1,721,056 research outputs found
A Fully Distributed Robust MPC Approach for Frequency and Voltage Regulation in Smart Grids with Active and Reactive Power Constraints
No full textShortly, power distribution grids will incorporate large amounts of distributed energy resources and flexible loads, allowing the operation of a portion of the network in islanded mode to increase the reliability and resilience of the whole power system. A fully distributed robust model predictive control (MPC) strategy for voltage and frequency regulation in interconnected distribution grids is stated. Each grid node represents a collection of prosumers with a large active and reactive power regulation capacity. The advantages of this approach rely on the capability to afford any type of uncertainties, without making any assumption on the probability density function, on distributed generation and load nowcasting. We propose a two-stage architecture: at the first stage, an MPC approach, based on the distributed alternating direction method of multipliers (dADMM), is performed, considering the data nowcasting; instead, the second stage (based on robust distributed team decision theory) takes as input the trajectory of the first stage to compensate the noise that affects the system. The developed architecture has been tested on a modified IEEE5 bus system, considering multiple loads and renewable generation
Optimal charging and routing of electric vehicles with power constraints and time-of-use energy prices
No full textIn this paper, a new mathematical formulation for the electric vehicle routing problem (EVRP) is proposed. This formulation extends the Green Vehicle Routing Problem (GVRP) considering time-of-use energy prices, and including a detailed model for the EVs' energy consumption. The main decisions for the considered EVRP are relevant to the choice among different types of charging modes at recharging stations, the speed of EVs, the loaded cargo and the battery charge. The model objective consists of minimizing the cost for the total travel distance and that for energy purchase, which depends on the selected recharging mode. A preprocessing algorithm used to reduce the problem dimension is presented. The experimental analysis performed on a large set of benchmark instances is reported
An Optimization Model For Electrical Vehicles Routing with time of use energy pricing and partial recharging
No full textThe reduction of greenhouse gas emissions is a priority for European Union. National Authorities are encouraged to promote the use of electric and hybrid vehicles. In this paper, the problem of planning a freight transportation service provided by electrical vehicles (EVs) is considered, and a new decision model is proposed. An extension of the Green Vehicle Routing Problem (GVRP) is formalized, adding time variant prices for energy purchase, a detailed model for EVs consumes and different types of charging modes. The main decisions for the considered problem also refer to the velocity of EVs, the loaded cargo and the battery charge at recharging nodes, with the objective of minimizing the cost for the total travel distance and that for energy purchase, which depends on the selected recharging mode. A case study is provided and the related results are discussed
A user equilibrium model for electric vehicles: Joint traffic and energy demand assignment
No full textThe development of policies for sustainable development has led to an increase in electric vehicles (EVs). This has given rise to new problems, such as the location and sizing of charging stations over the considered traffic network. The solution of such problems requires the application of analysis techniques in order to predict the joint allocation of transportation and energy demands. In this paper, an approach is proposed that extends the User Equilibrium (UE) principle in order to determine, besides to the flow over the network links, the service requests from the drivers to the various service stations. The application of this method is preliminary for the statement of any problem related to the optimization of location and sizing of service stations. The proposed approach is applied to a case study in Genoa Municipality (Liguria Region)
A decentralized optimization approach to the power management of electric vehicles parking lots
No full textTo reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model
Optimal Control of Hybrid Systems and Renewable Energies
No full textInternational policies for sustainable development have led to an increase in distributed power production based on renewable resources [...
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Flattening the Duck Curve: A Case for Distributed Decision Making
No full textThe large penetration of renewable resources has resulted in rapidly changing net loads, resulting in the characteristic 'duck curve'. The resulting ramping requirements of bulk system resources is an operational challenge. To address this, we propose a distributed optimization framework within which distributed resources located in the distribution grid are coordinated to provide support to the bulk system. We model the power flow of the multi-phase unbalanced distribution grid using a Current Injection (CI) approach, which leverages McCormick Envelope based convex relaxation to render a linear model. We then solve this CI-OPF with an accelerated Proximal Atomic Coordination (PAC) which employs Nesterov type acceleration, termed NST-PAC. We evaluate our distributed approach against a local approach, on a case study of San Francisco, California, using a modified IEEE-34 node network and under a high penetration of solar PV, flexible loads, and battery units. Our distributed approach reduced the ramping requirements of bulk system generators by up to 23%
Optimal scheduling and real-time control of a microgrid with an electrolyzer and a fuel cell systems using a reference governor approach
No full textThis paper proposes a novel approach for the optimal scheduling and control of a microgrid with an electrolyzer and fuel cell systems, both of proton exchange membrane (PEM) technology. It is based on a hierarchical procedure constituted of two levels of optimization: a higher level based on an economic optimization for the optimal scheduling of different components of the microgrid, and a lower level for the real-time control of the hydrogen systems: PEM electrolyzer (PEMEZ) and PEM fuel cell (PEMFC). The flexible operation imposed by the higher level leads to a violation of the limits designed by the manufacturer of the hydrogen components, specifically when switching from one power level to another. The current proportional-integral (PI) controllers integrated into those systems cannot handle this issue, which provokes a premature aging phenomenon of the materials and leads to poor performance of the systems. In the present paper, at the lower level, a reference governor (RG) real-time control approach has been added on top of the PI controller to ensure the respect of the operating limits and guarantee better performances. The focus has been given to the stack's temperature in both the electrolyzer and fuel cell systems as the control objective because of its direct influence on the material's durability and, by extension, on the efficiency. The bi-level optimization and control architecture has been applied and validated through simulations using data from a real-world case study, specifically the Savona Campus Smart Polygeneration Microgrid in Italy. The results showed a significant reduction in the overshoots of the stack's temperature compared to the PI controller
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