scholarly journals A novel optimization approach for sub-hourly unit commitment with large numbers of units and virtual transactions

2021 ◽  
Author(s):  
JIANGHUA WU ◽  
Peter Luh ◽  
Yonghong Chen ◽  
Mikhail Bragin ◽  
Bing Yan
Keyword(s):  
Unit Commitment ◽  
Computation Time ◽  
Poor Performance ◽  
Convergence Condition ◽  
Branch And Cut ◽  
Ordinal Optimization ◽  
Optimization Approach ◽  
Power System Operations ◽  
Large Numbers ◽  
Practice Method

Unit Commitment (UC) is an important problem in power system operations. It is traditionally scoped for 24 hours with one-hour time intervals. To improve system flexibility by accommodating the increasing net-load variability, sub-hourly UC has been suggested. Such a problem is larger and more complicated than hourly UC because of the increased number of periods and reduced unit ramping capabilities per period. The computational burden is further exacerbated for systems with large numbers of virtual transactions leading to dense transmission constraints matrices. Consequently, the state-of-the-art and practice method, branch-and-cut (B&C), suffers from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) is enhanced by embedding ordinal-optimization concepts for a drastic reduction in subproblem solving time. Rather than formally solving subproblems by using B&C, subproblem solutions that satisfy SAVLR’s convergence condition are obtained by modifying solutions from previous iterations or solving crude subproblems. All virtual transactions are included in each subproblem to reduce major changes in solutions across iterations. A parallel version is also developed to further reduce the computation time. Testing on MISO’s large cases demonstrates that our ordinal-optimization embedded approach obtains near-optimal solutions efficiently, is robust, and provides a new way on solving other MILP problems.

scholarly journals A novel optimization approach for sub-hourly unit commitment with large numbers of units and virtual transactions

2021 ◽  
Author(s):  
JIANGHUA WU ◽  
Peter Luh ◽  
Yonghong Chen ◽  
Mikhail Bragin ◽  
Bing Yan
Keyword(s):  
Unit Commitment ◽  
Computation Time ◽  
Poor Performance ◽  
Convergence Condition ◽  
Branch And Cut ◽  
Ordinal Optimization ◽  
Optimization Approach ◽  
Power System Operations ◽  
Large Numbers ◽  
Practice Method

Unit Commitment (UC) is an important problem in power system operations. It is traditionally scoped for 24 hours with one-hour time intervals. To improve system flexibility by accommodating the increasing net-load variability, sub-hourly UC has been suggested. Such a problem is larger and more complicated than hourly UC because of the increased number of periods and reduced unit ramping capabilities per period. The computational burden is further exacerbated for systems with large numbers of virtual transactions leading to dense transmission constraints matrices. Consequently, the state-of-the-art and practice method, branch-and-cut (B&C), suffers from poor performance. In this paper, our recent Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) is enhanced by embedding ordinal-optimization concepts for a drastic reduction in subproblem solving time. Rather than formally solving subproblems by using B&C, subproblem solutions that satisfy SAVLR’s convergence condition are obtained by modifying solutions from previous iterations or solving crude subproblems. All virtual transactions are included in each subproblem to reduce major changes in solutions across iterations. A parallel version is also developed to further reduce the computation time. Testing on MISO’s large cases demonstrates that our ordinal-optimization embedded approach obtains near-optimal solutions efficiently, is robust, and provides a new way on solving other MILP problems.

scholarly journals Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 495
Author(s):  
Jessica Thomsen ◽  
Noha Saad Hussein ◽  
Arnold Dolderer ◽  
Christoph Kost
Keyword(s):  
Computation Time ◽  
Energy Systems ◽  
Energy System ◽  
Variable Cost ◽  
Optimization Approach ◽  
Or Efficiency ◽  
Electricity Grid ◽  
Regional Energy ◽  
Long Run ◽  
Regional Energy Systems

Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into demand reduction or efficiency with high upfront costs as it cannot compute the benefits over the time span necessary for full cost recovery. These aspects should be considered when choosing the foresight horizon.

scholarly journals Comparative Assessment of Priority Listing and Mixed Integer Linear Programming Unit Commitment Methods for Non-Interconnected Island Systems

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 657 ◽  
Author(s):  
Georgios Psarros ◽  
Stavros Papathanassiou
Keyword(s):  
Linear Programming ◽  
Power Systems ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Droop Characteristic

The generation management concept for non-interconnected island (NII) systems is traditionally based on simple, semi-empirical operating rules dating back to the era before the massive deployment of renewable energy sources (RES), which do not achieve maximum RES penetration, optimal dispatch of thermal units and satisfaction of system security criteria. Nowadays, more advanced unit commitment (UC) and economic-dispatch (ED) approaches based on optimization techniques are gradually introduced to safeguard system operation against severe disturbances, to prioritize RES participation and to optimize dispatch of the thermal generation fleet. The main objective of this paper is to comparatively assess the traditionally applied priority listing (PL) UC method and a more sophisticated mixed integer linear programming (MILP) UC optimization approach, dedicated to NII power systems. Additionally, to facilitate the comparison of the UC approaches and quantify their impact on systems security, a first attempt is made to relate the primary reserves capability of each unit to the maximum acceptable frequency deviation at steady state conditions after a severe disturbance and the droop characteristic of the unit’s speed governor. The fundamental differences between the two approaches are presented and discussed, while daily and annual simulations are performed and the results obtained are further analyzed.

scholarly journals Hyper-optimized tensor network contraction

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 410
Author(s):  
Johnnie Gray ◽  
Stefanos Kourtis
Keyword(s):  
Computation Time ◽  
Quantum Circuit ◽  
Optimization Approach ◽  
Many Body ◽  
Tensor Networks ◽  
Randomized Protocols ◽  
Classical Simulation ◽  
Tensor Network ◽  
Speed Up ◽  
Many Body Systems

Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a variety of benchmarks, including the random quantum circuit instances recently implemented on Google quantum chips. We find that the paths obtained can be very close to optimal, and often many orders or magnitude better than the most established approaches. As different underlying geometries suit different methods, we also introduce a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding. The increase in quality of contraction schemes found has significant practical implications for the simulation of quantum many-body systems and particularly for the benchmarking of new quantum chips. Concretely, we estimate a speed-up of over 10,000× compared to the original expectation for the classical simulation of the Sycamore `supremacy' circuits.

scholarly journals An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

2011 ◽  
Vol 62 (1) ◽  
pp. 11-18 ◽  
Author(s):  
C. Christober ◽  
Asir Rajan
Keyword(s):  
Tabu Search ◽  
Unit Commitment ◽  
Computation Time ◽  
Evolutionary Programming ◽  
Search Method ◽  
Unit Commitment Problem ◽  
New Approach ◽  
Commitment Problem ◽  
Tabu Search Method ◽  
The Cost

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints This paper presents a new approach to solve the short-term unit commitment problem using An Evolutionary Programming Based tabu search method with cooling and banking constraints. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, lagrangian relaxation.

Hybrid Optimization Approach for Evaluating Photovoltaic Cell Parameters

2013 ◽  
Vol 373-375 ◽  
pp. 1261-1264
Author(s):  
Mei Ying Ye
Keyword(s):  
Cell Model ◽  
Computation Time ◽  
Photovoltaic Cell ◽  
Evaluation Procedure ◽  
Model Parameters ◽  
Optimization Approach ◽  
Problem Space ◽  
Local Optima ◽  
Cell Parameters ◽  
Intelligent Technique

A new hybrid intelligent technique is proposed to evaluate photovoltaic cell model parameters in this paper. The intelligent technique is based on a hybrid of genetic algorithm (GA) and LevenbergMarquardt algorithm (LMA). In the proposed hybrid intelligent technique, the GA firstly searches the entire problem space to get a set of roughly estimated solutions, i.e. near-optimal solutions. Then the LMA performs a local optima search in order to carry out further optimizations. An example has been used to demonstrate the evaluation procedure in order to test the performance of the proposed approach. The results show that the proposed technique has better performance than the GA approach in terms of the objective function value, the computation time and the reconstructedI-Vcurve shape.

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