Stochastic Optimal Scheduling Based on Scenario Analysis for Wind Farms

Xu, Jian; Xiankun, Yi; Sun, Yuanzhang; Lan, Tiankai; Sun, Huaijiang

doi:10.1109/tste.2017.2694882

Cited by 46 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…probability distribution function. In this study, we assume that wind speed follows Weibull Distribution [35], [36], as shown in Figure 13 with detailed parameters given in Table II, similar to [36]. The wind speed data for a year is generated using the wind speed distribution model in Figure 13, and this information is used to calculate the AEP.…”

Section: ) Annual Energy Production Of a Cluster With Different Methodsmentioning

confidence: 99%

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Bui

Nguyen

2020

IEEE Access

View full text Add to dashboard Cite

In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.

show abstract

Section: ) Annual Energy Production Of a Cluster With Different Methodsmentioning

confidence: 99%

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Bui

Nguyen

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The operation bounds of each WTG is shown in (12) considering its operation state. The available power of WTGs at a given interval is calculated by (13) based on the WTGs' parameters and wind characteristics [27]. Equation (14) shows the relation between changing the set-points of WTGs and mismatch power in a cluster.…”

Section: Minmentioning

confidence: 99%

Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints

et al. 2019

View full text Add to dashboard Cite

In this paper, a hybrid energy management system is developed to optimize the operation of a wind farm (WF) by combining centralized and decentralized approaches. A two-stage optimization strategy, including distributed information sharing (stage 1); and centralized optimization (stage 2) is proposed to find out the optimal set-points of wind turbine generators (WTGs) considering grid-code constraints. In stage 1, cluster energy management systems (CEMSs) and transmission system operator (TSO) interact with their neighboring agents to share information using diffusion strategy and then determine the mismatch power amount between the current output power of WF and the required power from TSO. This amount of mismatch power is optimally allocated to all clusters through the CEMSs. In stage 2, a mixed-integer linear programming (MILP)-based optimization model is developed for each CEMS to find out the optimal set-points of WTGs in the corresponding cluster. The CEMSs are responsible for ensuring the operation of WF in accordance with the requirements of TSO (i.e., grid-code constraints) and also minimizing the power deviation for the set-points of WTGs in each cluster. The minimization of power deviation helps to reduce the internal power fluctuations inside each cluster. Finally, to evaluate the effectiveness of the proposed method, several case studies are analyzed in the simulations section for operation of a WF with 20 WTGs in four different clusters.

show abstract

“…To date, wind energy has become a promising alternative and renewable energy resource to handle the problem of energy crisis and environmental deterioration. Along with the rapid development and extensive exploitation of wind energy throughout the world, calculations of the output power of wind farm (WF)s is widely required not only for the WF layout optimization problem [1,2] but for the WF output monitoring [3][4][5], analysis [6], prediction [7,8], and control [9][10][11][12][13] problems. However, in most of the literature, the wind turbine (WT) power curves are mathematically expressed by means of a piecewise function.…”

Section: Introductionmentioning

confidence: 99%

Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient

Tao

Feijóo

et al. 2019

Energies

View full text Add to dashboard Cite

This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.

show abstract

Stochastic Optimal Scheduling Based on Scenario Analysis for Wind Farms

Cited by 46 publications

References 27 publications

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints

Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient

Contact Info

Product

Resources

About