Robust Optimization Approach to Process Flexibility Designs with Price Differentials

2018 ◽  
Author(s):  
Shixin Wang ◽  
Xuan Wang ◽  
Jiawei Zhang
Keyword(s):  
Robust Optimization ◽  
Optimization Approach ◽  
Process Flexibility ◽  
Price Differentials

scholarly journals Robust Optimization Approach to Process Flexibility Designs with Contribution Margin Differentials

2020 ◽  
Author(s):  
Shixin Wang ◽  
Xuan Wang ◽  
Jiawei Zhang
Keyword(s):  
Robust Optimization ◽  
Performance Measures ◽  
Broad Class ◽  
Problem Definition ◽  
Partial Ordering ◽  
Group Index ◽  
Optimization Approach ◽  
Worst Case ◽  
Process Flexibility ◽  
Long Chain

Problem definition: The theoretical investigation of the effectiveness of limited flexibility has mainly focused on a performance metric that is based on the maximum sales in units. However, this could lead to substantial profit losses when the maximum sales metric is used to guide flexibility designs while the products have considerably large profit margin differences. Academic/practical relevance: We address this issue by introducing margin differentials into the analysis of process flexibility designs, and our results can provide useful guidelines for the evaluation and design of flexibility configurations when the products have heterogeneous margins. Methodology: We adopt a robust optimization framework and study process flexibility designs from the worst-case perspective by introducing the dual margin group index (DMGI). Results and managerial implications: We show that a general class of worst-case performance measures can be expressed as functions of a design’s DMGIs and the given uncertainty set. Moreover, the DMGIs lead to a partial ordering that enables us to compare the worst-case performance of different designs. Applying these results, we prove that under the so-called partwise independently symmetric uncertainty sets and a broad class of worst-case performance measures, the alternate long-chain design is optimal among all long-chain designs with equal numbers of high-profit products and low-profit products. Finally, we develop a heuristic based on the DMGIs to generate effective flexibility designs when products exhibit margin differentials.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

STOCHASTIC MODEL PREDICTIVE CONTROL FOR SPACECRAFT RENDEZVOUS AND DOCKING VIA A DISTRIBUTIONALLY ROBUST OPTIMIZATION APPROACH

2021 ◽  
pp. 1-19
Author(s):  
ZUOXUN LI ◽  
KAI ZHANG
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Robust Optimization ◽  
Predictive Control ◽  
Attitude Control ◽  
Optimization Approach ◽  
Distributionally Robust Optimization ◽  
Distributionally Robust ◽  
Stochastic Model Predictive Control ◽  
Rendezvous And Docking

Abstract A stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.

scholarly journals A Bidding Model for a Virtual Power Plant via Robust Optimization Approach

2017 ◽  
Vol 95 ◽  
pp. 15001
Author(s):  
Tianxiang Geng ◽  
Li Xiang ◽  
Maosheng Ding ◽  
Feng Li
Keyword(s):  
Power Plant ◽  
Robust Optimization ◽  
Optimization Approach ◽  
Virtual Power ◽  
Virtual Power Plant
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