This paper proposes a new distance-based distributionally robust unit commitment (DB-DRUC) model via Kullback-Leibler (KL) divergence, considering volatile wind power generation. The objective function of the DB-DRUC model is to minimize the expected cost under the worst case wind distributions restricted in an ambiguity set. The ambiguity set is a family of distributions within a fixed distance from a nominal distribution. The distance between two distributions is measured by KL divergence. The DB-DRUC model is a “min-max-min” programming model; thus, it is intractable to solve. Applying reformulation methods and stochastic programming technologies, we reformulate this “min-max-min” DB-DRUC model into a one-level model, referred to as the reformulated DB-DRUC (RDB-DRUC) model. Using the generalized Benders decomposition, we then propose a two-level decomposition method and an iterative algorithm to address the RDB-DRUC model. The iterative algorithm for the RDB-DRUC model guarantees global convergence within finite iterations. Case studies are carried out to demonstrate the effectiveness, global optimality, and finite convergence of a proposed solution strategy.

This paper proposes an optimal distributed control strategy for the coordination of multiple distributed generators in an islanded microgrid (MG). A finite-time secondary frequency control approach is developed to eliminate the frequency deviation and maintain accurate active power sharing in a finite-time manner. It is demonstrated that the traditional distributed control approach with asymptotical convergence is just a special case of the proposed finite-time control strategy under the specific control parameter settings. Then, a secondary voltage control approach is presented to regulate the average voltage magnitude of all distributed generators to the desired value and achieve accurate reactive power sharing. The implementation of the proposed distributed control strategy only requires information exchange among neighboring local controllers through a sparse communication network. Simulations with an islanded MG testbed built in MATLAB/Simulink are conducted to validate the effectiveness of the proposed distributed control strategy.

For a battery swapping station (BSS), the stochastic operation of electric buses (EBs) and the uncertainty of electricity prices cause unnecessary economic losses. To minimize the operating costs of the BSS, this paper applies the deep reinforcement learning (DRL) and proposes a BSS model to determine the optimal real-time charge/discharge power of the charging piles. The predictability of bus operation and the uncertainty of price can be directly captured from historical data without any assumption in the model. Moreover, Deep Deterministic Policy Gradient (DDPG), as the DRL algorithm, is implemented in the model to simultaneously control multiple charging piles. Numerical results illustrate that the proposed approach can bring less operating cost than the existing benchmark control methods for a BSS while providing adequate batteries ready for swapping.

This paper proposes a new distributed dispatch scheme to realize efficient coordinated real-time dispatch of the coupled transmission grid and active distribution grids (ADGs). In the proposed scheme, the concept of optimal power exchange interval is introduced to coordinate the transmission grid and ADGs so that the reserve capacity support from ADGs can be incorporated in the dispatch optimization of the transmission grid. Meanwhile, uncertainties of renewable distributions are considered to ensure the robustness of dispatch decisions. With the analytical target cascading (ATC) method, the centralized distributionally robust dispatch model for the integrated transmission-distribution system is decoupled, leading to a number of independent small local optimization problems for the transmission grid and ADGs. Meanwhile, the diagonal quadratic approximation is adopted to develop an iterative coordination strategy where all local optimization problems are solved in a parallel manner, increasing the computational efficiency. By using the constrained cost variable technique (CCV) and a new affine policy, the original non-convex dispatch model is reformulated as a linear optimization problem, which ensures the convergence of the iterative process and further reduces the computational burden. Case studies on test systems verify the effectiveness and efficiency of the proposed scheme.

To realize the potential of active distribution networks (ADNs) for improving power system flexibility and to cope with multiple uncertainties, a coordinated robust reserve scheduling (CRRS) model for the coupled transmission and distribution networks (CTD) is proposed in this paper. This model coordinates the generation resources both in the normal state and re-dispatch state to enhance the cost-effectiveness and reliability in a system perspective. A fully distributed framework based on the alternating direction method of multipliers (ADMM) is employed to solve this problem in a decentralized way. As the regional problem of the transmission system is non-convex, a two-layer iterative process (TIP) is presented to enhance the convergence property of standard ADMM. Since only the boundary information needs to be exchanged between the transmission and distribution systems, the communication burden is light and regional information is encrypted. Case studies on the T6D2 and T118D10 systems demonstrate the effectiveness of the proposed model and approach.