This paper presents an optimal energy control scheme for a grid independent photovoltaic (PV) solar system consisting of a PV array, battery energy storage, and time varying loads (a small critical load and a larger variable non-critical load). The optimal controller design is based on a class of adaptive critic designs (ACDs) called the action dependant heuristic dynamic programming (ADHDP). The ADHDP class of ACDs uses two neural networks, an "action" network (which actually dispenses the control signals) and a "critic" network (which critics the action network performance). An optimal control policy is evolved by the action network over a period of time using the feedback signals provided by the critic network. The objectives of the optimal controller in order of decreasing importance is to first fully dispatch the required energy to the critical loads at all times; secondly to dispatch energy to the battery whenever necessary so as to be able to dispatch energy to the critical loads in any absence of energy from the PV array; and lastly to dispatch energy to the non-critical loads while not interfering with the first two objectives. Results on three different US cities show that the ADHDP based optimal control scheme outperforms the conventional PV-priority control scheme in maintaining the stated objectives almost all the time.

Meeting Name

International Joint Conference on Neural Networks, 2007


Electrical and Computer Engineering

Keywords and Phrases

Action Dependant Heuristic Dynamic Programming (ADHDP Class); Battery Storage Plants; Neural Nets; Optimal Control; Photovoltaic Power Systems; Power Control; Solar Power Stations; Time-Varying Systems

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link