Abstract
In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. However, recent studies based on the uncertainty and worst-case scenario-oriented robust optimization methodology reveal the perplexities associated with renewable energy sources (RES). First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. Then, the joint dispatch of energy and spinning reserve capacity is performed with the integration of RES and battery storage system (BSS) to satisfy the predicted load demand. In addition, the generation system is penalized with a cost factor against load not served for the amount of energy demand which is not fulfilled due to generation constraints. Meanwhile, due to ramping of thermal units, the available surplus power will be stored in the backup energy storage system considering the state of charge of the storage system. The proposed method is applied on the IEEE-standard 6-Bus system and particle swarm optimization (PSO) algorithm is used to solve the cost minimization objective function. Finally, the proposed system performance has been verified along with the reliability during two worst-case scenarios, i.e., sudden drop in power demand and a short-fall at the generation end.
Highlights
The electrical system operator (ESO) is responsible to generate the power and he has to face plentiful multifaceted perplexities to make the system work fluently
In which the demand is cut-short compared with total available generation i.e., renewable energy sources (RES) and liquid fuel thermal generation units (LFTGU), and the surplus power will be stored, until the state of charge (SoC) level reaches to 90%
In this paper, the load forecasting results verified by comparing with a real time data of ISO-NECA, market data from January 2017 to December 2017, which has been taken from [17]
Summary
Rt Reserve requirement at tth hour [MW]. Pnt Dispatch of thermal unit n over tth hour [MW]. Ηnt Status of the nth thermal unit over tth hour. Ct Cost of power generation over the period t [$] Fnt Cost of nth generation unit over tth hour [$] Pk t Wind power output from unit k for tth hour [MW]. Pl t PV power output from unit l for tth hour [MW]. Pu,t Max. demand of uth user over tth hour [MW]. Pu,t Min. demand of uth user over tth hour [MW]. Pu,t Avg. power demand for uth user over tth hour [MW]. T Expected energy not supplied for tth hour [MW]. Gt Solar irradiance over the period t [W/m2]. Gt Max. limit on solar irradiance over the period t [W/m2]. Gt Min. limit on solar irradiance over the period t [W/m2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
