Manufactory load allocation can be used as an effective industrial demand response scheme to reduce operating costs for industrial multi-energy micro-grids (iMEMGs). In addition, combined cooling, heat and power (CCHP) plants with auxiliary devices can provide low-cost multiple energies for industrial plants. However, uncertain power generation from renewable energy sources impairs the iMEMG's operation, leading to challenges such as increased operating costs and energy supply deficiency. To conquer these challenges, this paper proposes a multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated in different timescales. In the weekly scheduling stage, industrial loads and CCHP units are scheduled for the following week and the hourly iMEMG operation is optimized within the week. Besides, this paper applies an adaptive robust optimization method where the uncertain renewable power generation is fully addressed. The proposed approach is tested on an iMEMG with various industrial manufactories and it is compared with conventional methods. The simulation results indicate that compared to the conventional ones, the proposed approach can guarantee a robustly optimal operation solution for the iMEMG against any uncertainty realization.

To address uncertainties, two-stage robust optimization (TSRO) methods have been widely used for power system operation and planning problems. However, solving TSRO problems with integer recourses of energy storage systems (ESSs) is challenging. This article proposes a feasibility pump based column and constraint generation (FP-CCG) solution algorithm to solve these TSRO problems. In this article, firstly, a TSRO problem of ESS operation with state-of-charge management is formulated. Then, the FP-CCG algorithm is proposed and applied to solve this TSRO problem. Simulation results indicate full solution robustness and high computation efficiency of the proposed FP-CCG solution algorithm.

This paper proposes a new separable model for the unit commitment (UC) problem and three deterministic global optimization methods for it ensuring convergence to the global optimum within a desired tolerance. By decomposing a multivariate function into several univariate functions, a tighter outer approximation methodology that can be used to improve the outer approximations of several classical convex programming techniques is presented. Based on the idea of the outer approximation (OA) method and the proposed separable model, an outer-inner approximation (OIA) approach is also presented to solve this new formulation of UC problem. In this OIA approach, the UC problem is decomposed into a tighter outer approximation subproblem and an inner approximation subproblem, where the former leads to a better lower bound than the OA method, and the later provides a better upper bound. The simulation results for systems of up to 100 units with 24 h are compared with those of previously published methods, which show that the OIA approach is very promising due to the excellent performance. The proposed approaches are also applied to the large-scale systems of up to 1000 units with 24 h, and systems of up to 100 units with 96 h and 168 h.