With increasing adoption of intermittent renewable generation, challenges to maintain stable frequencies for power systems are increasing. Demand-side resources, such as thermostatically controlled loads (TCLs), have been proven capable of providing regulation services. The district cooling system (DCS), a type of centralized TCL that provides cooling services for a group of buildings, is an ideal resource for this purpose. A DCS usually has significant regulation flexibility because its rated power capacity is large (up to 100 MW) and it can utilize thermal inertia of an aggregation of buildings. However, as a large-scale system with complex thermal dynamics, its effective regulation is nontrivial, and its traditional demand-driven control scheme has difficulty considering regulation signals from power systems. To address these challenges, we propose a bidirectional-driven control scheme in a DCS for the first time. On this basis, we formulate the thermal and electricity model of a DCS in the frequency domain and derive an equivalent model to control it like a traditional generator. Furthermore, we propose a sensitivity-based control strategy for a DCS to allocate mass flow adjustments among buildings, which effectively considers heterogeneity of buildings to balance temperature impacts. Numerical studies illustrate effectiveness of the proposed model and control strategy.

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.

Integration of distributed renewable generation (DRG) in distribution networks can be effectively promoted by scheduling flexible resources such as heating, ventilation, and air conditioning (HVAC) loads. However, finding the optimal scheduling for them is not trivial because DRG outputs are highly uncertain. To address this issue, this paper proposes a learning-based joint chance-constrained approach to coordinate HVAC loads with DRG. Unlike cutting-edge works adopting individual chance constraints to manage uncertainties, this paper controls the violation probability of all critical constraints with joint chance constraints (JCCs). This joint manner can explicitly guarantee operational security of the entire system based on operators’ preferences. To overcome intractability of JCCs, we first prove that JCCs can be safely approximated by robust constraints with proper uncertainty sets. A famous machine learning algorithm, one-class support vector clustering, is then introduced to construct a small enough polyhedron uncertainty set for these robust constraints. A linear robust counterpart is further developed based on the strong duality to ensure computational efficiency. Numerical results based on various distributed uncertainties confirm the advantages of the proposed model in optimality and feasibility.

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.