• Energy Research

Energy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage.

Energy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage.

In the paper, a new multi-temporal methodology is proposed for the determination of the daily demand profiles of the Fast Charging Stations and the calculation of the charging process waiting time. The methodology has been implemented within a Multi-Objective optimization algorithm, based on the Non-dominated Sorting Genetic Algorithm, for the simultaneous allocation and sizing of Fast Charging Stations using a probabilistic load flow. Results on the planning of two coupled distribution and transportation systems are presented to demonstrate the feasibility and consistency of the developed model, which gives the opportunity to make a proper trade-off between the contrasting interests of different stakeholders, i.e. the Distribution Network Operator, the Fast Charging Stations owner and the Plug-in Electric Vehicles drivers.

In the paper, a new multi-temporal methodology is proposed for the determination of the daily demand profiles of the Fast Charging Stations and the calculation of the charging process waiting time. The methodology has been implemented within a Multi-Objective optimization algorithm, based on the Non-dominated Sorting Genetic Algorithm, for the simultaneous allocation and sizing of Fast Charging Stations using a probabilistic load flow. Results on the planning of two coupled distribution and transportation systems are presented to demonstrate the feasibility and consistency of the developed model, which gives the opportunity to make a proper trade-off between the contrasting interests of different stakeholders, i.e. the Distribution Network Operator, the Fast Charging Stations owner and the Plug-in Electric Vehicles drivers.

The concept of electrical-mobility, in opposition to the present oil-mobility, is attracting the attention of politicians and of civil society worldwide. Electrical mobility means the usage of battery powered Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) as the main future technology to combat greenhouse gas emissions. The burden of electric mobility will be mainly on the distribution system that, particularly during the peak hours, will be exposed to critical operation conditions by a high number of high density simultaneous loads. Vehicle-to-Grid technology by adding control capabilities to charge and discharge of cars' batteries can exalt the benefits from their whole energy storage capacity. Distributors can then be helped in the active management of the network by the services offered (e.g., VAR/volt regulation, frequency regulation, spinning reserve, integration of renewable generation). Vehicle-to-Grid is perfectly part of the emerging Smart Grid technology and is based on intelligent stations fully integrated within the distribution management system. For a full exploitation of Vehicle-to-Grid potentialities, the role of the aggregator is essential to create value to customers by offering services to the distribution system operator. In the paper, a Particle Swarm Optimization is used to define the aggregator's optimal control strategy to optimize the recharge/discharge patterns of a fleet of EVs taking into account financial contracts, driver's behavior, energy prices, etc.

The concept of electrical-mobility, in opposition to the present oil-mobility, is attracting the attention of politicians and of civil society worldwide. Electrical mobility means the usage of battery powered Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) as the main future technology to combat greenhouse gas emissions. The burden of electric mobility will be mainly on the distribution system that, particularly during the peak hours, will be exposed to critical operation conditions by a high number of high density simultaneous loads. Vehicle-to-Grid technology by adding control capabilities to charge and discharge of cars' batteries can exalt the benefits from their whole energy storage capacity. Distributors can then be helped in the active management of the network by the services offered (e.g., VAR/volt regulation, frequency regulation, spinning reserve, integration of renewable generation). Vehicle-to-Grid is perfectly part of the emerging Smart Grid technology and is based on intelligent stations fully integrated within the distribution management system. For a full exploitation of Vehicle-to-Grid potentialities, the role of the aggregator is essential to create value to customers by offering services to the distribution system operator. In the paper, a Particle Swarm Optimization is used to define the aggregator's optimal control strategy to optimize the recharge/discharge patterns of a fleet of EVs taking into account financial contracts, driver's behavior, energy prices, etc.

A heuristic optimization algorithm based on the Dynamic Programming theory is proposed to find the optimal placement of measurement devices, i.e. to determine their number and position. The optimization procedure explicitly considers network reconfigurations (caused by random faults or by the active management of the network), so that the final measurement system allows the distribution state estimation to provide an accurate estimate of the system status in all the possible practical conditions. The branch currents are taken as state variables for improving the quality of the solution of the state estimator that exploits field measurements and load pseudo-measurements. The uncertainties introduced by the measurement chain are simulated with a Monte Carlo algorithm. Variations of both load demand and network parameters are also modeled in the Monte Carlo algorithm. The provided examples show the effectiveness of the optimization process.

A heuristic optimization algorithm based on the Dynamic Programming theory is proposed to find the optimal placement of measurement devices, i.e. to determine their number and position. The optimization procedure explicitly considers network reconfigurations (caused by random faults or by the active management of the network), so that the final measurement system allows the distribution state estimation to provide an accurate estimate of the system status in all the possible practical conditions. The branch currents are taken as state variables for improving the quality of the solution of the state estimator that exploits field measurements and load pseudo-measurements. The uncertainties introduced by the measurement chain are simulated with a Monte Carlo algorithm. Variations of both load demand and network parameters are also modeled in the Monte Carlo algorithm. The provided examples show the effectiveness of the optimization process.