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Robust chaotic systems, due to their inherent properties of mixing, ergodicity, and larger chaotic parameter space, constitute a perfect candidate for cryptography. This paper reports a novel method to generate random numbers using modified robust logistic map (MRLM). The non-smooth probability distribution function of robust logistic map (RLM) trajectories gives an un-even binary distribution in randomness test. To overcome this disadvantage in RLM, control of chaos (CoC) is proposed for smooth probability distribution function of RLM. For testing the proposed design, cryptographic random numbers generated by MRLM were vetted with National Institute of Standards and Technology statistical test suite (NIST 800-22). The results showed that proposed MRLM generates cryptographically secure random numbers (CSPRNG).

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Background: Current energy systems face multiple problems related to inflation in the energy prices, reduction of fossil fuels, and greenhouse gas emissions in disturbing the comfort zone of energy consumers and affordability of power for large commercial customers. This kind of problem can be alleviated with the help of optimal planning of Demand Response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of green DGs. To this end, a real-time-based campus is considered that is currently providing its loads from the utility grid only. Yet, according to the proposed given scenario, it contains the solar panels and wind turbine as a non-dispatchable DG while a diesel generator is considered as a dispatchable DG. It also incorporates the energy storage system with the optimal sizing of BESS to tackle with multiple disturbances that arise from solar radiations. Results: The resultant problem of linear mathematics has been simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of EMS minimizes the grid electricity costs by 31% in case of summer and 38% in case of winter respectively, while the reduction of GHG emissions per day is 780.68 and 730.46 kg for the corresponding summer and winter seasons. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs is also observed. Conclusion: The substantial environmental and economic benefits compared to the present case prompt campus owners to put investment in the DGs and to install large-scale energy storage.