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During the operation of the integrated energy system (IES), the dynamic behaviour of heat loads can affect the optimal operation strategy. This study proposes a novel optimal day-ahead operation method of user-level IES considering the dynamic behaviour of heat loads. Firstly, based on the energy hub, the model of heat loads with dynamic behaviour is added into IES. The dynamic behaviour is embodied by water temperature which can be represented by differential equations. Then, the optimal day-ahead operation model aimed at minimising the energy purchase cost is proposed. Piecewise linearisation is utilised to simplify the temperature constraints which can transform the initial non-convex and non-linear optimisation into a linear programming problem. Finally, case studies are carried out on a practical case, and the results indicate that with the consideration of dynamic behaviour of heat loads, the energy purchase cost can be reduced by 5.6%.

High-penetration renewable energy and AC/DC hybrid network are two main basic characteristics of future power systems. Evaluating different networks' structure adaptability is extremely important for the power system to keep economical, safe, reliable and flexible. Firstly, the basic characteristic and the definition of network structure adaptability are given under the background of strong randomness. On this basis, several probable indexes of structure adaptability are proposed. After that, a practical calculation method is put forward, in which the Monte Carlo simulation method and the optimal economic dispatch model are used. Case studies are carried out on the modified IEEE 30-bus system, and the simulation results show that the proposed indexes can effectively reflect the adaptability of different network structures with the integration of high-penetration renewable energy.

This study proposes a novel optimal day-ahead operation method for the user-level integrated energy system (IES), which can take into account the dynamic behaviour of heat loads and customers' heat satisfaction. First, based on the energy hub, the dynamic model of heat loads is added into IES. Additionally, the customers' heat satisfaction model is constructed by the variance of water temperature and punishment coefficient. Then, the optimal day-ahead operation model aimed at minimising the total cost is proposed. Constraints of energy purchase, operation of energy converters, loads, temperature and the variation of temperature are all contained. Piecewise linearisation and a standard modelling method are utilised to simplify the initial non-convex and non-linear problem to a quadratic programming problem. Finally, case studies are carried out on a practical calculation example. Results indicate that the total cost can be reduced by 5.6% when considering the dynamic behaviour of heat loads. Also, customers' heat satisfaction exerts a remarkable influence on the total cost.