Advanced distribution management system (DMS), an evolution of supervisory control and data acquisition obtained by extending its working principles from transmission to distribution, is the brain of a smart grid. Advanced DMS assesses smart functions in the distribution system and is also responsible for assessing control functions such as reactive dispatch, voltage regulation, contingency analysis, capability maximization, or line switching. Optimal power flow (OPF)-based tools can be suitably adapted to the requirements of smart distribution network and be employed in an advanced DMS framework. In this paper, the authors present a methodology for unbalanced three-phase OPF (TOPF) for DMS in a smart grid. In the formulation of the TOPF, control variables of the optimization problem are actual active load demand and reactive power outputs of microgenerators. The TOPF is based on a quasi-Newton method and makes use of an open-source three-phase unbalanced distribution load flow. Test results are presented on the IEEE 123-bus Radial Distribution Feeder test case.

Recent developments of distribution systems take advantages of new technical issues and networks equipments such as: DG (distributed generation), smart grids, smart metering, etc. The aim of the paper is to propose an approach for load/generation control in distribution grid based on the solution of an OPF (optimal power flow). The OPF dispatch local generation (DG), power injected by feeders and assess the load that needs to be curtailed in emergency conditions (isolated system, lack of, transport capacity onto the feeders, etc.). The proposed approach investigates about the possibility for distribution companies to reduce customer load up to 50% using signals coming from smart meters. The analysis also considers compensation costs for customers' supply interruption based on the actual Italian regulation.

The integration of electric vehicles (EVs) in power systems can be encouraged by charging station diffusion. These stations can perform smart charging processes, and can take advantage of the involvement of distributed generation sources in a microgrid framework. Furthermore, since photovoltaic batteries and EVs are sources based on direct current (DC), the realization of a DC microgrid structure is promising, though challenging. In this paper, a mixed-integer linear procedure for determining optimal operation planning of a DC-based electric vehicle supply infrastructure is proposed. The procedure aims at optimizing daily operational costs, based on forecast of photovoltaic production and EV exploitation. Peculiar aspects of energy storage devices and of the DC microgrid framework are accounted for through a non-linear iterative procedure. The proposed approach is applied to a test DC microgrid on different operation days and its effectiveness is compared to non-linear formulation solved by means of a genetic algorithm.

A cloud-based architecture developed for the control of dispatchable resources of active users is proposed. This architecture assumes the availability of renewables, dispatchable loads and storage resources in a demand response framework. The integration of a metering infrastructure with the cloud is a relevant aspect. The proposed methodology is based on Model Predictive Control (MPC) and is solved by Mixed Integer Linear Programming (MILP) to take into account non-linearities and discontinuities introduced by cost functions and storage systems. The control strategy was actually implemented on a pilot installation in a public school, requiring retrofit interventions, with particular regard to energy monitoring and cloud communication. An experimental gateway prototype (ER Gateway) was installed allowing communication between field and cloud control tiers. Experimental results are conducted to assess economic gains deriving by employing MPC when compared to rule-based ordinary controllers. Computational timings and communication latencies confirms the feasibility of the approach.

The paper describes the methodology used for developing an electric load microforecasting module to be integrated in the Energy Management System (EMS) architecture designed and tested within the “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers and prosumers with a monitoring and control device that would enable demand response through optimization of their own distributed energy resources (DERs). The optimal control of resources is organized with a hierarchical control structure and performed in two stages. A cloud-based computation platform provides global control functions based on model predictive control whereas a closed-loop local device manages actual monitoring and control of field components. In this architecture, load forecasts on a small scale (a single residential or tertiary building) are needed as inputs of the predictive control problem. The microforecasting module aimed at providing such inputs was designed to be flexible, adaptive, and able to treat data with low time resolution. The module includes alternative forecasting techniques, such as autoregressive integrated moving average (ARIMA), neural networks, and exponential smoothing, allowing the application of the right forecasting strategy each time. The presented test results are based on a dataset acquired during a monitoring campaign in two pilot systems, installed during the ER Project in public buildings.

The paper proposes a methodology for the optimal dispatch of energy sources in hybrid and isolated energy systems. The proposed approach is based on the formulation and solution of a nonlinear discrete optimization problem aimed at optimizing input and output time trajectories for a set of combined generating and storage technologies. Loads and interruptible loads are among controlled variables, and are modeled according to their interruption costs. The approach is general enough to be applied to any hybrid system configuration and was developed having in mind the complex hybrid system architectures comprising several competing storage technologies (battery, pumping, and hydrogen). Test results are aimed at showing the feasibility of the proposed methodology, comparing optimal trajectories to suboptimal system behavior given by load-following strategies.