Abstract
The risks faced by modern energy systems are increasing, primarily caused by natural disasters. As a new form of multi-level energy complimentary utilization, integrated energy systems are attracting more and more attention for their high-efficiency and low-cost. However, due to the deep coupling relationship between systems, they are more susceptible to natural disasters, resulting in a cascading failure. To enhance the resilience of the integrated electricity-gas system, this paper proposes a failure restoration strategy after a natural disaster occurs. First, the temporal constraints of the dispatching model are considered, and the failure restoration problem is molded into a multi-period mixed-integer linear programme, aiming to recover the interrupted loads as much as possible. Second, since the uncertain output of distributed generation sources (DGs) such as wind turbines and photovoltaic systems will threat the reliability of restoration results, the robust formulation model is incorporated to cope with this problem. Third, we propose a new modeling method for radial topology constraints towards failure restoration. Moreover, the Column and Constraints Generation (C&CG) decomposition method is utilized to solve the robust model. Then, the piecewise linearization technique and the linear DistFlow equations are utilized to eliminate the nonlinear terms, providing a model that could be easily solved by an off-shelf commercial solver. The obtained results include the sequence of line/pipeline switchgear actions, the time-series dispatching results of electricity storage system, gas storage system, and the coupling devices including the gas-fired turbine, power to gas equipment. Finally, the effectiveness of the proposed restoration strategy is verified by numerical simulation on a 13-6 node integrated energy system.
Highlights
In recent years, with the development of the integration of renewable energy and the growing demand for environmentally-friendly operation, integrated energy systems are believed to be a promising way to satisfy energy customers’ demand with high-reliability, high-efficiency and low-cost [1,2,3,4]
In an electric power system, failure restoration studies mainly focus on the two-stage failure restoration process, as shown in Figure 1a, including the faulty stage and the restored stage, but in an integrated electricity-gas system (IEGS), due to the tightly coupled nature, the two-stage failure restoration process cannot represent the temporal characteristics of the topology, distributed generation sources (DGs), battery storage system (BSS) and gas storage systems (GSS), which may lead to unfeasible operation results in the restoration process
Most of the existing research on failure-related aspects of integrated electricity-gas systems focuses on failure prevention and reliability assessment
Summary
With the development of the integration of renewable energy and the growing demand for environmentally-friendly operation, integrated energy systems are believed to be a promising way to satisfy energy customers’ demand with high-reliability, high-efficiency and low-cost [1,2,3,4]. Proposing a three-stage failure restoration process, including the action sequence of switchgear, so in this way the feasibility of restoration results can be ensured; Modeling the failure restoration problem of IEGS as a multi-period mixed integer linear problem, considering the temporal characteristics of various devices, including DGs, BSS, GSS and line/pipeline switchgear; Considering the bi-directional coupling of IEGS, the GFT and P2G are coordinated to achieve the failure restoration, and the reliability of the IEGS is improved; Proposing a new modeling method for radial topology constraints towards failure restoration; By introducing the linearization technique, the nonlinear mathematical model of the failure restoration is formulated into a mixed-integer linear programming problem, which can be solved by an off-the-shelf commercial solver; The Column & Constraint Generation (C&CG) [42] algorithm is used to solve the three-layer robust model.
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