Carmen Del Vecchio

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.

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.

Equilibrium and stability analysis of X-chromosome linked recessive diseases model

We present a mathematical model describing the population distribution of genetic diseases related to X chromosomes. The model captures the disease spread within a population according to the relevant inheritance mechanisms; moreover it allows to include de novo mutations (i.e., affected siblings born to unaffected parents). The resulting dynamic system is nonlinear, discrete time and positive. Among our contributions, we consider the analytical study 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 Lyapunov second method. In particular global exponential stability was demonstrated in the presence of significant mutation rates and global asymptotic stability for negligible mutation rates.

Non linear discrete time epidemiological model for X-linked recessive diseases

We developed a discrete time, structured, mathematical model describing the epidemiology of X-linked recessive diseases, a class of genetic disorders. The model accounts for both de novo mutations and distinct reproduction rates of procreating couples depending on their health conditions. We found the exact solution to the model when de novo mutations are not significant and negligible reproduction rates are assigned to affected males. Our results have relevance for both system modeling and genetic epidemiology.

Brief paper: Robust invariant sets for constrained storage systems

Several items are produced and stored into n buffers in order to supply an external demand without interruptions. We consider the classical problem of determining control laws and smallest buffer levels guaranteeing that an unknown bounded demand is always satisfied. A simple model with n decoupled integrators and n additive bounded disturbances is employed. The coupling arises from bounds on the total production capacity and on the total demand. Invariant set theory for linear and switched linear systems is exploited to compute robust positive invariant sets and controlled robust invariant sets for two commonly adopted scheduling policies. This paper provides the explicit expression of the invariant sets for any arbitrary n.

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.

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.

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.

Enabling technologies for Enterprise Wide Optimization

Current research in Enterprise Wide Optimization (EWO) is oriented more towards studying the interface between chemical engineering and operations research. This investigation studies the role of industrial automation and data mining for leveraging EWO. In particular, the role of field device integration (FDI), data models, OPC Unified Architecture (OPC UA) and information models that promote vertical data integration, and data mining techniques that create knowledge from aggregated data in enhancing EWO is studied. Further, the investigation shows that, integrating data mining and optimization models in EWO results in more realistic optimization problem that encapsulate the disturbance and uncertainties faced by process industries. As a result, EWO integrated with data mining techniques lead to more realistic solutions that are capable of dealing with uncertainties. Two illustrative examples from a rolling industry on energy and asset optimization are studied in this investigation. Our study reveals that emerging models in industrial automation and data mining are the key enablers of EWO in process industries.