Short-term generation scheduling in a hydrothermal power system.

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An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionAgencia Estatal de InvestigaciónComunidad de Madri

Evolution of microgrids with converter-interfaced generations: Challenges and opportunities

© 2019 Elsevier Ltd Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids

Power systems generation scheduling and optimisation using evolutionary computation techniques

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.Overseas Development Agenc

CHANGE-READY MPC SYSTEMS AND PROGRESSIVE MODELING: VISION, PRINCIPLES, AND APPLICATIONS

The last couple of decades have witnessed a level of fast-paced development of new ideas, products, manufacturing technologies, manufacturing practices, customer expectations, knowledge transition, and civilization movements, as it has never before. In today\u27s manufacturing world, change became an intrinsic characteristic that is addressed everywhere. How to deal with change, how to manage it, how to bind to it, how to steer it, and how to create a value out of it, were the key drivers that brought this research to existence. Change-Ready Manufacturing Planning and Control (CMPC) systems are presented as the first answer. CMPC characteristics, change drivers, and some principles of Component-Based Software Engineering (CBSE) are interwoven to present a blueprint of a new framework and mind-set in the manufacturing planning and control field, CMPC systems. In order to step further and make the internals of CMPC systems/components change-ready, an enabling modeling approach was needed. Progressive Modeling (PM), a forward-looking multi-disciplinary modeling approach, is developed in order to modernize the modeling process of today\u27s complex industrial problems and create pragmatic solutions for them. It is designed to be pragmatic, highly sophisticated, and revolves around many seminal principles that either innovated or imported from many disciplines: Systems Analysis and Design, Software Engineering, Advanced Optimization Algorisms, Business Concepts, Manufacturing Strategies, Operations Management, and others. Problems are systemized, analyzed, componentized; their logic and their solution approaches are redefined to make them progressive (ready to change, adapt, and develop further). Many innovations have been developed in order to enrich the modeling process and make it a well-assorted toolkit able to address today\u27s tougher, larger, and more complex industrial problems. PM brings so many novel gadgets in its toolbox: function templates, advanced notation, cascaded mathematical models, mathematical statements, society of decision structures, couplers--just to name a few. In this research, PM has been applied to three different applications: a couple of variants of Aggregate Production Planning (APP) Problem and the novel Reconfiguration and Operations Planning (ROP) problem. The latest is pioneering in both the Reconfigurable Manufacturing and the Operations Management fields. All the developed models, algorithms, and results reveal that the new analytical and computational power gained by PM development and demonstrate its ability to create a new generation of unmatched large scale and scope system problems and their integrated solutions. PM has the potential to be instrumental toolkit in the development of Reconfigurable Manufacturing Systems. In terms of other potential applications domain, PM is about to spark a new paradigm in addressing large-scale system problems of many engineering and scientific fields in a highly pragmatic way without losing the scientific rigor

Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.Мета. У цій статті пропонується нова стратегія управління перетворювачем KY (підвищення напруги постійного струму). Пропонована гібридна енергетична система, що живиться перетворювачем KY, використовується разом із контролером системи адаптивного нейро-нечіткого інтерфейсу. Відновлювані джерела енергії останнім часом набули величезного значення внаслідок зростання попиту на електроенергію, швидкого виснаження викопного палива та загрози глобального потепління. Однак через властиву їм уривчастість ці джерела забезпечують низьку надійність системи. Таким чином, гібридна енергетична система, що включає енергію вітру/фотоелектричних елементів/акумулятору, реалізована для отримання стабільної і надійної мікромережі. Як сонячна, так і вітрова енергія доступні з величезним невикористаним потенціалом, і разом вони забезпечують понад 60 % щорічного чистого приросту нових потужностей з виробництва електроенергії в усьому світі. Новизна. Перетворювач KY використовується тут для підвищення вихідної потужності фотоелектричної системи, і його робота керується за допомогою каскадного контролера системи з адаптивним нейро-нечітким інтерфейсом. Оригінальність. Підвищення загальної стабільності та надійності системи за допомогою гібридної енергетичної системи, що живиться перетворювачем KY і використовується разом з контролером системи з адаптивним нейро-нечітким інтерфейсом. Практична цінність. Пропорційний інтегральний контролер використовується в системі перетворення енергії вітру на основі асинхронного генератора з подвійним живленням для управління випрямлячою роботою з широтно-імпульсною модуляцією для забезпечення регульованої вихідної напруги постійного струму. Акумуляторна система накопичення енергії, в якій використовується акумуляторний перетворювач для підключення до кола постійного струму, зберігає надмірну потужність, що виробляється з відновлюваних джерел енергії. Залежно від стану заряду акумулятора, процес його зарядки і розрядки контролюється за допомогою пропорційного інтегрального контролера. Керована напруга кола постійного струму подається на трифазний інвертор джерела напруги для ефективного перетворення постійної напруги змінну. Інвертор підключений до трифазної мережі через LC-фільтр для ефективного придушення гармонік. Пропорційний інтегральний регулятор використовується для досягнення ефективної синхронізації напруги мережі. Результати. Запропонована модель змодельована з використанням MATLAB/Simulink, і з отриманих результатів випливає, що каскадний адаптивний нейро-нечіткий інтерфейс із системним контролером та перетворювачем KY здатний підтримувати стабільну роботу мікромережі з чудовим ККД 93 %

Decision-making frameworks for practical industrial applications in optimal process design and control

While economics are the driving force behind many of the decisions made by industrial stakeholders, the methodologies employed to make high-level decisions often utilize heuristics that may not be quantitatively optimal. In this dissertation, I develop optimization-based frameworks that enable quantitatively driven high-level decision-making for two problems of practical industrial significance. In the first part of the dissertation, I address the problem of deciding the operating mode (batch or continuous-flow) of a chemical process, while taking into account the fundamental differences in the natures of the two operating modes (such as the batch advantage of utilizing reactors for the manufacture of multiple products, or the batch disadvantage of reactor cleanup between campaigns), the size and cost of the respective reactor units, and the potential use of reactor networks to optimize performance. I develop a first-principles-based non-dimensionalization algorithm that unifies the model for all reactor types and chemical systems from the two operating modes which enables direct performance comparisons between reactors of the two operating modes. In addition, I introduce a novel discretization method, the orthogonal collocation on finite elements for reactors (OCFERE), that allows the consideration of networks of reactors of either of the two operating modes, and I unify the description of the economics of the two operating modes. This results in a framework that encompasses the solution of a single optimization problem to make the decision about operating mode and find the optimal reactor network design. In the second part of the dissertation, I address the problem of quantifying the monetary value of improvements in process control. While methods have been developed for quantifying the value of control in the case of predominantly steady-state processes, there has been no attempt to quantify the monetary value of control for predominantly transient processes. I first review the problem, and highlight the relationship between optimal scheduling and process control for transient processes. Then, I utilize the general framework of integrated scheduling and control to develop novel performance functions that enable the quantification of the monetary value of control from a scheduling perspective for a predominantly transient process. I posit that the transition time between one product and the next in a production sequence can be used as a performance metric over which the value of control can be quantified.Chemical Engineerin