Distributed model predictive control for energy management in a network of microgrids using the dual decomposition method

Razzanelli, Matteo; Crisostomi, Emanuele; Pallottino, Lucia; Pannocchia, Gabriele

doi:10.1002/oca.2504

Cited by 15 publications

(10 citation statements)

References 37 publications

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“…Similarly, it also naturally enforece ESSs not work in the charging and discharging mode at the same moment. Coincidentally, this is coherent with the assumptions proposed in [4, 22,28].…”

Section: Distributed Optimization Problem Formulationsupporting

confidence: 89%

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Preference-based Energy Exchange in a Network of Microgrids

Bai,

Thomopulos,

Crisostomi

2019

Preprint

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Peer-to-peer (P2P) energy trade may disrupt conventional electricity markets in the future for a network of multiple microgrids and entitle an individual microgrid (MG) to trade energy with alternative options instead of the main grid. In this paper, driven by the P2P electricity maket, a preference mechanism is added to the conventional energy management model of a network of microgrids, and a distributed method of alternating direction method of multipliers (ADMM) is adopted for its implementation. The effect of the preference mechanism on the customized power flow in the network is further studied for significant case studies. Results show that the customized power flows on the network of interconnected MGs can be achieved with different preference values locally chosen or imposed by the system operator.

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“…Similarly, it also naturally enforece ESSs not work in the charging and discharging mode at the same moment. Coincidentally, this is coherent with the assumptions proposed in [4, 22,28].…”

Section: Distributed Optimization Problem Formulationsupporting

confidence: 89%

“…ESSs are modeled by a first-order discrete time model, accounting for the energy losses in the charging and discharging process (see [27,28] ), as…”

Section: Ess Modelmentioning

confidence: 99%

Preference-based Energy Exchange in a Network of Microgrids

Bai,

Thomopulos,

Crisostomi

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In (15) and (16), γ + and γ − are slack variables, which represent corrective actions for compensating real-time infeasibilities. To deal with the violated random constraints, a second-stage or penalty function is included in the objective function.…”

Section: Two-stage Stochastic Programming With Recoursementioning

confidence: 99%

“…Although the final solution of distributed approaches will be sub-optimal compared to the centralized strategies, a substantial reduction in computational time will be acquired. Multi-agent systems [10,11], game theory [12,13], decentralized optimization techniques based on Alternating Direction Method of Multipliers (ADMM) [14], and distributed MPC [15][16][17] are among the most common methodologies that have been used to decompose the energy management problem of a network of IMGs into several smaller optimization problems. In [18], power transfer between multiple AC microgrids is scheduled by using distributed cooperative control.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Predictive Energy Management of Multi-Microgrid Systems

et al. 2020

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Next-generation power systems will require innovative control strategies to exploit existing and potential capabilities of developing renewable-based microgrids. Cooperation of interconnected microgrids has been introduced recently as a promising solution to improve the operational and economic performance of distribution networks. In this paper, a hierarchical control structure is proposed for the integrated operation management of a multi-microgrid system. A central energy management entity at the highest control level is responsible for designing a reference trajectory for exchanging power between the multi-microgrid system and the main grid. At the second level, the local energy management system of individual microgrids adopts a two-stage stochastic model predictive control strategy to manage the local operation by following the scheduled power trajectories. An optimal solution strategy is then applied to the local controllers as operating set-points to be implemented in the system. To distribute the penalty costs resulted from any real-time power deviation systematically and fairly, a novel methodology based on the line flow sensitivity factors is proposed. Simulation and experimental analyses are carried out to evaluate the effectiveness of the proposed approach. According to the simulation results, by adopting the proposed operation management strategy, a reduction of about 47% in the average unplanned daily power exchange of the multi-microgrid system with the main grid can be achieved.

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“…Recently, the distributed MPC (DMPC) has been investigated for smart grids applications to overcome the weakness of a centralized framework and the massive amount of control inputs/decision for MGs networks. In Reference 40, a DMPC approach is employed to compute the amount of active power should be exchanged among interconnected MMGs, and also using a tailored dual decomposition method that allows us to reach a feasible solution while guaranteeing the privacy of single MGs. Similarly, in Reference 41, the authors propose a resilient optimal solution for the power dispatch and reduce the economic costs.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical smart energy management strategy based on cooperative distributed economic model predictive control for multi‐microgrids systems

Brahmia

Wang

Oliveira

et al. 2020

Int Trans Electr Energ Syst

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Summary With the new edge of smart grids technology, the concept of efficient energy management among cooperative microgrids (MGs) distribution networks for the next generation of energy trading applications has become more and more challenging. This article presents smart energy dispatch for multi‐MGs (MMGs) systems based on the cooperative distributed economic model predictive control (DEMPC) within a hierarchical structure. The optimization framework consists of two levels. First, a DEMPC coordinator computes the optimal energy dispatch solution, taking into account the energy surplus with a power‐sharing policy, the retail electricity price based on the time of use (TOU), and the distributed generators units. For the second level, each local controller executes the energy scheduling reference from the first level. Thus, the MMGs system achieves a global supply‐demand balance. Moreover, the proposed approach improves the utilization of renewable power and demonstrates optimal energy dispatch while reducing operating costs. Finally, to validate the efficiency and resiliency of our proposed approach, two cases of study are presented based on three interconnected MGs system under a cooperative and non‐cooperative optimization. Simulation results show the advantage of the cooperative strategy with 28% for MG 1, and 20% for MG 2 in terms of daily operating costs savings.

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Distributed model predictive control for energy management in a network of microgrids using the dual decomposition method

Cited by 15 publications

References 37 publications

Preference-based Energy Exchange in a Network of Microgrids

Preference-based Energy Exchange in a Network of Microgrids

Stochastic Predictive Energy Management of Multi-Microgrid Systems

Hierarchical smart energy management strategy based on cooperative distributed economic model predictive control for multi‐microgrids systems

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