Unit commitment in microgrids by improved genetic algorithm

Liang, Huishi; Gooi, Hoay Beng

doi:10.1109/ipecon.2010.5697083

Cited by 29 publications

(14 citation statements)

References 18 publications

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“…The remaining part of optimizing the microgrid operation is done Fuel Cost Optimization of an Islanded Microgrid Considering Environmental Impact Vinod M. Raj and Saurabh Chanana through Mixed Integer programming. In [8], the unit commitment problem in the microgrid has been solved using Genetic Algorithm approach. A Mixed Integer Non Linear Programming approach has been used in [9] to solve economic dispatch problem in a microgrid consisting of Combined Heat and Power plants.…”

Section: Introductionmentioning

confidence: 99%

Fuel Cost Optimization of an Islanded Microgrid Considering Environmental Impact

Raj¹,

Chanana²

2015

JOCET

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Abstract-This paper shows how the total fuel cost generated by the distributed generating units of a microgrid is minimized by the application of unit commitment problem. The microgrid taken for case study consists of a wind power unit, a photovoltaic unit, a microturbine, a diesel generator and a battery bank. The unit commitment problem is solved using priority listing method. The priority is assigned on the basis of emissions released by each unit. The 24 hour forecasted power output of the renewable energy sources is subtracted from the given 24 hour load requirement and is not considered in the unit commitment problem. The fuel cost coefficients and generating constraints of the conventional units and the battery bank specifications are taken as data for solving the unit commitment problem. Minimum up time, minimum down time and startup costs of the conventional units are considered in this case study. Ramp rate constraints and reserve capacity constraints are ignored and shut down cost is taken as zero. Operating costs of the renewable units are taken as zero and the cost of energy delivered during battery discharge is considered as profit. The economic dispatch problem is solved using Quadratic Programming approach. Losses in the system are ignored while solving the economic dispatch problem. From the results obtained we can conclude that even if the unit commitment schedule generated may not be most economic, it still ensures an environment friendly operation by the microgrid.Index Terms-Microgrid, priority listing, quadratic programming, unit commitment problem. I. INTRODUCTIONThe unit commitment problem is generally applied in the field of power systems to find out the optimum generating schedule of the generating units to satisfy the load demand. The optimal scheduling of power is to be done in a way such that all constraints of the generating systems and the network are met. The unit commitment time period generally varies from one day to one week. It requires solving the unit commitment problem for each hour. Various optimization algorithms are applied in this problem to achieve the optimum power generation. The optimal fuel cost generated is calculated with the help of the power output obtained by the application of the optimization techniques.The technique used for solving the unit commitment problem mainly depends on the data that is available and the constraints in the problem. Sometimes the fuel cost function is quadratic or convex in nature and hence in such cases Priority Listing method or Lagrange method can be applied to solve the problem. However if the problem is non-convex, we need to use dynamic programming or other meta-heuristic Manuscript received November 30, 2014; revised April 23, 2015. The authors are with the Electrical Engineering Department, National Institute of Technology Kurukshetra, India (e-mail: vinodmraj1@gmail.com, s_chanana@rediffmail.com).approaches to solve the problem.In recent times, the traditional power system network is gradually transforming from a centralized...

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Section: Introductionmentioning

confidence: 99%

Fuel Cost Optimization of an Islanded Microgrid Considering Environmental Impact

Raj¹,

Chanana²

2015

JOCET

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“…Unit commitment (UC) is defined as an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads under different constraints and environments in [41]. A dynamic programming-based algorithm is derived to solve the unit commitment problem with a multi-objective function in order to reduce the economic cost and CO 2 equivalent emissions employing a modified genetic algorithm (GA) based method is proposed in [42]. Simulated annealing technique is used to improve the speed of convergence in genetic algorithm.…”

Section: Power Quality Controlmentioning

confidence: 99%

Optimization Techniques for Operationand Control of Microgrids � Review

Ramprabhakar

2018

JGE

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Microgrid systems show great promise in integrating large numbers of distributed generation systems, based on renewable energy sources into future power systems. The characteristics of power generated by renewable energy sources is random, unregulated and highly unreliable, vowing to the stochastic nature of the sources like wind, solar etc. While integrating the locally generated power into grid sounds very attractive, there are many operational and planning concerns. Also, a major chunk of power generated is weather dependent, this makes an energy storage system and/or backup power generation system an imperative part of microgrids. A proper planning and designing is the first step towards integrated power. Optimization techniques justify cost of investment of a Microgrid by enabling economic and reliable usage of resources. This paper summarizes various optimization methodologies and criterion for optimization of Microgrids. Extensive study of published literature show that computational alternatives like evolutionary, heuristic and non-classical algorithms show better results when compared to other conventional methods. The study of multi-objective optimization problems shows superior performance by combining intelligent optimization algorithms with adaptive techniques.

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“…As a result of the differences between traditional generation systems and microgrids, several publications in recent years have suggested new and different approaches and methods for solving ED and UC problems. For this purpose, for different microgrid structures, metaheuristic and heuristic methods have been proposed and applied to solve the problem of load dispatch [8], [9]. Some advanced control algorithms which consider system uncertainties, caused by demand and renewable sources of energy, have been developed to achieve optimal use of energy storage to compensate for physical imbalances, etc [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Unit Commitment in Microgrids based on Model Predictive Control

Alvarado-Barrios

Valerino

Nozal

et al. 2018

2018 International Conference on Smart Energy Systems and Technologies (SEST)

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This article deals with the problem of Stochastic Unit Commitment (SUC), considering the stochastic nature of demand and meteorological phenomena. This paper shows the optimal operation of a hybrid microgrid composed of the following generation units: wind turbine (WT), photovoltaic solar panel (PV), diesel engine generator (DE), micro-turbine (MT), as well as storage devices such as Battery Energy Storage (BES), considering its constraints and the requirements of the reserve generation. For this purpose, a Model-based Predictive Control (MPC), which uses dynamic models of prediction of renewable power and demand in real time, is developed, allowing feedback at each step of time, which corrects the uncertainty of the models. A comparison with a classic UC formulation has been made. The results reach a lower cost solution.

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Unit commitment in microgrids by improved genetic algorithm

Cited by 29 publications

References 18 publications

Fuel Cost Optimization of an Islanded Microgrid Considering Environmental Impact

Fuel Cost Optimization of an Islanded Microgrid Considering Environmental Impact

Optimization Techniques for Operationand Control of Microgrids � Review

Stochastic Unit Commitment in Microgrids based on Model Predictive Control

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