Significant Wind Power Penetration Research Articles

Design and Development of Wind Power Refined Simulation Software

A challenge to the steady operation of the system as well as the design, operation, and maintenance of wind power is posed by the wind power industry's rapid development and the rate at which wind power is incorporated into the power system. This work creates high-quality simulation software for wind power to address the complicated modeling requirements brought on by significant wind power penetration. The software implements the functions of fine modeling, offline simulation, real-time simulation, and data analysis and uses Python, Shell, and C++ language programming as its primary programming languages. Additionally, to confirm the software's functionality, this study employs OpenFAST single-unit and three-unit wind field samples. The reality demonstrates how easily real-time simulation software can be implemented and how well it does so. Software also has a cheap time and economic cost, and its application value is considerable.

Strategic Operation of Hydroelectric Power Plants in Energy Markets: A Model and a Study on the Hydro-Wind Balance

The European Union defined ambitious targets for the production of energy from renewable energy sources. Most European markets trade now high levels of variable renewable energy (VRE). Renewable generation increases the variability and uncertainty of the net-load (i.e., demand minus VRE). To a large extent, this variability and uncertainty can be compensated by hydroelectric power plants. Typically, hydro power producers (HPPs) consider the periods of time with low market prices (and normally low demand and/or high VRE production) to pump, and the periods with high market prices (and normally high demand and/or low VRE production) to produce energy. This article presents a model for hydro power plants and a study to analyse the hydro-wind balance in a real-world setting, namely a simplified version of the Portuguese power system, involving a significant penetration of hydro and wind power (more than 50%). The study is conducted with the help of the multi-agent system MATREM. The results confirm (and rebut) the typical behavior of hydroelectric power plants (to produce energy, to pump water or to stay idle).

Coordinated Control Using Backstepping of DFIG-Based Wind Turbine for Frequency Regulation in High Wind Energy Penetrated System

The expansion of renewable generation has raised some red flags in terms of power system stability, control, and management. For instance, unlike traditional synchronous energy sources, the doubly-fed induction generator- (DFIG-) based wind turbines (WTs) do not instinctively act against frequency deviations. In fact, the power electronics interfacing the generator, merely controlled to warrant maximum wind power conversion, make its output power and mechanical speed immune to the characteristics of the electric network frequency. Moreover, significant wind power penetration (WPP) promotes the retirement of many traditional generation groups, consequently curtailing the power system corresponding inertia and displacing the primary reserves that are essential to retain the frequency within an acceptable range of variation. This paper explores different control approaches, using backstepping, allowing DFIG-based WTs to engage actively in frequency regulation using a coordinated control of the rotor speed and pitch angle to regulate the system during both partial- and full-load operation modes. The first method momentarily discharges part of the kinetic energy stored in the WT spinning masses, and the second method follows a deloaded operation characteristic, so as to keep a specific power reserve that can be automatically activated at the events of frequency excursions. A study case considering an isolated power system that consists of synchronous generators, DFIG-based wind farm, static load, and a sudden frequency disturbance was performed. The simulation result in a Matlab/Simulink environment highlights the robustness and capability of the coordinated control scheme to furnish, under variant operation conditions, active power aid, consequently lifting the frequency nadir up to a superior level than that obtained with 0% wind power penetration in the system.

Competition and Efficiency of Coalitions in Cournot Games With Uncertainty

We investigate the impact of coalition formation on the efficiency of Cournot games where producers face uncertainties. In particular, we study a market model where firms must determine their output before an uncertain production capacity is realized. In contrast to standard Cournot models, we show that the game is not efficient when there are many small firms. Instead, producers tend to act conservatively to hedge against their risks. We show that in the presence of uncertainty, the game becomes efficient when firms are allowed to take advantage of diversity to form groups of certain sizes. We characterize the tradeoff between market power and uncertainty reduction as a function of group size. In particular, we compare the welfare and output obtained with coalitional competition, with the same benchmarks when output is controlled by a single system operator. We show that when there are N firms present, competition between groups of size Ω (√N) results in equilibria that are socially optimal in terms of welfare and groups of size Ω (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2/3</sup> ) are socially optimal in terms of production. We also extend our results to the case of uncertain demand by establishing an equivalency between the Cournot oligopoly and Cournot Oligopsony. We demonstrate our results with real data from electricity markets with significant wind power penetration.

A Dual-Step Integrated Machine Learning Model for 24h-Ahead Wind Energy Generation Prediction Based on Actual Measurement Data and Environmental Factors

Wind power generation output is highly uncertain, since it entirely depends on intermittent environmental factors. This has brought a serious problem to the power industry regarding the management of power grids containing a significant penetration of wind power. Therefore, a highly accurate wind power forecast is very useful for operating these power grids effectively and sustainably. In this study, a new dual-step integrated machine learning (ML) model based on the hybridization of wavelet transform (WT), ant colony optimization algorithm (ACO), and feedforward artificial neural network (FFANN) is devised for a 24 h-ahead wind energy generation forecast. The devised model consists of dual steps. The first step uses environmental factors (weather variables) to estimate wind speed at the installation point of the wind generation system. The second step fits the wind farm actual generation with the actual wind speed observation at the location of the farm. The predicted future speed in the first step is later given to the second step to estimate the future generation of the farm. The devised method achieves significantly acceptable and promising forecast accuracy. The forecast accuracy of the devised method is evaluated through several criteria and compared with other ML based models and persistence based reference models. The daily mean absolute percentage error (MAPE), the normalized mean absolute error (NMAE), and the forecast skill (FS) values achieved by the devised method are 4.67%, 0.82%, and 56.22%, respectively. The devised model outperforms all the evaluated models with respect to various performance criteria.

Interval Optimization-Based Unit Commitment for Deep Peak Regulation of Thermal Units

The deep peak regulation of thermal units is an important measure for coping with significant wind power penetration. In this paper, based on interval optimization, a novel multi-objective unit commitment method is proposed considering the deep peak regulation of thermal units. In the proposed method, a thermal power cost model was developed to accurately determine the economic performance of three different peak regulation scenarios, particularly of the deep peak regulation scenario. The midpoint and width of the cost interval are simultaneously considered in the optimization process. The non-dominated sorting GA-II (NSGA-II) algorithm was incorporated into the model for a coordinated control of the midpoint and width of the obtained cost interval for further optimization. Considering that significant wind penetration results in greater nodal variations, the affine arithmetic was employed to solve nodal uncertainties, so that all system variations can be addressed. The method proposed in this paper was validated by a modified IEEE-39 bus system. The results showed that it serves as a useful tool for power dispatchers to obtain robust and economic solutions at different wind power prediction accuracies.

Sliding mode load frequency control for multi‐area time‐delay power system with wind power integration

The interconnected time-delay power system has become an important issue for the open communication network. Meanwhile, due to the output power fluctuation of integrated wind energy, load frequency control (LFC) for power system with variable sources and loads has become more complicated. The novel decentralised sliding mode (SM) LFC strategy is proposed for multi-area time-delay power system with significant wind power penetration. The appropriate switching surface gain is selected to assure the stability of power system with mismatched uncertainties. The SM controller is constructed to satisfy the hitting condition. At last, the SM controller is proved by using the real-time digital simulator device under different case of time delay, wind penetration, load disturbance and operating point. The test results show that the proposed SM LFC can reduce frequency deviation and tie-line power fluctuation effectively.

Risk and unit commitment decisions in scenarios of wind power uncertainty

This paper addresses the problem of decision making in Unit Commitment in systems with a significant penetration of wind power. Traditional approaches to Unit Commitment are inadequate to fully deal with the uncertainties associated to wind, represented by scenarios of forecasted wind power qualified by probabilities. Departing from a critique of planning paradigms, the paper argues that a stochastic programming approach, while a step in the good direction, is insufficient to model all aspects of the decision process and therefore proposes the adoption of models based on a Risk Analysis paradigm. A case study is worked out reinforcing this perspective. In a multi-objective context, the properties of the cost vs. risk Pareto-optimal fronts are analyzed, where risk may be represented by aversion to a worst scenario or a worst event. It is shown that the Pareto-optimal front may not be convex, which precludes a simplistic use of tradeoff concepts. It is also shown that decisions based on stochastic programming may in fact put the system at risk. An evaluation of risk levels and cost of hedging against undesired events is proposed as the paradigm to be followed in Unit Commitment decision making.

A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing

As the share of wind power in the electricity system rises, the limited predictability of wind power generation becomes increasingly critical for operating a power system reliably. In most operational and economic models, the wind power forecast error (WPFE) is often assumed to follow a Gaussian or the so-called $\betab$ -distribution. However, these distributions might not be suited to fully describe the skewed and heavy-tailed character of WPFE data. In this paper, the Levy $\alphab$ -stable distribution is proposed as an improved description of the WPFE. The method presented allows us to quantify the probability of a certain error, given a certain wind power forecast. Based on recent historical wind power data, the feasibility of the Levy $\alphab$ -stable distribution as a WPFE description is demonstrated. The added value of this improved statistical model of the WPFE is illustrated in a state-of-the-art probabilistic reserve sizing method. Results show that this new statistical description of the WPFE can hold important information for short-term economic and operational (reliability) studies for power systems with a significant wind power penetration.

Limit-induced bifurcation by wind farm voltage supervisory control

This paper studies dynamic bifurcations induced by the limits of a wind farm voltage supervisory control. This is an important issue due to the significant wind power penetration of current systems and also because of the catastrophic effect that this bifurcation may cause—loss of system stability without any previous warning. Wind turbines (WTs) based on doubly fed induction generators are considered. Bifurcation analysis is used to study this phenomenon in a 15-bus test system. For comparison purposes, the analysis is repeated without and with supervisory control. When the supervisory control is inactive, eigenvalue trajectories are continuous and smooth as the load is increased. No dynamic interactions are observed between synchronous generators (SGs) and WTs. However, when the supervisory control is active, eigenvalues suffer discrete changes and a limit-induced bifurcation (LIB) point is found when the upper limit is reached. Dynamic interactions between SGs and WTs are observed, however, any influence of WTs in the instability is ruled out. Finally, two stability boundaries are found based on a Hopf bifurcation point defined by the voltage mode and the electromechanical mode, respectively. The results reveal that when a constant voltage trajectory is followed, the stability boundary based on the voltage mode is surpassed and the highest loading is reached when the electromechanical mode crosses the imaginary axis. The LIB appearance is highly dependent on the particular characteristics of the system under study, such as network parameters or the reactive power capability of system elements. In order to get the highest system loadability, this work supports the use of wind farm supervisory control to regulate bus voltage, but the possible existence of a LIB point must be ruled out by checking the system behavior under different operating conditions.

Special Issue on Smart Grid

Recently many utilities, manufacturers, researchers, government leaders around the world are working on a very sophisticated issue, Smart Grid, to modernize both the electric power transmission and distribution grids for the future. As a suitable subject for special issue for the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), I found that Smart Grid with its impacts on many fields is a timely subject though related to the fundamental concept of this Journal on Intelligence and real-world applications and so forth. Smart grids are intelligent and self-healing power systems which integrate intelligent transmission network with IT and collect, distribute, and process information about the behavior of all power suppliers and consumers in order to improve reliability, power quality, and to reduce electricity costs. Using a key issue, smart meter, enables smart grids to have smart real time monitoring on a regional and national scale to control and management the grids to avoid or mitigate the system-wide blackouts. In this special issue, we hope to explore breakthrough and new contributions useful to achieve the goal of smart grid. Three papers were selected for this special issue: The first paper proposes a novel idea though a strategic system in energy and environment required in smart grid. Managing sources combination including solar energy as well as the production trading is a new kind of risk management in smart grid. Important extensions of this study includes emissions management program accommodating uncertain and erratic renewable energy sources such as solar and wind energies. The second paper is related to communication aspect required for smart grid technology when renewable energy in small smart communities is interconnected to the smart grid. The simulation model developed in this paper is believed to be a useful tool in real-time power management system in smart grid. Third paper is selected as another hot subject in smart grids; the authors developed an extended procedure that obtains a unit commitment including a significant wind power penetration and PEVs as additional reserves. The shadow prices obtained by the trade-off analysis may provide a basis of evaluating the equivalent cost of the wind farms and the applying PEVs as the reserve and their contribution toward CO2 reduction. Finally, using this opportunity, I would like to thank the reviewers for spending their valuable time for evaluating the papers and quick response which made this special issue catch the time. I would also like to thank the JACIII editorial office for their great assistance for preparing this special issue.

Thermal Unit Scheduling for CO2Reduction Including Wind Power and Electric Vehicles

In this paper, we present a method to determine unit commitment schedules, while considering CO2emissions and costs along with the frequency regulation capability of the units, in order tomitigate fluctuations in wind power. We developed an extended procedure that obtains a trade-off solution of cost versus CO2emissions, including a significant wind power penetration, and developed Plug-in Electric Vehicles (PEVs) as additional reserves. The proposed method was tested on a 10-unit, 24-hour model system using the estimated wind power curve derived from an actual wind farm. The results, such as shadow prices of CO2obtained using the trade-off analysis, may provide a basis of evaluating the equivalent cost of wind farms and PEVs, and their contributions to CO2reduction.

Quantifying Spinning Reserve in Systems With Significant Wind Power Penetration

The traditional unit commitment and economic dispatch approaches with deterministic spinning reserve requirements are inadequate given the intermittency and unpredictability of wind power generation. Alternative power system scheduling methods capable of aggregating the uncertainty of wind power, while maintaining reliable and economic performance, need to be investigated. In this paper, a probabilistic model of security-constrained unit commitment is proposed to minimize the cost of energy, spinning reserve and possible loss of load. A new formulation of expected energy not served considering the probability distribution of forecast errors of wind and load, as well as outage replacement rates of various generators is presented. The proposed method is solved by mixed integer linear programming. Numerical simulations on the IEEE Reliability Test System show the effectiveness of the method. The relationships of uncertainties and required spinning reserves are verified.

Potential of Heat Pumps for Demand Side Management and Wind Power Integration in the German Electricity Market

Increasing power system flexibility by responsive demand is a central issue for the incorporation of higher levels of variable wind generation in future power systems. The electrification of the heat sector, except from energy efficiency gains, may offer a vast potential of new forms of flexible demand, by time-shifting of heat production in buildings. The assessment of this potential can, however, be performed only when the limitations imposed by the primary operation of the equipment (space heating) are realistically taken into account. In this paper, a methodology is presented for the quantification of the flexibility offered by the thermal storage of building stock equipped with heat pumps, to power systems with significant penetration of wind power. A model is proposed for the incorporation of the building stock thermal behavior as equivalent energy storage in electricity market models. At the same time, the model allows the coupling to a detailed dynamic thermal model of buildings for the assessment of the respective operational restrictions. The case study presents the results of a project for the evaluation of the flexibility offered by portfolios of high heat pump deployment in conjunction with high wind penetration scenarios for the future German electricity system.

What Happens When it's Windy in Denmark? An Empirical Analysis of Wind Power on Price Variability in the Nordic Electricity Market

High levels of wind power penetration will tend to affect prices in a deregulated electricity market. Much of the analysis in the literature has focused on the effect that wind power has on average electricity prices, this paper attempts to test the effect that wind power production has on the variability of wholesale electricity prices in the spot market. I use a simple distributed lag econometric model and five years worth of hourly and daily data from Denmark, which is one of the few places with a long history of significant wind power penetration. I show that wind power has the effect of reducing intra-day variability but that this result only partially carries over to price variation over weekly time windows. I suggest that the reduction in price variability in turn is due to a steeper supply schedule at peak-load times.

What Happens When It’s Windy in Denmark? An Empirical Analysis of Wind Power on Price Volatility in the Nordic Electricity Market

This paper attempts to test the effect that wind power production has on the variability of wholesale electricity prices in the spot market. I use a simple distributed lag econometric model and five years worth of hourly and daily data from Denmark, which is one of the few places with a long history of significant wind power penetration. I show that wind power has the effect of reducing intra-day variability but that this result only partially carries over to price variation over weekly time windows. I suggest that the reduction in price variability in turn is due to a steeper supply schedule at peak-load times.