Minimization of the energy storage requirements of a stand‐alone wind power installation by means of photovoltaic panels

Autonomous wind power systems are among the most interesting and environmentally friendly technological solutions for the electrification of remote consumers. In many cases, however, the battery contribution to the initial or the total operational cost is found to be dominant, discouraging further penetration of the available wind resource. This is basically the case for areas possessing a medium–low wind potential. On the other hand, several isolated consumers are located in regions having the regular benefit of an abundant and reliable solar energy supply. In this context the present study investigates the possibility of reducing the battery size of a stand‐alone wind power installation by incorporating a small photovoltaic generator. For this purpose an integrated energy production installation based exclusively on renewable energy resources is hereby proposed. Subsequently a new numerical algorithm is developed that is able to estimate the appropriate dimensions of a similar system. According to the results obtained by long‐term experimental measurements, the introduction of the photovoltaic panels considerably improves the operational and financial behaviour of the complete installation owing to the imposed significant battery capacity diminution. Copyright © 2006 John Wiley & Sons, Ltd.     

Integrated energy balance analysis of a stand‐alone wind power system for various typical Aegean Sea regions

The wind power industry is nowadays a mature energy production sector disposing to market commercial wind converters from 50 W up to 5 MW. In the present work the possibility of using stand‐alone electricity production systems based on a small wind turbine in order to meet the electricity requirements of remote consumers is analysed for selected Aegean Sea regions possessing representative wind potential types. The proposed configuration results from an extensive long‐term meteorological data analysis on a no‐load rejection condition basis during the entire time period examined. Accordingly, an integrated energy balance analysis is carried out for the whole time period investigated, including also the system battery depth‐of‐discharge distribution versus time. Finally, the predicted optimum system configuration is compared to other existing technoeconomic alternatives on a simplified total production cost basis. The results support the viability of similar solutions, especially for areas of high or medium wind potential. Copyright © 2002 John Wiley & Sons, Ltd.     

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 John Wiley & Sons, Ltd.     

Energy‐cost‐aware flow shop scheduling considering intermittent renewables,energy storage,and real‐time electricity pricing

The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on‐site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2‐stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on‐site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day‐ahead and real‐time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day‐ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real‐time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real‐time electricity supplied by the real‐time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on‐site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables.     

New architecture and SCADA for stand‐alone hybrid (medium‐sized asynchronous wind turbine + UPS with battery + photovoltaic array) power system without diesel generator

In general, the commercialized medium‐sized asynchronous wind turbines are fully automated facilities designed to operate in parallel connection to the grid; in case of isolated operation, they need to be combined with diesel generator. This paper aims at studying the method of producing electricity of maximal quality with the wind, by constructing a new stand‐alone hybrid (medium‐sized asynchronous wind turbines, UPS with battery, and photovoltaic array) power system without diesel generator. This paper proposes a new architecture of stand‐alone hybrid power system that consists of medium‐sized asynchronous wind turbine, UPS, current limiter (reactor), photovoltaic array, and consumer and dump loads; accordingly, a supervisory control and data acquisition (SCADA) for this system is suggested along with the operation strategies depending on the output power of the UPS and wind turbine, consumer load, and the battery voltage of UPS. The case study was confirmed through the simulation results of the operation of a new stand‐alone hybrid (two 110 kW of asynchronous wind turbines, 250 kVA of UPS with battery, reactor, 36 kW of photovoltaic array, and consumer and dump loads) power system. The results of the simulation showed that the system frequency change of the new stand‐alone hybrid power system was 60 ± 0.5 Hz, and the one of the wind + diesel stand‐alone hybrid system was 60 ± 1 Hz, for the sudden change of consumer load and gust. This new system can be eligible as a standardizing option for the architecture of nondiesel stand‐alone hybrid system and its SCADA system.     

Cost–benefit analysis of remote hybrid wind–diesel power stations: Case study Aegean Sea islands

More than one third of world population has no direct access to interconnected electrical networks. Hence, the electrification solution usually considered is based on expensive, though often unreliable, stand-alone systems, mainly small diesel-electric generators. Hybrid wind–diesel power systems are among the most interesting and environmental friendly technological alternatives for the electrification of remote consumers, presenting also increased reliability. More precisely, a hybrid wind–diesel installation, based on an appropriate combination of a small diesel-electric generator and a micro-wind converter, offsets the significant capital cost of the wind turbine and the high operational cost of the diesel-electric generator. In this context, the present study concentrates on a detailed energy production cost analysis in order to estimate the optimum configuration of a wind–diesel-battery stand-alone system used to guarantee the energy autonomy of a typical remote consumer. Accordingly, the influence of the governing parameters—such as wind potential, capital cost, oil price, battery price and first installation cost—on the corresponding electricity production cost is investigated using the developed model. Taking into account the results obtained, hybrid wind–diesel systems may be the most cost-effective electrification solution for numerous isolated consumers located in suitable (average wind speed higher than 6.0 m/s) wind potential regions.     

Modeling,design, and optimization of a cost‐effective and reliable hybrid renewable energy system integrated with desalination using the division algorithm

People in the Middle East are facing the problem of freshwater shortages. This problem is more intense for a remote region, which has no access to the power grid. The use of seawater desalination technology integrated with the generated energy unit by renewable energy sources could help overcome this problem. In this study, we refer a seawater reverse osmosis desalination (SWROD) plant with a capacity of 1.5 m³/h used on Larak Island, Iran. Moreover, for producing fresh water and meet the load demand of the SWROD plant, three different stand‐alone hybrid renewable energy systems (SAHRES), namely wind turbine (WT)/photovoltaic (PV)/battery bank storage (BBS), PV/BBS, and WT/BBS are modeled and investigated. The optimization problem was coded in MATLAB software. Furthermore, the optimized results were obtained by the division algorithm (DA). The DA has been developed to solve the sizing problem of three SAHRES configurations by considering the object function's constraints. These results show that this improved algorithm has been simpler, more precise, faster, and more flexible than a genetic algorithm (GA) in solving problems. Moreover, the minimum total life cycle cost (TLCC = 243 763$), with minimum loss of power supply probability (LPSP = 0%) and maximum reliability, was related to the WT/PV/BBS configuration. WT/PV/BBS is also the best configuration to use less battery as a backup unit (69 units). The batteries in this configuration have a longer life cycle (maximum average of annual battery charge level) than two other configurations (93.86%). Moreover, the optimized results have shown that utilizing the configuration of WT/PV/BBS could lead to attaining a cost‐effective and green (without environmental pollution) SAHRES, with high reliability for remote areas, with appropriate potential of wind and solar irradiance.     

Thermoeconomic optimization for a finned‐tube evaporator configuration of a roof‐top bus air‐conditioning system

This paper presents a methodology of a design optimization technique that can be useful in assessing the best configuration of a finned‐tube evaporator, using a thermoeconomic approach. The assessment has been carried out on a direct expansion finned‐tube evaporator of a vapor compression cycle for a roof‐top bus air‐conditioning (AC) system at a specified cooling capacity. The methodology has been conducted by studying the effect of some operational and geometrical design parameters for the evaporator on the entire cycle exergy destruction or irreversibility, AC system coefficient of performance (COP), and total annual cost. The heat exchangers for the bus AC system are featured by a very compact frontal area due to the stringent space limitations and structure standard for the system installation. Therefore, the current study also takes in its account the effect of the variation of the design parameters on the evaporator frontal area. The irreversibility due to heat transfer across the stream‐to‐stream temperature difference and due to frictional pressure drops is calculated as a function of the design parameters. A cost function is introduced, defined as the sum of two contributions, the investment expense of the evaporator material and the system compressor, and the operational expense of AC system that is usually driven by an auxiliary engine or coupled with the main bus engine. The optimal trade‐off between investment and operating cost is, therefore, investigated. A numerical example is discussed, in which a comparison between the commercial evaporator design and optimal design configuration has been presented in terms of the system COP and evaporator material cost. The results show that a significant improvement can be obtained for the optimal evaporator design compared with that of the commercial finned‐tube evaporator that is designed based on the conventional values of the design parameters. Copyright © 2007 John Wiley & Sons, Ltd.     

Long‐term loads for a monopile‐supported offshore wind turbine

Accurate prediction of long‐term ‘characteristic’ loads associated with an ultimate limit state for design of a 5‐MW bottom‐supported offshore wind turbine is the focus of this study. Specifically, we focus on predicting the long‐term fore–aft tower bending moment at the mudline and the out‐of‐plane bending moment at the blade root of a monopile‐supported shallow‐water offshore wind turbine. We employ alternative probabilistic predictions of long‐term loads using inverse reliability procedures in establishing the characteristic loads for design. Because load variability depends on the environmental conditions (defining the wind speed and wave height), we show that long‐term predictions that explicitly account for such load variability are more accurate, especially for environmental states associated with above‐rated wind speeds and associated wave heights. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of battery storage on economics of hybrid wind‐diesel power systems in commercial applications in hot regions

Integration of wind machines and battery storage with the diesel plants is pursued widely to reduce dependence on fossil fuels. The aim of this study is to assess the impact of battery storage on the economics of hybrid wind‐diesel power systems in commercial applications by analyzing wind‐speed data of Dhahran, East‐Coast, Kingdom of Saudi Arabia (K.S.A.). The annual load of a typical commercial building is 620,000 kWh. The monthly average wind speeds range from 3.3 to 5.6 m/s. The hybrid systems simulated consist of different combinations of 100‐kW commercial wind machines (CWMs) supplemented with battery storage and diesel generators. National Renewable Energy Laboratory's (NREL's) (HOMER Energy's) Hybrid Optimization Model for Electric Renewables (HOMER) software has been employed to perform the economic analysis. The simulation results indicate that for a hybrid system comprising of 100‐kW wind capacity together with 175‐kW diesel system and a battery storage of 4 h of autonomy (i.e. 4 h of average load), the wind penetration (at 37‐m hub height, with 0% annual capacity shortage) is 25%. The cost of generating energy (COE, $/kWh) from this hybrid wind–battery–diesel system has been found to be 0.139 $/kWh (assuming diesel fuel price of 0.1$/L). The investigation examines the effect of wind/battery penetration on: COE, operational hours of diesel gensets. Emphasis has also been placed on un‐met load, excess electricity, fuel savings and reduction in carbon emissions (for wind–diesel without battery storage, wind–diesel with storage, as compared to diesel‐only situation), cost of wind–battery–diesel systems, COE of different hybrid systems, etc. The study addresses benefits of incorporation of short‐term battery storage (in wind–diesel systems) in terms of fuel savings, diesel operation time, carbon emissions, and excess energy. The percentage fuel savings by using above hybrid system is 27% as compared to diesel‐only situation Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply‐demand‐based optimization (SDO) is applied to the HES sizing problem based on long‐term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big‐bang‐big‐crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.7 kW from PV arrays, 231.6 kW from WTs, 192.5 kWh from battery banks, and 267.6 kW from the DGS. The optimal HES exhibits a high REF (66.4%) and is economically feasible ($104 323.10/year) and environmentally friendly. The entire load demand of the area under study is met without power loss (LPSP = 0%).     

Power characteristics of a fuel cell micro‐grid with wind power generation

An independent micro‐grid connected with renewable energy has the potential to reduce energy costs, and reduce the amount of greenhouse gas discharge. However, the frequency and voltage of a micro‐grid may not be stable over a long time due to the input of unstable renewable energy, and changes in short‐period power load that are difficult to predict. Thus, when planning the installation of a micro‐grid, it is necessary to investigate the dynamic characteristics of the power. About the micro‐grid composed from 10 houses, a 2.5 kW proton exchange membrane fuel cell is installed in one building, and it is assumed that this fuel cell operated corresponding to a base load. A 1 kW PEM‐FC is installed in other seven houses, in addition a 1.5 kW wind turbine generator is installed. The micro‐grid to investigate connects these generating equipments, and supplies the power to each house. The dynamic characteristics of this micro‐grid were investigated in numerical analysis, and the cost of fuel consumption and efficiency was also calculated. Moreover, the stabilization time of the micro‐grid and its dynamic characteristics accompanied by wind‐power generation and fluctuation of the power load were studied. Copyright © 2007 John Wiley & Sons, Ltd.     

Study on optimal configuration of the grid-connected wind–solar–battery hybrid power system

The capacity allocation of each energy unit in the grid-connected wind–solar–battery hybrid power system is a significant segment in system design. In this paper, taking power grid dispatching into account, the research priorities are as follows: (1) We establish the mathematic models of each energy unit in the hybrid power system. (2) Based on dispatching of the power grid, energy surplus rate, system energy volatility and total cost, we establish the evaluation system for the wind–solar–battery power system and use a number of different devices as the constraint condition. (3) Based on an improved Genetic algorithm, we put forward a multi-objective optimisation algorithm to solve the optimal configuration problem in the hybrid power system, so we can achieve the high efficiency and economy of the grid-connected hybrid power system. The simulation result shows that the grid-connected wind–solar–battery hybrid power system has a higher comprehensive performance; the method of optimal configuration in this paper is useful and reasonable.     

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

Demand response is considered to be a realistic and comparatively inexpensive solution aimed at increasing the penetration of renewable generations into the bulk electricity systems. The work in this paper highlights the demand response in conjunction with the optimal capacity of installed wind energy resources allocation. Authors proposed a total annual system cost model to minimize the cost of allocating wind power generating assets. This model contains capacity expansion, production, uncertainty, wind variability, emissions, and elasticity in demand to find out cost per hour to deliver electricity. A large‐scale electric grid (25 GW) is used to apply this model. Authors discovered that demand response based on interhourly system is not as much helpful as demand response grounded on intrahourly system. According to results, 32% wind generation share will provide the least cost. It is also worth noting that optimal amount of wind generation is much sensitive to installation cost as well as carbon tax.     

Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north‐east of Aegean Sea bearing full potential of wind energy generation. A multi‐objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright © 2009 John Wiley & Sons, Ltd.     

Bi‐objective optimization of a grid‐connected decentralized energy system

Motivated by the increasing transition from fossil fuel–based centralized systems to renewable energy–based decentralized systems, we consider a bi‐objective investment planning problem of a grid‐connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon‐intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation‐optimization algorithm and then adapt multi‐objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi‐objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well‐known measures from the literature.     

Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting

Accurate short‐term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high‐resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower‐resolution models. Recent computational advances have enabled the use of large‐eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence‐resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof‐of‐concept study on the prospect of leveraging these ultra high‐resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high‐frequency information is lost. Therefore, a statistical post‐processing approach is explored on the basis of smoothing and feature engineering from the high‐frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.     

Multicamera measurement system to evaluate the dynamic response of utility‐scale wind turbine blades

Wind turbine blade certification requires static and fatigue testing at a large‐scale facility similar to the Wind Technology Testing Center (WTTC) located in Charlestown, Massachusetts. Usually, these tests are conducted by using wire‐based sensors such as strain gages, accelerometers, and string potentiometers. These systems are expensive, require a time‐consuming installation (e.g., up to 3 weeks and $35 k–$50 k for a strain gage system on a 55‐m‐long blade), are difficult to deploy on large‐sized structures, require additional instrumentations (e.g., power amplifiers and data acquisition systems), and produce results only at a handful of a discrete number of measurement points. In this study, a multicamera measurement system is implemented and experimentally evaluated to obtain full‐field displacement and strain over a ~12‐m‐long portion of a ~60‐m utility‐scale wind turbine blade. The proposed system has the potential to streamline the certification process by reducing the blade's preparation and sensor installation cost and time to a few hundreds of dollars (for painting equipment) and a few days for preparing the surface of the blade for the test. Furthermore, operational modal analysis was used in conjunction with the multicamera system to estimate the natural frequencies and mode shapes of the wind turbine blade. The obtained results have shown that the proposed approach can detect in‐plane displacement as low as 0.2 mm, mechanical strain with an error below 3% when compared with measurement performed using strain gages, and the first five natural frequencies with an error below 2% when compared with data recorded using traditional wire‐based accelerometers. This paper presents these results and provides a summary of the strengths and weaknesses of the proposed optical measurement approach in the context of streamlining the blade certification/testing process and performing vision‐based structural dynamic measurements on large‐scale structures.     

Economic evaluation for wind power generation–hybrid energy storage system based on game theory

The installation of energy storage system to smooth the fluctuations of wind power output connected to the grid can effectively improve the electric quality and increase economic benefits of wind‐generated electricity. Aiming at the overall profit maximization of wind power generation and storage system (WPGSS), taking the smoothing effect of active power output, the cost of hybrid energy storage system, and the earnings of wind power connected to the grid into consideration, this paper brings forward a game theory‐based coordination and optimization control methodology for WPGSS adopting two low‐pass filters to smooth the fluctuation of wind power output. The smoothing control to the active power of wind power and the power distribution of the hybrid energy storage system is respectively achieved by regulating the time constants of the two filters. With the combinations of wind power–batteries and wind power–super‐capacitors as game's participants and the maximization of the WPGSS's overall profit as game's goal constraint conditions, game theory is introduced to realize the coordination and optimization between the time constants of the two filters. For comparative analysis, the model's optimal solution is respectively worked out by genetic algorithm and particle swarm optimization. The simulation results verify the effectiveness of the proposed method and provide a theoretical basis for the economic evaluation of the WPGSS. Copyright © 2016 John Wiley & Sons, Ltd.     

Supporting security and adequacy in future energy systems: The need to enhance long‐term energy system models to better treat issues related to variability

As the shares of variable renewable generation in power systems increase, so does the need for, inter alia, flexible balancing mechanisms. These mechanisms help ensure the reliable operation of the electricity system by compensating for fluctuations in supply or demand. However, a focus on short‐term balancing is sometimes neglected when assessing future capacity expansions with long‐term energy system models. Developing heuristics that can simulate short‐term system issues is one way of augmenting the functionality of such models. To this end, we present an extended functionality to the Open Source Energy Modelling System (OSeMOSYS), which captures the impacts of short‐term variability of supply and demand on system adequacy and security. Specifically, we modelled the system adequacy as the share of wind energy is increased. Further, we enable the modelling of operating reserve capacities required for balancing services. The dynamics introduced through these model enhancements are presented in an application case study. This application indicates that introducing short‐term constraints in long‐term energy models may considerably influence the dispatch of power plants, capacity investments, and, ultimately, the policy recommendations derived from such models. Copyright © 2014 John Wiley & Sons, Ltd.