• Energy Research

Abstract This paper focuses on the modelling and predictive control of a real medium temperature solar cooling system made up of a solar collector, an absorption chiller, a gas heater, hot and cold water storage tanks, connecting pipes, pumps, and a number of valves, allowing to modify the plant configuration in order to optimize its performance in terms of electric power and gas consumption. A detailed model of the plant is first obtained as a mix of first principle equations, validated with data collected on the plant, and identification techniques. Then, the model is simplified and used to formulate a hybrid Model Predictive Control (MPC) problem computing the optimal plant configuration over a prediction horizon of six hours, or more, and based on the available forecast of the solar radiation and user demand. The developed MPC algorithm has been used to control the plant; some of the collected results are reported here to witness the potentialities of the proposed approach.

Abstract This paper focuses on the modelling and predictive control of a real medium temperature solar cooling system made up of a solar collector, an absorption chiller, a gas heater, hot and cold water storage tanks, connecting pipes, pumps, and a number of valves, allowing to modify the plant configuration in order to optimize its performance in terms of electric power and gas consumption. A detailed model of the plant is first obtained as a mix of first principle equations, validated with data collected on the plant, and identification techniques. Then, the model is simplified and used to formulate a hybrid Model Predictive Control (MPC) problem computing the optimal plant configuration over a prediction horizon of six hours, or more, and based on the available forecast of the solar radiation and user demand. The developed MPC algorithm has been used to control the plant; some of the collected results are reported here to witness the potentialities of the proposed approach.

Abstract: Due to the widespread diffusion of renewables, the energy management and control of microgrids with non-dispatchable generation units, storage systems, and uncontrollable loads is becoming a topic of paramount interest. This problem is made complex by the need to consider both the continuous dynamics of generators and loads, and a number of logic constraints representing the feasible operating conditions of the devices, such as latency times or charge/discharge constraints. A promising solution, already proposed in various papers, is to resort to hybrid modeling and optimization; however, this leads to very large and intractable mixed integer optimization problems when the number of devices increases. For these reasons, in this paper a multilayer control scheme of microgrids is proposed: the higher layer defines the daily energy exchange with the main network based on a simplified model of the microgrid where dispatchable devices with similar characteristics are organized in clusters. The intermediate, or supervisory, layer allocates the energy requests among the units of each cluster. Finally, the lower layer is made by local controllers of the units. Simulation results are reported to witness the potentialities of this approach.

Abstract: Due to the widespread diffusion of renewables, the energy management and control of microgrids with non-dispatchable generation units, storage systems, and uncontrollable loads is becoming a topic of paramount interest. This problem is made complex by the need to consider both the continuous dynamics of generators and loads, and a number of logic constraints representing the feasible operating conditions of the devices, such as latency times or charge/discharge constraints. A promising solution, already proposed in various papers, is to resort to hybrid modeling and optimization; however, this leads to very large and intractable mixed integer optimization problems when the number of devices increases. For these reasons, in this paper a multilayer control scheme of microgrids is proposed: the higher layer defines the daily energy exchange with the main network based on a simplified model of the microgrid where dispatchable devices with similar characteristics are organized in clusters. The intermediate, or supervisory, layer allocates the energy requests among the units of each cluster. Finally, the lower layer is made by local controllers of the units. Simulation results are reported to witness the potentialities of this approach.

Abstract Volatile renewable energy resources are gaining more and more diffusion throughout the power system, and their intermittent production calls for enhanced balancing efforts. With a recent regulation, the European Union endorsed the participation of aggregated microgrids to the balancing of power system. The resulting assets optimization problem, however, features privacy constraints that prevent a full exchange of information, making fully centralized approaches not suitable. To this purpose, this work proposes a hierarchical approach allowing microgrids' aggregators to provide balancing services in an efficient and privacy-friendly fashion. This approach is based on a novel method to describe the power exibility that each microgrid can provide, allowing to significantly decrease the computational effort

Abstract Volatile renewable energy resources are gaining more and more diffusion throughout the power system, and their intermittent production calls for enhanced balancing efforts. With a recent regulation, the European Union endorsed the participation of aggregated microgrids to the balancing of power system. The resulting assets optimization problem, however, features privacy constraints that prevent a full exchange of information, making fully centralized approaches not suitable. To this purpose, this work proposes a hierarchical approach allowing microgrids' aggregators to provide balancing services in an efficient and privacy-friendly fashion. This approach is based on a novel method to describe the power exibility that each microgrid can provide, allowing to significantly decrease the computational effort

The Model Predictive Control (MPC) approach is used in this paper to control the voltage profiles in MV networks with distributed generation. The proposed algorithm lies at the intermediate level of a three-layer hierarchical structure. At the upper level a static Optimal Power Flow (OPF) manager computes the required voltage profiles to be transmitted to the MPC level, while at the lower level local Automatic Voltage Regulators (AVR), one for each Distributed Generator (DG), track the reactive power reference values computed by MPC. The control algorithm is based on an impulse response model of the system, easily obtained by means of a detailed simulator of the network, and allows to cope with constraints on the voltage profiles and/or on the reactive power flows along the network. If these constraints cannot be satisfied by acting on the available DGs, the algorithm acts on the On-Load Tap Changing (OLTC) transformer. A radial rural network with two feeders, eight DGs, and thirty-one loads is used as case study. The model of the network is implemented in DIgSILENT PowerFactory, while the control algorithm runs in Matlab. A number of simulation results is reported to witness the main characteristics and limitations of the proposed approach.

The Model Predictive Control (MPC) approach is used in this paper to control the voltage profiles in MV networks with distributed generation. The proposed algorithm lies at the intermediate level of a three-layer hierarchical structure. At the upper level a static Optimal Power Flow (OPF) manager computes the required voltage profiles to be transmitted to the MPC level, while at the lower level local Automatic Voltage Regulators (AVR), one for each Distributed Generator (DG), track the reactive power reference values computed by MPC. The control algorithm is based on an impulse response model of the system, easily obtained by means of a detailed simulator of the network, and allows to cope with constraints on the voltage profiles and/or on the reactive power flows along the network. If these constraints cannot be satisfied by acting on the available DGs, the algorithm acts on the On-Load Tap Changing (OLTC) transformer. A radial rural network with two feeders, eight DGs, and thirty-one loads is used as case study. The model of the network is implemented in DIgSILENT PowerFactory, while the control algorithm runs in Matlab. A number of simulation results is reported to witness the main characteristics and limitations of the proposed approach.