buses. • The calculation method of carbon quota for battery-exchange buses to participate in carbon trading market is put forward. • The battery-exchange buses participation in ancillary service market has economic benefits. The mode of battery replacement will become the main means of urban public transport energy supply, which will have a significant impact on the energy cost of public transport companies and the stability of the power system. Therefore, this paper proposes a charging and discharging optimization model for electric buses to participate in the carbon trading market and the peak shaving auxiliary service market. The upper model aims to minimize the total daily energy cost of the bus company, and the lower model is optimized based on the upper optimization results to minimize grid load fluctuations. Then, the scheme proposed in this paper is compared with disordered charging and orderly charging based on time of use price. Finally, a bus company in Beijing is taken as an example of simulation. The results show that compared with the disordered charging and the orderly charging based on time of use price, the charging and discharging schemes proposed in this paper can reduce the daily energy cost of 6052.68 yuan and 617.42 yuan respectively, reduce the fluctuation of power grid load by 92.14 % and 13.85 %, and reduce the carbon emission of the "bus power" combined system by 3930 kg and 2080 kg. [ABSTRACT FROM AUTHOR]

• The coupling measure between network and demand structure are defined. • A bi-level model is formulated to handle reversible lane network design problems. • A heuristic algorithm is designed to solve the approximate optimal solution. • The effectiveness of the proposed approaches is verified by numerical experiments. This paper investigates demand-originated reversible lane design plans for transportation networks, where the tidal and asymmetrical characteristics of the demand structure are taken into consideration. Compared to traditional network design methods, this reversible lane technique has some advantages. It not only can improve the performance of the considered network by taking full advantage of the potential resources and existing infrastructures but is also easy to operate, returns rapidly, is less costly and requires no additional land. Specifically, this study first defines a coupling measure to quantify the relationship between network structure and demand structure. Then, focusing on maximizing the coupling measure, a nonlinear bilevel mixed-integer programming model is presented to find the optimal lane combination strategy in the considered network from the viewpoint of systematology. For solving the proposed model, we design heuristic algorithms to obtain its approximate optimal solution. Finally, the effectiveness of the proposed approaches is examined on the Sioux Falls network and Beijing main road network. [ABSTRACT FROM AUTHOR]