Abstract
This study uses a minimum cost flow method to solve a dispatch problem in order to minimize the consumption of coal in the dispatching of a thermal power system. Low-carbon generation dispatching is also considered here since the scheduling results are consistent with energy-saving generation dispatch. Additionally, this study employs minimum coal consumption as an objective function in considering the output constraints, load balance constraints, line loss, ramp rate limits, spinning reverse needs, prohibited operating zone requirements, security constraints, and other common constraints. The minimum cost flow problem, considering the loss of network flow, is known as a generalized network flow problem, which can be expressed as a quadratic programming problem in mathematics. Accordingly, the objective function was solved by LINGO11, which was used to calculate a network in a single time; a continuous period dispatch plan was obtained by accumulating each period network flow together. This analysis proves it feasible to solve a minimal cost flow problem with LINGO11. Theoretical analysis and numerical results prove the correctness and effectiveness of the proposed method.
Highlights
There seems to be rather compelling evidence that global warming is an issue that we seriously need to be concerned about today [1, 2]
The main objective of this study is to introduce convex quadratic programming to solve the energy-saving generation dispatching (ESGD) problem, since coal consumption and network losses are all convex functions of the power flow through a network
Achieving the lowest carbon emission is the target of low-carbon generation dispatching
Summary
There seems to be rather compelling evidence that global warming is an issue that we seriously need to be concerned about today [1, 2]. Many mathematical techniques have been developed and applied to dispatch problem such as linear programming [7], interior-point method [8], Lagrangian relaxation algorithm [9], quadratic programming [10] and other traditional algorithms. In pursuit of the optimal solution for economic dispatch, various hybrid methods have been investigated and implemented [5, 19,20,21,22] These hybrid algorithms normally take lengthy calculation time when compared with the mathematical optimization methods. A continuous period of ESGD planning was obtained by accumulating period network flow results This process confirmed that the minimal cost flow method was successful for solving the ESGD problem and, has value for these types of applications
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