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Beihua Fang

Publications and source records attributed to Beihua Fang.

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High Dimensional Three-Periods Locally Ideal MIP Formulations for the UC Problem

The thermal unit commitment (UC) problem often can be formulated as a mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale instances. The tighter characteristic reduces the search space, therefore, as a natural conse-quence, significantly reduces the computational burden. In the literature, many tightened formulations for single units with parts of constraints were reported without presenting how they were derived. In this paper, a sys-tematic approach is developed to formulate the tight formulations. The idea is using more new variables in high dimension space to capture all the states for single units within three periods, and then, using these state variables systematic derive three-periods locally ideal expressions for a subset of the constraints in UC. Meanwhile, the linear dependence relations of those new state variables are leveraged to keep the compactness of the obtained formulations. Based on this approach, we propose two tighter models, namely 3P-HD and 3P-HD-Pr. The proposed models and other four state-of-the-art models were tested on 51 instances, including 42 realistic instances and 9 8-unit-based instances, over a scheduling period of 24 h for systems ranging from 10 to 1080 generating units. The simulation results show that our proposed MIQP UC formulations are the tightest and can be solved most efficiently. After using piecewise technique to approxi-mate the quadratic operational cost function, the six UC MIQP formulations can be approximated by six corre-sponding mixed-integer linear programming (MILP) formulations. Our experiments show that the proposed 3P-HD and 3P-HD-Pr MILP formulations also perform the best in terms of tightness and solution times.

math.OC

A Center-Point Algorithm for Unit Commitment with Carbon Emission Trading

This paper proposes a global optimization method for it ensures finding good solutions while solving the unit commitment (UC) problem with carbon emission trading (CET). This method con-sists of two parts. In the first part, a sequence of linear inte-ger-relaxed subproblems are first solved to rapidly generate a tight linear relaxation of the original mixed integer nonlinear pro-gramming problem (MINLP) model. In the second part, the algo-rithm introduces the idea of center-cut so that it can quickly find good solutions. The approach tested on 10 test instances with units ranging from 35 to 1560 over a scheduling period of 24h, and compared with state-of-the-art solver CPLEX. The results show that the proposed algorithm can find better solutions than CPLEX in a short time. And it is more suitable to solve large scale UC problem than CPLEX.

math.OC