Robust design and optimization for autonomous PV-wind hybrid power systems

This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.

Overview of Maximum Power Point Tracking Control Methods for PV Systems

Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

Research and practice of designing hydro/ photovoltaic hybrid power system in microgrid

Small-hydro power station is often used in remote areas beside a river,but it doesn't match electricity demand so well,especially in dry seasons. A photovoltaic (PV) system with battery is a suitable option to complement the electricity gap. In this paper,a new structure of megawatt-class PV system integrating battery at DC-bus (DC: direct current) is proposed to be used in hydro/PV hybrid power system,and 4 main designing considerations and several key equipments are discussed. In 2011,a 2 MWp PV station with the proposed structure was built up in Yushu,China. From stability analysis,the station shows a strong stability under load cut-in/off and solar irradiance's fluctuation.

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

Design of Real Time Battery Management Unit for PV-Hybrid System by Application of Coulomb Counting Method

This paper presents a real-time battery management unit designed by applying the Coulomb counting method and intended for use in an integrated renewable energy system for PV-Hybrid power supply. Battery management is required to stabilize hybrid systems and extend battery lifetimes. The battery management unit is divided into three main stages. Firstly, analysis of the basic components of the battery type used in the system is considered. Secondly, the state of charge (SOC) estimation method and the deterioration factor of the battery are analyzed. Finally, the overall battery management system, including a computer-based measurement and control unit, is constructed. The control system displays real-time information through LabVIEW 8.5 by estimating the state of charge through various measurements. The system will issue alerts when malfunctions are detected, and the operator can analyze and react to the system in real time to stabilize the system and extend the battery lifetime.

Calculation Method and Analysis for the Annual Power Generation of PV Faades in China

The application of PV faades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand,this paper investigates PV faade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV faade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV faades. The results show that with various cities,building orientations,building forms,materials and arrangements of PV modules,there is a distinct difference in the electrical output energy of PV faades. Weather conditions play a very important role in terms of PV generation performance of PV faades.

Modeling and Optimization of Stand-Alone Hybrid Power System : a Case Study for a Household in Urumqi,China

In order to alleviate environmental pollution of Urumqi,China,a stand-alone hybrid wind / PV / battery power system was studied for this region. An accurate structure and model of the standalone hybrid wind / PV / battery power system for a household in that region was presented in this study. On the basis of wind speed,solar radiation,ambient temperature,and load data,the optimal design of the hybrid system is determined using a genetic algorithm( GA). As a result,the optimal hybrid system for a household consists of one wind turbine,21. 407 6 m^2 of PV arrays,and 20. 958 kW ·h of battery bank capacity. The system has a loss of power supply probability( LPSP) of 0. 019 9 and the minimum total annualized cost is $ 35 333.

Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other

Influence of Time Complementarity on Energy Storage through Batteries in Hydro PV Hybrid Energy System

The notion of energetic complementarity can be a tool for energy resource managers to prioritize energy generation projects based on renewable resources in both interconnected and independent systems. As a tool in decision-making, it is important to know better the influence of energetic complementarity on the performance of hybrid systems especially with regard to energy shortages but also in relation to other parameters. In recent years, hydro PV hybrid systems have become a growing target of researchers and designers for the idea of installing photovoltaic modules on the water surface of reservoirs. Energetic complementarity has three components: time-complementarity, energy-amplitude and amplitude-complementarity. This paper is dedicated to the study of the influence of time-complementarity on the storage of energy through batteries in hydro PV hybrid systems. The method applied is in the literature and suggests the simulation of the system under study with the idealization of energy availabilities, to remove the effects of climatic variations and the characteristic intermittency of renewable resources. Simulations were performed with the well-known software Homer. The results provided the variations of the states of charge of the batteries as a function of different time-complementarities, indicating as expected better performances associated to higher time-complementarities. The results indicated that the cost of energy for a hybrid system with 28 batteries was equal to US$ 0.502 per kWh and that this cost increased as the time complementarity between energy resources moved away from the situation corresponding to full complementarity. The simulations also showed that the maintenance of the zero failure condition supplying the demands of the consumer loads requires that the load be reduced to 52% if the complementarity is reduced from the full complementarity to zero complementarity, with the cost of energy going from US$ 0.502 per kWh to US$ 0.796 per kWh. The results also allow a better understanding of the influence of time complementarity on the performance of hybrid systems.

Power Electronics and Its Application to Solar Photovoltaic Systems in India

Out of many renewable energy resources, solar energy is one of the conspicuous sources of energy which can supply the increasing demand of energy. As of May 2014, India has an installed PV capacity of 2.5 GW. The solar photovoltaic project includes power electronics with high quality performance devices, incorporated with smart energy management principles. Power electronics is used to improve the energy efficiency of apparatus, and help the generation of environmentally clean energy. In this article the explanation of role of power electronics and the discussion about similar and future concepts in solar photovoltaic systems related to reliability and advancement of each technology in India has been presented.

Economic Analysis and Environmental Impacts of a Hybrid PV System in Arid Climate Considering Different Types of Solar Trackers

This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.

Study and Design of a DC/AC Energy Converter for PV System Connected to the Grid Using Harmonic Selected Eliminated (HSE) Approach

Nowadays, distributing network-connected photovoltaic (PV) systems are expanded by merging a PV system and a Direct Current (DC)/Alternating Current (AC) energy converter. DC/AC conversion of PV energy is in great demand for AC applications. The supply of electrical machines and transfer energy to the distribution network is a typical case. In this work, we study and design a DC/AC energy converter using harmonic selective eliminated (HSE) method. To this end, we have combined two power stages connected in derivation. Each power stage is constituted of transistors and transformers. The connection by switching of the two rectangular waves, delivered by each of the stages, makes it possible to create a quasi-sinusoidal output voltage of the inverter. Mathematical equations based on the current-voltage characteristics of the inverter have been developed. The simulation model was validated using experimental data from a 25.2 kWp grid-coupled (PV) system, connected to Gridfit type inverters. The data were exported and implemented in programming software. A good agreement was observed and this shows all the robustness and the technical performances of the energy converter device. It emerges from this analysis that the inverter output voltage and the phase angle thus simulated are very important to control in order to orientate the transfer of the power flow from the continuous cell to cell to the alternating part. Simulated and field-testing results also show that increases in the value of the modulation factor (m) for low power output are highly significant. This study is an important tool for DC/AC inverter designers during initial planning stages. A short presentation of the design model of the inverter has been proposed in this article.

防水式BIPV一体化系统设计与示范研究

本研究为一种防水式BIPV一体化光伏系统的设计及发电仿真预测。项目选址在天津市滨海新区,对该光伏项目的设备选型、组件组串和电气接入进行设计,利用PVsyst软件对该BIPV一体化的光伏电站进行建模和仿真,结果表明该光伏电站年发电量可达126.23MWh,系统发电效率可达到87.5%。最终,在某公司自有建筑屋面实施建设了该BIPV一体化光伏电站示范项目,该光伏电站兼具了发电及屋顶防水功能。

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

Frequency Control of Power System with Solar and Wind Power Stations by Using Frequency Band Control and Deadband Control of HVDC Interconnection Line

In recent years, environmental problems are becoming serious and renewable energy has attracted attention as their solutions. However, the electricity generation using the renewable energy has a demerit that the output becomes unstable because of intermittent characteristics, such as variations of wind speed or solar radiation intensity. Frequency fluctuations due to the installation of large scale wind farm (WF) and photovoltaics (PV) into the power system is a major concern. In order to solve the problem, this paper proposes two control methods using High Voltage Direct Current (HVDC) interconnection line to suppress the frequency fluctuations due to large scale of WF and PV. Comparative analysis between these two control methods is presented in this paper. One proposed method is a frequency control using a notch filter, and the other is using a deadband. Validity of the proposed methods is verified through simulation analyses, which is performed on a multi-machine power system model.

Economic Performance Optimization of a PV-BESS Power Generator: A Case Study La Reunion Island

This paper proposes an economic performance optimization strategy for a PV plant coupled with a battery energy storage system. The case study of La Reunion Island, a non-interconnected zone (NIZ) with a high level of renewable energy sources (RES), is considered. This last decade, to reach the ambitious target of electricity autonomy by 2030 set by the local authorities, local and national plans have been launched to promote renewable energy sources integration that led to a noticeable development of photovoltaic (PV) systems. To avoid a decrease of the grid reliability due to a large integration of intermittent energy sources into a non-interconnected grid, the authorities have introduced new regulatory rules for RES producers. The proposed optimization strategy relies on these new regulatory rules and takes into account the energy market data, the amount of PV production subject to penalties for imbalance, the batteries and the PV technological characteristics together with a PV production forecast model. Due to its high convergence rate to the true global minimum and its perfect suitability to practical engineering optimization problems, the recently developed Modified Cuckoo Search algorithm is used as optimization algorithm. The effectiveness and relevance of the proposed strategy are assessed on experimental data collected on a real PV power plant. An economical analysis demonstrates that the proposed optimization strategy is able to fulfill the new regulatory rules requirements while increasing the economic performance of the system.

基于RPC的光储接入牵引供电系统协调控制方法

为将光伏发电接入牵引供电系统进行有效利用,同时兼顾牵引供电系统电能质量改善及列车再生制动能量回收利用,提出了一种基于铁路功率调节器的光储系统接入牵引供电系统的协调控制方法.首先,搭建基于铁路功率调节器的光储系统接入牵引供电拓扑结构,并对其电能质量补偿机理进行理论分析;在此基础上,充分考虑光储系统的运行条件以及列车牵引、制动、空载或惰行工况,提出光储系统接入牵引供电的联合协调控制方法,实现了“光+储+荷”三者间的协调控制、牵引供电系统负序的动态补偿以及谐波的有效抑制;在RT-Lab实时仿真系统中建立了基于铁路功率调节器的光伏接入牵引供电系统仿真模型,并结合实际光照和牵引工况数据验证本文所提出的系统结构以及协调控制方法的有效性.结果表明:本文所提出的系统结构以及协调控制方法能够实现牵引供电系统中光储系统的有效接入,再生制动能量回收率提升至86.7%,功率因数提升6.4%,对谐波特别是高次谐波含有率有较大改善,满足光伏电能高效消纳及综合利用、电能质量动态综合补偿、再生制动能量回收多重需求.