Coordinated planning is an effective method to balance investment costs and benefits in achieving high renewable target under the renewable-driven power system expansion wave. This paper proposes a coordinated planning model to support the efficient achievement of renewable target considering economy of the system by accounting for the interaction among source, grid, and energy storage system. An adaptive two-stage min-max-min robust optimization model is formulated to take into account renewable target as well as the uncertainty associated with renewable production and load demand. To reduce the conservatism of robust optimization, uncertain budget, multiple uncertain sets, and data-driven method are used to design uncertain sets. The resulting model is transformed into a tractable bi-level programming through strong duality theory and big-M method. A customized column-and-constraint generation algorithm is used to solve the bi-level programming. Simulation results presented for the modified IEEE 30-bus test system corroborates the effectiveness of the methodology, which finds siting and sizing of renewable energy sources and energy storage systems as well as transmission expansion schemes. It is capable to provide a flexible planning tool driven by renewable target under a reasonable computational burden.

This paper proposes a distance-based distributionally robust energy and reserve (DB-DRER) dispatch model via Kullback–Leibler (KL) divergence, considering the volatile of renewable energy generation. Firstly, a two-stage optimization model is formulated to minimize the expected total cost of energy and reserve (ER) dispatch. Then, KL divergence is adopted to establish the ambiguity set. Distinguished from conventional robust optimization methodology, the volatile output of renewable power generation is assumed to follow the unknown probability distribution that is restricted in the ambiguity set. DB-DRER aims at minimizing the expected total cost in the worst-case probability distributions of renewables. Combining with the designed empirical distribution function, the proposed DB-DRER model can be reformulated into a mixed integer nonlinear programming (MINLP) problem. Furthermore, using the generalized Benders decomposition, a decomposition method is proposed and sample average approximation (SAA) method is applied to solve this problem. Finally, simulation result of the proposed method is compared with those of stochastic optimization and conventional robust optimization methods on the 6-bus system and IEEE 118-bus system, which demonstrates the effectiveness and advantages of the method proposed.

Large-scale renewable energy integration brings unprecedented challenges to electric power system planning and operation. The paper aims at economic dispatch and the safe operation of high penetration renewable energy power systems. According to the principle of power system dispatchability, the assessment of wind energy accommodation is formulated into a two-stage robust optimization problem with a min-max-min structure. Based on the benders algorithm, the intractable robust optimization problem is transformed into the form of sub-problem and master problem. Strong duality theory and big-M method are used to recast the sub problem into a mixed integer linear programming. The envelope of wind energy accommodation can be obtained by using commercial software to solve the master problem and sub problem alternately. For the realization of arbitrary wind power within the envelope, the amount of wind energy leakage and load shedding in power system operation are acceptable. An example of modified IEEE 39-bus test systems is used to verify the effectiveness and practicability of the evaluation method.