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This paper presents a direct load control based demand side management (DSM) algorithm that performs peak shaving considering time-varying renewable generation, and thermal comfort of the buildings. The demand side operator of the microgrid (MG) uses the DSM algorithm for peak-shaving, and reducing the energy costs. The DSM controller has a hierarchical control architecture, wherein there is a central controller (CC) and numerous local controllers (LCs). The CC uses information on demand and renewable generation to compute the load to be curtailed. The LCs that supply the consumers, reduce the demand by curtailing heating loads in buildings without breaching thermal comfort limits. Building models, information on comfort margins, and an optimization routine are used by the LCs to implement the DSM algorithm. As the algorithm guarantees thermal comfort, reluctance among consumers to employ direct load control based DSM algorithm is eliminated. Further, in the proposed algorithm demand side operator controls the consumption by monitoring the temperature, therefore need to instal smart thermostats/controllers, and continuous monitoring of prices in buildings is eliminated. The working of the DSM algorithm is illustrated using simulations performed on data obtained from residential heating system with 50 buildings in Norwegian living lab in Steinkjer. Our results indicate that the algorithm performs peak-shaving considering information on renewable generation without breaching thermal comfort margins of the consumer.

The epidemiology of X-linked recessive diseases, a class of genetic disorders, is modeled with a discrete-time, structured, non linear mathematical system. The model accounts for both de novo mutations (i.e., affected sibling born to unaffected parents) and selection (i.e., distinct fitness rates depending on individual's health conditions). Assuming that the population is constant over generations and relying on Lyapunov theory we found the domain of attraction of model's equilibrium point and studied the convergence properties of the degenerate equilibrium where only affected individuals survive. Examples of applications of the proposed model to two among the most common X-linked recessive diseases (namely the red and green color blindness and the Hemophilia A) are described.

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Abstract-In this paper a control strategy for the optimal energy management of a district heating power plant is proposed using a stochastic formulation. The main goal of the control strategy is to reduce the production and maintenance costs by optimally managing the boilers, the thermal energy storage and the flexible loads while satisfying a time-varying request and operation constraints. The optimization model includes a detailed modeling of boilers operating constraints, energy thermal energy exchange and the operating modes of the power plant layout. Furthermore, the uncertainty in power demand and renewable power output, as well as in weather conditions, is handled by formulating a two-stage stochastic problem and incorporating it into a model predictive control framework. A simulation evaluation based on the real data and the layout of a Finnish power plant is conducted to assess the performance of our proposed framework. The cost analysis shows the advantages of using the predictive control strategy.

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