Robust multi-objective management of a multi-energy industrial park under scheduled and unscheduled gas rationing programs, incorporating the green power-to-x process and load control strategy

Supply management under restrictions, such as disasters, system failures, load growth, inaccessibility, and inadequate infrastructure and technology, is a critical issue for achieving a sustainable society. These conditions compel governments and stakeholders to fairly allocate resources among consumers through rationing programs, according to their priorities to accomplish a specific level of satisfaction. Fossil fuels, as primary input feed for residential, commercial, and industrial activities, are subject to rationing in some countries, posing significant challenges and leaving them dependent on imports or alternative energy sources. This study examines both scheduled and unscheduled natural gas rationing programs in an industrial park, which is a multi-vector consumer that relies primarily on gas for its operations, in the presence of a green power-to-x process and coordinated load control. The green power-to-x process uses renewable energy from solar and wind to produce hydrogen via electrolyzers, which is then converted into methane or electricity via methanization or fuel cells. Given the numerous uncertainties inherent in such complex systems, p-robust optimization is employed to manage fluctuations and mitigate decision-making risk. The presented model formulates a multi-objective optimization problem that combines load curtailment and day-ahead market trading cost functions, and solves it using the ε-constraint method to compute the Pareto set. The results indicate that scheduled rationing is easier to manage operationally than unscheduled rationing, due to the uncertainty associated with the latter. In this regard, the objective functions for a risk-averse strategy with maximum robustness increase by about 3.94% and 4.32% compared with the risk-neutral strategy under scheduled rationing, respectively. In contrast, such values are 11.22% and 11.38% under unscheduled rationing, respectively. The outcomes confirm that the green power-to-x process remains unable to address the intended gas rationing problem due to its low overall efficiency and high-rate gas demand; however, considering a load control strategy, even with limited flexibility, is significantly effective. It should be noted that increasing the size of power-to-x components is not a comprehensive solution, as it relies directly on uncertain inputs that, in turn, affect the sizes of other units, such as storage systems. The sensitivity analysis of power-to-x parameters indicates that improving overall process efficiency and increasing the penetration of renewables can somewhat relieve the problem. The rationing period, storage system, and load control are additional factors that must be optimally managed. Hence, a hybrid approach that combines various procedures should be considered for handling gas rationing.