The tight coupling of power distribution system (PDS) and district heating system (DHS) makes it imperative to devise a coordinated strategy for the resilience enhancement of integrated electric and heating system (IEHS) in natural disasters. The PDS reconfiguration serves as a critical tool for service restoration by the optimal adjustment of PDS topology with designated tie and sectionalizing switches. Similarly, the DHS reconfiguration provides a viable solution for countering disasters by the optimal adjustment of heating supply topology with targeted tie and sectionalizing valves in DHS. In this paper, a multi-stage resilience enhancement strategy considering the coordinated reconfiguration of PDS and DHS is proposed, which coordinates degradation, fault isolation, and service restoration stages after disasters. The proposed model is formulated as a mixed-integer nonlinear problem, which addresses the coordinated regulation of PDS switches and DHS valves to achieve efficient fault isolation and provide a better service restoration. To make the proposed model tractable, a linearized Distflow model is proposed by introducing current-oriented auxiliary variables and the approximation of power flow and voltage drop equations. Extensive case studies are conducted to validate the efficiency and accuracy of the proposed model and illustrate the performance of coordinated reconfiguration of PDS and DHS on resilience enhancement.

The tight coupling of electric and heating systems would make it necessary to consider the coordinated planning of the energy facilities. The seasonal district heating network (DHN) reconfiguration can notably impact the planning results and offer a potential approach for better wind penetration. To address this issue, a coordinated planning of electric and heating (CPEH) model is proposed in this paper by considering the seasonally DHN reconfiguration, which is formulated as a mixed-integer linear programming problem. Considering the computation difficulties brought in by numerous operation scenarios involved in the CPEH problem, a scenario-oriented generalized Benders decomposition (S-BD) algorithm is proposed to solve the CPEH model with higher efficiency by generating feasibility and optimality cuts with a unified form. The numerical simulations and a practical case in Jilin, China, are simulated to demonstrate the efficiency of the S-BD and illustrate the potential benefits of DHN reconfiguration in enhancing wind energy penetration.

Extensive deployment of combined heat and power (CHP) units into the electric power system (EPS) makes it imperative to investigate the utilization of flexible resources in a district heating system (DHS) and avoid unnecessary wind power curtailment. In this paper, a combined heat and power dispatch (CHPD) model is developed by considering the flexibility of heat sources, pipelines and DHS loads. Specifically, the adjustable region of the CHP units, heat storage of the heating networks and thermal inertia of the buildings are taken into account. Moreover, the heterogeneity of the EPS and DHS requires the independent control over the two systems. We propose a novel decentralized algorithm for solving the CHPD problem based on the heterogeneous decomposition method. The algorithm only requires the locational marginal prices at boundary buses of the EPS and the injected boundary power of the DHS to be exchanged. Numerical simulations are carried out for real data in Jilin. The flexible resources are optimally utilized and the wind power curtailment is reduced in the CHPD, with the alleviation of congestion in the EPS. Besides, simulations demonstrate that the proposed algorithm is efficient and robust, with a light communication burden to solve CHPD.

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.

A multi-energy virtual power plant can be defined as flexible cooperation of multi-energy distributed energy resources including heating and cooling devices that operates as a single entity and participates in the power markets. To exploit the potential of multi-energy distributed resources, this paper proposes a model for the aggregation of a multi-energy virtual power plant that participates in day-ahead energy and reserve markets. The reserve capacity brought by the multi-energy quick-start devices is considered in this paper, which adds binary variables to the recourse problem. The proposed model is an adjustable robust optimization problem that guarantees all possible deployment requests can be realized by the virtual power plant. Moreover, the optimal offering strategy is determined, and the total cost of the multi-energy virtual power plant is ensured to not exceed the optimal value of the objective function. A modified nested column-and-constraint generation algorithm is proposed for solving the adjustable robust optimization problem with mixed-integer recourse in specified precision efficiently compared with other KKT-based algorithms. The results from a case study of an actual industrial park demonstrate the satisfactory performance of the proposed model.

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.