Study on wind power consumption based on demand response

Today, wind power is becoming an important energy source for the future development of electric energy due to its clean and environmentally friendly characteristics. However, due to the uncertainty of incoming wind, the utilization efficiency of wind energy is extremely low, which means the problem of wind curtailment becomes more and more serious. To solve the issue of wind power large-scale consumption, a two-stage stochastic optimization model is established in this paper. Different from other research frameworks, a novel two-side reserve capacity mechanism, which simultaneously takes into account supply side and demand side, is designed to ensure the stable consumption of wind power in the real-time market stage. Specifically, the reserve capacity of thermal power units is considered on the supply side, and the demand response is introduced as the reserve capacity on the demand side. At the same time, the compensation mechanism of reserve capacity is introduced to encourage generation companies (GENCOs) to actively participate in the power balance process of the real-time market. In terms of solution method, compared with the traditional k-means clustering method, this paper uses the K-means classification based on numerical weather prediction (K-means-NWP) scenario clustering method to better describe the fluctuation of wind power output. Finally, an example simulation is conducted to analyze the influence of reserve capacity compensation mechanism and system parameters on wind power consumption results. The results demonstrate that with the introduction of reserve capacity compensation mechanism, the wind curtailment quantity of the power system has a significant reduction. Besides, the income of GENCOs is gradually increasing, which motivates their enthusiasm to provide reserve capacity. Furthermore, the reserve capacity mechanism designed in this paper promotes the consumption of wind power and the sustainable development of renewable energy.

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Research on Optimal Scheduling Strategy of Wind Power Integration Based on Demand Response

2019 IEEE Sustainable Power and Energy Conference (iSPEC) ◽

10.1109/ispec48194.2019.8975376 ◽

2019 ◽

Author(s):

Mingyang Zhu ◽

Changyong Yin ◽

Yan Zhao

Keyword(s):

Wind Power ◽

Demand Response ◽

Optimal Scheduling ◽

Scheduling Strategy ◽

Wind Power Integration ◽

On Demand

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Operation Optimization of Regional Integrated Energy System Based on Electric Heating Gas Demand Response

E3S Web of Conferences ◽

10.1051/e3sconf/202021801002 ◽

2020 ◽

Vol 218 ◽

pp. 01002

Author(s):

Nan Wang ◽

Jialin Yang ◽

Xichao Zhou ◽

Zhen Li ◽

Yaling Sun ◽

...

Keyword(s):

Power Consumption ◽

Wind Power ◽

Demand Response ◽

Energy Cost ◽

Energy System ◽

Multi Objective Optimization ◽

Total Energy Consumption ◽

Multi Objective ◽

Trade Offs ◽

Integrated Energy System

Taking into account the energy cost, pollution emission, wind power consumption, and other dispatching objectives in the regionally integrated energy system (RIES), the RIES multi-objective optimization model considering the integrated demand response is established. Firstly, the RIES modeling of equipment including electricity-to-gas, energy storage systems, cogeneration units, etc., and the introduction of a comprehensive demand response that specifically considers load reduction, load transfer, and load replacement in the region, aimed at reducing system load peaks and valleys difference. Then, the objective function to minimize the system energy cost, the abandoned wind power, and the pollutant treatment cost was established respectively, and the multi-objective optimization method was adopted—the Pareto front was solved by fuzzy weighted programming traversal weights, and then the decision was made based on evidence Method to find the optimal scheduling strategy. Finally, based on a typical case study, the results show that the proposed multi-objective optimization algorithm can effectively make trade-offs among multiple scheduling objectives, and RIES considering comprehensive demand response has advantages in terms of total energy consumption, environmental friendliness, and wind power consumption.

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