Incorporating Wind Generation Research Articles

A risk-based machine learning approach for probabilistic transient stability enhancement incorporating wind generation

ABSTRACT Power systems are becoming more complex than ever and are consequently operating close to their limit of stability. Considering its significance in power system security, it is important to propose a novel approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables. Moreover, the time-domain simulation approach for transient stability evaluation can be very computationally intensive, especially for a large-scale system. The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this paper proposes a risk-based, machine learning decision-making approach, for probabilistic transient stability enhancement, by replacing circuit breakers, including the impact of wind generation. The IEEE 14-bus test system was used to test and validate the effectiveness of the proposed approach. DIgSILENT PowerFactory and MATLAB were utilised for transient stability simulations (for obtaining training data for machine learning), and applying machine learning algorithms, respectively.

Scenario-based probabilistic multi-stage optimization for transmission expansion planning incorporating wind generation integration

Integrated transmission expansion planning (TEP) and generation expansion planning (GEP) with Wind Farms (WFs) is addressed in this paper. The optimal number of expanded lines, the optimal capacity of WFs installed capacity, and the optimal capacity of wind farms lines (WFLs) are determined through a new TEP optimization model. Furthermore, the optimum capacity additions including conventional generating units is obtained in the proposed model. The Benders decomposition approach is used for solving the optimization problem, including a master problem and two sub-problems with internal scenario analysis. In order to reduce the computational burden of the multi-year and multi-objective expansion planning problem, a multi-stage framework is presented in this paper. The uncertainties of wind speed and system demand along with contingency scenarios lead to a probabilistic optimization problem. Moreover, in the proposed model, the planning time horizon is divided into three predefined stages. This multi-stage approach is used to increase the proposed model accuracy in a power system with a high level of wind power penetration. Hence, in this paper a scenario-based probabilistic multi-stage model for transmission expansion planning is proposed, incorporating optimal WFs integration. It is recognized that high wind penetration increases the transmission expansion investment cost, but based on the reduction of the investment cost of conventional units, the total system cost will be smaller. This result emphasizes the main advantage of wind generating system over the conventional generating system. This planning methodology is applied to the modified IEEE 24-bus test system and simplified Iran 400-kV real system to show the feasibility of the proposed algorithm.

Optimal Power Flow considering intermittent Wind Power using Particle Swarm optimization

Inclusion of renewable generation in the existing network is necessary due to the increase in raw material cost for generating electricity and growing demand. Optimal power flow incorporating wind generation is solved using Particle swarm optimization (PSO) in this paper. Weibull distribution function is used for modelling the intermittent nature of wind farm and then it is incorporated in the existing power system network. A direct cost function of the wind power purchased is presented in the paper. Cases without and with wind power are solved using PSO due to its ability in solving the non linear problems. The analysis is carried out on IEEE 30 bus test system and the obtained results are compared with the few existing methods. From the results it can be inferred that this method provides enhanced results.

Effects of Wind Generation Uncertainty and Volatility on Power System Small Signal Stability

This paper discusses the impacts of large scale grid-connected wind farm equipped with permanent magnet synchronous generator (PMSG) on power system small signal stability (SSS) incorporating wind generation uncertainty and volatility. Firstly, a practical simplified PMSG model with rotor-flux-oriented control strategy applied is derived. In modeling PMSG generator side converter, the generator-voltage-oriented control strategy is utilized to implement the decoupled control of active and reactive power output. In modeling PMSG grid side converter, the grid-voltage-oriented control strategy is applied to realize the control of DC link voltage and the reactive power regulation. Based on the Weibull distribution of wind speed, the Monte Carlo simulation technique based is carried out on the IEEE 16-generator-68-bus test system as benchmark to study the impacts of wind generation uncertainty and volatility on small signal stability. Finally, some preliminary conclusions and comments are given.

A new approach of available transfer capability incorporating wind generation

The approach of available transfer capability (denoted as ATC) incorporating wind generation has been paid very high attention since the development of wind generation. Based on the maximum function, this paper presents an ATC model. The characteristic of the new model is twofold. First, it considers wind turbines connected to power system and static security of power system simultaneously. Second, it is a system of semismooth equations and can be solved easily. By using the smoothing strategy, a smoothing Newton method is adopted for solving the proposed new ATC model. Numerical simulation results of the IEEE 30-bus and 118-bus system show that the new model and algorithm are feasible and effective. The impact of wind turbines connected to power system on ATC is also analyzed.

Stabilization and control of tie-line power flow of microgrid including wind generation by distributed energy storage

High penetration of wind generation in electrical microgrids causes fluctuations of tie-line power flow and significantly affects the power system operation. This can lead to severe problems, such as system frequency oscillations, and/or violations of power lines capability. With proper control, a distribution static synchronous compensator (DSTATCOM) integrated with superconducting magnetic energy storage (SMES) is able to significantly enhance the dynamic security of the power system. This paper proposes the use of a SMES system in combination with a DSTATCOM as effective distributed energy storage (DES) for stabilization and control of the tie-line power flow of microgrids incorporating wind generation. A new detailed model of the integrated DSTATCOM-SMES device is derived and a novel three-level control scheme is designed. The dynamic performance of the proposed control schemes is fully validated using MATLAB/Simulink.