Bi-level optimization model applications in managing air emissions from ships:A review

Ship air emissions are recognized as one of the key concerns of the maritime industry.Competent authorities have issued various regulations to manage air emissions from ships.Although the authorities are policy makers,the effectiveness of policies is up to the shipping industry who operates the vessels and terminals to fulfill maritime transportation works.Given this characteristic,bi-level optimization model has been widely adopted in studies that optimize policy design or evaluate its effectiveness.The framework of a typical bi-level optimization model for ship emission management problem is given to show the basic structure of similar issues.A series of applications of bi-level optimization model in managing ship emissions is reviewed,including cases of Energy Efficiency Design Index,Emissions Control Area,Market Based Measure,Carbon Intensity Indicator,and Vessel Speed Reduction Incentive Program.We hope this paper can enlighten scholars interested in this area and provide help for them.

Optimal Nonlinear Distance Toll for Cordon-Based Congestion Pricing Considering Equity Issue

In order to address the optimal distance toll design problem for cordon-based congestion pricing incorporating the issue of equity,this paper presents a toll user equilibrium( TUE) model based on a transformed network with elastic demand,to evaluate any given toll charge function. A bi-level programming model is developed for determining the optimal toll levels,with the TUE being represented at the lower level.The upper level optimizes the total equity level over the transport network,represented by the Gini coefficient,where a constraint is imposed to the total travel impedance of each OD pair after the levy. A genetic algorithm( GA) is implemented to solve the bi-level model,which is verified by a numerical example.

Coordinated optimal allocation of water resources and industrial structure in the Beijing-Tianjin-Hebei regions of China

Optimizing the allocation of water resources is critical for promoting the optimization and upgrading of industrial structure and coordinated development in the Beijing-Tianjin-Hebei regions of China.Based on specific regional and water conditions,to strengthen the constraints on water resources,the“three-step”adaptive management approach of“scheme design-scheme diagnosis-scheme optimization”of water resource allocation are adopted to facilitate the coordinated optimal allocation of water resources and industrial structure in the Beijing-Tianjin-Hebei regions.First,from the level of overall industry,a water resource allocation scheme for the regions is designed by applying the master-slave hierarchical mode and a bi-level optimal model to determine the ideal amount of water resource allocation for the regions and respective industries.Second,the diagnostic criteria of spatial balance,structural matching,and coordinated development are constructed to determine the rationality of the water resource allocation scheme.Then a benefit compensation function with water market transactions is developed,to adaptively adjust the water resource allocation scheme.Finally,the optimization and upgrading of industrial structure are promoted to improve water consumption efficiency and the coordinated development of the Beijing-Tianjin-Hebei regions.The study can provide reference for the Beijing-Tianjin-Hebei regions to realize the comprehensive optimal allocation of water resources in the regions and improve the adaptability of water resources and industrial structure optimization.

Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system.Mobile energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability.However,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of MESS.This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power networks.The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the system.With fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of MESS.Therefore,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme.Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model.The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion.Besides,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

A quantitative and systematic methodology to investigate energy consumption issues in multimodal intercity transportation systems

Energy issues in transportation systems have garnered increasing attention recently.This study proposes a systematic methodology for policy-makers to minimize energy consumption in multimodal intercity transportation systems considering suppliers’operational constraints and travelers’mobility requirements.A bi-level optimization model is developed for this purpose and considers the air,rail,private auto,and transit modes.The upper-level model is a mixed integer nonlinear program aiming to minimize energy consumption subject to transportation suppliers’operational constraints and traffic demand distribution to paths resulting from the lower-level model.The lower-level model is a linear program seeking to maximize the trip utilities of travelers.The interactions between the multimodal transportation suppliers and intercity traffic demand are considered under the goal of minimizing system energy consumption.The proposed bi-level mixed integer model is relaxed and transformed into a mathematical program with complementarity constraints,and solved using a customized branch-and-bound algorithm.Numerical experiments,conducted using multimodal travel options between Lafayette,Indiana and Washington,D.C.reiterate that shifting traffic demand from private cars to the transit and rail modes significantly reduce energy consumption.Moreover,the proposed methodology provides tools to quantitatively analyze system energy consumption and traffic demand distribution among transportation modes under specific policy instruments.The results illustrate the need to systematically incorporate the interactions among traveler preferences,network structure,and supplier operational schemes to provide policy-makers insights for developing traffic demand shift mechanisms to minimize system energy consumption.Hence,the proposed methodology provide policy-makers the capability to analyze energy consumption in the transportation sector by a holistic approach.

Conic Optimal Energy Flow of Integrated Electricity and Natural Gas Systems

In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGSs). Conic alternating current optimal power flow (ACOPF) is formulated in the upper-level model, and the generation cost of natural gas fired generation units (NGFGUs) is calculated based on natural gas locational marginal prices (NG-LMPs). The market clearing process of natural gas system is modeled in the lower-level model. The bi-level model is then transferred into a mixed-integer second-order cone programming (MISOCP) problem. Simulation results demonstrate the effectiveness of the proposed conic OEF model.

A Chaotic Approach for the Bi-level Discrete Equilibrium Network Design Problem

A chaotic algorithm for providing a solution to the bi-level Discrete Equilibrium Network Design Problem （NDP） is discussed following an introduction of the Discrete Network Design Problem （DNDP） model and Chaos Optimization Algorithms （COA）. A description of the chaotic approach for the DNDP model is described in details. Then a numerical example for the DNDP is carried out to investigate the chaotic approach. The results have been encouraging, indicating that the chaotic approach has great potential ability in finding the optimal solution of DNDP models.

Planning and Real-time Pricing of EV Charging Stations Considering Social Welfare and Profitability Balance

This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.