Data-driven energy management in residential areas leveraging demand response

A distributed data-driven coordinated design is proposed to achieve efficient energy management of a residential grid, where controllable distributed resources such as electric vehicles (EVs) and thermostatically controlled loads (TCLs) are adjusted by balancing the end-use electricity cost, charging preference, and thermal comfort. The motivation for the control pattern is to minimize the total system cost by directly utilizing the measured input–output data instead of intractable model identification and state estimation. Firstly, we formulate a data based optimization problem with persistently exciting data sets and show the equivalence with the model-based problem. To protect the privacy of each consumer, we design a distributed pattern by the gradient of the augmented Lagrangian such that TCLs and EVs implement demand response individually. Moreover, the proposed algorithm is enhanced by a receding control scheme to tackle the uncertainties in the predictions. Standard test systems are used to illustrate the proposed design and demonstrate its effectiveness and benefits in the residential community.