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An energy systems engineering approach for the design and operation of microgrids in residential applications

CHEMICAL ENGINEERING RESEARCH & DESIGN 2013年第10期91卷 2054-2069页

作者： Liu, Pei Georgiadis, Michael C. Pistikopoulos, Efstratios N. Tsinghua Univ Dept Thermal Engn State Key Lab Power Syst Beijing 100084 Peoples R China Aristotle Univ Thessaloniki Dept Chem Engn Thessaloniki 54124 Greece Univ London Imperial Coll Sci Technol & Med Dept Chem Engn CPSE London SW7 2AZ England

A distributed energy system refers to an energy system where energy production is close to end use, typically relying on small-scale energy distributed technologies. It is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. However, distributed energy systems such as micro-grids in residential applications may not be able to produce the potential benefits due to lack of appropriate system configurations and suitable operation strategies. The optimal design, scheduling and control of such a complex system are of great importance towards their successful practical realization in real application studies. This paper presents a short review and an energy systems engineering approach to the modeling and optimization of micro-grids for residential applications, offering a clear vision of the latest research advances in this field. Challenges and prospects of the modeling and optimization of such distributed energy systems are also highlighted in this work. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Distributed energy systems Energy scheduling Energy supply chains MILP unit commitment problem CHP

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An evolving neural network approach in unit commitment solution

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MICROPROCESSORS AND MICROSYSTEMS 2000年第5期24卷 251-262页

作者： Wong, MH Chung, TS Wong, YK Hong Kong Polytech Univ Dept Elect Engn Kowloon Hong Kong Peoples R China

In this paper, the Genetic Algorithm (GA) is used to evolve the weight and the interconnection of the neural network to solve the unit commitment problem. We will emphasize on the determination of the appropriate GA parameters to evolve the neural network, i.e. the population size and probabilities of crossover and mutation, and the method used for selection amongst generations such as Tournament selection, Roulette Wheel selection and Ranking selection. Performance comparisons are conducted to analyze the learning curve of different parameters, to find out which has a dominant influence on the effectiveness of the algorithm. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： genetic algorithm unit commitment problem neural network

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A genetic algorithm solution to a new fuzzy unit commitment model

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ELECTRIC POWER SYSTEMS RESEARCH 2004年第2期72卷 171-178页

作者： Mantawy, AH King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

This paper proposes a new fuzzy model for the unit commitment problem (UCP). A solution method for the proposed UCP model based on the genetic algorithms (GAs) is presented (FZGA). The model treats the uncertainties in the load demand and the spinning reserve constraints in a new fuzzy logic (FL) frame. The proposed FL model is used to determine a penalty factor that could be used to guide the search for more practical optimal solution. The implemented fuzzy logic system consists of two inputs: the error in forecasted load demand and the amount of spinning reserve, and two outputs: a fuzzy load demand and a penalty factor. The obtained fuzzy load demand is more realistic than the forecasted crisp one;hence the solution of the UCP will be more accurate. In the proposed FZGA algorithm, coding of the solution is based on mixing binary and decimal representation. The fitness function is taken as the reciprocal of the total operating cost of the UCP in addition to penalty terms resulted from the fuzzy membership functions for both load demand and spinning reserve. Results show that the fuzzy-based penalty factor is directly related to the amount of shortage in the committed reserve;hence will properly guide the search, when added to the objective function, in the solution algorithm of the UCP. Accordingly, acceptable level of reserve with better-cost savings was achieved in the obtained results. Moreover, the proposed FZGA algorithm was capable of handling practical issues such as the uncertainties in the UCR Numerical results show the superiority of solutions obtained compared to methods with traditional UCP models. (C) 2004 Published by Elsevier B.V.

关键词： genetic algorithms (GAs) FZGA unit commitment problem

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Risk-averse energy management system for isolated microgrids considering generation and demand uncertainties based on information gap decision theory

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IET RENEWABLE POWER GENERATION 2019年第6期13卷 940-951页

作者： Nasr, Mohamad-Amin Nasr-Azadani, Ehsan Rabiee, Abbas Hosseinian, Seyed Hossein Amirkabir Univ Technol Ctr Excellence Power Syst Iran Dept Elect Engn Tehran Iran Microsoft Corp Redmond WA 98052 USA Univ Zanjan Dept Elect & Comp Engn Zanjan Iran

There are many technical challenges for integration of renewable energy sources (RESs) in the context of microgrids. Among these challenges, spinning reserve energy management should be accurately considered in the microgrid scheduling system for a better system operation. This study presents a methodology to model and analyse a novel scheme to integrate RESs, particularly photovoltaic (PV) systems, in diesel generation-based isolated microgrids. The proposed approach considers the uncertainties of PV power generation and demand, simultaneously, by solving a bi-level multi-objective optimisation problem using information gap decision theory (IGDT). The proposed energy management system is formulated considering spinning reserve constraints and the uncertainties associated with PV power generation and load, by solving a unit commitment problem. This method, a non-probabilistic approach, does not require the probability density function of uncertain parameters and provides a robust framework to better understand the potential savings due to the PV integration. In order to test and perform the analysis, realistic data from a 20MW hybrid PV project is used as a case study. Furthermore, the proposed method is compared with probabilistic techniques, such as Monte Carlo simulations and scenario-based stochastic programming technique. The presented studies demonstrate applicability of the proposed model for real microgrids.

关键词： stochastic processes power generation dispatch probability diesel-electric generators renewable energy sources linear programming photovoltaic power systems demand side management distributed power generation stochastic programming power generation scheduling decision theory power markets Monte Carlo methods power generation economics risk-averse energy management system demand uncertainties information gap decision theory technical challenges renewable energy sources reserve energy management microgrid scheduling system system operation photovoltaic systems diesel generation-based isolated microgrids PV power generation active power demand bi-level multiobjective optimisation problem reserve constraints unit commitment problem PV integration scenario-based stochastic programming technique generation uncertainties hybrid PV project power 20 0 MW

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Perspective cuts for a class of convex 0-1 mixed integer programs

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MATHEMATICAL PROGRAMMING 2006年第2期106卷 225-236页

作者： Frangioni, A Gentile, C Univ Pisa Dept Comp Sci I-56124 Pisa Italy CNR Ist Anal Sistemi & Informat Antonio Ruberti I-00185 Rome Italy

We show that the convex envelope of the objective function of Mixed-Integer Programming problems with a specific structure is the perspective function of the continuous part of the objective function. Using a characterization of the subdifferential of the perspective function, we derive "perspective cuts", a family of valid inequalities for the problem. Perspective cuts can be shown to belong to the general family of disjunctive cuts, but they do not require the solution of a potentially costly nonlinear programming problem to be separated. Using perspective cuts substantially improves the performance of Branch & Cut approaches for at least two models that, either "naturally" or after a proper reformulation, have the required structure: the unit commitment problem in electrical power production and the Mean-Variance problem in portfolio optimization.

关键词： Mixed-Integer Programs valid inequalities unit commitment problem portfolio optimization

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A primary unit commitment approach with a modification process

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APPLIED SOFT COMPUTING 2013年第2期13卷 1007-1015页

作者： Amiri, Mohsen Khanmohammadi, Sohrab Islamic Azad Univ Abhar branch Dept Engn Elect Engn Div Abhar Zanjan Iran Univ Tabriz Dept Elect & Comp Engn Div Automat Control Tabriz Iran

This paper introduces an approach to one of the most important problems in electrical power system called the unit commitment (UC). The proposed method PUC-MP which stands for the primary unit commitment-modification process, addresses this problem firstly by using a simple and new priority for operating the generating units in each hour, and then, using a modification process which enhances the solution quality with lower cost. The PUC-MP takes advantage of both deterministic and stochastic algorithms in its structure to solve the discrete-variable part of the UC problem for choosing a suitable combination of units in each hour, and also, continuous-variable part of it which is dispatching the operating units' output power to the power network load economically. The latter part which is called economic dispatch (ED) has been solved using an intelligent algorithm which in turn has been customized by two new ideas to increase its efficiency. Simulation results show that this new approach even without using its modification process can be considered as an effective approach which surpasses some other popular and recently reported methods in producing near-optimal and robust solutions. (C) 2012 Elsevier B.V. All rights reserved.

关键词： unit commitment problem Primary UC Economic dispatch Modification process

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A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints

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APPLIED ENERGY 2015年 158卷 310-331页

作者： Koltsaklis, Nikolaos E. Georgiadis, Michael C. Aristotle Univ Thessaloniki Dept Chem Engn Thessaloniki 54124 Greece

This work presents a generic mixed integer linear programming (MILP) model that integrates the unit commitment problem (UCP), i.e., daily energy planning with the long-term generation expansion planning (GEP) framework. Typical daily constraints at an hourly level such as start-up and shut-down related decisions (start-up type, minimum up and down time, synchronization, soak and desynchronization time constraints), ramping limits, system reserve requirements are combined with representative yearly constraints such as power capacity additions, power generation bounds of each unit, peak reserve requirements, and energy policy issues (renewables penetration limits, CO2 emissions cap and pricing). For modelling purposes, a representative day (24 h) of each month over a number of years has been employed in order to determine the optimal capacity additions, electricity market clearing prices, and daily operational planning of the studied power system. The model has been tested on an illustrative case study of the Greek power system. Our approach aims to provide useful insight into strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level by providing the optimal energy roadmap under real operating and design constraints. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Mixed integer linear programming unit commitment problem Long term energy planning Electricity markets CO2 emissions System marginal price

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Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage

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RENEWABLE ENERGY 2020年 146卷 2060-2069页

作者： Alvarado-Barrios, Lazar Rodriguez del Nozal, Alvaro Boza Valerino, Juan Garcia Vera, Ignacio Martinez-Ramos, Jose L. Univ Loyola Andalucia Dept Ingn Seville Spain Univ Seville Dept Elect Engn Seville Spain

A Stochastic Model for the unit commitment (SUC) problem of a hybrid microgrid for a short period of 24 h is presented. The microgrid considered in the problem is composed of a wind turbine (WT), a photovoltaic plant (PV), a diesel generator (DE), a microturbine (MT) and a Battery Energy Storage System (BESS). The problem is addressed in three stages. First, based on the historical data of the demanded power in the microgrid, an ARMA model is used to obtain the demand prediction. Second, the 24-h-ahead SUC problem is solved, based on generators' constraints, renewable generation and demand forecast and the statistical distribution of the error in the demand estimation. In this problem, a spinning reserve of the dispatchable units is considered, able to cover the uncertainties in the demand estimation. In the third stage, once the SUC problem has been solved, a case study is established in real time, in which the demand estimation error in every moment is known. Therefore, the objective of this stage is to select the spinning reserve of the units in an optimal way to minimize the cost in the microgrid operation. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Microgrid Distributed generation Energy storage sources unit commitment problem

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Short-term scheduling solved with a particle swarm optimiser

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第11期5卷 1091-1104页

作者： Inostroza, J. C. Hinojosa, V. H. Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso Chile

unit commitment problem is an important optimising task in daily operational planning of power systems which can be mathematically formulated as a large scale non-linear mixed-integer minimisation problem for which there is no exact solution technique. The solution to the problem can be obtained only by complete enumeration, often at a prohibitively computation time requirement for realistic power systems. This research discusses the planning of short-term scheduling on thermal power systems through the particle swarm optimisation technique. The formulation of the problem considers the operating costs of the units, the minimum up and down times, the spinning reserve and the power balance. A methodology is presented, and a computational tool is developed to solve the problem of unit commitment, which handles the problem constraints with correction operators. In the research also is proposed a new way to update the swarm individuals and a mutation stage of the best solution. The method is applied to standard systems and the results are compared with the ones previously reported in the literature. The results obtained by applying the proposed algorithm to the Chile Large Northern Interconnected System are also discussed.

关键词： particle swarm optimisation Thermal power stations and plants Power system planning and layout thermal power stations Optimisation techniques nonlinear programming power system operational planning integer programming large scale nonlinear mixed-integer minimisation problem Chile Large Northern interconnected system power generation scheduling power generation planning particle swarm optimiser thermal power system short-term scheduling power balance minimisation unit commitment problem power system interconnection

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unit commitment by an improved binary quantum GSA

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APPLIED SOFT COMPUTING 2017年 60卷 180-189页

作者： Barani, Fatemeh Mirhosseini, Mina Nezamabadi-pour, Hossein Farsangi, Malihe M. Higher Educ Complex Bam Dept Comp Sci Bam Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

unit commitment (UC) problem is an important optimizing task for scheduling the on/off states of generating units in power system operation over a time horizon such that the power generation cost is minimized. Since, increasing the number of generating units makes it difficult to solve in practice, many approaches have been introduced to solve the UC problem. This paper introduces an improved version of the binary quantum-inspired gravitational search algorithm (BQIGSA) and proposes a new approach to solve the UC problem based on the improved BQIGSA, called QGSA-UC. The proposed approach is applied to unit commitment problems with the number of generating units in the range of 10120 along with 24-h scheduling horizon and is compared with nine state-of-the-art approaches. Furthermore, four different versions of gravitational approach are implemented for solving the UC problem and compared with those obtained by QGSA-UC. Comparative results clearly reveal the effectiveness of the proposed approach and show that it can be used as a reliable tool to solve UC problem. (C) 2017 Elsevier B.V. All rights reserved.

关键词： unit commitment problem Economic dispatch Quantum computing Gravitational search algorithm

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