• Energy Research

With the increasing complexity of the power grid and the concerns on major blackouts, there is an urgent need for an efficient and effective tool to assess the power system structural vulnerability. To tackle this challenge, this paper proposes a maximum flow-based complex network approach to identify the critical lines in a system. The proposed method consists of two major steps. First, the power network is modeled as a graph with edges (transmission lines, transformers, etc.) and nodes (buses, substations, etc.). The critical scenarios are identified by using the principal component analysis and convex hull. Then the second step is to use an improved maximum flow-based complex network approach for topology analysis. Weighted vertices in the network are considered, enabling taking the selected operating conditions into consideration when identifying the vulnerable lines. The proposed method is validated using the western Danish power system. The vulnerable lines in the network are ranked. Simulation results demonstrate the effectiveness of the method by intentional attacks and comparison with the planning strategy from the system operator.

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 mixed-integer 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 decomposes the original multitime periods MINLP problem into single-time period nonlinear programming 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.

Sufficient dynamic Var reserve (DVR) can efficiently enhance the immunity against continuous commutation failures (CCF) to avoid DC blocking and further security accidents in the line-commutated converter (LCC) based HVDC receiving-end power system. This article proposes the problem formulation and efficient solution algorithm for the DVR-constrained coordinated scheduling considering contingencies and wind uncertainties. The DVR sufficiency is proposed for the post-contingency CCF immunity in scheduling problems, which combines the voltage sensitivity-based DVR capacity calculation and the off-line DVR requirement tests. HVDC transmission and W/Var sources are coordinately scheduled to accommodate contingencies and wind uncertainties and to improve the operational economy. The reduced event-based algorithm (REA) is developed to subdivide the complex scheduling problem and reduce the considered constraints for efficient calculation. Case studies demonstrate the guaranteed DVR sufficiency, the improved economy, and the enhanced computational efficiency of the proposed methods.

This work focuses on the optimal operation of the integrated gas and electrical power system with bi-directional 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. And only a single stage linear programming is needed to obtain the optimal operation strategy for both gas and power systems. Simulation on the test case illustrates the success of the modelling 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 letter proposes a parametric cost function approximation (CFA)-based on-line operation strategy for microgrids, considering the uncertainty from distributed renewable energy. It aims to obtain on-line scheduling solutions with the minimal expectation of the operational cost. For the non-convex optimization problem in this letter, the stochastic gradient descent-based parameter optimization method is proposed to find the optimal parameter used in the parametric CFA policy. The proposed algorithm is demonstrated to be highly effective by using numerical experiments, indicating the high potential for practical applications.