Fluctuations Of Wind Generation Research Articles

Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

The optimal hourly electricity price considering wind electricity uncertainty based on conditional value at risk

ABSTRACT Wind generation fluctuation results in the electricity supply uncertainty of the company in electricity market. And this uncertainty has an important impact on hourly electricity price. In our paper, we use game models to optimize hourly price for different risk appetites’ companies considering the uncertainty. For the risk-neutral company, we solve the optimal electricity price by maximizing the expected profit. For the risk-averse or risk-preferring company, we consider wind electricity generation as a Weibull distribution and obtain the optimal electricity price based on conditional value at risk. In numerical examples, we find that the risk-preferring company sets the highest electricity price, demonstrating the importance of modeling risk appetites of the company in electricity price. And the results indicate that a risk-averse (risk-preferring) company will set a higher (lower) electricity price for a larger risk confidence level. Besides, we find that the company will lower electricity price with the decrement of users’ price elasticity. Furthermore, our finding shows that the profit of the company increases with the wind penetration due to the decreased cost, which supports the observed and investigated results in electricity market.

Coordination of PSSs and SVC Damping Controller to Improve Probabilistic Small-Signal Stability of Power System With Wind Farm Integration

A modified fruit fly optimization algorithm (MFOA) combined with a probabilistic approach are proposed in this paper to coordinate and optimize the parameters of power system stabilizers (PSSs) and static VAR compensator (SVC) damping controller for improving the probabilistic small-signal stability of power systems with large-scale wind generation, taking into consideration the stochastic uncertainty of system operating conditions. It is generally accepted that there is a threat to the stability of power system with penetration of wind farm. In addition, the stochastic fluctuations of wind generation make PSSs tuning more difficult. In this paper, PSSs and SVC damping controller are employed for suppressing local and inter-area low frequency oscillation. In order to eliminate the adverse effect between PSSs and SVC damping controller, the MFOA based on the probabilistic eigenvalue is applied to coordinate and optimize their parameters. The effectiveness of the proposed approach is verified on two test systems.

Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast

Wind ramps introduce significant uncertainty into wind power generation. Reliable system operation, however, requires accurate detection and forecast of wind ramps, especially at high penetration levels. In this paper, to deal with the wind ramp dynamics, a support vector machine (SVM)-enhanced Markov model is developed for short-term wind power forecast, based on one key observation from the measurement data that wind ramps often occur with specific patterns. Specifically, using the historical data of the wind turbine power outputs recorded at an actual wind farm, data analytics-based finite-state Markov models are first developed to model the normal fluctuations of wind generation, while taking into account the diurnal nonstationarity and the seasonality of wind generation. Next, the forecast by the SVM is integrated cohesively into the finite-state Markov models. Based on the SVM-enhanced Markov model, both short-term distributional forecasts and point forecasts are then derived. Numerical test results, using real wind generation data traces, demonstrate the significantly improved accuracy of the proposed forecast approach.

Improving the Integration of Wind Power Generation Into AC Microgrids Using Flywheel Energy Storage

The connection of wind power generation into ac microgrids (MGs) is steadily increasing. This incorporation can bring problems onto the power quality and dynamics of the electrical grid due to the lack of controllability over the wind. In this work, a flywheel energy storage (FES) is used to mitigate problems introduced by wind generation into MGs. A dynamic model of the FES device is briefly presented and a technique to control the power exchanged between the device and the power system is proposed. The control technique has three modes, namely: voltage control (VC), frequency control (FC), and active power stabilization (APS). Simulation tests on the behavior of the FES device are analyzed when it works in combination with wind generation in the electrical microgrid. Results show an acceptable performance of the proposed control techniques along with a high effectiveness to smooth the active power fluctuations of wind generation.

Combined control of a distribution static synchronous compensator/flywheel energy storage system for wind energy applications

The integration of wind power generation in power systems is steadily increasing around the world. This incorporation can bring problems onto the dynamics of power systems owing to the lack of controllability over the wind and the type of generation used. In this work, a distribution static synchronous compensator (DSTATCOM) coupled with a flywheel energy storage system (FESS) is used to mitigate problems introduced by wind generation in the electrical systems. A dynamic model of the DSTATCOM/FESS device is briefly presented and a technique to control the active power exchanged between the device and the power system is proposed. The control technique has two control modes. One control mode mitigates the power fluctuations of wind generators, and it is based on fuzzy logic and a special filter. The other control mode contributes to recover the frequency when significant faults arise in the system. Simulation tests on the behaviour of the device are analysed when it works in combination with wind generation in the electrical system. Results show a satisfactory performance of the proposed control techniques along with a high effectiveness to smooth the active power fluctuations of wind generation and to contribute to the recovery of the frequency.

Evaluating operational risk in a power system with a large amount of wind power

Reserve definition is a compromise between economic issues (additional capacity costs) and reliability (risk of loss of load due to outages of the generators), generally approached by deterministic criteria (e.g. the percentage rule defined by UCTE in Europe) and probabilistic methods like PJM (Pennsylvania–New Jersey, Maryland) and its enhancements, based on the concept of risk. With wind power generation increasing in power systems worldwide, these operational issues gain a renewed interest due to the volatile nature of this kind of energy. The aim of this paper is therefore to address this issue from a risk evaluation point of view, showing that it is possible to extend classical probabilistic methods to this new situation, by introducing a detailed Markov model of wind parks that accounts both for machine failures and different wind power levels. This evaluation, where wind generation fluctuation and uncertainty is included, can be helpful for transmission system operators (TSO), when defining the reserve requirements for the next hours. In fact, the results obtained for the risk can be used by TSO to check if the reserve levels that results from traditional deterministic rules are acceptable or need to be increased.