peak load ensemble prediction and multi-agent reinforcement learning for der demand response management in smart grids
abstract
the increasing number of distributed energy resources (ders), such as home batteries and electrical vehicles (evs), provides an opportunity for utility companies to
develop demand response mechanisms to balance the demand and supply of energy during peak times. however, it is challenging to shave the grid’s peak load efficiently and
effectively as it requires accurate energy forecasting and coordinated management of
ders. to address this challenge, this thesis proposes a system consisting of an imagebased ensemble prediction model and a multi-agent reinforcement learning (marl)
mechanism for demand response (dr) management in smart grids. for the imagebased prediction model, we hypothesize that the approximate curve of the daily power
consumption graph has some specific patterns that can be used to separate each day
into different groups based on the pattern of the energy consumption curve. to this
end, we use a convolution neural network model to classify and extract the features
of the curve image. then, we apply the k-means mechanism for image clustering to
select better training sets and optimize the forecasting mechanism. our results show
an overall improvement in prediction during the season-changing period. the proposed
marl mechanism takes the prediction results as input to the agents to coordinate the
discharging time of ders to maximize the peak shaving performance. this mechanism
requires centralized training and allows distributed execution. the system’s evaluations
and experiments are conducted on a real-life dataset, and our results show the proposed
system’s effectiveness.