The paradigm shift from a coal-based power system to a renewable-energy-based power system brings more challenges to the supply-demand balance of the grid. Distributed energy resources (DERs), which can provide operating reserve to the grid, are regarded as a promising solution to compensate for the power fluctuation of the renewable energy resources. Small-scale DERs can be aggregated as a virtual power plant (VPP), which is eligible to bid in the operating reserve market. Since the DERs usually belong to different entities, it is important to investigate the VPP operation framework that coordinates the DERs in a trusted manner. In this paper, we propose a blockchain-assisted operating reserve framework for VPPs that aggregates various DERs. Considering the heterogeneity of various DERs, we propose a unified reserve capacity evaluation method to facilitate the aggregation of DERs. By considering the mismatch between actual available reserve capacity and the estimated value, the performance of VPP in the operating reserve market is improved. A hardware-based experimental system is developed, and numerical results are presented to demonstrate the effectiveness of the proposed framework.

District cooling system (DCS) provides centralized chilled water to multiple buildings for air conditioning with high energy-efficiency and operational flexibility. It is one of the most popular cooling systems for large buildings in modern cities and an important demand response source for power systems. In order to enhance its energy efficiency and utilize its flexibility, strategic operation is indispensable. However, finding an optimal policy for DCS operation is a challenging task because of the high inter-connectivity among components. The evolution of cooling load uncertainties further increases the difficulties. This paper addresses the aforementioned challenges by proposing a novel optimal power dispatch model for DCS. The proposed model optimizes water temperature and mass flow rates simultaneously to improve the energy efficiency as much as possible. It also explicitly describes the uncertainty accumulation and propagation. Chance-constrained programming is employed to guarantee the cooling service quality. We further propose a more time-efficient formulation to overcome the computational intractability caused by the non-smooth and non-convex constraints. Numerical experiments based on a real DCS confirm that a time-efficient formulation can save about half of solution time with negligible cost increase.