In this article, we propose a new bi-level approach for the location and sizing of charging stations (CSs) based on the integration of power distribution and traffic networks. At the lower level, the user choices and the energy demands are modeled through the user equilibrium (UE) traffic assignment conditions that are derived for the case of electric vehicles (EVs). At the higher level, an optimization problem is formalized for the optimal planning of locations, sizes, and unit prices of a set of new charging stations in a territory, which embeds the UE conditions and the power flow model as constraints. The main novelties lie in the integration of traffic and electric networks, the optimization problem formalization, and the inclusion of detailed electrical issues [such as the complete alternating current (AC) power flow equations and the modeling of CSs with different phases]. The developed model has been applied to a case study in the Genoa Municipality. Note to Practitioners —In the past decade, particular attention has been posed on EVs and renewable resources to reduce pollutants’ emissions. Critical issues regard the setting of adequate recharging infrastructure and the integration of huge distributed and intermittent loads and production into the electrical grid. This article aims to provide a bi-level optimization model that can be used by companies or public administrations that want to install charging stations in a territory. In particular, the transportation and the electrical networks are integrated: at a lower level, the UE traffic assignment conditions are derived to represent users’ choices and to assess the energy demand. At the upper level, an optimization problem is formalized, to minimize costs of installation and power losses and to maximize benefits, which embeds as constraints the UE conditions for the case of EVs and the model of the electrical grid (load flow equations, bounds over-voltage, apparent power, etc.).

Smart grid planning and control is becoming a theme of high interest in the last years. This is due to the presence of distributed generation, power from renewable resources and storage systems, to the different actors present over the territory, and to the difficulty of defining appropriate models for decision support. A bilevel optimal control scheme is proposed for grids characterized by renewable and traditional power production, bidirectional power flows, dynamic storage systems, and stochastic modeling issues. In this scheme, the upper level decision maker (UDM) views the lower level decision makers (LDMs) or microgrids as single nodes. In the statement of the UDM problem, the LDM control strategies are structurally and parametrically constrained inside a nonlinear optimization problem that includes load flow equations. Then, the LDMs can follow references from the UDM and use available information at the local level to solve a stochastic optimization problem. The proposed control architecture has been applied to a specific case study (Savona, Italy).

In this paper, a survey is presented on the use of optimization models for the integration of electric vehicles (EVs) and charging stations (CSs) in the energy system, paying particular attention both to planning problems (i.e., those problems related to long term decisions such as the siting and sizing of CSs), and operational management problems (i.e., the optimal scheduling of EVs in smart grids, microgrids and buildings taking into account vehicle-to-grid (V2G) capabilities). Moreover, specific attention was dedicated to decision problems that couple transportation and electrical networks, such as the energy demand assessment for a vehicle over a path and routing and charging decision problems for goods and people transportation. Finally, an effort was dedicated to highlighting the integration and the use of EVs in very recent regulation frameworks, with specific reference to the participation in the balancing market through the figure of an aggregator and the inclusion in the management of Energy Communities (ECs) and sustainable districts.

This article considers an optimization problem aiming at planning the service stations to recharge a fleet of electric buses (EBs) for public transportation. The planning problem includes the selection of the sites of the stations (among a set of pre-specified eligible sites) and their sizing (both in terms of the number of sockets and maximum output power). The optimization problem also integrates issues related to the assignment of the various lines to the activated stations and the bus fleet's sizing. The formalization of the optimization problem is carried out by taking into account the nonlinear charging process characteristic of the batteries of EBs. The optimization problem is defined as a minimum cost design while guaranteeing a minimum quality level of the service, represented in this case by a minimum frequency for each of the lines. The overall decision problem turns out to be a mixed nonlinear one. It can be efficiently solved by common commercial software tools for moderate-medium problem sizes, as demonstrated in the presented application.

The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.