scholarly journals Optimization-Driven Scenario Grouping

2020 ◽  
Vol 32 (3) ◽  
pp. 805-821
Author(s):  
Kevin Ryan ◽  
Shabbir Ahmed ◽  
Santanu S. Dey ◽  
Deepak Rajan ◽  
Amelia Musselman ◽  
...  
Keyword(s):  
Large Scale ◽  
Unit Commitment ◽  
Standard Test ◽  
Mixed Integer ◽  
Feasible Region ◽  
Decomposition Algorithms ◽  
Two Stage ◽  
Stochastic Programs ◽  
Stochastic Unit Commitment ◽  
Nonanticipativity Constraints

Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. We develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups. Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.

scholarly journals A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3777
Author(s):  
Cristian Camilo Marín-Cano ◽  
Juan Esteban Sierra-Aguilar ◽  
Jesús M. López-Lezama ◽  
Álvaro Jaramillo-Duque ◽  
Juan G. Villegas
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Reaction Times ◽  
Mixed Integer ◽  
Feasible Region ◽  
Solution Strategy ◽  
Unit Commitment Problem ◽  
Joint Strategy

The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied.

scholarly journals Two-stage minimax stochastic unit commitment

2018 ◽  
Vol 12 (4) ◽  
pp. 947-956 ◽  
Author(s):  
Ping Che ◽  
Lixin Tang ◽  
Jianhui Wang
Keyword(s):  
Unit Commitment ◽  
Two Stage ◽  
Stochastic Unit Commitment

A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

scholarly journals Backbone—An Adaptable Energy Systems Modelling Framework

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3388 ◽  
Author(s):  
Niina Helistö ◽  
Juha Kiviluoma ◽  
Jussi Ikäheimo ◽  
Topi Rasku ◽  
Erkka Rinne ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Unit Commitment ◽  
Energy Systems ◽  
Mixed Integer ◽  
Investment Planning ◽  
Planning And Scheduling ◽  
Modelling Framework ◽  
Wide Range ◽  
Systems Modelling

Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.

The Optimal Controlled Partitioning Strategy for Power System Based on Master-Slave Problem

2013 ◽  
Vol 385-386 ◽  
pp. 999-1006
Author(s):  
Wei Wang ◽  
Ting Yu ◽  
Tian Jiao Pu ◽  
Ai Zhong Tian ◽  
Ji Keng Lin
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Mixed Integer ◽  
Master Problem ◽  
Decomposition Algorithms ◽  
Integer Programming Problem ◽  
Complete Model ◽  
New Approach ◽  
Mixed Integer Programming Problem ◽  
Partitioning Strategy

Controlled partitioning strategy is one of the effective measures taken for the situation when system out-of-step occurs. The complete splitting model, mostly solved by approximate decomposition algorithms, is a large-scale nonlinear mixed integer programming problem. A new alternate optimization method based on master-slave problem to search for optimal splitting strategy is proposed hereby. The complete model was converted into master-slave problems based on CGKP (Connected Graph Constrained Knapsack Problem). The coupling between master problem and slave problem is achieved through load adjustment. A better splitting strategy can be obtained through the alternating iteration between the master problem and the salve problem. The results of the examples show that the method can obtain better splitting strategy with less shed load than other approximate algorithms, which verifies the feasibility and effectiveness of the new approach presented.

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