Browsing by Author "Franco, John F."
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- Joint Optimal Allocation of Electric Vehicle Charging Stations and Renewable Energy Sources Including CO2 EmissionsPublication . Lima, Tayenne Dias de; Franco, John F.; Lezama, Fernando; Soares, João; Vale, ZitaIn the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made towards the development of EVCSs, with the ability to recharge the vehicle at a similar time than conventional vehicle filling stations. Besides, EVs can bring environmental benefits by reducing greenhouse gas emissions. However, depending on the energy matrix of the country in which the EVs fleet circulates, there may be indirect emissions of polluting gases. Therefore, the development of this technology must be combined with the growth of renewable generation. Thus, this proposal aims to develop a mathematical model that includes EVs integration in the distribution system. To this end, a mixed-integer linear programming (MILP) model is proposed to solve the allocation problem of EVCSs including renewable energy sources. The model addresses the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. Moreover, an EV charging forecast method is proposed, subject to the uncertainties related to the driver's behavior, the energy required by these vehicles, and the state of charge of the EVs. The proposed model was implemented in the AMPL modelling language and solved via the commercial solver CPLEX. Tests with a 24-node system allow evaluating the proposed method application
- Local Electricity Markets for Electric Vehicles: An Application Study Using a Decentralized Iterative ApproachPublication . Faia, Ricardo; Soares, João; Fotouhi Ghazvini, Mohammad Ali; Franco, John F.; Vale, ZitaLocal electricity markets are emerging solutions to enable local energy trade for the end users and provide grid support services when required. Various models of local electricity markets (LEMs) have been proposed in the literature. The peer-to-peer market model appears as a promising structure among the proposed models. The peer-to-peer market structure enables electricity transactions between the players in a local energy system at a lower cost. It promotes the production from the small low–carbon generation technologies. Energy communities can be the ideal place to implement local electricity markets as they are designed to allow for larger growth of renewable energy and electric vehicles, while benefiting from local transactions. In this context, a LEM model is proposed considering an energy community with high penetration of electric vehicles in which prosumer-to-vehicle (P2V) transactions are possible. Each member of the energy community can buy electricity from the retailer or other members and sell electricity. The problem is modeled as a mixed-integer linear programing (MILP) formulation and solved within a decentralized and iterative process. The decentralized implementation provides acceptable solutions with a reasonable execution time, while the centralized implementation usually gives an optimal solution at the expense of reduced scalability. Preliminary results indicate that there are advantages for EVs as participants of the LEM, and the proposed implementation ensures an optimal solution in an acceptable execution time. Moreover, P2V transactions benefit the local distribution grid and the energy community.
- A Specialized Long-Term Distribution System Expansion Planning Method With the Integration of Distributed Energy ResourcesPublication . De Lima, Tayenne D.; Franco, John F.; Lezama, Fernando; Soares, JoãoThe electrical distribution system (EDS) has undergone major changes in the last decade due to the increasing integration of distributed generation (DG), particularly renewable energy DG. Since renewable energy resources have uncertain generation, energy storage systems (ESSs) in the EDS can reduce the impact of those uncertainties. Besides, electric vehicles (EVs) have been increasing in recent years leveraged by environmental concerns, bringing new challenges to the operation and planning of the EDS. In this context, new approaches for the distribution system expansion planning (DSEP) problem should consider the distributed energy resources (DG units, ESSs, and EVs) and address environmental impacts. This paper proposes a mixed-integer linear programming model for the DSEP problem considering DG units, ESSs, and EV charging stations, thus incorporating the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. In contrast to other approaches, the proposed model includes the simultaneous optimization of investments in substations, circuits, and distributed energy resources, including environmental aspects (CO 2 emissions). The optimization method was developed in the modeling language AMPL and solved via CPLEX. Tests carried out with a 24-node system illustrate its effectiveness as a valuable tool that can assist EDS planners in the integration of distributed energy resources.