Resilience-Oriented Planning of Multi-Carrier Microgrids under Cyber-Attacks - Azimian, Mahdi; Amir, Vahid; Javadi, Saeid; Mohseni, Soheil; Brent, Alan C. (Sustainable cities and society, 2022)
•A generic microgrid decision support model is developed.•An efficient mechanism is developed to generate attack-resilient multi-carrier microgrids.•Critical physical assets are identified and reinforced against cyber-physical attacks.•Financial viability of multi-carrier microgrids is explored from various economic aspects.•The model aids investors in deciding whether or not to install multi-carrier microgrids. Microgrids are inherently subject to a variety of cyber-physical threats due to potential vulnerabilities in their cyber systems. In this context, this paper introduces a cyber-attack-resilient design of a multi-carrier microgrid to avoid the loss of critical loads. The objective of the proposed model is to minimize the total planning cost of multi-carrier microgrids, which incorporates the investment and replacement costs of distributed energy resources, operation and maintenance costs, peak demand charges, emission costs, unserved energy costs, and potential reinforcement costs to handle cyber-physical attacks. Not only is the proposed multi-carrier microgrid planning approach able to determine the optimal size of multi-carrier microgrids, but it also identifies and reinforces the system to handle cyber-physical attacks by serving critical loads. The proposed multi-carrier microgrid planning model is formulated as a mixed-integer programming problem and solved using the GAMS 24.1 software. To evaluate the effectiveness of the proposed integrated resource planning model, it is applied to a real-world industrial park test-case system. Numerical simulations demonstrate the effectiveness of the resilience-oriented multi-carrier microgrid planning model. Importantly, the simulation results indicate the economic viability of multi-carrier microgrids optimized by the proposed model. Also, the model sensitivity of various decision variables has been analyzed.