Influence of different downstream users on single multipurpose reservoir operation by chance constraints

1988 ◽  
Vol 15 (4) ◽  
pp. 596-600
Author(s):  
S. Simonovic
Keyword(s):  
Chance Constraints ◽  
Programming Algorithm ◽  
Chance Constrained Programming ◽  
Control Space ◽  
Constrained Model ◽  
Constrained Programming ◽  
Release Strategy ◽  
Control Trajectory ◽  
Multipurpose Reservoir

A chance-constrained model is applied to solve the problem of long-term planning of the operation of a single multipurpose reservoir. Apart from the direct multipurpose use of water from the reservoir downstream, releases are available for use. The influence of downstream users is taken into account by a special form of objective function. Releases are bounded by the capacity of the outlet works above and by the guaranteed minimum from below. These two bounds are constraints on the control space. The model uses a chance-constrained programming algorithm to yield the control trajectory (releases) closest to the optimum. The properties of the chance-constrained program reveal a strong influence of changes of the control space on releases. This enables a release strategy to be derived which will increase the utilization of water from the reservoir. The application and results presented in this paper refer to the Prohor Reservoir on the Pcinja River in the Vardar River basin, Yugoslavia. Key words: multipurpose reservoir, mathematical modeling, optimization, chance constraints, control space.

scholarly journals Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4071
Author(s):  
António Sérgio Faria ◽  
Tiago Soares ◽  
Tiago Sousa ◽  
Manuel A. Matos
Keyword(s):  
Large Scale ◽  
Uncertainty Budget ◽  
Fast Response ◽  
Frequency Regulation ◽  
Test Case ◽  
Chance Constrained Programming ◽  
Risk Levels ◽  
Constrained Model ◽  
Constrained Programming ◽  
Reserve Market

The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

scholarly journals Distributed Robust Chance-Constrained Empty Container repositioning Optimization of the China Railway Express

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 706
Author(s):  
Lei Xing ◽  
Qi Xu ◽  
Jiaxin Cai ◽  
Zhihong Jin
Keyword(s):  
Partial Information ◽  
Service Level ◽  
Chance Constraints ◽  
Chance Constrained Programming ◽  
Intermodal Transportation ◽  
Empty Container ◽  
Total Cost ◽  
Empty Container Repositioning ◽  
China Railway ◽  
Constrained Programming

In order to reduce the total cost of empty container repositioning, a multi-period empty container repositioning optimization model of the China Railway Express was established by using distributed robust chance-constrained programming based on partial information such as mean and variance of demand. This established model considered sea-land intermodal transportation, uncertain empty container demand and foldable containers. To simplify the model, the distributed robust chance constraints were transformed into equivalent ones that could be easily solved, and the empty container demands were determined. Numerical experiments were carried out to analyze the influence of different parameters on the total cost. The results showed that the total cost could be greatly reduced by sea-land intermodal transportation. Using foldable containers could reduce the total cost of empty container repositioning. With the improvement of service level, the numbers of empty container repositioning increased owing to the distributional robust chance constraints. When standard and foldable containers were used simultaneously, the total cost could be greatly reduced by appropriately using foldable containers under three different supply–demand relationships of containers. The optimization results may provide a greatly feasible reference for the decision makers of the China Railway Express.

scholarly journals A simulation based research on chance constrained programming in robust facility location problem

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 87-96
Author(s):  
Leng Kaijun ◽  
Shi Wen ◽  
Song Guanghua ◽  
Pan Lin
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Chance Constraints ◽  
Random Fluctuation ◽  
Location Decisions ◽  
Chance Constrained Programming ◽  
Supply Uncertainty ◽  
Actual Operation ◽  
Constrained Programming

AbstractSince facility location decisions problem include long-term character and potential parameter variations, it is important to consider uncertainty in its modeling. This paper examines robust facility location problem considering supply uncertainty, in which we assume the supply of the facility in the actual operation is not equal to the supply initially established, the supply is subject to random fluctuation. The chance constraints are introduced when formulating the robust facility location model to make sure the system operate properly with a certain probability while the supply fluctuates. The chance constraints are approximated safely by using Hoeffding’s inequality and the problem is transformed to a general deterministic linear programming. Furthermore, how the facility location cost change with confidence level is investigated through a numerical example. The sensitivity analysis is conducted for important parameters of the model and we get the main factors that affect the facility location cost.

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