• Energy Research

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 CCHP's 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.

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 CCHP's 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.

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.

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.

Abstract 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.

Abstract 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.

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.

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.