Day-ahead optimal dispatch of integrated energy system considering wind power forecasting and integrated demand response

2020 ◽  
Author(s):  
Shaoqi Yu ◽  
Shuhe Yan ◽  
Xingyang Liu ◽  
Bin Liu ◽  
Chang Xiong ◽  
...  
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Energy System ◽  
Wind Power Forecasting ◽  
Optimal Dispatch ◽  
Integrated Energy System ◽  
Power Forecasting

scholarly journals Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2539
Author(s):  
Zhengjie Li ◽  
Zhisheng Zhang
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Solar Power ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Scheduling ◽  
Scheduling Scheme ◽  
Load Power ◽  
Integrated Energy System ◽  
Operation Cost

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

scholarly journals Optimal Dispatch of Integrated Energy System Considering Demand Response and Load Inertia

2021 ◽  
Vol 2022 (1) ◽  
pp. 012033
Author(s):  
Feng Hong ◽  
Haixin Wang ◽  
Junyou Yang ◽  
Yunlu Li ◽  
Xiran Zhou ◽  
...  
Keyword(s):  
Demand Response ◽  
Energy System ◽  
Optimal Dispatch ◽  
Integrated Energy System

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

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.

scholarly journals An optimized demand-response operation method of regional integrated energy system considering 5G base station energy storage

2021 ◽  
Vol 2121 (1) ◽  
pp. 012007
Author(s):  
Yunli Yue ◽  
Beibei Sun ◽  
Yiming Xue ◽  
Jianmin Ding ◽  
kerui Liang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Demand Response ◽  
Energy System ◽  
Optimization Method ◽  
Base Station ◽  
Low Carbon ◽  
Side Peak ◽  
Operation Method ◽  
Integrated Energy System

Abstract The scheduling technology of regional integrated energy system is one of the key technologies to realize carbon neutralization by utilizing wind-power. Aiming at the optimal scheduling problem of regional electrothermal integrated energy system considering wind-power utilization and load side energy consumption, this paper proposes an optimized demand-response operation method of regional integrated energy system considering 5G base station energy storage. The regional integrated energy system of load side demand response is constructed based on the comprehensive consideration of technical and economic factors such as wind-power utilization and economic costs and load side peak valley difference. Finally, a two-layer particle swarm optimization method is proposed to solve the model. The experimental results show that the proposed method can effectively achieve wind-power utilization, economic dispatch and reduce the peak valley difference through load side demand response, which can improve the economic efficiency, environmental protection and low-carbon operation of regional integrated energy system.

Development and Operational Status of Wind Power Forecasting System

2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Time Series Model of Wind Power Forecasting Error by using Beta Distribution for Optimal Sizing of Battery Storage

2019 ◽  
Vol 139 (3) ◽  
pp. 212-224
Author(s):  
Xiaowei Dui ◽  
Masakazu Ito ◽  
Yu Fujimoto ◽  
Yasuhiro Hayashi ◽  
Guiping Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Beta Distribution ◽  
Time Series Model ◽  
Battery Storage ◽  
Wind Power Forecasting ◽  
Optimal Sizing ◽  
Forecasting Error ◽  
Power Forecasting

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

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