scholarly journals A Robust Optimization Model to the Day-Ahead Operation of an Electric Vehicle Aggregator Providing Reliable Reserve

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7456
Author(s):  
Antonio Jiménez-Marín ◽  
Juan Pérez-Ruiz
Keyword(s):  
Robust Optimization ◽  
Real Time ◽  
Electric Vehicle ◽  
Optimization Model ◽  
Linear Problem ◽  
The Other ◽  
Mixed Integer ◽  
Robust Approach ◽  
Proposed Model

This paper presents a robust optimization model to find out the day-ahead energy and reserve to be scheduled by an electric vehicle (EV) aggregator. Energy can be purchased from, and injected to, the distribution network, while upward and downward reserves can be also provided by the EV aggregator. Although it is an economically driven model, the focus of this work relies on the actual availability of the scheduled reserves in a future real-time. To this end, two main features stand out: on one hand, the uncertainty regarding the EV driven pattern is modeled through a robust approach and, on the other hand, a set of non-anticipativity constraints are included to prevent from unavailable future states. The proposed model is posed as a mixed-integer robust linear problem in which binary variables are used to consider the charging, discharging or idle status of the EV aggregator. Results over a 24-h case study show the capability of the proposed model.

scholarly journals A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingyou Yan ◽  
Qian Zhang ◽  
Xin Zou
Keyword(s):  
Optimization Model ◽  
Cost Optimization ◽  
Resource Usage ◽  
Project Duration ◽  
Resource Demand ◽  
Resource Leveling ◽  
Proposed Model ◽  
Liquidated Damages ◽  
The Relationship

The study of traditional resource leveling problem aims at minimizing the resource usage fluctuations and obtaining sustainable resource supplement, which is accomplished by adjusting noncritical activities within their start and finish time. However, there exist limitations in terms of the traditional resource leveling problem based on the fixed project duration. This paper assumes that the duration can be changed in a certain range and then analyzes the relationship between the scarce resource usage fluctuations and project cost. This paper proposes an optimization model for the multiresource leveling problem. We take into consideration five kinds of cost: the extra hire cost when the resource demand is greater than the resource available amount, the idle cost of resource when the resource available amount is greater than the resource demand, the indirect cost related to the duration, the liquidated damages when the project duration is extended, and the incentive fee when the project duration is reduced. The optimal objective of this model is to minimize the sum of the aforementioned five kinds of cost. Finally, a case study is examined to highlight the characteristic of the proposed model at the end of this paper.

Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

2021 ◽  
Author(s):  
Lei Xu ◽  
Tsan Sheng (Adam) Ng ◽  
Alberto Costa
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Rail Transit ◽  
Distributionally Robust Optimization ◽  
Performance Function ◽  
Transit Networks ◽  
Disruption Tolerance ◽  
Distributionally Robust

In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.

Efficient and Robust Approach for Heartbeat Detection of ECG Signal

2019 ◽  
Vol 29 (08) ◽  
pp. 2050133
Author(s):  
Anas Fouad Ahmed ◽  
Mohammed Abdulmunem Ahmed ◽  
Hussain Mustafa Bierk
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Suggested Method ◽  
The Other ◽  
Ecg Signal ◽  
Excellent Performance ◽  
Robust Approach ◽  
Different Types ◽  
Three Stages ◽  
Electrocardiogram Ecg

This paper introduces an efficient and robust method for heartbeat detection based on the calculated angles between the successive samples of electrocardiogram (ECG) signal. The proposed approach involves three stages: filtering, computing the angles of the signal and thresholding. The suggested method is applied to two different types of ECG databases (QTDB and MIT-BIH). The results were compared with the other algorithms suggested in previous works. The proposed approach outperformed the other algorithms, in spite of its simplicity and their fast calculations. These features make it applicable in real-time ECG diagnostics systems. The suggested method was implemented in real-time using a low cost ECG acquisition system and it shows excellent performance.

scholarly journals Multimodal Capacitated Hub Location Problems with Multi-Commodities: An Application in Freight Transport

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Alan Osorio-Mora ◽  
Francisco Núñez-Cerda ◽  
Gustavo Gatica ◽  
Rodrigo Linfati
Keyword(s):  
Latin American ◽  
Location Problems ◽  
Transport Cost ◽  
Mixed Integer ◽  
Hub Location ◽  
Proposed Model ◽  
Multimodal Transport ◽  
Two Factors ◽  
Commercial Solver

Hub location problems (HLPs) support decision making on multimodal transport strategic planning. It is related to the location of hubs and the allocation of origin/destination (O/D) flow in a system. Classical formulations assume that these flows are predefined paths and direct delivery is not available. This applied research presents a mixed integer linear programming (MILP) model for a capacitated multimodal, multi-commodity HLP. Furthermore, an application on the export process in a Latin American country is detailed. The new proposed model, unlike the traditional HLP, allows direct shipment, and its O/D flows are part of the decision model. Situations with up to 100 nodes, six products, and two transport modes are used, working with initial and projected flows. All instances can be solved optimally using the commercial solver, Gurobi 7.5.0, in computational times less than a minute. Results indicate that only one hub is profitable for the case study, both for the initial and projected scenarios. The installation of a hub generates transport savings over 1% per year. Two factors affect the location decision: low concentration and distance between the hubs and destinations. Long distances involve an exhaustive use of trains instead of trucks, which leads to lower transport cost per unit.

Mixed-integer nonlinear optimization for district heating network expansion

2020 ◽  
Vol 68 (12) ◽  
pp. 985-1000
Author(s):  
Marius Roland ◽  
Martin Schmidt
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Model ◽  
Thermal Effects ◽  
Nonlinear Models ◽  
Valid Inequalities ◽  
Average Power ◽  
District Heating ◽  
Mixed Integer ◽  
Mixed Integer Nonlinear Optimization

AbstractWe present a mixed-integer nonlinear optimization model for computing the optimal expansion of an existing tree-shaped district heating network given a number of potential new consumers. To this end, we state a stationary and nonlinear model of all hydraulic and thermal effects in the pipeline network as well as nonlinear models for consumers and the network’s depot. For the former, we consider the Euler momentum and the thermal energy equation. The thermal aspects are especially challenging. Here, we develop a novel polynomial approximation that we use in the optimization model. The expansion decisions are modeled by binary variables for which we derive additional valid inequalities that greatly help to solve the highly challenging problem. Finally, we present a case study in which we identify three major aspects that strongly influence investment decisions: the estimated average power demand of potentially new consumers, the distance between the existing network and the new consumers, and thermal losses in the network.

scholarly journals OPTIMIZING THE CONTAINER TRUCK PATHS WITH UNCERTAIN TRAVEL TIME IN CONTAINER PORTS

Transport ◽  
2021 ◽  
Vol 36 (6) ◽  
pp. 444-462
Author(s):  
Jiaming Liu ◽  
Bin Yu ◽  
Wenxuan Shan ◽  
Baozhen Yao ◽  
Yao Sun
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Programming Model ◽  
Mixed Integer ◽  
A Algorithm ◽  
Shuffled Complex Evolution ◽  
Proposed Model ◽  
Container Ports ◽  
Yard Template

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

Integrating Price-Incentive and Trip-Selection Policies to Rebalance Shared Electric Vehicles

2020 ◽  
Vol 12 (4) ◽  
pp. 148-173
Author(s):  
Zihao Jiao ◽  
Lun Ran ◽  
Xin Liu ◽  
Yuli Zhang ◽  
Robin G. Qiu
Keyword(s):  
Electric Vehicle ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Exact Algorithm ◽  
Labor Cost ◽  
Environmental Benefits ◽  
Mixed Integer ◽  
Fleet Size ◽  
Integrated Strategy

Because electric vehicle sharing (EVS) offers the advantages of high flexibility and convenience, it has been receiving increasing attention worldwide as an effective approach to easing traffic congestion and environmental pollution. However, unbalanced electric vehicle distribution is an obstacle in the development of EVS. In this paper, we propose an integrated strategy to mitigate the imbalance issue and enhance customers’ adoption of EVS. We construct an integrated strategy that combines the price-incentive approach with the trip-selection policy and models uncertain travel demand in a continuous trip-adopting process based on our integrated strategy. Aiming to improve EVS operating profits, we apply spatiotemporal nonlinear mixed-integer programming to formulate the travel pricing and rebalancing plan. Additionally, we approximate the model in a tractable form after analyzing the optimal service adoption and develop an efficient exact algorithm to handle the nonlinear items. The computational results of a real-world car2go Amsterdam case study demonstrate several economic and environmental benefits generated by our integrated policy, including (i) higher profits for EVS operators, (ii) improved service satisfaction for consumers, and (iii) a higher level of carbon emissions reduction, from 381 grams per mile to 225 grams per mile, beneficial for the social environment. Moreover, according to the case study, an appropriate initial fleet size, high rebalancing frequency, low labor cost, high potential travel demands, and short charging time also benefit EVS operation.

scholarly journals Probabilistic Model for Real-Time Flood Operation of a Dam Based on a Deterministic Optimization Model

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3206
Author(s):  
Víctor Cuevas-Velásquez ◽  
Alvaro Sordo-Ward ◽  
Jaime H. García-Palacios ◽  
Paola Bianucci ◽  
Luis Garrote
Keyword(s):  
Real Time ◽  
Optimization Model ◽  
Flood Control ◽  
Evaluation Method ◽  
Risk Index ◽  
Mixed Integer ◽  
Global Risk ◽  
Case Based Learning ◽  
Milp Model ◽  
Conventional Models

This paper presents a real-time flood control model for dams with gate-controlled spillways that brings together the advantages of an optimization model based on mixed integer linear programming (MILP) and a case-based learning scheme using Bayesian Networks (BNets). A BNet model was designed to reproduce the causal relationship between inflows, outflows and reservoir storage. The model was trained with synthetic events generated with the use of the MILP model. The BNet model produces a probabilistic description of recommended dam outflows over a time horizon of 1 to 5 h for the Talave reservoir in Spain. The results of implementing the BNet recommendation were compared against the results obtained while applying two conventional models: the MILP model, which assumes full knowledge of the inflow hydrograph, and the Volumetric Evaluation Method (VEM), a method widely used in Spain that works in real-time, but without any knowledge of future inflows. In order to compare the results of the three methods, the global risk index (Ir) was computed for each method, based on the simulated behavior for an ensemble of hydrograph inflows. The Ir values associated to the 2 h-forecast BNet model are lower than those obtained for VEM, which suggests improvement over standard practice. In conclusion, the BNet arises as a suitable and efficient model to support dam operators for the decision making process during flood events.

scholarly journals Robust Optimization Model with Shared Uncertain Parameters in Multi-Stage Logistics Production and Inventory Process

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 211
Author(s):  
Lijun Xu ◽  
Yijia Zhou ◽  
Bo Yu
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Model ◽  
Multi Stage ◽  
Proposed Model ◽  
Dual Forms

In this paper, we focus on a class of robust optimization problems whose objectives and constraints share the same uncertain parameters. The existing approaches separately address the worst cases of each objective and each constraint, and then reformulate the model by their respective dual forms in their worst cases. These approaches may result in that the value of uncertain parameters in the optimal solution may not be the same one as in the worst case of each constraint, since it is highly improbable to reach their worst cases simultaneously. In terms of being too conservative for this kind of robust model, we propose a new robust optimization model with shared uncertain parameters involving only the worst case of objectives. The proposed model is evaluated for the multi-stage logistics production and inventory process problem. The numerical experiment shows that the proposed robust optimization model can give a valid and reasonable decision in practice.

Sign in / Sign up

Export Citation Format

Share Document