Distributed Strategy for Optimal Dispatch of Unbalanced Three-Phase Islanded Microgrids

Vergara, Pedro P.; Rey, Juan M.; Shaker, Hamid Reza; Guerrero, Josep M.; Jôrgensen, Bo Nørregaard; Silva, Luiz C. P. da

doi:10.1109/tsg.2018.2820748

Cited by 39 publications

(30 citation statements)

References 34 publications

(116 reference statements)

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“…However, this method depends on the prediction of load demand and generation capacity, as well as the knowledge of power line impedances. An extension of [5] is presented in [6] as a decentralized hierarchical control, on which a first-order consensus protocol is used to offer power sharing among DERs. In this case, DERs have their power references set by the solution of an optimal problem based on primal-dual constrained decomposition.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Finally, the optimal power terms are applied to (6) and (7), at the secondary-level layer-2, resulting in optimal values to grid power references (i.e., * ( + 1) and * ( + 1)). Afterwards, the grid and UI power references are passed from the layer-2 on to the PBC algorithm (secondary-level layer-1) and to the UI converter.…”

Section: B Multiobjective Optimization Algorithmmentioning

confidence: 99%

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Optimal Multiobjective Control of Low-Voltage AC Microgrids: Power Flow Regulation and Compensation of Reactive Power and Unbalance

Brandão

Ferreira

Alonso

et al. 2020

IEEE Trans. Smart Grid

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The presence of single-phase distributed generators unevenly injecting active power in three-phase microgrids may create undesired upstream current unbalance. Consequently, voltage asymmetry and even active power curtailment may occur in such networks with negative economic impact. Thus, this paper proposes an optimal multiobjective approach to regulate the active and reactive power delivered by distributed generators driven by a three-layer hierarchical control technique in low-voltage microgrids. This method does not require previous knowledge of network parameters. The multiobjective algorithm is implemented in the secondary level achieving optimal dispatch in terms of maximizing the active power generation, as well as minimizing the reactive power circulation and current unbalance. By the existence of a utility interface three-phase converter placed at the point-ofcommon-coupling, the proposed control can regulate the power circulating among the microgrid phases, and the microgrid structure can withstand grid-connected and islanded operating modes. The path for interphase power circulation through the DClink of the utility interface allows the multiobjective algorithm to achieve better results in terms of generation and compensation compared to the system without utility interface. The proposed method is assessed herein by computational simulations in a threephase four-wire microgrid under realistic operational conditions.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: B Multiobjective Optimization Algorithmmentioning

confidence: 99%

Optimal Multiobjective Control of Low-Voltage AC Microgrids: Power Flow Regulation and Compensation of Reactive Power and Unbalance

Brandão

Ferreira

Alonso

et al. 2020

IEEE Trans. Smart Grid

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“…Our research aims at increasing energy efficiency by supporting the user-specific selection of DR methods that are used on HEMS in residential buildings (here Smart Homes) (Goebel et al 2014). Similar research activities fail to compare, evaluate, and benchmark DR methods, although they address, for example, distributed and renewable energy resources (e.g., (Berthold et al 2017;Dyson et al 2014)), islanded grids (e.g., (Vergara et al 2018; Ma and Billanes 2016)) or grids in general (e.g., (Steinbrink et al 2017)), the energy grid in smart cities (e.g., (Masera et al 2018;Stoyanova et al 2017)), security concerns (e.g., (Fulli et al 2017)), EVs and batteries in general (e.g., (Park et al 2016;Ma et al 2010)), energy contracts (e.g., (Basmadjian et al 2016)), or data and privacy concerns (e.g., (Cupelli et al 2018)).…”

Section: Related Workmentioning

confidence: 99%

Empowering the selection of demand response methods in smart homes: development of a decision support framework

et al. 2018

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Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users' cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the bestperforming DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework's applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.

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“…The deviations produced on variables as frequency and local voltage magnitudes are restored by the secondary layer. Finally, with the slowest time of response, the tertiary layer coordinates the power dispatch based on economical and operational constraints [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…However, they do not consider the control of the voltage at PCC. Furthermore, the lack of a power dispatch mechanism impedes optimal management of the operation and generation costs [7].…”

Section: Introductionmentioning

confidence: 99%

Droop‐free hierarchical control strategy for inverter‐based AC microgrids

Rey

Vergara

Castilla³

et al. 2020

IET Power Electronics

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Hierarchical schemes are widely used for the design of inverter-based AC microgrids control strategies. To ensure a reliable operation, hierarchical control must consider together all the functionalities that allow the regulation of key variables and guarantee a safe transition between operation modes. Conventionally, in the literature are proposed three-layer schemes which present relevant drawbacks: they include limited functionalities and they use droop method for the primary layer which, despite its decentralized nature, suffers from issues that have motivated the development of alternative strategies. Considering this, the contribution of this paper is twofold. First, a droop-free hierarchical control strategy that satisfy a proper operation of AC microgrids is proposed. Control objectives such as power-sharing, frequency regulation, optimal power dispatch, and voltage regulation are considered. Second, a closed-loop small-signal model, which facilitates the control parameters design and fills a gap in the literature is presented. Differences between the proposal and previous controls are discussed. Selected tests are carried out in a laboratory microgrid under different conditions, including normal operation and the response to failures in the central controller and to communication impairments. The experimental results show a good performance of the proposal even in adverse conditions.

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Distributed Strategy for Optimal Dispatch of Unbalanced Three-Phase Islanded Microgrids

Cited by 39 publications

References 34 publications

Optimal Multiobjective Control of Low-Voltage AC Microgrids: Power Flow Regulation and Compensation of Reactive Power and Unbalance

Optimal Multiobjective Control of Low-Voltage AC Microgrids: Power Flow Regulation and Compensation of Reactive Power and Unbalance

Empowering the selection of demand response methods in smart homes: development of a decision support framework

Droop‐free hierarchical control strategy for inverter‐based AC microgrids

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