Giulio Binetti

Distributed Consensus-Based Economic Dispatch With Transmission Losses

A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems.

A Distributed Auction-Based Algorithm for the Nonconvex Economic Dispatch Problem

This paper presents a distributed algorithm based on auction techniques and consensus protocols to solve the nonconvex economic dispatch problem. The optimization problem of the nonconvex economic dispatch includes several constraints such as valve-point loading effect, multiple fuel option, and prohibited operating zones. Each generating unit locally evaluates quantities used as bids in the auction mechanism. These units send their bids to their neighbors in a communication graph that supports the power system and which provides the required information flow. A consensus procedure is used to share the bids among the network agents and resolves the auction. As a result, the power distribution of generating units is updated and the generation cost is minimized. The effectiveness of this approach is demonstrated by simulations on standard test systems.

Scalable Real-Time Electric Vehicles Charging With Discrete Charging Rates

Large penetration of electric vehicles (EVs) can have a negative impact on the power grid, e.g., increased peak load and losses, that can be largely mitigated using coordinated charging strategies. In addition to shifting the charging process to the night valley when the electricity price is lower, this paper explicitly considers the EV owner convenience that can be mainly characterized by a desired state of charge at the departure time. To this end, the EV charging procedure is defined as an uninterruptible process that happens at a given discrete charging rate and the coordinated charging is formulated as a scheduling problem. The scalable real-time greedy (S-RTG) algorithm is proposed to schedule a large population of EVs in a decentralized fashion, explicitly considering the EV owner criteria. Unlike the majority of existing approaches, the S-RTG algorithm does not rely on iterative procedures and does not require heavy computations, broadcast messages, or extensive bi-directional communications. Instead, the proposed algorithm schedules one EV at a time with simple computations, only once (i.e., at the time the EV connects to the grid), and only requires low-speed communication capability making it suitable for real-time implementation. Numerical simulations with significant EVs penetration and comparative analysis with scheduling policies demonstrate the effectiveness of the proposed algorithm.

Distributed solution for the economic dispatch problem

A distributed approach for the economic dispatch of generating units is presented. It is assumed that the generators are connected by a communication graph. Each unit has information about itself, and can exchange information only with a few neighboring units in such a graph. Using graph theory and consensus algorithms, the proposed approach solves the economic dispatch problem in a distributed manner instead of existing centralized approaches. The proposed solution is shown to be optimal, independent of the initial power distribution, and to respond automatically to real-time load demand changes. Moreover, it requires only that the communication graph be connected. The effectiveness of the proposed algorithm is verified by simulating a standard IEEE test system.

Decentralized task allocation for surveillance systems with critical tasks

This paper considers the problem of assigning a set of tasks to a set of heterogeneous agents under the additional assumptions that some tasks must be necessarily allocated and therefore are critical for the assignment problem, and that each agent can execute a limited number of tasks. In order to solve this problem in a decentralized way (i.e., without any form of central supervision), we develop an extension of an algorithm proposed in the recent literature. After analyzing convergence and communication requirement of the algorithm, a set of numerical simulations is provided to confirm the effectiveness of the proposed approach.

Toward consensus-based balancing of smart batteries

Smart reconfigurable battery systems allow access to individual cells for monitoring and control purposes, enabling an efficient and flexible energy management among cells that can also be used for voltage balancing. This paper studies consensus protocols and cooperative control tools to illustrate the use of distributed control strategies for voltage balancing in such reconfigurable battery systems. By considering the individual cell as agents, the voltage balancing problem can be reformulated as a consensus problem. In case of leaderless consensus, the criteria required to obtain average consensus is discussed. Moreover, in case of leader-follower consensus approach, the leader selection and its impact on the time-to-reach consensus is analyzed. Several case studies are presented to show the effectiveness of the proposed approaches.

Distributed Power Profile Tracking for Heterogeneous Charging of Electric Vehicles

Coordinated charging of plug-in electric vehicles (PEVs) can effectively mitigate the negative effects imposed on the power distribution grid by uncoordinated charging. Simultaneously, coordinated charging algorithms can accommodate the PEV user’s needs in terms of desired state-of-charge and charging time. In this paper, the problem of tracking an arbitrary power profile by coordinated charging of PEVs is formulated as a discrete scheduling process, while accounting for the heterogeneity in charging rates and restricting the charging to only the maximum rated power. Then, a novel distributed algorithm is proposed to coordinate the PEV charging and eliminate the need for a central aggregator. It is guaranteed to track, and not exceed, the power profile imposed by the utility, while maximizing the user convenience. A formal optimality analysis is provided to show that the algorithm is asymptotically optimal in case of Homogeneous charging, while it has a very small optimality gap for the heterogeneous case. Numerical simulations considering realistic charging scenarios with different penetration levels and tracking of a valley-filing profile are presented to validate the proposed charging algorithm.

Consensus-Based Approach for the Economic Dispatch Problem

Abstract This paper presents a distributed consensus-based approach to solve the economic dispatch problem with power generator constraints and transmission losses. Buses and transmission lines in the power system are modeled as nodes and edges in a communication graph, respectively. Each node exchanges information with its neighbors and runs two consensus algorithms in parallel, without relying on a centralized decision maker. A consensus algorithm plus a correction term is run to reach consensus on a Lagrangian variable to satisfy the generation-demand equality constraint, while another consensus algorithm is used to estimate the power mismatch in the network. Thus, each generating unit computes its output power according to its cost function. Advantages and limitations of the proposed approach are discussed. Finally, the algorithm is validated by means of numerical simulations on several benchmarks.

Decentralized task allocation for heterogeneous agent systems with constraints on agent capacity and critical tasks

This paper considers the task allocation problem for a network of heterogeneous agents under the additional assumptions that each agent can execute a limited number of tasks due to its physical limitations and that some tasks considered critical (i.e., mandatory) for the assignment problem must be necessarily assigned. In order to solve this problem in a decentralized way, we propose an algorithm that builds an initial assignment through a market-based approach and resolves conflicts with a consensus procedure based on local communications between neighboring agents. Numerical simulations are performed to evaluate the effectiveness of the proposed approach.

Comparison of Model-free and Model-based Control Techniques for a Positioning Actuator based on Magnetic Shape Memory Alloys

This paper addresses the control issue of a precise positioning system based on Magnetic Shape Memory Alloys (MSMAs). This family of smart materials exhibits a hysteresis phenomenon that needs to be properly addressed in order to build effective devices. A model-free control scheme is compared with two different model-based approaches which exploit an accurate hysteresis model to perform hysteresis cancellation or feedforward compensation. All the control schemes are based on a PID controller which is automatically tuned by solving a set of Linear Matrix Inequalities (LMIs) able to guarantee a desired exponential rate for the error convergence to zero. Finally, the comparison of model-free and model-based control schemes is performed using an experimental set-up to emphasize both the advantages and disadvantages of the different control strategies.