Deep Reinforcement Learning-Based Trading Strategy for Load Aggregators on Price-Responsive Demand

Table 1

Description of the variables of the DDPG.

Name	Meaning	Meaning of this study
Agent	Intelligence to be controlled	Price load-responsive load aggregator
State, S	Status of the agent	Current electricity spot market price (, )
Action, a	Actions that an agent can take	Purchase and sale of electricity (output sell/buy)
Reward, r	Timely return value of the environment used to evaluate the quality of an action on an agent	Revenue from load aggregators
Policy, P	Agent decides the strategy of the next action based on the current state	Buying and selling actions in the next cycle are determined based on the status of the load aggregator in the previous cycle
Value	Return value of an agent action’s long-term value, distinguished from the short-term return represented by reward	Total revenue over the period of operation
Environment	Environment of the agent	Fluctuations in electricity prices (input t real-time locational marginal price, input t real-time l demand)