Framework design of a hybrid energy system by combining wind farm with small gas turbine power plants

Owing to the stochastic characteristic of natural wind speed, the output fluctuation of wind farm has a negative impact on power grid when a large-scale wind farm is connected to a power grid. It is very difficult to overcome this impact only by wind farm itself. A novel power system called wind-gas turbine hybrid energy system was discussed, and the framework design of this hybrid energy system was presented in detail in this paper. The hybrid energy system combines wind farm with several small gas turbine power plants to form an integrated power station to provide a relatively firm output power. The small gas turbine power plant has such special advantages as fast start-up, shutdown, and quick load regulation to fit the requirement of the hybrid energy system. Therefore, the hybrid energy system uses the output from the small gas turbine power plants to compensate for the output fluctuation from the wind farm for the firm output from the whole power system. To put this hybrid energy system into practice, the framework must be designed first. The capacity of the wind farm is chosen according to the capacity and units of small gas turbine power plants, load requirement from power grid, and local wind energy resource distribution. Finally, a framework design case of hybrid energy system was suggested according to typical wind energy resource in Xinjiang Autonomous Region in China.     

Modeling and performance analysis of renewable hydrogen energy hub connected to an ac/dc hybrid microgrid

This paper proposes a system modeling and performance analysis of a renewable hydrogen energy hub (RHEH) connected to an ac/dc hybrid microgrid (MG). The proposed RHEH comprises a photovoltaic (PV)-based renewable energy source (RES) as the primary source, a proton exchange membrane fuel cell (PEMFC) as the secondary power source, and a proton exchange membrane electrolyzer (PEMELZ) that can generate and store hydrogen in a hydrogen tank. All these resources are directly connected at the dc bus of the ac/dc microgrids. The PEMFC operates and utilizes the hydrogen from the hydrogen tank when the energy generated by RES cannot meet the load demand. A coordinated power flow control approach has been developed for the RHEH to mitigate the mismatch between generation and demand in the ac/dc microgrid and produce renewable hydrogen when renewable power is in excess. The paper also proposes a modified hybrid Perturb & Observe-Particle Swarm Optimization (Hybrid PO-PSO) algorithm to ensure the maximum power point tracking (MPPT) operation of the PV and the PEMFC. The operation of the proposed RHEH is validated through simulations under various critical conditions. The results show that the proposed RHEH is effective to maintain the system power balance and can provide power-to-hydrogen and hydrogen-to-power when required.     

Energy storage based MG connected system for optimal management of energy: An ANFMDA technique

This paper proposes an efficient hybrid technique for the system modeling and the optimal energy management of the MG with low cost. The novelty of the proposed approach is the combination of the ANFIS and MDA named as ANFMDA that performs the decision making with the multi-objective problem. Moreover, the proposed method is the cost-effective power production of the microgrids and effective utilization of renewable energy sources without wasting the available energy. The strategy is worried about the mathematical optimization problems that include in excess of one objective function to be optimized at the same time. The MDA algorithm optimizes the MG configuration at minimum fuel cost to take care of the required load demand by using the inputs of MG like WT, PV array, MT, and ESS with corresponding cost functions. In the proposed approach, the ANFIS learning phase is employed to predict the load demand. Based on the predicted load demand the minimum annual fuel cost characteristics, the operation cost and also the replacement cost is decreased with all the subsequent points of the MDA. The performance of the proposed method is examined by comparison with the other techniques such as ABC algorithm, DA, and HOMER. The comparison results demonstrate the superiority of the proposed technique and confirm its potential to solve the problem.     

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.     

Modelling and optimization of the smart hybrid renewable energy for communities (SHREC)

The future energy system in community level should be more ‘smart’ to secure reliability, enhance market service, minimize environmental impact, reduce costs and improve the use of renewable energy source (RES). Therefore, this paper proposes an energy integration system – smart hybrid renewable energy for communities (SHREC). It considers both thermal (heating and cooling) and electricity market in a large community level and highlight the interactions between them through utilizing RES, combined heat and power (CHP) and energy storages. A planning model based on CHP modelling is developed for the SHREC system. A linear programming (LP) algorithm is developed to optimize the SHREC system in a weekly period and the results are compared with an existing energy optimization software. We also demonstrate the model in a sample SHREC system during three typical weeks with cold, warm and mid-season weather in the year 2011. The results indicate that the developed modelling and optimization method is more efficient and flexible for the smart hybrid renewable energy systems.     

分数阶PID混合算法的水轮机调速系统优化控制与建模分析

为深入研究水轮机调速系统的非线性动态特征,分析水轮机在负荷波动时调速系统对水轮机转速、出力及输出电压电流波形的控制情况,提出一种采用混合算法(BP-FOA)的分数阶PID(FOPID)双目标函数控制系统;并利用Matlab平台建立了能够反映动态负荷变化及发电机情况的水轮机模型,使其更具实际适应性。仿真及实例分析表明,经混合算法优化后的水轮机调速器在调节性能与鲁棒性上均有一定的提升,同时亦为水轮机动态模型建模提供了借鉴。     

Domestic integration of micro-renewable electricity generation in Ireland – The current status and economic reality

The utilisation of renewable energy resources for power generation is extremely important for Ireland due to the lack of indigenous fossil fuel resources. A micro-wind turbine is by far the most commonly used grid-connected micro-renewable electricity generation system for domestic applications in Ireland, followed by solar PV. Unfortunately, neither a single micro-wind turbine nor a single solar PV system can provide a continuous power supply due to variations in weather and climate conditions. The coupling of these two systems however can improve the power supply reliability by using the complementary characteristics of wind and solar energy. In this paper, a micro-renewable electricity-generation-system integration technique, tailored for applications in Ireland but generally applicable, is presented. Net present value is the parameter used to identify the optimal system. The optimal system can be a mono system, formed from a single micro-wind turbine or a single solar PV system, or a hybrid system formed from a combination of both. A renewable energy requirement is a constraint used in the integration to eliminate systems that cannot provide sufficient energy from renewable energy resources. The integration technique is applied to find the optimal system, under current Irish conditions, that can be formed from six sample micro-wind turbines and/or solar PV systems assembled from three sample solar PV modules. The analyses show that, with a 50% renewable energy requirement, the optimal system is a mono system containing a 2.4 kW micro-wind turbine; however, critically, the system is not economically viable. Four parameter studies assessing the effect of household electrical load, imported electricity price, exported electricity tariff and wind speed have also been conducted. From these studies it is seen that the most effective way to improve the financial performance of all systems is to offer a higher exported electricity tariff; installing a mono/hybrid system containing a micro-wind turbine in a location with a good wind resource can also have a significant effect.     

解决新疆风能资源大规模开发瓶颈的探讨

本文对如何解决新疆风能资源进行大规模开发时所遇到的瓶颈进行了探讨,提出了一种解决问题的新思路。用详细的数据介绍了新疆丰富的风能资源分布,讨论了在大规模开发时遇到的电网稳定性、有效负荷需求不足以及没有联网电网的支持等问题,最后,根据新疆拥有丰富的天然气资源,提出了采用一种新型的风电-燃气轮机互补发电系统来彻底解决风电场负荷的波动对电网稳定性不利的影响,为新疆的风能大规模开发提供了一种新的可能。     

水轮机及其调速系统建模与参数辨识方法

介绍了时域法、频域法和遗传算法等常用的电力系统模型辨识方法。以现场试验数据为基础,分别用PLPF法和遗传算法对水轮机及调速系统进行了参数辨识,结果表明遗传算法能较准确地辨识出系统各个环节的数学模型,仿真曲线更接近实际,体现了遗传算法在非线性模型辨识方面的优越性。     

Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, MG (Micro-Grid) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a CLS (Chaotic Local Search) mechanism and a FSA (Fuzzy Self Adaptive) structure is utilized. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization).     

风电与燃气轮机互补发电系统发电成本分析

在采用风电场与小型燃气轮机组成的互补系统发电特性参数的基础上,详细分析了互补系统发电成本的构成和各自的计算方法。采用新疆达坂城风电场的风速数据,基于互补系统的发电特性参数和风电场与燃气轮机电站的发电成本构成,应用改进过的等额支付折算法,在当前的技术条件和价格下,计算了风电场子系统和燃气轮机电站子系统各自的折旧成本、燃料成本和运行维护成本,得到了整滚发电系统发电成本的计算方法,为在新疆地区实现这种互补发电系统提供经济分析基础。     

风气互补发电系统与电力系统相互影响的研究

为研究风气互补发电系统对电网的影响,首先搭建了由风电机组、燃气轮机、电网线路、静止无功补偿器、电力系统稳定器和大型水力发电机组成的仿真系统,并对该系统的负载侧和电网线路中部节点进行了稳定性分析。仿真结果表明,电网在加载了风气互补系统后运行能保持稳定,并能在发生短时故障后恢复到原来状态。该文为进一步研究风气互补系统与电网的相互影响提供了良好的模型基础。     

垂直轴螺旋型风轮-光伏电池互补发电装置

提出一种新型的垂直轴式阻力差螺旋型风轮一光伏电池互补智能化控制发电照明系统。风力机由相互缠绕的两螺旋面叶片构成。通过优化螺旋面的曲率、螺距、风轮高度等几何参数,优化设计风速下风力机的叶尖速比值,提高了风能利用率;通过智能化控制不同风速下的风一光互补性能,提高了灯具的整体节能水平与智能化控制效果,延长了蓄电池的使用寿命。该垂直轴螺旋型风轮一光伏电池互补发电技术可向偏远村落、海岛渔民供电等应用领域拓展。     

Power flow control of grid connected hybrid renewable energy system using hybrid controller with pumped storage

This paper presents a grid-connected HRES using a hybrid controller with PHS for optimal power flow control and minimizing the production cost. The novelty of the proposed approach is the joined execution of the SSA and CSA named as SSA-CS are apparently a very new metaheuristic algorithm. Moreover, the proposed method is the cost-effective power production of the microgrids and effective utilization of renewable energy sources without wasting the available energy. Here, the energy sources in particular PV system, WT, MT and battery with PHS are utilized to generate the power of the MG system. In the proposed approach, the required power demand of the energy system is predicted by the ANN technique. After that, the production cost minimization is done in view of the anticipated load demand by utilizing the optimization approaches to be a specific SSA-CS algorithm. The result of the proposed approach is actualized in the MATLAB/Simulink working platform. The performance of the proposed approach is examined by comparing the current methodologies such as SSA and PSO with the proposed SSA-CS approach. The simulation results show that the proposed method generates maximum power and furthermore the proposed framework has less production cost in light of the power demand.     

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

This paper proposes an efficient hybrid approach–based energy management strategy (EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasi‐oppositional‐chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid‐connected MG system is continuously tracked by the RF technique. The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two‐strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques.     

The concept of a smart wind turbine system

A smart wind turbine concept with variable length blades and an innovative hybrid mechanical-electrical power conversion system was analyzed. The variable length blade concept uses the idea of extending the turbine blades when wind speeds fall below rated level, hence increasing the swept area, and thus maintaining a relatively high power output. It is shown for a typical site, that the annual energy output of such a wind turbine that could double its blade length, could be twice that of a corresponding turbine with fixed length blades. From a cost analysis, it is shown that the concept would be feasible if the cost of the rotor could be kept less than 4.3 times the cost of a standard rotor with fixed length blades. Given the variable length blade turbine system exhibits a more-or-less linear maximum power curve, as opposed to a non-linear curve for the standard turbine, an innovative hybrid mechanical-electrical power conversion system was proposed and tested proving the feasibility of the concept.     

Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m . This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.     

The importance of detailed data utilization on the performance evaluation of a grid-independent hybrid renewable energy system

In this study, a multi-source hybrid power system consisting of wind turbine (WT), photovoltaic (PV) solar unit, proton exchange membrane (PEM) FC and battery is proposed. The WT and PV generation systems are considered as the main power sources for utilizing the available renewable energy. The FC system is proposed as the back-up generation combined with electrolyzer unit and battery picks up the fast load transients and ripples. In such a hybrid system, energy management plays an important role for the overall system performance and durability. From this perspective, a fuzzy logic based intelligent controller is considered in this study. Besides, a detailed minute-scale meteorological and load demand data is utilized in the simulation process and the importance of utilization of such detailed data is presented. This detailed analysis may be valuable for evaluating the feasibility of grid-independent hybrid renewable energy units for upcoming power systems.     

Experimental performance assessment of an online energy management strategy for varying renewable power production suppression

Lately, interest in renewable sources, especially wind and solar energy, has shown a significant increase in all over the world that mostly depends on climate-threatening conventional fossil fuels. Besides, hybrid use of these power sources with suitable back-up units provides many advantages compared to sole use of these sources. In this regard, a hybrid system consisting of a wind turbine for utilizing the wind energy, photovoltaic panels for solar energy, fuel cell for providing back-up power and a battery unit for storing the possible excess energy production and supplying the transient load is considered in this study. Experimental assessment of this system in different case studies including the real time measured dynamic power demand of an office block is realized. The collaborative actions of the proposed hybrid system with a fuzzy logic based energy management strategy during fluctuations of renewable-based power production are investigated. Thus, results of this study may be valuable for evaluating the feasibility of stand-alone hybrid renewable energy units for future power systems.     

Performance investigation of a novel near-isothermal compressed air energy storage system with stable power output

As one of the grid-scale energy storage technologies, compressed air energy storage (CAES) is promising to facilitate the permeability of renewable energies. By integrating CAES into renewable sources, the fluctuation and intermittence of renewable energies could be effectively restrained. Among various CAES system configurations, isothermal CAES (I-CAES) is considered as a most competitive technology with expected high efficiency. However, most of the existing I-CAES systems have trouble in keeping a stable power output. To address this issue, a novel near-isothermal CAES system is proposed in this article to acquire a near stable power output. Imitating the concept of hydraulic accumulator, a two pressure vessels structure is employed to maintain the gas pressure stable during discharging. Besides, the turbine power output can be controlled by adjusting the liquid flow rate of the Pelton turbine under this near constant pressure condition. Based on the system transient model and economic model, the system components transient behavior, parametric analysis, off-design performance analysis and economic evaluation issues are also conducted. Results show that system round trip efficiency (RTE) with 61.42% and energy density (ED) with 0.2015 kWh/m³ can be achieved under design condition. In the discharge process, the gas pressure in vessel varies in a small range, from 68 to 72 bar, which is relatively stable. The power output from Pelton turbine can be maintained around 1 kW. Meanwhile, the initial pressure, the pipe diameter, and the spraying flow rates of circulating pumps have significant effects on system RTE and ED. Furthermore, the Pelton turbine power output level can be adjusted by adding jets number, and the higher storage pressure can make the power output unsteady.