Abstract: Electricity market is the key to promoting China's energy transformation. The high proportion of renewable energy has significantly changed the resource allocation characteristics of the electricity market. Different from traditional thermal power units, the high uncertainty of renewables makes the market-clearing boundary fuzzy. With the existing deterministic market-clearing model, it is difficult to consider the flexible resource cost required to stabilize the uncertainty of renewable energy. Explicitly considering the uncertainty of renewable energy in the market pricing method is the key to the above-mentioned problems. This paper proposes an electricity market pricing method considering the uncertainty of renewable energy explicitly based on the chance-constrained stochastic optimization method. A multi-statistical features-oriented renewable energy uncertainty pricing method is developed through the transformation and derivation of the Lagrange duality problem. To solve the problem that the uncertainty of renewable energy under arbitrary distribution produces a large number of statistical parameters, resulting in the unclear physical meaning of market price, this paper proposes a sensitivity-based renewable uncertainty pricing method by introducing the existing uncertainty measurement methods. The simulations performed on PJM 5-bus, IEEE 118-bus systems and practical 661-bus system show that the proposed method can provide a clear price signal to guide the market resource allocation, and the computational efficiency meets the requirements of the market-clearing time window.