Optimal energy dispatch in a smart micro-grid system using economic model predictive control

The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently addressed using classical control or ad-hoc methods. This paper discusses the application of Economic Model Predictive Control (EMPC) to the management of a smart micro-grid system connected to an electrical power grid. The considered system is composed of several subsystems, namely some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the PV panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Modeling smart grids components is based on the generalized flow-based networked systems paradigm, and assuming energy generators to be stable, load demands and energy prices are known. This study shows that EMPC is economically superior to a two-layer hierarchical MPC.Peer ReviewedPostprint (author's final draft

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances

A survey of differential flatness-based control applied to renewable energy sources

Conference ProceedingsThis paper presents an overview of various methods used to minimize the fluctuating impacts of power generated from renewable energy sources. Several sources are considered in the study (biomass, wind, solar, hydro and geothermal). Different control methods applied to their control are cited, alongside some previous applications. Hence, it further elaborates on the adoptive control principles, of which includes; Load ballast control, dummy load control, proportional integral and derivative (PID) control, proportional integral (PI) control, pulse-width modulation (PWM) control, buck converter control, boost converter control, pitch angle control, valve control, the rate of river flow at turbine, bidirectional diffuser-augmented control and differential flatnessbased controller. These control operations in renewable energy power generation are mainly based on a steady-state linear control approach. However, the flatness based control principle has the ability to resolve the complex control problem of renewable energy systems while exploiting their linear properties. Using their flatness properties, feedback control is easily achieved which allows for optimal/steady output of the system components. This review paper highlights the benefits that range from better control techniques for renewable energy systems to established robust grid (or standalone generations) connections that can bring immense benefits to their operation and maintenance costs

Neural Network Predictive Controller for Improved Operational Efficiency of Shiroro Hydropower Plant

The development of e fficient models and controllers is central to better understanding and analysis of operational efficiency of modern hydropower plants. In this work, an intelligent Levenberg - Marquardt b ased Neural Network Predictive C ontroller (NNPC) was developed for Shiroro hydroelectric power station us ing actual data obtained from the plant operation . Results obtained after training and simulation of the system show that neural network technique serves as an efficient approach of designing hydroelectric power station models and controllers

Predicción de caudales en río Colorado, Argentina

The identification of suitable models for predicting daily water flow is important for planning and management of water storage in reservoirs of Argentina. Long-term prediction of water flow is crucial for regulating reservoirs and hydroelectric plants, for assessing environmental protection and sustainable development, for guaranteeing correct operation of public water supply in cities like Catriel, 25 de Mayo, Colorado River and potentially also Bahía Blanca. In this paper, we analyze in Buta Ranquil flow time series upstream reservoir and hydroelectric plant in order to model and predict daily fluctuations. We compare results obtained by using a three-layer artificial neural network (ANN), and an autoregressive (AR) model, using 18 years of data, of which the last 3 years are used for model validation by means of the root mean square error (RMSE), and measure of certainty (Skill). Our results point out to the better performance to predict daily water flow or refill them of the ANN model performance respect to the AR model. La identificación de modelos adecuados para predecir caudales diarios es importante para la planificación y la gestión de almacenamiento de agua en los embalses de la Argentina. La predicción a largo plazo del caudal es crucial para la regulación de los embalses y centrales hidroeléctricas, evaluar la protección del medio ambiente y el desarrollo sostenible, garantizar el correcto funcionamiento del abastecimiento público de agua en ciudades como Catriel, 25 de Mayo, río Colorado y también, eventualmente, en Bahía Blanca. En este trabajo, se analizan series de tiempo de caudales de agua, arriba del embalse y de la planta hidroeléctrica en Buta Ranquil, para modelar y predecir las fluctuaciones diarias. Se comparan los resultados obtenidos mediante el uso de una red neuronal artificial (ANN) de tres capas y un modelo autoregresivo (AR), con 18 años de datos, cuyos últimos 3 años se utilizan para la validación del modelo por medio de la raíz del error cuadrado medio (RMSE) y medida de certeza (Skill). Para predecir o rellenar el caudal diario, los resultados indican que el mejor desempeño es del ANN con respecto al modelo AR.Fil: Pierini, Jorge Omar. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: Gomez, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Tecnologica Nacional; ArgentinaFil: Telesca, Luciano. Istituto di Metodologie per l’Analisi Ambientale; Itali

MLP ANN Condition Assessment Model of the Turbogenerator Shaft A6 HPP Đerdap 2

This paper describes a model for estimating the condition of the shafts of turbines of the current generator in Hydropower plant Đerdap 2. For this purpose, an integral diagnostic approach was used. Based on the diagnostics of the condition of the shaft and the estimated lifetime, a multi-layer perceptron (MLP) based artificial neural network (ANN) is built, which is able to estimate the remaining lifespan of the turbine shaft. The MLP ANN model has not been made in this way on turbogenerators of hydroelectric power plant Đerdap 2 until now. The significance of this approach is that experiment brings about topology of ML ANN (number of neurons and layers) which is optimal for this model, training and testing. Results obtained from the neural network can be further used for decision-making about the moment of diagnosis or maintenance actions, as well as reducing stagnation and production losses

A novel condition monitoring methodology based on neural network of pump-turbines with extended operating range

Due to the entrance of new renewable energies, water-storage energy has to be regulated more frequently to keep the stability of power grid. Consequently, pump-turbines have to work under offdesign conditions more than before, which will cause more damage and decrease their useful life. Advanced monitoring methodologies that can balance the degradation of machine and revenues to the power plant has been required. To develop an innovative condition monitoring approach, vibration data was collected from different components of a pump-turbine which is running in an extended operating range. The consequences of operating range extension on the vibration of the pump-turbine have been studied by analysing the vibration signatures. The changing rule of the vibration behavior of the machine with the operating parameters has been obtained. An artificial neural network based model has been applied to build an autoregressive normal behavior model. The results indicated that the normal behavior model based on multi-layer neural net has the ability to predict the vibration characteristics of the machine in different operating conditions. This monitoring method can be adapted to the similar type of hydraulic turbine units.Peer ReviewedPostprint (published version

An artificial neural network approach for predicting the performance of ship machinery equipment

Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting ship performance, having a great impact in terms of business losses by reducing ship availability and increasing downtime and moreover increasing the potential of major accidents occurring, endangering lives onboard. Efforts have being made to transform corrective/preventive maintenance techniques into predictive ones. Condition monitoring is considered as a major part of predictive maintenance. It assesses the operational health of equipment, in order to provide early warning of potential failure such that preventive maintenance action may be taken. Condition monitoring is defined as the collection and interpretation of the relevant equipment parameters for the purpose of the identification of the state of equipment changes from normal conditions and trends of the health of the equipment. The equipment condition and the fault developing trend are often highly nonlinear and time-series based. Artificial Neural Networks (ANNs) can be used due to their potential ability in nonlinear time-series trend prediction. Therefore this paper proposes the use of an autoregressive dynamic time series ANN in order to monitor and predict selected physical parameters of ship machinery equipment that contribute to the overall performance and availability, in order to predict their future values that will illustrate their performance state that will eventually lead to the correct maintenance actions and decisions

A Theory of Renewable Energy from Natural Evaporation

About 50% of the solar energy absorbed at the Earth’s surface is used to drive evaporation, a powerful form of energy dissipation due to water’s large latent heat of vaporization. Evaporation powers the water cycle that affects global water resources and climate. Critically, the evaporation driven water cycle impacts various renewable energy resources, such as wind and hydropower. While recent advances in water responsive materials and devices demonstrate the possibility of converting energy from evaporation into work, we have little understanding to-date about the potential of directly harvesting energy from evaporation. Here, we develop a theory of the energy available from natural evaporation to predict the potential of this ubiquitous resource. We use meteorological data from locations across the USA to estimate the power available from natural evaporation, its intermittency on varying timescales, and the changes in evaporation rates imposed by the energy conversion process. We find that harvesting energy from natural evaporation could provide power densities up to 10 W m-2 (triple that of present US wind power) along with evaporative losses reduced by 50%. When restricted to existing lakes and reservoirs larger than 0.1 km2 in the contiguous United States (excluding the Great Lakes), we estimate the total power available to be 325 GW. Strikingly, we also find that the large heat capacity of water bodies is sufficient to control power output by storing excess energy when demand is low. Taken together, our results show how this energy resource could provide nearly continuous renewable energy at power densities comparable to current wind and solar technologies – while saving water by cutting evaporative losses. Consequently, this work provides added motivation for exploring materials and devices that harness energy from evaporation