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.

In this paper, a novel joint planning framework is proposed to coordinate the investment and operation of renewable energy sources and energy storage systems (ESS) in energy and ancillary services markets. Based on this framework, coordinated planning and operation model under centralized and deregulated market mechanism is studied, and multiple factors such as siting and sizing of wind turbine and ESS, the efficiency of ESS, transmission lines constraints are considered. For the centralized market mechanism, the coordinated model aiming at maximizing social welfare is established, which is a tractable single-level optimization problem. For the deregulated market mechanism, the coordinated model aiming at maximizing investment profits is established, which is an intractable bi-level optimization problem as the locational marginal price in the objective function. The bi-level optimization problem is reformulated into a mathematical problem with equilibrium constraints by Karush-Kuhn-Tucker conditions. The big-M method and strong dual theory are used to deal with the nonlinearity in constraints and objective functions, and the problem is transformed into a mixed-integer linear programming, which can be solved by commercial software. Furthermore, the impact of production tax credit and investment tax credit financial incentive policies on investment behavior has been studied, and the evaluation indexes of electrical information and economy have been established. The proposed approach has been implemented on the IEEE 6-bus and the IEEE 30-bus test systems, and results justify the efficiency of the model proposed. INDEX TERMS Investment and operation, market mechanism, mathematical problem with equilibrium constraints, Karush-Kuhn-Tucker conditions, financial incentive. NOMENCLATURE SETS AND INDICES The associate editor coordinating the review of this manuscript and approving it for publication was Pierluigi Siano .

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In order to help achieve the goal of carbon peak and carbon neutrality, the large-scale development and application of clean renewable energy, like wind generation and solar power, will become an important power source in the future. Large-scale clean renewable energy generation has the uncertain characteristics of intermittency, randomness, and volatility, which brings great challenges to the balance regulation and flexible operation of the power system. In addition, the rapid development of renewable energy has led to strong fluctuations in electricity prices in the power market. To ensure the safe, reliable, and economic operation of the power system, how to improve the power system flexibility in an uncertain environment has become a research hotspot. Considering the uncertainties, this article analyzes and summarizes the research progress related to power system flexibility from the perspective of power system planning, operation, and the electricity market. Aiming at the modeling technology of uncertainty, the related modeling methods including stochastic programming, robust optimization, and distributionally robust optimization are summarized from the perspective of mathematics, and the application of these methods in power system flexibility is discussed.