With the accelerating integration of variable renewable energies (VREs), power systems become more vulnerable to active power disturbances, and more drastic frequency dynamics emerge. The battery energy storage system (BESS) is able to handle the uncertainties of VREs, and the decreasing system inertia and frequency regulation capability. This paper proposes a chance-constrained optimal configuration scheme for the BESS to maintain both the uncertain power fluctuations and frequency deviation within predefined limits. First, the required frequency regulation capability of the BESS constrained by the maximum transient frequency deviation (MTFD) and quasi-steady-state frequency deviation (QSSFD) is estimated. Then, the kernel density estimation method is utilized to model the net power fluctuations of VREs and load. A multi-objective chance-constrained programming model accounting for the life cycle cost, energy arbitrage, uncertain power fluctuation, MTFD, and QSSFD is established to optimize the capacity of the BESS. Furthermore, the Bernstein approximation is utilized to process the chance constraint, and transform the optimization model into a deterministic form. Based on the linear weighted method and Benders decomposition, the optimization model is solved through alternating iteration. Case studies were conducted to validate the proposed scheme, showing superior performance in smoothing uncertain power fluctuations, and reducing frequency deviation under contingencies.

Considering the complementary characteristics of wind and solar energy, expanding an existing wind farm with photovoltaic panels can significantly smooth fluctuation of output power and improve operation economy. This paper proposes a two-stage approach to optimize the wind farm expansion. Based on the National Aeronautics and Space Administration data, modified meteorological models are developed considering the correlation between wind speed and solar irradiation. Taking into account fluctuation of output power, utilization of electrical equipment, and losses of renewable energy, a multi-objective optimization model is established. Two scenarios with different transformer ratings are analyzed to determine whether to expand electrical equipment. The Monte Carlo simulation is utilized to generate meteorological data in the first stage. The Pareto optimal solution set is searched by the multi-objective particle swarm optimization algorithm to determine the final solution in the second stage. A case study was conducted to validate the proposed approach.

The inherent complementarity of wind and solar energy resources is beneficial to smooth aggregate power and reduce ramp reserve capacity. This article proposes a progressive approach to assess the wind‐solar complementarities in Shandong province, China for the preliminary planning of hybrid energy systems. Based on the NASA database, the long‐term wind speed and solar irradiation data are obtained and transformed into capacity factors by virtual energy system models. Then, the local assessment that focuses on temporal characteristics is carried out to measure the complementarities in different time scales and search the optimal scale. Progressively, the space scale is extended from a local site to the whole study region and the global assessment is conducted to extract the spatial complementary characteristics. The assessment results are useful to provide the optimal temporal scale and spatial combination of wind‐solar complementarity and the proposed approach can be generalised to other regions.