A mixed-integer-linear-logical programming interval-based model for optimal scheduling of isolated microgrids with green hydrogen-based storage considering demand response

2022 ◽  
Vol 48 ◽  
pp. 104028
Author(s):  
Marcos Tostado-Véliz ◽  
Salah Kamel ◽  
Hany M. Hasanien ◽  
Rania A. Turky ◽  
Francisco Jurado
Keyword(s):  
Demand Response ◽  
Optimal Scheduling ◽  
Mixed Integer ◽  
Logical Programming

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.

Optimal Scheduling of Commercial Demand Response by Technical Virtual Power Plants

2021 ◽  
Author(s):  
Matthew Gough ◽  
Sergio F. Santos ◽  
Joao M. B. A. Matos ◽  
Juan M. Home-Ortiz ◽  
Mohammad S. Javadi ◽  
...  
Keyword(s):  
Demand Response ◽  
Power Plants ◽  
Optimal Scheduling ◽  
Virtual Power ◽  
Virtual Power Plants

Optimal Scheduling of Hydrogen Storage under Economic and Environmental Priorities in the Presence of Renewable Units and Demand Response

2019 ◽  
Vol 46 ◽  
pp. 101406 ◽  
Author(s):  
Sahar Seyyedeh-Barhagh ◽  
Majid Majidi ◽  
Sayyad Nojavan ◽  
Kazem Zare
Keyword(s):  
Hydrogen Storage ◽  
Demand Response ◽  
Optimal Scheduling

scholarly journals Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm

2020 ◽  
Vol 17 (3) ◽  
pp. 1632
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Demand Response ◽  
Operating Cost ◽  
Optimal Scheduling ◽  
Energy Resources ◽  
Successful Implementation ◽  
Scheduling Problem ◽  
Solar Pv ◽  
Teaching Learning Based Optimization ◽  
Diesel Generators ◽  
Teaching Learning

<p>This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.</p>

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