Francesca Verrilli

Model Predictive Control-Based Optimal Operations of District Heating System With Thermal Energy Storage and Flexible Loads

Operating heating power plant (DHPP) with fluctuating load is a complex problem. Thermal energy storage (TES), flexible loads, and operating constraints compound this complexity further. This investigation focuses on the design of a model predictive controller (MPC) that reduces the operating and maintenance cost in a DHPP, considering TES and flexible loads. The MPC accomplishes this task by scheduling boilers, TES units, and flexible loads. To handle the fluctuating demand, the MPC uses forecasts and combines it with a constrained optimization problem. The objective function reflects the cost, whereas the generator limits, TES dynamics, thermal loads, including supply temperature, power plant layout, and reliability, are the constraints. The resulting optimization problem is modeled as a mixed-integer linear program with both continuous and logic variables. Here the logic variables model the operating modes of the boiler and storage units. The use of receding horizon approach enhances the robustness to the forecast errors. The constraints modeling plant layout, supply temperature, and grid reliability lead to a more realistic solution. The MPC is illustrated using simulation on historical data and experiments on a DHPP at Ylivieska, Finland. Our results demonstrate the cost benefits of the proposed approach.

Model Predictive Control for Optimization of Combined Heat and Electric Power Microgrid

Abstract This is a contribution to the economic dispatch problem of combined electrical and heat power microgrids. A mixed integer linear microgrid model has been developed; the microgrid operations optimization problem has been formulated using Mixed-Integer Linear Programming and Model Predictive Control technique has been applied to take system uncertainties into account. The proposed optimization algorithm has been applied to a tertiary site microgrid, located in Finland; the obtained numerical results have been compared with a heuristic algorithm.

Optimal operation of a district heating power plant with thermal energy storage

This paper presents an optimal control strategy for a district heating power plant with thermal energy storage. The main goal of the control strategy is to reduce the operation costs of the power plant, by scheduling the boilers, the operation of the thermal energy storage and the curtailment on the loads. The problem is stated as a constrained optimization in the form of a Mixed Integer Linear Program (MILP), embedded on an Model Predictive Control (MPC) framework. Particular attention is paid to modeling of boilers operating constraints, including the outlet water flow temperature, to the energy exchanged with the thermal energy storage and to the operating modes of the power plant layout, including the constraints related to the supply water temperature needed from the network. The results are performed using the data and the layout of the power plant located in the city of Ylivieska, in Finland. The cost analysis performed shows the advantages of using the predictive control strategy.

Real-Time Control of a PRV in Water Distribution Networks for Pressure Regulation: Theoretical Framework and Laboratory Experiments

AbstractPressure-reducing valves (PRVs) are often used in water distribution networks (WDNs) to regulate pressure for leakage reduction. Optimal management would require the pressure to be constant...

Effects of selection and mutation on epidemiology of X-linked genetic diseases

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 individuals health conditions). Assuming that the population is constant over generations and relying on Lyapunov theory we found the domain of attraction of models 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.

Optimization of energy exchanges in utility grids with applications to residential, industrial and tertiary cases

This paper focuses on an efficient Energy Management System (EMS) developed within the European project e-Gotham. Relying on previous results on modeling and controlling microgrids operations, we propose an optimal control strategy to manage the operations of an utility grid. The goal of the proposed strategy is to minimize the operations, maintenance and generations costs balancing a time-varying power demand. The overall problem is stated as a Mixed Integer Linear Programming (MILP) problem. A Model Predictive Control (MPC) technique is used to compensate the system uncertainties. As case of study a residential, an industrial and a tertiary pilot are considered to test the proposed control strategy.

When sex matters: a complete model of X-linked diseases

X-linked recessive diseases are genetic disorders caused by genes abnormalities placed on the X chromosome. Due to differences between males and females in sex chromosomes, the transmission mechanisms of these diseases vary in the two sexes. Other than the results of the well-known patterns of inheritance, the current spread of genetic disorders is influenced by spontaneous genetic mutations and individuals reduced reproduction capacities (fertility) conditioned by the disease severity. We developed a structured, continuous-time mathematical model describing how the disease spreads along time; the model accounts for a different fertility of affected individuals and for spontaneous mutations. Through Lyapunov analysis, we gained insights into systems asymptotic behaviour, that is how individuals fitness or spontaneous genetic mutations affect diseases diffusion. To the best of our knowledge, our model is the first one specifically designed to describe X-linked recessive diseases diffusion.

Optimal operations and load allocation of a power plant equipped with a CCHP feeding power, steam and cold water to an industrial plant

This paper focuses on the optimal operations of an industrial power plant equipped with a Combined Cooling, Heat and Power (CCHP). The goal of the control strategy is to minimize the generation and maintenance costs of the power plant, scheduling the CCHPs operations, the usage of the auxiliary generators (used to meet the demand when the CCHP is turned OFF), the purchasing/selling phase from/to the main grid and integrating the renewable energy sources. The overall problem is stated as a constrained mixed integer linear optimization problem with both continuous and logical variables. A Model Predictive Control (MPC) approach is used to compensate forecasts uncertainties in the control strategy. Being the industrial work load related to the production lines consumption, an optimal load allocation algorithm is implemented to optimally schedule the production lines over the planning day. The simulation results are performed on an industrial plant layout located in the city of Benevento, in Italy.

Stochastic model predictive control for optimal energy management of district heating power plants

In this paper a control strategy for the optimal energy management of a district heating power plant is proposed. The main goal of the control strategy is to reduce the running 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.

A complete epidemiological model of a class of genetic diseases

The epidemiology of X-linked recessive diseases, a class of genetic disorders, has been modeled through a discrete time, structured, non linear mathematical system. The model version presented in this paper completely captures the disease epidemiology as it includes the spread of affected women within a population that has not been considered in other works. Moreover the model allows for de novo mutations (i.e. affected sibling born to unaffected parents) and distinct reproduction rates of individuals depending on their health conditions. Among our contributions, we consider the analytical study of the properties of models equilibrium point, that is the distribution of the population among healthy, carrier and affected subjects, and the proof of the stability properties of the equilibrium point through the Lyapunov method. Model sensitivity analysis has been carried out to quantify the influence of model parameters on system response.