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Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach. [ABSTRACT FROM AUTHOR]

Power production by conversion of salt gradient energy (osmotic power production) has the potential for global commercialization. Research on the net-power output and osmotic power plant configurations could result in viable methods for improvement of plant efficiency. In this paper, a novel equivalent electric-circuit model of the pressure-retarded osmosis (PRO) process is described and is used to develop a power-hardware-in-the-loop (PHIL) emulator to represent the osmotic power plant. The model considers many dynamics involved with PRO including reverse salt leakage, concentration polarization, and the salt storage capacity of water. The proposed model facilitates real-time dynamic simulation and analysis of the PRO power plant, and its interaction with the rest of the PRO power system, namely the impulse turbine and the synchronous generator supplying power to off-grid or isolated loads. The response of the multidomain system including hydraulic, mechanical, and electrical components of the system is observed, given changes in the input parameters such as source flow rate. The proposed PHIL emulator provides insight into the operational dynamics and behavior of the PRO system. The proposed real-time emulator serves as a powerful tool that can advance research and development of PRO power generation systems. Simulation and experimental results are presented in this paper to validate the PRO plant model and the operation of the proposed real-time PHIL PRO emulator. [ABSTRACT FROM PUBLISHER]

Concerns over environmental impact and high infrastructure cost heavily influence the uptake of wind turbine technology. Recently, researchers have started to look into small wind turbines, which are much smaller in size and capacity. Small wind turbines have less environmental impact and low infrastructure cost. These small turbines have limited power generation capacity, requiring multiple turbines to be connected together to produce useful power. In order to capture power from multiple wind turbines, researchers have proposed numerous topologies in the literature. However, these traditional topologies were designed for high-power wind turbines and cannot be used to connect very small wind turbines since a significant amount of the generated power would be lost in driving the complex electronic circuits used in traditional topologies. In this paper, we present a multiple-input nonisolated dc–dc converter design that collects the output power from series-connected very small wind turbines. The proposed system was implemented in our smart energy laboratory and experimental results were gathered, showing a significant improvement over existing converters. [ABSTRACT FROM AUTHOR]

This paper focuses on a review of the state of the art of future power grids, where new and modern technologies will be integrated into the power distribution grid, and will become the future key players for electricity generation, transmission, and distribution. The current power grids are undergoing an unprecedented transformation from the original design, changing the way how energy has been produced, delivered, and consumed over the past century. This new energy era includes the integration of renewable sources such as wind and solar, supported by the distributed or community energy storage, to power distribution grids through innovative high-frequency magnetic links and power-electronic converters. The use of emission free transportation, such as electric vehicles, and energy efficient technologies, such as superconducting generators and storage systems, are also rapidly emerging and will be integrated into the power grids in the foreseeable future. However, it is necessary to reconsider the current paradigms of system analysis and plan with a focus on how to achieve the most flexible, efficient, and reliable power grid for the future––the one that enables operation in a domain which is very different than the current one to deliver the services to consumers at an affordable cost. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness. [ABSTRACT FROM AUTHOR]

After years of dormant scenarios, the Brazilian industry of photovoltaic (PV) energy systems is now growing fast. Therefore, improving interconnection and certification codes, as well as standards, has become undeniably important to establish a well-regulated market under concern of businesses, equipment, and power quality matters. Thus, considering international standards and world-recognized scenarios for distributed PV systems, this paper aims to discuss the existing local energy regulations, grid connection codes, and other related limitations that restrain a faster market and technology development in Brazil. Special attention is given to the certification requirements for grid-connected inverters and other PV equipment, along with the new trends in energy policies, focusing on their impacts on Brazilian PV industry. In the end, it demonstrates that despite its current underdeveloped status, the Brazilian scenario is expected to be quite enthusiastic in the following years and may represent an attractive and dynamic market for professionals, companies, and energy investors. [ABSTRACT FROM AUTHOR]

This paper presents a new method for controlling the exchange of power between a single-phase distributed generation system and the grid. Rather than controlling the active and reactive powers separately and through the media of current signal as is done by the conventional techniques, the proposed controller acts directly on the instantaneous power. This eliminates the conventional need for calculating the active and reactive powers; a calculation that involves filtering/phase-shifting and slows down the system responses and adds to computational complexity. This paper first formulates a nonlinear structure from a purely mathematical approach based on minimizing a cost function. The minimization procedure generates a reference for the current signal which is subsequently used in the current control loop. This paper then derives an equivalent linear counterpart for the nonlinear structure. Moreover, it is also shown that the idea of controlling the instantaneous power does not require a separate loop for the current. Having replaced the nonlinear part with its linear equivalent, a control loop that comprises linear time-varying elements is obtained. This paper further develops a linear time-invariant model of the loop for stability and design purposes. The proposed control system is successfully applied to a photovoltaic system and performance evaluation results (using computer simulations and a laboratory experimental setup) are presented. Desired performance and robustness of the proposed method is verified by testing it within different operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents a new active/reactive power closed-loop control system for a hybrid renewable energy generation system used for single-phase residential/commercial applications. The proposed active/reactive control method includes a hybrid estimator, which is able to quickly and accurately estimate the active/reactive power values. The proposed control system enables the hybrid renewable energy generation system to be able to perform real-time grid interconnection services such as active voltage regulation, active power control, and fault ride-through. Simulation and experimental results demonstrate the superior performance of the proposed closed-loop control system. [ABSTRACT FROM PUBLISHER]

The European Union is targeting a 20% share of energy from renewable resources by 2020, and this increase is in turn expected to lead to operational challenges that will require various congestion management actions by system operators. In this paper, we deal with topology control of the transmission network as a congestion management resource and evaluate the impacts of topology control on the European electricity network. To do this, we co-optimize unit commitment and transmission switching over 24 h, and we use a decomposition scheme to tackle the resulting large-scale problem. Our analysis is conducted on different scenarios of load and renewable power generation. We find that topology control results in significant cost savings within Europe which tend to be inversely related to net load. The robustness of our results is supported by an extensive sensitivity analysis. [ABSTRACT FROM AUTHOR]

This paper examines the application of various classical root-finding methods to digital maximum power point tracking (DMPPT). An overview of root-finding methods such as the Newton Raphson method, Secant method, bisection method, regula falsi method, and a proposed modified regula falsi method (MRFM) applied to photovoltaic (PV) applications is presented. These methods are compared among themselves. Some of their features are also compared with other commonly used maximum power point (MPP) tracking methods. Issues found when implementing these root-finding methods based on continuous variables in a digital domain are explored. Some of these discussed issues include numerical stability, digital implementation of differential operators, and quantization error. Convergence speed is also explored. The analysis is used to provide practical insights into the design of a DMPPT based on classical root-finding algorithms. A new DMPPT based on an MRFM is proposed and used as the basis for the discussion. It is shown that this proposed method is faster than the other discussed methods that ensure convergence to the MPP. The discussion is approached from a practical perspective and also includes theoretical analysis to support the observations. Extensive simulation and experimental results with hardware prototypes verify the analysis. [ABSTRACT FROM AUTHOR]

The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry (mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and/or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry. [ABSTRACT FROM AUTHOR]

In this paper, a control scheme is proposed for a three-phase isolated photovoltaic (PV)-diesel microgrid without energy-storage element. The scheme aims to: track maximum power from the PVA, regulate the load voltage, compensate the load unbalance viewed by the diesel generator, and to control the diesel-engine speed. The first three tasks are achieved by controlling the pulse width modulation inverter interfacing the PV array to the system. The fourth is realized by a modified fuzzy logic controller of the diesel engine. The obstacles encountered on operating the system under certain probable loading conditions are addressed. Two different operation strategies are proposed to provide high-quality power under all loading scenarios, and to achieve the targets of the control scheme. The system operation is investigated under a variety of conditions to prove the aptness of the proposed techniques. [ABSTRACT FROM PUBLISHER]

This paper presents a fuzzy-optimization approach for solving the generation scheduling problem with consideration of wind and solar energy systems. Wind and solar energy are being considered in the power system to schedule unit power output to minimize the total thermal unit fuel cost. When performing the generation scheduling problem in conventional methods, the hourly load, available water, wind speed, solar radiation must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy-optimization approach is that the forecast hourly load, available water, wind speed and solar radiation errors can be taken into account using fuzzy sets. Fuzzy set notations in the hourly load, available water, wind speed, solar radiation, spinning reserve and total fuel cost are developed to obtain the optimal generation schedule under an uncertain environment. To demonstrate the effectiveness of the proposed method, the generation scheduling problem is performed in a simplified generation system. The results show that a proper generating schedule for each unit can be reached using the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a generalized approach for the estimation of average displaced or avoided system emissions by intermittent renewable sources such as wind. The quantification of the environmental benefits of renewable energy projects is of utmost importance for: 1) the creation and trading of certified emission reductions or credits and 2) the design of green energy government policies that recognize the contribution of renewable energy for the reduction of national/regional carbon emissions. The proposed approach is based on the correlation factor between time-evolution of system marginal emissions and wind power generation. Results show that average displaced emissions by wind generation can be estimated once typical power system dispatch data and regional wind generation is available, thus circumventing the use of proprietary power dispatch models. The main objective of this work is to contribute towards the development of simplified methodologies that will facilitate the assessment of renewable energy projects in a variety of regulatory and regional settings. [ABSTRACT FROM AUTHOR]

Due to the current concern about the environment, there is a growing interest in distributed generation from renewable energy Sources. Usually a power electronic convener is required to interface renewable generation units with the utility grid. The power electronic converter can be designs to provide nonactive power in addition to active power supply in order to compensate distorted currents. This paper proposes a distributed control method for converter-interfaced renewable generation units with active filtering capability. Agent-based communication makes coordination between the generation units possible. Experimental results are included to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]