  Price-based demand response (DR) of heating, ventilating, and
air-conditioning (HVAC) systems is a challenging task, requiring comprehensive
models to represent the building thermal dynamics and game theoretic
interactions among participants. This paper proposes an online learning-based
strategy for a distribution system operator (DSO) to determine optimal
electricity prices, considering the optimal DR of HVAC systems in commercial
buildings. An artificial neural network (ANN) is trained with building energy
data and represented using an explicit set of linear and nonlinear equations,
without physics-based model parameters. An optimization problem for price-based
DR is then formulated using this equation set and repeatedly solved offline,
producing data on optimal DR schedules for various conditions of electricity
prices and building thermal environments. Another ANN is then trained online to
directly predict DR schedules for day-ahead electricity prices, which is
referred to as meta-prediction (MP). By replacing the DR optimization problem
with the MP-enabled ANN, an optimal electricity pricing strategy can be
implemented using a single-level decision-making structure, which is simpler
and more practical than a bi-level one. In simulation case studies, the
proposed single-level strategy is verified to successfully reflect the game
theoretic relations between the DSO and commercial building operators, so that
they effectively exploit the operational flexibility of the HVAC systems to
make the DR application profitable, while ensuring the grid voltage stability
and occupants thermal comfort.
