Author
Andrea Mercurio
Bio: Andrea Mercurio is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Energy management system & Distributed generation. The author has an hindex of 8, co-authored 13 publications receiving 168 citations.
Papers
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TL;DR: An optimization problem is formulated, which minimizes the cost of enabling the access points and maximizes the reliability of PLC network paths in a multi-objective optimization fashion and returns the optimal routing.
Abstract: This paper deals with the problem of deploying a PowerLine Communication (PLC) network over a medium voltage (MV) power grid. The PLC network is used to connect the end nodes (ENs) of the MV grid to the service provider by means of PLC network nodes enabled as access points. In particular, a network planning problem is faced wherein we require to define the PLC network topology by deciding which MV network nodes are to be enabled as access points. An optimization problem is then formulated, which minimizes the cost of enabling the access points and maximizes the reliability of PLC network paths in a multi-objective optimization fashion. This work also considers resiliency (i.e., it guarantees the PLC network connectivity even in case of link faults) and capacity constraints (i.e., it checks that there are enough resources to transmit the estimated amount of traffic over the PLC network paths). As a byproduct, the optimization algorithm also returns the optimal routing. Simulations based on realistic MV network topologies validate the proposed approach.
33 citations
TL;DR: A system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS) and the solution of a Walrasian market equilibrium and the design of a distributed algorithm are presented.
Abstract: In this paper we present a system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS). Within the proposed architecture, control methodologies allow to adapt Distributed Energy Resources (DER) generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at local level a Load Area Controller (LAC) optimally calculates the FEVs charging sessions, while at higher level a Macro Load Area Aggregator (MLAA) provides DER with energy production profiles, and LACs with energy withdrawal profiles. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed algorithm.
24 citations
03 Jul 2012
TL;DR: In this paper, a network planning problem aiming to enable underground medium voltage (MV) power grids to resilient PowerLine Communications (PLCs) is faced, where a multi-objective optimization approach is used, in order to keep in balance the needs of minimizing the cost of equipment allocation and maximizing the reliability of PLC network paths.
Abstract: In this paper a network planning problem aiming to enable underground Medium Voltage (MV) power grids to resilient PowerLine Communications (PLCs) is faced. The PLC network is used to connect PLC End Nodes (ENs) located into the secondary substations to the energy management system of the utility by means of PLC network nodes enabled as Access Points. An optimization problem is formulated, aiming to optimally allocate the Access Points to the substations and the repeaters to the MV feeders. A multi-objective optimization approach is used, in order to keep in balance the needs of minimizing the cost of equipment allocation and maximizing the reliability of PLC network paths. Resiliency and capacity constraints are properly modeled, in order to guarantee the communications even under faulted link conditions. As a byproduct, the optimization algorithm also returns the optimal routing. Simulations performed on a realistic underground MV distribution grid validate the proposed approach
23 citations
23 Jun 2010
TL;DR: In this paper, the control problem of the wind turbine driven doubly fed induction generator (DFIG) is faced, both in wind park operator and system operator perspective, both from an ancillary services to the system operator and the maximization of wind park operators profitability.
Abstract: In this paper the control problem of the wind turbine driven doubly fed induction generator (DFIG) is faced, both in wind park operator and system operator perspective. Two control schemes are proposed, based on feedback linearization theory for MIMO systems and PI controllers: the first one for simultaneous active and reactive power regulation, the second one for simultaneous propeller angular speed and reactive power regulation. They are shown to allow the deliver of ancillary services to the system operator and the maximization of wind park operator profitability respectively.
21 citations
25 Jun 2013
TL;DR: In this article, the authors present a system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS), within the proposed architecture, control methodologies allow to adapt Distributed Energy Resources (DER) generation profiles and active loads to ensure economic benefits to each actor.
Abstract: In this paper we present a system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS). Within the proposed architecture, control methodologies allow to adapt Distributed Energy Resources (DER) generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at local level a Load Area Controller (LAC) optimally calculates the FEV's charging sessions, while at higher level a Macro Load Area Aggregator (MLAA) provides DER with energy production profiles, and LACs with energy withdrawal profiles. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed algorithm.
19 citations
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TL;DR: In this article, an optimization-based model is proposed to perform load shifting in the context of smart grids, where agents responsible for load, generation and storage management are defined, in particular some of them are electric vehicle aggregators.
195 citations
01 Jan 2016
TL;DR: The nonlinear programming analysis and methods is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
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173 citations
TL;DR: Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.
169 citations
TL;DR: In this article, an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid is presented, where the objective is to minimize the cost of energy consumption while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator).
138 citations
TL;DR: A new optimization framework for achieving computational scalability based on the alternating directions method of multipliers, which allows for distributing the optimization process across several servers/cores is proposed.
Abstract: One of the main challenges for electric vehicle (EV) aggregators is the definition of a control infrastructure that scales to large EV numbers. This paper proposes a new optimization framework for achieving computational scalability based on the alternating directions method of multipliers, which allows for distributing the optimization process across several servers/cores. We demonstrate the performance and versatility of our framework by applying it to two relevant aggregator objectives: 1) valley filling; and 2) cost-minimal charging with grid capacity constraints. Our results show that the solving time of our approach scales linearly with the number of controlled EVs and outperforms the centralized optimization benchmark as the fleet size becomes larger.
100 citations