Chapter (1)
Introduction
1.1 Introduction
Renewable energy technologies considered clean sources of energy that have a much lower impact in environmental than traditional energy technologies such as coal, oil, nuclear and natural gas. In addition, renewable energy resources will never give out while conventional sources of energy are finite and will someday be used up. Most renewable energy (solar, bio, wave, and the wind) is derived from the sun, directly or indirectly, while other types of renewable energy resources (geothermal, hydrogen, and tidal) come from different sources other than the sun. For example, the geothermal energy comes from the Earth internal heat. One of the most utilized renewable energy sources is wind energy.
The first wind turbines for electricity generation already had been developed at the beginning of the twentieth century. Since the early 1970s, the technology has improved step by step. By the end of the 1990s, wind energy became the most important resources of sustainable energy. During the last decade of the last century, worldwide wind capacity has multiplied approximately every three years. Wind power development and the issues concerning with wind integration are very vital. Since the 1990s, power generation from renewable resources has seen rapid development this is due to a result of high fossil fuel prices and greenhouse effects [1].
According to the news from the Global Wind Energy Council (GWEC), the annual installed wind capacity for the period 1997-2014 years is shown in Fig.(1-1). The wind power growth rate in the global market for five years is forecasted by GWEC and the report in the Global report is published 2014 [2]. As shown in Fig.(1-2), The data forecasting.
Fig. (1-1): Wind capacity installed in world annually
Fig.(1-2): Growth rate of wind power
Onshore wind energy technology is already a mature technology since it has been largely installed throughout in the last years. However, suitable places onshore are becoming rare. Therefore, countries are now starting to install wind turbines offshore, where space is more abundant and the wind has higher speeds since there are no obstacles in the open sea. So, offshore wind is able to provide higher and steadier energy yields.
European Wind Energy Association (EWEA) shows the growth of both onshore and offshore wind power plants from 1990 and it is projected till 2030 as depicted in Fig.(1-3). By the year 2030, the total installed wind power capacity will be 400 GW, out of which 150 GW will be offshore wind power installations. However, due to better capacity factor, the offshore wind power will be contributing around approximately half of the total wind energy production, as shown in Fig.(1-4). By the year 2050, it is projected that there will be 350 GW onshore and 250 GW offshore wind power installation. Thereby, it will be able to meet 50% of the electricity demand in the European Union [1].
Fig.(1-3): Total installed wind power capacity in the EU, projected till 2030
Fig.(1-4): Wind power energy production in the EU, projected till 2030
For large offshore wind farms (OWFs), the distance is long (over 50-80 km) to shore makes the connection of high voltage alternating current (HVAC) unsuitable economically and technically. When connecting to the grid through HVAC, there will be a great influence on the stability and power quality of the grid. The electricity transmission should be done in direct current (DC); otherwise, a large amount of the energy would be lost in the transport. Voltage source converter high voltage direct current (VSC-HVDC) transmission becomes more effective to combine large scale OWFs into the onshore AC power grid, due to its high capacity, stabilization potential and advanced controllability for AC grids etc.
The wind fluctuations over the wind farms lead to variations in the generated active power of the wind turbine generators. These fluctuations in wind speeds may cause some serious problems with respect to voltage and frequency oscillations when a large number of wind power generators are linked to the grid system. For quick and effective compensation in components of fluctuation which generated by a wind farm (WF), the generators of a wind farm are combined with an energy storage system. Several energy systems storage like compressed air energy storage (CAES) units, battery energy storage system (BESS) units, and superconducting magnetic energy storage (SMES) units combined with a WF to reduce the generated power fluctuation.
1.2 Background Literature Survey
This paper [4] operated with the OWFs connected to the grid through multi-terminal HVDC (MT-HVDC) transmission system. It studied three MT-HVDC configurations, each with different control strategies. This paper aimed to study dynamic behavior and stability of MT-HVDC system through PSCAD/EMTDC simulations. It used droop control strategy to perform independent coordination between converters without the need of communication. The results of simulation results showed the performance of control during a change in power demand from the grid side converter and disconnect of VSC.
Ref [5] proposed reduced models of aggregated wind turbines of various DFIG. It operated with a wind farm (WF) with various wind turbines connected to the same AC grid connection point. The paper presents a comparative research of four equivalent models for aggregating wind turbines of various DFIG. It concluded firstly that the cluster equivalent model is suitable for application to wind farms with identical wind turbines in a cluster on simplex terrains (offshore or smooth). Secondly, the compound model with equivalent wind for WFs composed of wind turbines with similar rated power is suitable on simplex terrains (smooth or offshore). Finally, the compound model without aggregation in mechanical and the mixed equivalent model for those with different wind turbines are suitable for any kind of terrain.
The aim of work [6] was done to represent a method of multi-machine dynamic equivalent which basis on a fuzzy C-means taking into account DFIG active power characteristics. Firstly, it can characterize the active output power performance of a DFIG. Secondly, the algorithm of a fuzzy C-means clustering is employed for modeling the WF. DFIGs are split by analyzing the indicator data into groups with a fuzzy C-means. Finally, for achieving the equivalent dynamic modeling of a WF with DFIG, the same group of DFIGs is equivalent as one DFIG. A dynamic equivalent model of a DFIG WF is established based on MATLAB/Simulink program. The results of simulation show the validation of the proposed modeling method; meanwhile, the WF model is simplified and complexity of computation is decreased.
This work [7] present the adaptive artificial neural network(ANN) to control SMES for enhancing the wind generator dynamic stability which coupled to the main AC grid. It used cascade control for the voltage source converter(VSC) and a two quadrant DC-DC chopper. It compared the proposed system and a conventional PI controlled superconducting magnetic energy storage using the standard PSCAD/EMTDC environment. It deduced that the system using adaptive ANN controlled SMES is found better damped than using PI controlled SMES and it improve the wind turbine generator transient stability.
Ref [8] proposed a double fuzzy logic control strategy optimizing the management of the SMES by combining the forecasting wind power and the real time control of the wind power system. The double fuzzy logic control algorithm is applied to regulate the variable time constant of the low-pass filter so as to indirectly control the charging–discharging power of the SMES. It makes SMES can not only smooth the power fluctuations from the wind turbine, but also prevent the SMES from occurring of the state of deep-discharge / over-charge. It concluded the proposed control method can more fully utilize SMES capacity to smooth the active power fluctuations from a wind turbine, and achieve the better smoothing effect, in comparison with the traditional control strategy.
The work carried out by [9] described a new aggregation technique using incorporation of a mechanical torque compensating factor (MTCF) to the aggregated wind farm (AWF) model. MTCF deals with the wind turbines nonlinearity in the partial load area and to make it close to a detailed model of the WF. It uses a large scale offshore wind farm (OWF) including of 72 wind turbines of DFIG type to verify the validation of the proposed AWF model. The simulation results displayed that the proposed technique can give more accurate results and save computation time.
The aim of work [10] was done to represent a probabilistic clustering for aggregating of wind farms modeling. The technique is proposed to set the equivalent number of wind turbines and their corresponding parameters that can be applied most frequently throughout the year to model a WF accurately. The paper used a technique of support vector clustering to group wind turbines based on incoming wind and WF design. The method is illustrated on a 98 MW WF consisting of 49 WT each driving a 2 MW doubly fed induction generator (DFIGs). Results obtained using the full dynamic model of the WF is compared against those obtained with the equivalent probabilistic model.
Reference [11] operated on increasing the precision of the single machine equivalent model of the WF. It proposed a method for an equivalent model for DFIG WF employing equivalent of maximum power curve taking into account the wake effect. The system is built in Matlab to proof the proposed equivalent modeling method effectiveness. Simulations have been executed under fluctuations in the wind speed and grid fault, and the simulation results display that the precision of the single equivalent model with the equivalent of maximum power curve of the DFIG WF has been improved.
Reference [12] discussed the aggregated control of active power in a VSC-HVDC system, interconnecting two asynchronous onshore AC grids and one OWF. Two different configurations are used, namely, a multi-terminal HVDC and a system using 2 point-to-point connections configuration. It presented for both two configurations an aggregated control system. The simulation results in MATLAB/Simulink show the validity of the two control strategies to distribute the power coming from an OWF and thereby allowing additional power transfers between two non-synchronized AC systems.
1.3 Thesis Outline
The thesis contains six chapters can be summarized as follow:
Chapter one:
This chapter presents a preface about wind farm and summarized some previous work.
Chapter two:
This chapter studies some types of aggregation techniques models in a wind farm. It also studies the detailed model of DFIG and permanent magnet synchronous generator (PMSG) wind turbines then compare the complete model of each wind farm type and their equivalent model. MATLAB/Simulink program is used to carry out the wind farm.
Chapter three:
This chapter puts a detector to link the complete wind farm and the AWF model. As when some of the wind turbines are tripping out during simulation we can’t get the actual output power of the AWF model. This detector detects the tripping out wind turbines of the complete model and sends data to the aggregated wind farm model to tripping out the power curve of the same wind tripping wind turbines without stopping the simulation. It also studies the detailed model squirrel cage induction generator (SCIG) wind turbine.
Chapter four:
This chapter presents offshore wind farms consisting of DFIG and PMSG wind turbines connected to Active network (AC grid) and Passive network (loads) through multi-terminal HVDC transmission system. It discusses the effect of using an SMES unit in a hybrid power system contains OWF. MATLAB/SIMULINK program is used to carry out this system to prove the SMES unit effectiveness during tripping some of the turbines, fluctuation in wind speeds, load change and voltage dips.
Chapter five:
This chapter includes conclusions obtained from the study.