Intelligent Decision Support Algorithm Based on Self-Adaption Reasoning

This paper analyzes the logic deduction and reasoning techniques used in several intelligent decision support algorithms, and proposes a flexible planning method GARIv using fuzzy descriptive logic in media enterprise management. Combined with experiments, the above methods are illustrated in terms of effectiveness and feasibility. In the end, the challenges and possible solutions of intelligent decision support algorithms with self-adaption reasoning are discussed

Transmission expansion planning and unit commitment with large-scale integration of wind power

The large-scale integration of wind generation into the power system brings great challenges to transmission expansion planning (TEP) and unit commitment (UC). The intermittence nature of wind generation needs to be fully considered in these two problems, which stimulates the research of this thesis. The selection of candidate lines is the prerequisite for the TEP problem. Considering the limitations of manual selection approach, a method to select candidate lines automatically is proposed, which consists of five stages to reinforce existing corridors and new corridors. Results of the two test systems illustrate that the locational marginal price difference is neither sufficient nor necessary condition for candidate lines. The uncertainty of load demand and wind power is studied both in the TEP and UC problems. In the term of TEP, a two-stage stochastic formulation of TEP is proposed. The stochastic dual dynamic programming (SDDP) approach is applied to consider the uncertainty, and the whole model is solved by Benders decomposition (BD) technique. In the term of UC, the chance-constrained two-stage programming formulation is proposed for the day-ahead UC problem. The chance-constrained stochastic programming formulation is converted into an equivalent deterministic formulation by a sequence of approximation and verification