No abstract

In systems with high penetration of renewables, demand side resources have been aggregated to facilitate system operation. However, the natural uncertainty and randomness of users' behaviour may deteriorate the demand aggregation performance, including a large mismatch from the expected aggregation target and unnecessary cost while executing aggregation. Here, the most fast‐growing demand side resource, electric vehicle is targeted, and an algorithm based on a multi‐armed bandit approach is proposed to aggregate those electric vehicle demands. In the proposed multi‐armed bandit model, each electric vehicle user's behaviour is viewed as two arms. Then, a combinatorial upper confidence bound mixed sorting algorithm, which selects the optimal set of users participating in demand aggregation, is developed. The case studies show that the proposed method can reduce the demand aggregation mismatch and eliminate the unnecessary cost. Additionally, it can be observed that the user experience is also improved.

Maintaining frequency stability is a crucial but challenging task for the stable operation of a power system. The distributed energy storage (DES) can charge or discharge for both upward and downward frequency regulation, exploring and effectively using the regulation capabilities will provide a strong backup for frequency regulation. Here, a dynamic DES control strategy for providing primary frequency regulation is proposed. The different behaviours of storage owners are considered when they respond to the regulation requests from the aggregator. This kind of uncertainty would lead to a mismatch between the final aggregation result and the expected target. Hence, the multi-armed bandit approach is applied to learn users' response behaviour and select the optimal set of users to mitigate the mismatch. Case studies on the IEEE RTS 24-bus system demonstrate that the proposed method's mismatch between the actual aggregation result and the regulation target is less than half as much as the conventional method, and it can restore the frequency 20 events earlier than the traditional method.

The regional integrated energy system is an effective way to realize energy cascade utilization and improve the flexibility and economy of the load side operation. Combine cooling heating and power (CCHP) system is a typical form of integrated energy systems. However, due to heat-load-based operation mode, the peak modulation capacity of CCHP units and the accommodation of renewable energy are limited. This paper explores methods of increasing the flexibility and economy of the system. An optimal scheduling model is proposed for integrated energy systems containing an electric heat pump, considering heating network characteristics. Simulation results show that the electric heat pump can help the system realize heat-and-power decoupling, improve the flexibility of the system and reduce the operating cost of the system.