Xiaomeng Ai

Dynamic Optimal Energy Flow in the Integrated Natural Gas and Electrical Power Systems

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.

Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming

This paper proposes an approximate dynamic programming (ADP)-based approach for the economic dispatch (ED) of microgrid with distributed generations. The time-variant renewable generation, electricity price, and the power demand are considered as stochastic variables in this paper. An ADP-based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo 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.

Data-adaptive Robust Optimization Method for the Economic Dispatch of Active Distribution Networks

Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from power system operators. This paper proposes a novel data-adaptive robust optimization method for the economic dispatch of active distribution network with renewables. The scenario-generation method and two-stage robust optimization are combined into the proposed method. To reduce the conservativeness, a few extreme scenarios selected from historical data are used to replace the conventional uncertainty set. The proposed extreme-scenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all possible scenarios if they hold in the selected extreme scenarios, which guarantees the robustness of the decision. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm with the selected uncertainty set is less conservative but equally as robust as the existing two-stage robust optimization approaches. This leads to the improved economy of the decision with uncompromised security.

Hybrid approximate dynamic programming approach for dynamic optimal energy flow in the integrated gas and power systems

This paper proposes a hybrid approximate dynamic programming (ADP) approach for the multiple time-period optimal power flow in integrated gas and power systems. ADP successively solves Bellmans equation to make decisions according to the current state of the system. So, the updated near future forecast information is not fully utilized. While model predictive control (MPC) as a look ahead policy can integrate the updated forecast in the optimization process. The proposed hybrid optimization approach makes full use of the advantages of ADP and MPC to obtain a better solution by using the real-time updated forecast information. The simulation results demonstrate the effectiveness of the proposed algorithm.

Constraints of wind power ramp event in robust unit commitment

The stochastic nature of wind power brings about large ramp events and poor prediction accuracy, which challenges the secure operation of power systems. Several stochastic unit commitment and economic dispatch methods are proposed to handle the prediction error of wind power, but few of them have attended to the ramp events. Therefore, this paper elaborately discusses the necessities to account for wind power rampings in unit commitment and proposes corresponding constraints of them. And possible decomposition implementation on the modified problem is discussed for computational efficiency in large systems. Case studies are carried out based on several benchmark systems. Numerical results show that the proposed constraints for ramp events are sufficient and essential to guarantee the security operation of power systems against rapid variations of wind power without significantly increasing the calculation burden.