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A proposal for a cooperative cross-entropy method to tackle the unit commitment problem

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷 107764-107764页

作者： Amaya, Jhon Edgar Camargo, Edgar Aguilar, Jose Tarazona, Maria Univ Nacl Expt Tachira San Cristobal Tachira Venezuela Cobuild Lab Miami FL USA Univ Los Andes Fac Ingn CEMISID Merida Venezuela Red Tepuy RD Merida Venezuela Univ EAFIT GIDITIC Medellin Colombia Univ Alcala Dept Automat Alcala De Henares Spain

The paper presents a proposal about the Cooperative Cross-Entropy to solve Combinatorial Optimization Prob-lems. The cooperative scheme consists of the definition of several instances of the Cross-Entropy method and the establishment of an exchange policy of solutions between the different instances participating in the model. Each instance consists of the implementation of a cross-entropy algorithm with particular parameters that originates a different probability density function for the generation of new solutions. The exchange policy is implemented synchronously between the instances, and the solutions for the exchange process are chosen within the elite solutions of each instance of the cooperative model. For the experimentation, we use a simplified version of the unit commitment problem (UCP). The computational results show that for large instances of the UCP, the cooperative model is effective. Furthermore, the proposed model shows a better balance between intensification and exploration for a certain combination of parameters, and therefore, it seems to be a good approach to tackle other optimization problems.

关键词： Cooperative cross-entropy Combinatorial optimization problems unit commitment problem

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Is learning for the unit commitment problem a low-hanging fruit?

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ELECTRIC POWER SYSTEMS RESEARCH 2022年 207卷 107851-107851页

作者： Pineda, S. Morales, J. M. Univ Malaga Malaga Spain

The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer of Occam's razor principle. In this letter, we use the unit commitment problem (UCP), an NP hard mathematical program that is fundamental to power system operations, to show that simplicity must guide any strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, we apply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sized power systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. To the best of our knowledge, this is the first work in the technical literature that quantifies how challenging learning the solution of the UCP actually is for real-size power systems. Our claim is thus that any sophistication of the learning method must be backed up with a statistically significant improvement of the results in this letter.

关键词： unit commitment problem Machine learning Computational burden Power system operations

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Solving unit commitment problem Employing Proposed Hybrid BBO-discrete Hopfield Neural Network

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Research Journal of Applied Sciences, Engineering and Technology 2016年第3期12卷 328-338页

作者： J. Chitra S.N. Deepa Department of EEE Anna University Regional Campus Coimbatore Tamil Nadu India

A novel hybrid approach is developed based on the hybridization of Biogeography Based Optimization and Discrete Hopfield Neural Network. BBO algorithm is employed to tune for the optimal weights of discrete Hopfield Neural Network leading to the minimization of energy function. The proposed hybrid BBO-DHNN is implemented for 10, 20, 40 and 60 units power system under consideration. Based on the simulation results presented, it is clearly noted that the proposed HBDHNN approach results in better solutions for the unit commitment problem considered and this in turn reduces the computational burden to a significant extent. The proposed approaches are developed in MATLAB environment version 7.8.0.347 and executed in a PC with Intel core 2 Duo processor with 2.27 GHz speed and 2 GB RAM with 64 bit operating system.

关键词： Artificial neural network biogeography based optimization discrete hopfield neural network gravitational search algorithm unit commitment problem

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Thermodynamic Approach to unit commitment problem

Thermodynamic Approach to Unit Commitment Problem

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Annual International Conference on Emerging Research Areas

作者： Bijo Reji P. C. Thomas PG Scholar EEE Department Amaljyothi College of Engineering Kanjirapally H.O.D EEE Department Amaljyothi College of Engineering Kanjirapally

ISBN: (纸本)9781467351508

Power system operation planning involves the best utilization of all available energy sources subjected to various constraints. It includes transfer of electric energy from generating companies to the consumers with maximum safety, continuity, quality and minimum operating cost. The minimum operating cost is obtained by scheduling available cheaper units. unit commitment involves hour to hour ordering of the units in the system to match the anticipated load and allow losses with sufficient safety margin. Simulated annealing is a derivative free thermodynamic optimization method. It provides unit scheduling with minimum running cost, where temperature is the control parameter. With time and power demand as the comparative factors, scheduling of units in a cost effective manner was done on a small power system with three generating units. The case study reveals that the total cost of production reduced to a considerable amount.

关键词： unit commitment problem Operating cost Thermodynamic approach Simulated annealing Control parameter Probability

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Robust unit commitment problem with Demand Response and Wind Energy

Robust Unit Commitment Problem with Demand Response and Wind...

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IEEE Power and Energy Society General Meeting

作者： Long Zhao Bo Zeng the Department of Industrial and Management Systems Engineering University of South Florida Tampa FL 33620

ISBN: (纸本)9781467327275

Currently, both demand response (DR) strategy and renewable energy have been adopted to improve power generation efficiency and reduce greenhouse gas emission. However, the uncertainty and intermittent generation pattern in wind farms and the complexity of demand side management pose huge challenges. In this paper, we analytically investigate how to integrate DR and wind energy with fossil fuel generators to (i) minimize power generation cost;and (2) fully take advantage of the wind energy with the managed demand to reduce greenhouse emission. We first build a two-stage robust unit commitment (UC) model to obtain day-ahead generator schedules where wind uncertainty is captured by a polytopic uncertainty set. Then, we extend our model to include DR strategy such that both price levels and generator schedules will be derived for the next day. For these two challenging models, we derive their mathematical properties and develop a novel solution method. Our computational study on an IEEE 118-bus system with 36 units shows that robust UC models can fully make use of wind generation with less generation cost. Also, the developed algorithm is computationally superior to classical Benders decomposition method.

关键词： unit commitment problem robust optimization cutting plane algorithm Wind energy Demand-Responsive machine unit DR generating efficiency Wind energy generation dynamos Greenhouse gas emissions Benders decomposition Wind farms Power production

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Improved PSO Approach for The Solution of unit commitment problem

Improved PSO Approach for The Solution of Unit Commitment Pr...

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2015 International Conference on Artificial Intelligence and Industrial Engineering(AIIE 2015)

作者： Y.R.Guo Y.H.Wu J.R.Zhang J.D.Ren Department of Mechanical and Electrical Engineering Shenzhen Research Institute of Xiamen University Xiamen University Department of Mechanical and Electrical Engineering Xiamen University Department of Mechanical and Electrical Engineering Xiamen University

ISBN: (纸本)9781510806450

unit commitment problem(UCP) is a large scale nonlinear hybrid integer programming problem and it involves determining on/off states of units and generations of units. This paper proposes an improved particle swarm optimization(IPSO) for the solution of UCP. In the proposed approach, the on/off states of units are limited into feasible schedules by providing a new method related to a time order at first. After that, the problem is transformed into a simple economic load dispatch problem. Then this dispatch problem is solved by an improved priority list technique instead of the classical equal lambdaiteration method. All the above improvements are embedding into the framework of PSO approach for UCP. It is seen from the numerical results that the presented algorithm surely possesses a high quality and a high convergence speed.

关键词： unit commitment problem particle swarm optimization time order priority list

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The Significance of Time Constraints in unit commitment problems

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IEEE ACCESS 2024年 12卷 31515-31522页

作者： Bukhsh, Waqquas Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

Electricity market clearing models are crucial for operational and investment planning in modern power systems. Two commonly used models for market clearing are the merit-order and unit commitment models. While the merit-order employs a snapshot approach and lacks precision in representing time-based variations in demand and generation, the unit commitment model incorporates crucial temporal constraints. This paper highlights the significance of temporal constraints in market clearing models, emphasizing the potential for significant errors in generation commitment if these constraints are not carefully considered. Using real data from the Irish electricity system, the impact of temporal constraints on intra-day market clearing is demonstrated, comparing with the merit-order rule with the unit commitment solution. The findings reveal 6% underestimation in market clearing prices when compared to unit commitment solutions. The minimum stable operating constraints of thermal units were identified as having the most substantial impact on the unit commitment solution. This study underscores the risk of significant errors in generation planning if temporal constraints are not adequately factored in, advocating for a more accurate approach for market clearing models.

关键词： Generators Renewable energy sources Mathematical models Costs Biological system modeling Electricity supply industry Power markets Linear programming Optimization methods Market research Power system planning Investment Incentive schemes Electricity markets integer programming optimization unit commitment problem merit order dispatch linear programming

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New Mixed-Integer Nonlinear Programming Formulations for the unit commitment problems with Ramping Constraints

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OPERATIONS RESEARCH 2024年第5期72卷 iii-vii, 1751-2261, C2-C3页

作者： Bacci, Tiziano Frangioni, Antonio Gentile, Claudio Tavlaridis-Gyparakis, Kostas CNR Ist Anal Sistemi Informat Antonio Ruberti I-00185 Rome Italy Univ Pisa Largo B Dipartimento Informat I-56127 Pisa Italy

The unit commitment (UC) problem in electrical power production requires to optimally operate a set of power generation units over a short time horizon. Operational constraints of each unit depend on its type and can be rather complex. For thermal units, typical ones concern minimum and maximum power output, minimum up-and down-time, startup and shut-down limits, ramp-up and ramp-down limits, and nonlinear objective function. In this work, we present the first mixed-integer nonlinear program formulation that describes the convex hull of the feasible solutions of the single-unit commitment problem comprising all the above constraints and convex power generation costs. The new formulation has a polynomial number of both variables and constraints, and it is based on the efficient dynamic programming algorithm proposed by Frangioni and Gentile together with the perspective reformulation. The proof that the formulation is exact is based on a new extension of a result previously only available in the polyhedral case, which is potentially of interest in itself. We then analyze the effect of using it to develop tight formulations for the more general UC problem. Because the formulation is rather large, we also propose two new formulations, based on partial aggregations of variables, with different trade-offs between quality of the bound and cost of solving the continuous relaxation. Our results show that navigating these trade-offs may lead to improved performances.

关键词： unit commitment problem ramp constraints MIP formulations dynamic programming convex costs

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A multi-agent approach to unit commitment problems

A multi-agent approach to unit commitment problems

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Winter Meeting of the IEEE-Power-Engineering-Society

作者： Nagata, T Ohono, M Kubokawa, J Sasaki, H Fujita, H Hiroshima Inst Technol Saeki Ku Hiroshima 7315193 Japan

ISBN: (纸本)0780373227

This paper proposes a multi-agent system for solving unit commitment problems. Multi-agent is a new paradigm for developing software applications. Coordinating the behavior of autonomous agents is a key issue in agent oriented programming techniques today. In this paper, we develop a power system unit commitment application using multi-agent architecture. Our model has the following characteristics: (1) the system consists of a single facilitator agent, several generator-agents, and two kinds of mobile agent. (2) The facilitator agent is developed to act as a manager for the process by using the singleton design pattern. The mobile agent and generator agent have simple negotiation strategies. (3) Message object is developed to communicate between agents using KQML-like object. The proposed approach is applied to a simple system, and the results show that the multi-agent system is able to find optimal solutions for unit commitment problems.

关键词： agent technology mixed integer programming multi-agents unit commitment problem

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Operation Planning for High unit Utilization in Distributed Energy Management System with Storage Devices

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ELECTRICAL ENGINEERING IN JAPAN 2017年第2期199卷 33-46页

作者： Shibata, Kodai Miyamoto, Toshiyuki Kitamura, Shoichi Mori, Kazuyuki Izui, Yoshio Osaka Univ Grad Sch Engn Suita Osaka Japan Osaka Univ Grad Sch Engn Elect Engn Suita Osaka Japan Mitsubishi Electr Corp Adv Technol R&D Ctr Tokyo Japan

Suppose a special district (group) that is composed of multiple companies (agents) imposed energy demand. We have proposed a distributed energy management system (DEMS) that optimizes energy consumption of a group through energy trading in the group. In this paper, we propose an efficient operation planning method for DEMS with storage device. At first, we formulate the optimization problem for agents. Second, we propose a method to search a unit commitment state that depends on group composition. We confirmed that the proposed method enables efficient use of generators in groups. (C) 2017 Wiley Periodicals, Inc.

关键词： distributed energy management system Walras auction storage battery unit commitment problem

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