To decrease the investment cost of energy storage for urbanization purposes, a stochastic bi-level optimal allocation approach of intelligent buildings (IBs) considering energy storage sharing (ESS) services is proposed. First, based on the thermal inertia (TI) of buildings and the lifecycle of energy storage devices, a detailed thermal dynamic model of IBs equipped with air conditioning (AC) systems and the ESS station model are established respectively. Then, a stochastic bi-level optimal allocation model of IBs considering ESS services is established to consider the uncertainties of electricity prices. Given the different interests of the ESS station and IBs, the upper-level model aims to reduce the planning costs of the ESS station, and the lower-level model aims to reduce the annual operating costs of IBs. Finally, the nonlinear bi-level model is converted into a single-level mixed-integer linear programming problem using the Karush–Kuhn–Tucher (KKT) condition. The results show that while meeting the temperature comfort demands of IBs users, the proposed stochastic bi-level optimal allocation approach can guarantee the interests of different agents under the uncertainties of electricity prices and achieve a win-win situation for the ESS station and IBs.

The electricity network (EN) and transportation network (TN) are becoming increasingly coupled and to ensure timely energy supply restoration of the coupling network after disasters; this study proposes an asynchronous decentralized load restoration method of the resilient electricity-transportation network (ETN) considering building and electric bus (E-Bus) coresponse. Initially, a coordinated restoration model of ETNs integrating building envelope thermal dynamic and E-Bus path planning models is proposed. Then, we leverage the natural cooling flexibility of the air conditioning (AC) and the spatial-temporal flexibility of the E-Bus to maximize the power supply time of loads and minimize the driving distance of E-Buses. Finally, to protect the information privacy between different subjects of the EN and TN, and reduce the computational burden, the double-judgment asynchronous decentralized alternating direction method of multipliers (DJAD-ADMMs) algorithm is proposed to solve the problem in a parallel manner. The results show that our method effectively enhances the restoration effect of critical loads by coordinating the potential flexibility of buildings and E-Buses while satisfying the temperature comfort demand of users and the terminal destination of E-Buses with enhanced solution efficiency.

The inherent linkage between a power distribution system (PDS) and a district heating system (DHS) necessitates coordinated load restoration after natural disasters. To guarantee optimal load restoration during a recovery process, a coordinated dispatch strategy of the maintenance crew for the PDS/DHS considering the optimal reconfiguration of their respective networks is proposed in this paper. The proposed solution focuses on the intricate mutual interaction of the DHS and PDS and coordinates the fault isolation and service restoration stages. The proposed optimization is modeled as a mixed-integer second-order cone problem (MISOCP), which contains numerous integer variables. To lessen the computational burden, a two-stage acceleration algorithm is proposed, which divides the solution procedure into two stages based on two types of integer variables: load status variables and variables associated with the maintenance path and network topology. Then, the acceleration principles are proposed to determine the load status variables. The effectiveness and accuracy of the proposed model are validated by extensive cases, which demonstrate the performance of the coordinated maintenance and reconfiguration in integrated energy systems for fault recovery.

To solve the profit allocation problem among multiagent energy systems (MAESs), this study proposes a cooperative operation strategy for the MAES integrated with wind, hydrogen, and buildings based on the asymmetric Nash bargaining (NB) approach. The modeling of the MAES, including wind farms, hydrogen systems, and buildings is conducted, with detailed electrical and thermal characteristics used to improve operation flexibility while ensuring the users’ comfort. To solve the issue of equal profit allocation in symmetric NB, this article proposes using asymmetric NB to achieve a rational profit allocation. Functions are constructed to quantify the agents’ contributions, which are then used as bargaining power in the asymmetric NB process to realize an asymmetric profit allocation. Furthermore, the proposed distributed algorithm three-stage predictor-corrector accelerated alternating direction method of multipliers (ADMM) algorithm can improve solving efficiency and preserve information privacy. The case studies show that compared with equal profit allocation by symmetric NB, the proposed asymmetric NB-based strategy can acknowledge each agent's contribution and allocate agents with profits that correspond to their respective contributions. The fairness and rationality of the asymmetric profit allocation method can benefit the cooperative relationships among agents.

Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi‐objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second‐order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air‐conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak‐valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN.