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 Using Generalized Generation Distribution Factors in a MILP Model to Solve the Transmission-Constrained Unit Commitment Problem

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2232 ◽  
Author(s):  
Guillermo Gutierrez-Alcaraz ◽  
Victor Hinojosa
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Mixed Integer ◽  
Transmission Network ◽  
Unit Commitment Problem ◽  
Linear Distribution ◽  
Technical Literature ◽  
Network Constraints ◽  
Milp Model

This study proposes a mixed-integer linear programming (MILP) model to figure out the transmission-constrained direct current (DC)-based unit commitment (UC) problem using the generalized generation distribution factors (GGDF) for modeling the transmission network constraints. The UC problem has been reformulated using these linear distribution factors without sacrificing optimality. Several test power systems (PJM 5-bus, IEEE-24, and 118-bus) have been used to validate the introduced formulation. Results demonstrate that the proposed approach is more compact and less computationally burdensome than the classical DC-based formulation, which is commonly employed in the technical literature to carry out the transmission network constraints. Therefore, there is a potential applicability of the accomplished methodology to carry out the UC problem applied to medium and large-scale electrical power systems.

A MIQCP formulation to solve the unit commitment problem for large-scale power systems

2012 ◽  
Vol 36 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Juan Álvarez López ◽  
José L. Ceciliano-Meza ◽  
Isaías Guillén Moya ◽  
Rolando Nieva Gómez
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Commitment Problem

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 Hydrothermal Unit-Commitment Problem of a Large-Scale System with Representation of Forbidden Zones

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 39
Author(s):  
Bruno Colonetti ◽  
Erlon Finardi ◽  
Lucas Borges Picarelli
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Electrical Networks ◽  
Unit Commitment Problem ◽  
Large Scale System ◽  
Marginal Costs ◽  
Commitment Problem ◽  
Electrical Components ◽  
The Impact

As we move towards electrical networks with a growing presence of renewable generation, the representation of the electrical components becomes more important. In hydro-dominated power systems, modelling the forbidden zones of hydro plants becomes increasingly challenging as the number of plants increases. Such zones are ranges of generation that either should be avoided or are altogether unreachable. However, because representing the forbidden zones introduces a substantial computational burden, hydrothermal unit-commitment problems (HTUC) for large systems are usually formulated ignoring the forbidden zones. Nonetheless, this simplification may demand adjustments to the solution of the HTUC, because the generation of the hydro stations may fall in forbidden zones. In practice, the adjustments are usually performed based on the experience of system operators and, then, can be far from an optimal correction. In this paper, we study the impact of explicitly representing the hydro-generation forbidden zones in a large-scale system with more than 7000 buses, 10,000 lines, and 700 hydro units. Our findings show that the simplified model that is current used can deviate significantly from the model with forbidden zones, both in terms of the generation of hydro plants, as well as the generation of thermal plants and the system marginal costs.

scholarly journals Practical Robust Optimization Method for Unit Commitment of a System with Integrated Wind Resource

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yuanchao Yang
Keyword(s):  
Power System ◽  
Large Scale ◽  
Unit Commitment ◽  
Optimization Method ◽  
Northwest China ◽  
Mixed Integer ◽  
Problem Solution ◽  
Unit Commitment Problem ◽  
Solution Approach ◽  
Commitment Problem

Unit commitment, one of the significant tasks in power system operations, faces new challenges as the system uncertainty increases dramatically due to the integration of time-varying resources, such as wind. To address these challenges, we propose the formulation and solution of a generalized unit commitment problem for a system with integrated wind resources. Given the prespecified interval information acquired from real central wind forecasting system for uncertainty representation of nodal wind injections with their correlation information, the proposed unit commitment problem solution is computationally tractable and robust against all uncertain wind power injection realizations. We provide a solution approach to tackle this problem with complex mathematical basics and illustrate the capabilities of the proposed mixed integer solution approach on the large-scale power system of the Northwest China Grid. The numerical results demonstrate that the approach is realistic and not overly conservative in terms of the resulting dispatch cost outcomes.

scholarly journals Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 576
Author(s):  
Mostafa Nasouri Gilvaei ◽  
Mahmood Hosseini Imani ◽  
Mojtaba Jabbari Ghadi ◽  
Li Li ◽  
Anahita Golrang
Keyword(s):  
Energy Storage ◽  
Solar Power ◽  
Unit Commitment ◽  
Thermal Power ◽  
System Structure ◽  
Compressed Air ◽  
Mixed Integer ◽  
Compressed Air Energy Storage ◽  
Concentrating Solar Power ◽  
Unit Commitment Problem

With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.

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