Wind Power Units Research Articles

Short-Term Scheduling Strategy for Wind-Based Energy Hub: A Hybrid Stochastic/IGDT Approach

This paper evaluates the scheduling problem for energy hub system consisting of wind turbine, combined heat and power units, auxiliary boilers, and energy storage devices via hybrid stochastic/information gap decision theory (IGDT) approach. Considering that energy hub plays an undeniable role as the coupling among various energy infrastructures, still it is essential to be investigated in both modeling and scheduling aspects. On the other hand, penetration of wind power generation is significantly increased in energy infrastructures in recent years. In response, this paper aims to focus on the hybrid stochastic/IGDT optimization method for the optimal scheduling of wind integrated energy hub considering the uncertainties of wind power generation, energy prices and energy demands explicitly in a way that not only global optimal solution can be reached, but also volume of computations can be lighten. In addition, by the proposed hybrid model, the energy hub operator can pursue two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. This method optimizes energy hub scheduling problem in uncertain environment by mixed-integer nonlinear programming. This formulation is proposed to minimize the expected operation cost of energy hub where different energy demands of energy hub would be efficiently met. The forecast errors of uncertainties related to wind power generation and energy demands are modeled as a scenario, while an IGDT optimization approach is proposed to model electricity price uncertainty.

Optimal Power Flow of a Power System Incorporating Stochastic Wind Power Based on Modified Moth Swarm Algorithm

The combined heat and power (CHP) generator not only generates electrical power but also generates heat energy in a single process, which decreases the emission level significantly. The integration of wind power into the power system will lead to an impact on the economic operation of the system as well as the bus voltage and transmission losses. In this paper, a formulation of optimal power flow (OPF) problem of a power system incorporating stochastic wind power is presented. To solve this problem, a modified version of the moth swarm algorithm (MMSA) is proposed. Three objective functions, which are the minimization of operating cost, the minimization of transmission power loss, and the voltage profile improvement, are considered in this paper. To minimize the operating cost, the direct, overestimation, and underestimation costs of wind power units are considered. Two test systems are considered to prove the effectiveness and the superiority of the proposed MMSA in comparison with other methods. The comparison with other methods proves the efficiency and the superiority of the proposed MMSA.

Real‐time energy management of a smart virtual power plant

The real-time (RT) energy management of virtual power plants (VPPs) is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This article presents a comparative analysis on alternative energy management models for a VPP that includes a wind power unit, an energy storage unit, and some flexible demands, which are interconnected within a small size electric energy system. The smart grid technology enables RT operation of the VPP by taking advantage of bidirectional communication infrastructure. To accomplish this task, the RT energy management is implemented through alternative decision-making tools, namely, (i) a robust model, (ii) a stochastic programming model, and (iii) a stochastic robust model. To handle uncertainties, these optimisation models use prediction intervals, scenarios, and a combination of both, respectively. A realistic case study provides a comparative out-of-sample analysis considering the impact of the different parameters used to manage risk in these three models.

Research on optimisation of integrated energy system scheduling based on weak robust optimisation theory

This study establishes a multi-objective model for comprehensive energy system scheduling optimisation with wind farms, combined heat and power units, power to gas technology, circulating fluidised bed boilers and gas boilers. The goal is to minimise the operating costs and minimise the discharge of pollutants while meeting the constraints of the material balance of the circulating fluidised bed boiler. The improved weak robust optimisation theory is used to deal with the uncertainty of wind power, and the weak robust coefficient is introduced. By adjusting the coefficient, the optimal solution under the different tolerances of the deterioration of the objective function is obtained. The improved bacterial population chemotaxis algorithm is used to optimise the comprehensive energy system scheduling model, and a feasible solution with certain robustness is obtained. Sensitivity analysis under different risk levels is done. The simulation verifies the correctness and effectiveness of the optimised scheduling model and the improved algorithm.

Optimal bidding strategy of coordinated wind power and gas turbine units in real-time market using conditional value at risk

Nowadays, the incorporation of wind power in electrical grids and electricity markets is grown. Due to the fluctuation of wind speed, one of the main challenges of wind power would be selling power directly to the wholesale markets. A method for solving this challenge is coordination of wind power with energy storages, cascaded hydro, or gas turbine units in bidding strategy and operation. By coordinating with gas turbine units, wind power can be incorporated in real-time markets with fewer capital costs. In this paper, a stochastic bi-level optimization is proposed for coordinated wind power and gas turbine units in the real-time market. The uncertainties of wind power, demands, rivals' biddings, and limitations of natural gas are considered. The optimal bidding strategy is determined by adding a barrier term into the objective function of the proposed optimization model. The added term is based on the concept of conditional value at risk.

Fault Diagnosis of Wind Turbine Gearbox Based on Wavelet Transform

As a kind of clean and renewable energy, wind energy is attracting more and more attention from all over the world. But the development of wind power is always hindered by the failure of downtime. The gearbox of wind power unit is one of the components with the highest failure rate in the whole wind turbine. Therefore, it is of great significance to study the fault diagnosis of gearbox in wind turbine. This paper first introduces the mathematical principle of wavelet analysis, and takes Matlab as an experimental platform to analyze and deal with the pitting fault data of a fan gearbox, and identifies the fault frequency by wavelet packet energy method, and diagnoses the type of failure. Finally, the experimental results are compared with other methods to deal with the data. Through the research in this paper, we can see that the data processing method based on wavelet analysis is simple and practical, especially in the engineering field represented by gearbox fault diagnosis.

A Solution to Non-convex/Convex and Dynamic Economic Load Dispatch Problem Using Moth Flame Optimizer

Moth flame optimization algorithm is novel nature inspired heuristic paradigm inspired by navigation method of moths in nature and based on the concept that the moth eventually converges toward the light. This paper presents the application of MFO algorithm for the solution of non-convex and dynamic economic load dispatch problem of electric power system. The performance of MFO algorithm is tested for non-convex, convex and dynamic economic load dispatch problem of seven IEEE benchmarks and the results are verified by a comparative study with lambda iteration method, particle swarm optimization algorithm, genetic algorithm (GA), artificial bee colony, evolutionary programming (EP) and grey wolf optimizer (GWO). Also, in the proposed research, the impact of renewable energy sources (i.e. wind and solar) has been taken into consideration along with conventional thermal power generating units. Also, critical analysis has been made for percentage cost saving with due consideration of solar and wind power units and it has been experimentally observed that the addition of renewable energy sources to conventional thermal power system results in significant cost saving. Comparative results show that the performance of Moth Flame Optimizer Algorithm is better than recently developed GWO algorithm and other well known heuristics and meta-heuristics search algorithms.

A multistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning

ABSTRACTIn this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning.

Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating

The penetration of renewable energy resources into the power system is rising prominently worldwide, and is causing increased uncertainty in power system operations. Increasing loads and scarce transmission resources have exacerbated this issue, imposing critical operational challenges, such as congestion issues, on the power system operator (SO). This study proposes a unit commitment that employs dynamic line rating (DLR) to develop a method that enables efficient use of network resources. A mathematical formulation is suggested, based on two-stage stochastic programming, to manage the newly introduced uncertainties related to DLR properly. In addition, we build approximated power-flow equations for the proposed model. The performance of the proposed method is verified using a six-bus system and the modified IEEE 118-bus system integrated with wind power units. The simulation results clearly demonstrate that, with DLR, an SO can significantly reduce the expected operating cost. This advantage stems from the sufficiently effective utilization of the existing transmission line, which leads to the optimal commitment of the generating units and dispatched volume, and efficient use of the harvested wind energy. Moreover, the results, which demonstrate that the expected-energy-not-supplied is markedly reduced by incorporating the DLR, prove that the proposed method is highly reliable.

МЕТОДЫ ИССЛЕДОВАНИЯ ПРОЦЕССОВ ТРАНСПОРТИРОВКИ И ПЕРЕГРУЗКИ УЗЛОВ ВЕТРОЭНЕРГОУСТАНОВОК

The article considers the possibility of using modeling tools to improve transportation process of wind power units to the places of their operation. Optimization processes of handling and moving heavy and long parts of wind power plants using software for simulation modeling PTV Vision are taken as a subject of research. The aim of the study is finding ways how to avoid accidents and reduce the risk of damage to both elements of wind power plants and lifting and transportation equipment

Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth

In recent years, scientists have extensively focused on renewable energy resources such as wind power and solar units to reduce the consumption of fossil fuels as the main source of environmental pollutions. One of the main challenges for the production of wind and solar energies is that they often involve uncertainties due to the stochastic natures of wind speeds and solar radiation. Therefore, the existing uncertainties in the resources of wind and solar units must be considered in the planning procedure of distribution systems for having a reliable performance. To consider uncertainties in the output power of renewable energy resources, a new formulation is proposed to determine the optimal location and sizing of solar and wind units in the distribution system. To achieve this, Particle Swarm Optimization (PSO) algorithm as a robust technique is employed to find the best location and sizing of wind, solar and fuel cell units in the distribution system. The main objective of the present work is to minimize the Total Harmonic Distortion (THD), the total power losses, the total cost of DG units (including investments, replacement, operation and maintenance costs) and greenhouse gas emissions. Different types of loads such as linear and non-linear loads as well as load growth are also considered in the distribution system. To demonstrate the effectiveness of the proposed algorithm, 31-bus test system is considered as the case study. Our findings show that the use of renewable energy resources significantly reduces the greenhouse gas emissions in the mentioned system. Furthermore, the simulation results reveal that DG units must be added at some years to keep the voltage within its allowable limits.

Load Shedding Control of Wind Turbine Based on Model Predictive Control Algorithm

With the increasing capacity of wind turbines, the key parts of wind turbine bear the increasing load, and the structural reliability have become increasingly demanding, thus, Requirements of wind turbine control system control algorithm, can not only realize power optimization control, also can achieve down load control. Based on the theory of model prediction, design a nonlinear, variable parameter, wind turbines model predictive controller for transmission chain load slow target, designed the Matlab and GL bladed joint simulation model predictive controller. Case study adopted 2MW doubly-fed wind power unit model parameter, design variable pitch model predictive controller respectively MPC and MMPC composite model predictive controller, and simulation comparison and application of a wide range of PI control, it is shown the results that the model predictive control to reduce the wind turbine speed fluctuation amplitude, leading to smaller gear dynamic torque ripple; With the soft logic composite model predictive control algorithm, action frequency of variable pitch control is decreased.

Reliability assessment of generating systems containing wind power and air separation unit with cryogenic energy storage

The acceptance of wind power has been increased in power systems because of environmental problems and production cost of conventional generation units. High penetration of intermittent wind power can increase risk level in power systems. The combination of energy storage with wind power is considered as a solution for problems of high wind integration. Energy storage can increase the reliability of power systems with high penetration of renewable energies like wind farms. In this paper, the cryogenic energy storage is used, which stores the air in the form of liquid and recover the power when it is needed. The copula theory is employed to generate correlated random variables to determine the wind speed of different locations. Also, for calculating the probability of availability of wind farm and energy storage system, Markov approach is adopted. Monte Carlo Simulation (MCS) method is implemented for obtaining reliability index of the system. The proposed method is verified using comprehensive simulations on Roy Billinton test system (RBTS) reliability test system considering the capacity of wind farms, level of wind penetration and size energy storage.

Scenario-based stochastic optimal operation of wind/PV/FC/CHP/boiler/tidal/energy storage system considering DR programs and uncertainties

BackgroundMicro-grid (MG) can be described as a group of controllable loads and distributed energy resources that can be connected and disconnected from the main grid and utilized in grid-connected or islanded modes considering certain electrical constraints.MethodsThe objective of this article are as follows: (1) predict the uncertainties through the hybrid method of WT-ANN-ICA and (2) determine the optimal generation strategy of a MG containing wind farms (WFs), photovoltaic (PV), fuel cell (FC), combined heat and power (CHP) units, tidal steam turbine (TST), and also boiler and energy storage devices (ESDs). The uncertainties include wind speed, tidal steam speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT) in order to reduce fluctuations in the historical input data. An improved artificial neural network (ANN) based on the nonlinear structure is applied for better training and learning. Furthermore, the imperialist competitive algorithm (ICA) is applied to find the best weights and biases for minimizing the mean square error of predictions.ResultThe scenario-based stochastic optimization problem is proposed to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties.ConclusionsIn this study, three cases are assessed to confirm the performance of the proposed method. In the first case study programming, MG is isolated from grid. In the second case study, which is grid-connected mode, the WT-ANN-ICA and WT-ANN uncertainty prediction methods are compared. In the third case, which is grid-connected mode, the effect of DR programs on the expected profit of energy resources is assessed.

Optimal operation of a micro-grid containing energy resources and demand response program

In profit-based unit commitment, the objective of programming is to maximize profit and optimize generation. Practically, the gross profit depends not only on the revenue but also on the total expenditures. In this article, an efficient algorithm is suggested to assess the effect of uncertainties in determining 24-hour optimal strategy of a microgrid (MG) containing wind farms, photovoltaic, fuel cell, combined heat and power units, boiler, and energy storage devices (ESDs). The optimization problem is presented to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. The uncertainties include wind speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT)-artificial neural network (ANN)-imperialist competitive algorithm (ICA). In this study, three cases are assessed to confirm the performance of the proposed method. In the first case study, programing MG is isolated from the grid. In the second case study, which is grid-connected mode the WT-ANN-ICA and WT-ANN uncertainties predictions methods are compered. In the third case, which is grid-connected mode the effect of DR programs on the expected profit of energy resources is assessed.

Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid

Abstract This paper presents a new coordination framework to optimize the joint operation of pumped-storage unit, irrigation system and intermittent wind power generation in an agricultural microgrid. The microgrid is an agricultural complex connected to the medium voltage network. This complex contains a farm needing water to be irrigated every day. The irrigation system has two large scale reservoirs located in different levels. These reservoirs have been planned to act as pumped-storage unit reservoirs. The microgrid contains different electrical loads such as industrial livestock, agricultural products packaging factory, drip irrigation system, lighting and other small loads. The microgrid is connected to the upstream network at the grid supply point (GSP) and supplies loads with power from the upstream network and a wind power unit located in the farm. The proposed framework optimizes day-ahead (DA) scheduling of power exchange with the upstream network, pumped-storage unit and irrigation system based on forecasted wind power, microgrid load demand and water needed for irrigation in a market environment. The two-point estimate method (TPEM) is implemented to model the uncertainties associated with wind power and wholesale market prices. Finally, the effectiveness of the proposed framework is evaluated on several case studies.

Autonomous Electrothermal Facility for Oil Recovery Intensification Fed by Wind Driven Power Unit

This paper describes the structure of autonomous facility fed by wind driven power unit for intensification of viscous and heavy crude oil recovery by means of heat impact on productive strata. Computer based service simulation of this facility was performed. Operational energy characteristics were obtained for various operational modes of facility. The optimal resistance of heating element of the downhole heater was determined for maximum operating efficiency of wind power unit.

A Stochastic Adaptive Robust Optimization Approach for the Offering Strategy of a Virtual Power Plant

This paper proposes a novel approach for the offering strategy of a virtual power plant that participates in the day-ahead and the real-time energy markets. The virtual power plant comprises a conventional power plant, a wind-power unit, a storage facility, and flexible demands, which participate in the day-ahead and the real-time markets as a single entity in order to optimize their energy resources. We model the uncertainty in the wind-power production and in the market prices using confidence bounds and scenarios, respectively, which allows us to formul-ate the strategic offering problem as a stochastic adaptive robust optimization model. Results of a case study are provided to show the applicability of the proposed approach.

Wind Generation and Electric Vehicles coordination in Microgrids for Peak Shaving purposes

Nowadays, the urgent need to reduce emissions has led to the development of Renewable Energy Sources (RES). Wind energy via Wind Generators is a very common RES, which, however, is characterized by great intermittency. Consequently, wind energy production may vary from zero to excessive (way above the load demand), which makes wind power plants’ penetration very difficult, making the need for energy storage essential. On the other hand, Electric Vehicles (EVs) offer a new, eco-friendly and more effective means of transportation. The energy stored in their batteries is used to move the EVs. Nevertheless, the penetration of EVs will increase the energy demand from Thermal Plants, and will therefore harm the environment unless it is combined with an increased RES integration. The advantages of combined use of RES and EVs are evident. EVs can augment Wind Power Plants penetration through their storage capabilities and use some of their excessive wind energy output for environmental-friendly mobility. In this paper, the combination of EVs, RES (Wind Generators) and Thermal Plants of an island is examined as an alternative for the future grids. Two scenarios are being studied with focus on peak shaving. Taking into account the Medium Voltage (MV) line load demand, wind power and thermal units output hourly data in a yearly basis, a special algorithm is developed so as to study the case of absorbing the excess energy via off peak charging and reintroducing it into the Microgrid scheme in peak demand periods. The studies are conducted for two cases: a) low penetration and b) high penetration of EVs. All findings are thoroughly discussed in conclusions section.

Wind-based Flow Battery Energy Storage Control System

A methanesulfonate lead flow battery (MLB) stack under control is proposed. The same electrolyte was used as both the negative and positive electrodes without film separation avoiding maintenance costs and short life. MLB is designed and related charging discharging character is studied. Furthermore, the remote island case study is given and related control system is implemented. Wind turbine determines the volatility and uncertainty of wind power unit mass grid, which will seriously affect the power system operation state, wind power grid problem increasingly prominent. So that it will solve off-grid, abandoned wind and other problems that Non-grid wind power using local storage by methanesulfonate lead flow battery stack.