Wind-based Generation Research Articles

Power management of grid-connected PV wind hybrid system incorporated with energy storage system

Renewable Energy Sources (RES) such as solar photovoltaic (PV) and wind-based generation cannot meet dynamic load demand all the time because of their intermittent nature. Battery Energy Storage System (BESS) is integrated with RES to meet dynamic load demands. Proper power management is needed for the efficient and reliable performance of the system. This paper presents power management of PV-Wind- Battery hybrid system for both grid-connected and islanded modes of operation as well. The power balance is maintained by a power management system (PMS) under different environmental conditions and load conditions, and modes of operation. The control method for state-of-charge (SOC) and battery charge/discharge is applied to ensure efficient performance and safe operation of BESS. Different cases, such as surplus/deficit power available from RES with respect to load demand and battery capacity, have been considered and carried out to verify the performance of PMS using the MATLAB/Simulink platform.

Power quality enhancement of solar–wind grid connected system employing genetic-based ANFIS controller

Abstract The demand for electricity globally has led to the search for renewable energy resources for power generation and attaining it in an eco-friendly manner. The solar photovoltaic systems and wind-based generators of power are regarded as primary resources of renewable energy and are called Distributed Generation units as they are scattered in nature. These are operated with bidirectional converters by providing auxiliary services at grid side and load side in either mode of microgrid operation. Besides, the DC power generation units’ integration gets converted into AC system by means of inverters. These types of systems not only increase voltage and current harmonics and power frequency deviations but also drive the distribution system to risky operating zone. This emphasizes the stipulation of advanced control schemes for microgrid architecture. Consequently, power electronic converters introduce harmonics in the system and affect system performance. To report these expanded issues, the authors recognized an advanced custom power device entitled Distributed Power Flow Controller (DPFC). In this study, the proposed system on solar–wind-based hybrid energy approach has been examined primarily through the strategy of DPFC mechanism. Later, the system has been examined with Genetic Algorithm (GA)-based fuzzy logic controller and GA-based adaptive neuro fuzzy inference system controller for shunt control of context built with DPFC mechanism. Furthermore, the validated results are verified using MATLAB/Simulink software.

GRMWBO: design of a general-purpose repowering model for wind farms via bioinspired optimization

Wind farm optimization requires efficient design of placement models which can assist in identification of deployment locations for higher conversion efficiency. To perform this task, various methods are proposed by researchers, and most of them are applicable for new deployments. Due to limited availability of wind farm sites and cost constraints, it is impractical to replace old equipments. Thus, repowering of wind farms is necessary, and must be planned under existing deployment constraints. Existing repowering methods apply static modelling techniques, which limits their scalability and usability for real-time deployments. To overcome this limitation, a novel general-purpose repowering model via bioinspired optimization is discussed in this text. The model proposes design of a genetic algorithm that considers inflow wind speed, wind direction angle, height of hub, surface roughness, horizontal-axis wind turbine rotor diameter, thrust coefficient, turbine efficiency, air density, and wake expansion rate to estimate location of wind-based generators. These locations are optimized for maximum output power, and minimum deployment costs, which can be estimated for multiple farm areas. This text also proposes various case studies and hypothesis about these deployments, and showcases performance optimizations w.r.t. optimum placement for a given number of turbines. Based on this evaluation, it was observed that the proposed GRMWBO model is capable of improving power efficiency by 18.5%, reduce cost by 8.3%, and reduce number of wind-based generators by 2.9% when compared with various state-of-the-art models. It was also observed that the proposed model showcased consistent performance under different use cases. Thus, making it applicable for a wide variety of real-time deployments.

Aqueous organic flow batteries for sustainable energy storage

Aqueous Organic Redox Flow Batteries (RFBs) have the potential to address the large-scale need for storing electrical energy from intermittent sources like solar- and wind-based generation. Unlike metal-based redox systems, small organic molecules present the prospect of achieving sustainability, by being synthesizable from abundantly available carbon dioxide, water, nitrogen, sulfur, and renewable energy. This mini-review focuses on the progress and challenges in designing water-based RFBs based on small organic molecules that can address the requirements of large-scale energy storage. Much of the recent research in this area involves discovering new molecular architectures via computational screening, understanding degradation, and cell configurations to address the techno-economic challenges of extraordinary electrochemical durability, recyclability, low cost of precursor materials, and good solubility. Redox materials for the positive side of the cell are notably few. Future research must continue to focus on these techno-economic challenges through rapid materials discovery. • Redox molecules must satisfy competing requirements of durability, low cost, and good solubility. • Specific degradation pathways are avoidable by tuning of electronic and steric factors. • Symmetric cells with mixed electrolyte is a practical solution for mitigating effects of crossover. • The use of sustainable biomaterials and low-cost waste products is an exciting prospect.

Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs

The importance of the accurate forecasting of power outputsof wind-based generation systems is increasing, as their contributions to the total system generation are rising. However, wind energy resource exhibits strong and stochastic spatio-temporal variations, which further combine with the uncertainties in WF operating regimes, i.e., numbers of wind turbines in normal operation, under curtailment, or that are faulty/disconnected. This paper presents a novel approach for efficient dealing with uncertainties in hour-ahead forecasted WF power outputs. It first applies Bayesian convolutional neural network-bidirectional long short-term memory (Bayesian CNN-BiLSTM) method, which allows for a more accurate probabilistic forecasting of wind speed, air density and wind direction, using the nearby WFs as additional input data. The WF operating regimes are also predicted using the same Bayesian CNN-BiLSTM structure. Afterwards, a high-dimensional Vine-Gaussian mixture Copula model is combined with Bayesian CNN-BiLSTM model to evaluate uncertainties in the WF outputs based on a cross-correlational conditioning of the forecasted weather variables and operating regimes. The proposed combined model is applied and validated using the actual field measurements from two WF clusters in close locations in Croatia and is also benchmarked against several other models.

Seasonal energy storage for zero-emissions multi-energy systems via underground hydrogen storage

The deployment of diverse energy storage technologies, with the combination of daily, weekly and seasonal storage dynamics, allows for the reduction of carbon dioxide (CO2) emissions per unit energy provided. In particular, the production, storage and re-utilization of hydrogen starting from renewable energy has proven to be one of the most promising solutions for offsetting seasonal mismatch between energy generation and consumption. A realistic possibility for large-scale hydrogen storage, suitable for long-term storage dynamics, is presented by salt caverns. In this contribution, we provide a framework for modeling underground hydrogen storage, with a focus on salt caverns, and we evaluate its potential for reducing the CO2 emissions within an integrated energy systems context. To this end, we develop a first-principle model, which accounts for the transport phenomena within the rock and describes the dynamics of the stored energy when injecting and withdrawing hydrogen. Then, we derive a linear reduced order model that can be used for mixed-integer linear program optimization while retaining an accurate description of the storage dynamics under a variety of operating conditions. Using this new framework, we determine the minimum-emissions design and operation of a multi-energy system with H2 storage. Ultimately, we assess the potential of hydrogen storage for reducing CO2 emissions when different capacities for renewable energy production and energy storage are available, mapping emissions regions on a plane defined by storage capacity and renewable generation. We extend the analysis for solar- and wind-based energy generation and for different energy demands, representing typical profiles of electrical and thermal demands, and different CO2 emissions associated with the electric grid.

Robust Energy and Reserve Scheduling Under Wind Uncertainty Considering Fast-Acting Generators

The large-scale integration of wind power generation has significantly boosted the role played by conventional fast-acting generating units in power system operation. Due to their short response time, these relatively expensive devices are particularly suited to cope with the intermittency and volatility featured by uncertain wind-based generation. Within the context of network-constrained generation scheduling under wind-related uncertainty, this paper presents a new robust approach wherein the operation of both slow- and fast-acting generating units is precisely characterized in a day-ahead co-optimized energy and reserve market. As salient modeling features, the ramping-related effect of dispatch nonanticipativity and nonspinning reserve offers are both accounted for. The resulting robust counterpart is formulated as a challenging trilevel program with lower-level binary variables that is effectively solved by an exact nested column-and-constraint generation algorithm. Numerical results including a practical out-of-sample validation procedure demonstrate the operational advantages and the computational effectiveness of the proposed approach.

Life-cycle sustainability assessment of key electricity generation systems in Portugal

This article presents a life-cycle sustainability assessment of electricity generation systems in Portugal, namely coal, natural gas, hydro (large and small), wind, and photovoltaic systems, and evaluates their environmental and socioeconomic impacts. The environmental life-cycle assessment methodology was used to quantify impacts in metal depletion, fossil fuel depletion, global warming, ozone depletion, terrestrial acidification, freshwater eutrophication, aquatic acidification, water scarcity footprint, and toxicity (towards freshwater ecosystems and humans). Socioeconomic impacts were estimated for employment provision, dependence on fossil fuels, capacity factor, and levelised cost of electricity. The results show that electricity based on coal has the highest environmental impacts in all categories except: metal depletion (wind had the highest impacts), ozone depletion (natural gas), water scarcity footprint (large hydro), and freshwater ecotoxicity and human toxicity, non-cancer (photovoltaic). For socioeconomic categories, there was a greater variability. Photovoltaic generated the most employment and wind-based generation was estimated to have the lowest levelised cost, while coal generated the second-highest total employment and the third-lowest levelised cost. Overall, the life-cycle sustainability assessment shows small hydro as the most environmentally and socioeconomically sustainable system. By exposing the trade-offs amongst technologies and sustainability dimensions, this work provides an informed basis for future energy policy in Portugal.

Least-Cost Reserve Offer Deliverability in Day-Ahead Generation Scheduling Under Wind Uncertainty and Generation and Network Outages

The relevant role played by the procurement of reserves becomes essential to hedge against the growing levels of penetration of intermittent wind-based generation. Within the context of unit-commitment-based day-ahead energy and reserve electricity markets, this paper proposes a novel approach for network-constrained energy and reserve scheduling based on adjustable robust optimization (ARO). Unlike previously reported generation scheduling models using ARO, offers for up- and down-spinning reserves and on/off generation statuses are explicitly considered in the problem formulation to jointly cope with uncertain nodal net injections and system component outages in a multiperiod setting. Moreover, the feasibility of the deployment of awarded reserve offers across the network is guaranteed at minimum offer cost. In other words, in contrast to current industry practice relying on deterministic models, the optimal scheduling of energy and reserve offers ensures power balance for all possible uncertainty realizations and credible contingencies. The proposed model is formulated as an instance of mixed-integer trilevel programming. The robust counterpart is effectively solved by a modified column-and-constraint generation algorithm featuring two novel acceleration strategies that improve the overall computational performance. Numerical simulations demonstrate the effectiveness of the proposed approach and its operational advantages over the conventional model relying on pre-specified reserve requirements.

Harmonic Modelling of the Wind Turbine Induction Generator for Dynamic Analysis of Power Quality

Given the increasing integration of wind-based generation systems into the electric grid, efforts have been made to deal with the problem of power quality associated with the intermittent nature of these systems. This paper presents a new modelling approach oriented towards harmonic distortion analysis of the induction machine for wind power applications. The model is developed using companion harmonic circuit modelling, which is a natural approach for analysis of the adverse effects of harmonic distortion in electric power systems, and represents an easier solution method than the well known dynamic harmonic domain, since it solves algebraic equations instead of state-space differential equations. The structure of the companion circuits simplifies both the formulation and solution for power systems with wind-based generation systems. This approach is especially useful for analysis of the harmonic interaction in transient and steady states between the wind power generator and the power system, whose interconnection is made through electronic converters. The proposed model allows us to compute the dynamics of the wind turbine, which are influenced by disturbances such as changes in the wind velocity, voltage fluctuations, electric waveform distortion, and mechanical vibrations, among other factors. Moreover, the cross-coupling between harmonic components at different frequencies is considered. The proposed model represents an integral framework of the electrical and mechanical subsystems of a wind turbine, allowing for analysis of the interactions between them, and understanding power quality degradation behaviour as well as causes and consequences, while also giving useful information on the field of simulation and control. To test the performance of the proposed model, a test power system is used to obtain the behaviour of a wind turbine induction generator in response to typical power quality disturbances, i.e., harmonic distortion, and voltage sags and swells. Then, the dynamics of the variables considering their harmonic interactions are analysed.

Battery Management in a Green Fog-Computing Node: a Reinforcement-Learning Approach

In the last years, Internet is evolving towards the cloud-computing paradigm complemented by fog-computing in order to distribute computing, storage, control, networking resources, and services close to end-user devices as much as possible, while sending heavy jobs to the remote cloud. When fog-computing nodes cannot be powered by the main electric grid, some environmental-friendly solutions, such as the use of solar- or wind-based generators could be adopted. Their relatively unpredictable power output makes it necessary to include an energy storage system in order to provide power, when a peak of work occurs during periods of low-power generation. An optimized management of such an energy storage system in a green fog-computing node is necessary in order to improve the system performance, allowing the system to cope with high job arrival peaks even during low-power generation periods. In this perspective, this paper adopts reinforcement learning to choose a server activation policy that ensures the minimum job loss probability. A case study is presented to show how the proposed system works, and an extensive performance analysis of a fog-computing node highlights the importance of optimizing battery management according to the size of the Renewable-Energy Generator system and the number of available servers.

Flicker Mitigation via Dynamic Volt/VAR Control of Power-Electronic Interfaced WTGs

The emerging trend for electric utilities to install wind-based generation on a large scale into distribution networks, employing a doubly fed induction generator or full-converter turbine technologies is raising issues for distribution planning engineers with regard to flicker among other power-quality (PQ) issues. This paper investigates dynamic volt/var control, a viable flicker mitigation technique which exploits the reactive power capabilities of modern power-electronic interfaced wind turbine generators. Dynamic volt/var control essentially mitigates flicker by solely dispatching reactive power based on voltage fluctuations within the flicker frequency band. This technique can be incorporated as part of a wind farm multiobjective reactive power-control scheme, which facilitates regulating: 1) the voltage level at the point of common coupling (PCC), employing slow-acting voltage control or power factor control and 2) the flicker level at the PCC with fast-acting dynamic volt/var control. This two-level approach can reduce the wind farm flicker level up to 80% and maintains the voltage level at the PCC well within acceptable bands. The results presented in this paper focus on a typical 25-kV medium-voltage distribution feeder integrating wind-based generation on a large scale. The performance of the two-level controller was tested for varying levels of penetration and wind speed conditions. The applicable standards and utility grid-code requirements are considered throughout.

Understanding the Performance of Aqueous Organic Redox Flow Batteries

Organic redox flow batteries (ORFB) are considered to be one of the most promising technologies for large-scale energy storage needed for integrating renewable energyrom intermittent sources such as solar photovoltaic and wind-based generation systems [1]. ORFBs are attractive because the proposed materials are potentially low cost, eco-friendly, and sustainable. Organic compounds, such as quinones and their derivatives, can be used as both the positive and negative active material of ORFB, due to their electrochemical reversibility and relatively high charge capacity of 200-490 Ah/kg. The use of water as the solvent lowers the cost and makes the system safe, non-flammable, and highly eco-friendly. Heavy metals such as chromium, vanadium, zinc and lead are completely avoided. Thus, the material cost can be reduced substantially for achieving the DOE-specified target of $100/kWh for the entire battery system.In the present study, we have constructed redox flow cells operating on 0.2 M benzoquinone disulfonic acid (DSQ) and 0.2 M anthraquinone sulfonic acid (AQS). We have measured the rechargeability and current-voltage characteristics. We find that the cells are rechargeable and no loss of capacity was observed in 12 cycles (Figure 1). This result suggests that the quinones are chemically stable under the conditions of cycling. The current-voltage curves were measured as a function of state-of-charge. We found that the power density decreased significantly below the 50% state of charge. This decrease is attributed to the mass transport limitations that can be severe at low reactant concentrations of 0.2 M (Figure 2). Analysis of the current-voltage and impedance data indicate that the power density can be increased by enhancing the solubility of the quinones, increasing the temperature of operation and by modifying the electrode structure to allow for rapid mass transport. In this presentation, we will discuss the improvements in power density achieved by these various approaches. Acknowledgement The work presented here was funded by ARPA-E (Open FOA) and the Loker Hydrocarbon Institute of University of Southern California.

Addressing supply-side risk in uncertain power markets: stochastic Nash models, scalable algorithms and error analysis

Increasing penetration of volatile wind-based generation into the fuel mix is leading to growing supply-side volatility. As a consequence, the reliability of the power grid continues to be a source of much concern, particularly since the impact of supply-side risk exposure, arising from aggressive bidding,1 is not felt by risk-seeking generation firms; instead, the system operator is largely responsible for managing shortfalls in the real-time market. We propose an alternate design in which the cost of such risk is transferred to firms responsible for imposing such risk. The resulting strategic problem can be cast as a two-period generalized stochastic Nash game with shared strategy sets. A subset of equilibria is given by a solution to a related stochastic variational inequality, that is shown to be both monotone and solvable. Computing solutions of this variational problem is challenging since the size of the problem grows with the cardinality of the sample space, network size and the number of participating firms. Consequently, direct schemes are inadvisable for most practical problems. Instead, we present a distributed regularized primal–dual scheme and a dual projection scheme where both primal and dual iterates are computed separately. Rates of convergence estimates are provided and error bounds are developed for inexact extensions of the dual scheme. Unlike projection schemes for deterministic problems, here the projection step requires the solution of a possibly massive stochastic programme. By utilizing cutting plane methods, we ensure that the complexity of the projection scheme scales slowly with the size of the sample space. We conclude with a study of a 53-node electricity network that allows for deriving insights regarding market design and operation, particularly for accommodating firms with uncertain generation assets.

Optimal Allocation of Wind Turbines Considering Different Costs for Interruption Aiming at Power Loss Reduction and Reliability Improvement Using Imperialistic Competitive Algorithm

In recent years, utilization of wind-based distributed generation (DG) as one of the most widespread used types of green generation technologies has attracted remarkable consideration. Recently, several contributions have accomplished in case of wind-based generation. In this research, in order to minimize the costs of annual energy losses and Energy Not Supplied (ENS) of the distribution networks, a multi-objective probabilistic based approach is proposed to determine the optimal location and capacity of wind-based units along with providing assurance for a desirable level of voltage profile. An Imperialist Competitive Algorithm (ICA) is employed to overcome non-convexity and complexity of the mixed integer optimization problem of DG allocation in the radial distribution networks. For this purpose, a Fuzzy C-Means (FCM) clustering is used to categorize historical data of load demands and output power of DG units. Then, given the fuzzy center point of the clusters and their respective probability generated from FCM clustering implementation, uncertainty consideration is involved to the probabilistic calculation of both of the wind-based DG and load demand. Moreover, during the calculation process of ENS, different costs of interruption are considered for various customers of the network. Finally, presented technique is exerted for an IEEE 33-bus standard test system subject to the system constrains under different cases and effectiveness and capability of the method is assessed. Obtained results demonstrate competence of the method to produce a significant reduction in ENS value and annual energy losses

Performance Assessment of Micro and Small-Scale Wind Turbines in Urban Areas

This paper addresses some general issues related to the difficulties and uncertainties during the assessment and characterization of micro and small-scale wind-based generation in urban areas. This paper proposes four generic wind turbine models, which could be used for the analysis and selection of optimal wind turbines in target applications, as they accurately represent the range of wind turbines currently available on the market. The analysis in this paper compares results for the expected annual energy outputs and cost-benefit analysis obtained using steady state and dynamic wind turbine models of actual and generic micro/small wind turbines, applying both low-resolution and high-resolution measurements of available wind energy resources. The presented results demonstrate the significance of the accurate assessment of wind resources in urban areas, as well as the importance of the correct modeling of wind turbine characteristics. It is shown that substantial errors, typically overestimating wind turbine performance, are obtained if standard assumptions and current recommendations are used for the analysis of wind turbine performance.

Short-term optimal wind power generation capacity in liberalized electricity markets

Mainly because of environmental concerns and fuel price uncertainties, considerable amounts of wind-based generation capacity are being added to some deregulated power systems. The rapid wind development registered in some countries has essentially been driven by strong subsidizing programs. Since wind investments are commonly isolated from market signals, installed wind capacity can be higher than optimal, leading to distortions of the power prices with a consequent loss of social welfare. In this work, the influence of wind generation on power prices in the framework of a liberalized electricity market has been assessed by means of stochastic simulation techniques. The developed methodology allows investigating the maximal wind capacity that would be profitably deployed if wind investments were subject to market conditions only. For this purpose, stochastic variables determining power prices are accurately modeled. A test system resembling the size and characteristics of the German power system has been selected for this study. The expected value of the optimal, short-term wind capacity is evaluated for a considerable number of random realizations of power prices. The impact of dispersing the wind capacity over statistical independent wind sites has also been evaluated. The simulation results reveal that fuel prices, installation and financing costs of wind investments are very influential parameters on the maximal wind capacity that might be accommodated in a market-based manner.