scholarly journals Bi-Level Optimization for Electricity Transaction in Smart Community With Modular Pump Hydro Storage

2020 ◽  
Author(s):  
Yang Chen ◽  
Xiao Kou ◽  
Mohammed Olama ◽  
Helia Zandi ◽  
Chenang Liu ◽  
...  
Keyword(s):  
Stackelberg Game ◽  
Cost Minimization ◽  
Necessary Optimality Conditions ◽  
Local Energy ◽  
Energy Prices ◽  
Profit Distribution ◽  
Level Model ◽  
Upper Level ◽  
Smart Community ◽  
Best Responses

Abstract Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.

scholarly journals Hierarchical Scheduling Scheme for AC/DC Hybrid Active Distribution Network Based on Multi-Stakeholders

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2830 ◽  
Author(s):  
Chang Ye ◽  
Shihong Miao ◽  
Yaowang Li ◽  
Chao Li ◽  
Lixing Li
Keyword(s):  
Optimization Model ◽  
Distribution Network ◽  
Hierarchical Optimization ◽  
Active Distribution Network ◽  
Scheduling Scheme ◽  
Multi Stage ◽  
Flexible Loads ◽  
Level Model ◽  
Load Regulation ◽  
Upper Level

This paper presents a hierarchical multi-stage scheduling scheme for the AC/DC hybrid active distribution network (ADN). The load regulation center (LRC) is considered in the developed scheduling strategy, as well as the AC and DC sub-network operators. They are taken to be different stakeholders. To coordinate the interests of all stakeholders, a two-level optimization model is established. The flexible loads are dispatched by LRC in the upper-level optimization model, the objective of which is minimizing the loss of the entire distribution network. The lower-level optimization is divided into two sub-optimal models, and they are carried out to minimize the operating costs of the AC/DC sub-network operators respectively. This two-level model avoids the difficulty of solving multi-objective optimization and can clarify the role of various stakeholders in the system scheduling. To solve the model effectively, a discrete wind-driven optimization (DWDO) algorithm is proposed. Then, considering the combination of the proposed DWDO algorithm and the YALMIP toolbox, a hierarchical optimization algorithm (HOA) is developed. The HOA can obtain the overall optimization result of the system through the iterative optimization of the upper and lower levels. Finally, the simulation results verify the effectiveness of the proposed scheduling scheme.

scholarly journals Solving Signal Control Problems with Second-Order Sensitivity Information of Equilibrium Network Flows

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Hsun-Jung Cho ◽  
You-Heng Huang
Keyword(s):  
Control Problem ◽  
Network Flows ◽  
Stackelberg Game ◽  
Nonlinear Approximation ◽  
Second Order ◽  
Signal Control ◽  
Reaction Function ◽  
Lower Level Problem ◽  
Level Problem ◽  
Upper Level

The equilibrium network signal control problem is represented as a Stackelberg game. Due to the characteristics of a Stackelberg game, solving the upper-level problem and lower-level problem iteratively cannot be expected to converge to the solution. The reaction function of the lower-level problem is the key information to solve a Stackelberg game. Usually, the reaction function is approximated by the network sensitivity information. This paper firstly presents the general form of the second-order sensitivity formula for equilibrium network flows. The second-order sensitivity information can be applied to the second-order reaction function to solve the network signal control problem efficiently. Finally, this paper also demonstrates two numerical examples that show the computation of second-order sensitivity and the speed of convergence of the nonlinear approximation algorithm.

scholarly journals Bi-Level Operation Scheduling of Distribution Systems with Multi-Microgrids Considering Uncertainties

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1441
Author(s):  
Saeid Esmaeili ◽  
Amjad Anvari-Moghaddam ◽  
Erfan Azimi ◽  
Alireza Nateghi ◽  
João P. S. Catalão
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Point Of View ◽  
Mixed Integer ◽  
Wholesale Market ◽  
System Operator ◽  
Level Model ◽  
Upper Level ◽  
Operation Scheduling

A bi-level operation scheduling of distribution system operator (DSO) and multi-microgrids (MMGs) considering both the wholesale market and retail market is presented in this paper. To this end, the upper-level optimization problem minimizes the total costs from DSO’s point of view, while the profits of microgrids (MGs) are maximized in the lower-level optimization problem. Besides, a scenario-based stochastic programming framework using the heuristic moment matching (HMM) method is developed to tackle the uncertain nature of the problem. In this regard, the HMM technique is employed to model the scenario matrix with a reduced number of scenarios, which is effectively suitable to achieve the correlations among uncertainties. In order to solve the proposed non-linear bi-level model, Karush–Kuhn–Tucker (KKT) optimality conditions and linearization techniques are employed to transform the bi-level problem into a single-level mixed-integer linear programming (MILP) optimization problem. The effectiveness of the proposed model is demonstrated on a real-test MMG system.

scholarly journals Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Hua Wang ◽  
Ling Xiao ◽  
Zhang Chen
Keyword(s):  
Network Design ◽  
Transportation Network ◽  
Bilevel Optimization ◽  
User Equilibrium ◽  
Emergency Vehicle ◽  
Stochastic Demands ◽  
Road Users ◽  
Level Model ◽  
Access Rights ◽  
Upper Level

We study transportation network design with stochastic demands and emergency vehicle (EV) lanes. Different from previous studies, this paper considers two groups of users, auto and EV travelers, whose road access rights are differentiated in the network, and addresses the value of incorporating inverse-direction lanes in network design. We formulate the problem as a bilevel optimization model, where the upper-level model aims to determine the optimal design of EV lanes and the lower-level model uses the user equilibrium principle to forecast the route choice of road users. A simulation-based genetic algorithm is proposed to solve the model. With numerical experiments, we demonstrate the value of deploying inverse-direction EV lanes and the computational efficiency of the proposed algorithm. We reach an intriguing finding that both regular and EV lane users can benefit from building EV lanes.

scholarly journals Bi-Level Model for Public Rail Transportation under Incomplete Data

2017 ◽  
Vol 17 (3) ◽  
pp. 75-91 ◽  
Author(s):  
Kristina Pavlova ◽  
Todor Stoilov ◽  
Krasimira Stoilova
Keyword(s):  
Incomplete Data ◽  
Optimization Problem ◽  
Transportation Network ◽  
Flow Distribution ◽  
Rail Transport ◽  
Rail Transportation ◽  
Maximal Flow ◽  
Level Model ◽  
Upper Level ◽  
Transportation Flows

Abstract The increase of the utilization of public rail transportations is searched in directions for redistribution of the passenger travels between rail and bus transportation. The rail transport benefits by redistribution of the transportation flows on paths, predominantly supported by rails. The redistribution of the transportation is formalized by bi-level optimization problem. The upper level optimization estimates the maximal flow, which can be transported through a transportation network, supported both by bus and rail transports. The lower level optimization gives priority to the rail transport by decreasing the costs of flow distribution, using rail transport. This bi-level optimization problem was applied for the case of optimization of the rail exploitation in Bulgaria, defining priorities in transportation of the National transport scheme.

scholarly journals Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

2019 ◽  
Vol 11 (11) ◽  
pp. 3169 ◽  
Author(s):  
Ho-Chul Park ◽  
Yang-Jun Joo ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Byung-Jung Park
Keyword(s):  
South Korea ◽  
Injury Severity ◽  
The Elderly ◽  
Type I ◽  
Severe Injuries ◽  
Crash Data ◽  
Level Model ◽  
Multi Level ◽  
Pedestrian Crashes ◽  
Upper Level

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

scholarly journals The Shrinking of Beijing and the Rising of Xiong’an: Optimize Population Migration in terms of Transport Service

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Hongzhi Lin
Keyword(s):  
Bilevel Programming ◽  
Central Government ◽  
Programming Model ◽  
Service Level ◽  
User Equilibrium ◽  
Population Migration ◽  
Transport Service ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

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.

Integrated optimization of production planning and scheduling in uncertain re-entrance environment for fixed-position assembly workshops

2022 ◽  
pp. 1-18
Author(s):  
Nan-Yun Jiang ◽  
Hong-Sen Yan
Keyword(s):  
Genetic Algorithm ◽  
Production Planning ◽  
Programming Model ◽  
Expected Value ◽  
Fixed Position ◽  
Planning And Scheduling ◽  
Integrated Optimization ◽  
Production Planning And Scheduling ◽  
Level Model ◽  
Upper Level

For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.

Bi-Level Model for Design of Transit Short-Turning Service Considering Bus Crowding

2017 ◽  
Vol 2649 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Xiyu Yang ◽  
Yuxiong Ji ◽  
Yuchuan Du ◽  
H. Michael Zhang
Keyword(s):  
Optimal Design ◽  
Logit Model ◽  
Operational Cost ◽  
Time Cost ◽  
Total Cost ◽  
Proposed Model ◽  
Level Model ◽  
Upper Level ◽  
Vehicle Travel Time

A bi-level model was developed to design the short-turning strategy on a bus route. The upper-level model aimed at minimizing the total cost, including operational cost, passengers’ waiting time cost, and in-vehicle travel time cost. The lower-level model was a logit model to capture the service choices of passengers. The effects of bus crowding and seat availability were considered explicitly in the proposed model. An algorithm was developed to determine the frequencies of different services and the turnback points of the short-turning service. A case study demonstrates the superiority of the proposed model over alternative models. Sensitivity of the optimal design to seat capacity was also investigated.

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