Abstract
This paper proposes a hybrid approximate dynamic programming (HADP) approach for the optimal operation of integrated gas and power systems (IGPS) under the stochastic environment. The proposed HADP, combining the advantages of the model predictive control (MPC) and approximate dynamic programming (ADP), decomposes the multi-time-period optimization into multiple sequential subproblems by solving Bellman’s equation forward through time. Historical data is utilized to build the approximate value functions so that the influence of current decisions on the future is estimated. And hence, the HADP algorithm can obtain a near-optimal solution through the whole time horizon of interest. Meanwhile, the MPC policy is embedded in the HADP to replace the long-term forecast with short-term or even real-time prediction. This further improves the optimality of the decisions made by the proposed HADP. The simulation results on the IGPS demonstrate the proposed HADP outperforms alternative solutions.
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