This paper focuses on the optimal operation of the integrated gas and electrical power system with bidirectional energy conversion. Considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated gas and power systems. With proper assumptions and simplifications, the problem is transformed into a single-stage linear programming to obtain the optimal operation strategy for both gas and power systems. Simulation on the test case illustrates the success of the modeling and the beneficial roles of the power-to-gas are analyzed. The proposed model can be used in the decision support for both planning and operation of the coordinated natural gas and electrical power systems.

This paper proposes an approximate dynamic programming (ADP) based approach for the economic dispatch (ED) of microgrid with distributed generations (DGs). The time-variant renewable generation, electricity price and the power demand are considered as stochastic variables in this work. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo (MC) method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.

This paper proposes an approximate dynamic programming (ADP) based algorithm for the real-time operation of the microgrid under uncertainties. First, the optimal operation of the microgrid is formulated as a stochastic mixedinteger nonlinear programming (MINLP) problem, combining the AC power flow and the detailed operational character of the battery. For this NP-hard problem, the proposed ADP based energy management algorithm (ADP-EMA) decomposes the original multi-time periods MINLP problem into singletime period nonlinear programming (NLP) problems. Thus the sequential decisions can be made by solving Bellman's equation. Historical data is utilized offline to improve the optimality of the real-time decision, and the dependency on the forecast information is reduced. Comparative numerical simulations with several existing methods demonstrate the effectiveness and efficiency of the proposed algorithm.