Abstract
This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.
Highlights
Advancements in technology are always accompanied by changes, and the energy arena is no exception
An Energy Storage Systems (ESS) algorithm for participating in economic demand response (DR) is suggested in [8], but the results show that there is no economic rationale for participating in the DR with ESSs because of the low-level benefits of economic DR
For the N.E. of the non-cooperative indirect energy trading (IET), consider that two players are under symmetric conditions for convenience
Summary
Advancements in technology are always accompanied by changes, and the energy arena is no exception. The use of RE is rapidly spreading in Korea following the Renewable Energy 3020 Plan, which was announced by the Ministry of Trade, Industry & Energy in 2017 The goal of this plan is to increase the capacity of RE by 20% of the total generation capacity by. 2030; the current status of RE capacity in Korea is about 178 GW [3] Under these conditions, flexible resources, such as Energy Storage Systems (ESS), high ramping rate generators, and demand response (DR) have become essential for handling the intermittency of RE generation. Flexible resources, such as Energy Storage Systems (ESS), high ramping rate generators, and demand response (DR) have become essential for handling the intermittency of RE generation Among these candidates, DR can be deployed to handle this flexibility efficiently [4]
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