Resilient microgrid system design for disaster impact mitigation

Chen, Xi; Su, Wencong; Kavousi‐Fard, Abdollah; Skowronska, Annette; Mourelatos, Zissimos P.; Hu, Zhen

doi:10.1080/23789689.2019.1708176

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…To model these uncertainties, hybrid stochasticrobust programming is implemented. The reliability indicator (EENS) is here an important index that is not associated with the price of energy, while it strongly depends on the accessibility/inaccessibility of the network components uncertainty [23][24][25][26]. In addition, as the EVs load and consumption energy uncertainty alters, it does not mainly change because of their low predicted error.…”

Section: Hybrid Stochastic-robust Programming Of Uncertaintiesmentioning

confidence: 99%

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A novel comprehensive energy management model for multi‐microgrids considering ancillary services

Veisi

Adabi

Kavousi‐Fard

et al. 2022

IET Generation Trans & Dist

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This article proposes a novel comprehensive multi-layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi-level optimization to address the participation of multi-microgrids (MMGs) in day-ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMGconnected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not-supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC-power flow, reliability, security, and flexibility of the MGs. To obtain a singlelevel model, the Karush-Kuhn-Tucker method is applied, and a hybrid stochastic-robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi-layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability).

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Section: Hybrid Stochastic-robust Programming Of Uncertaintiesmentioning

confidence: 99%

“…The operational model limitations of renewable and non-renewable resources are presented by (23) and (24), showing their apparent power capacity limitations. Employing (25), the reserve power can be computed, which is almost positive. The EENS constraints of every MG are shown in (26).…”

Section: The Bilevel Power Management Of the Sdn-connected Mgs Operationmentioning

confidence: 99%

A novel comprehensive energy management model for multi‐microgrids considering ancillary services

Veisi

Adabi

Kavousi‐Fard

et al. 2022

IET Generation Trans & Dist

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“…Compared with residential loads, commercial loads, known as commercial buildings (CBs) [4], normally have more mission-critical systems and components, resulting in higher resilience requirements [5]. Accordingly, large numbers of hardening and restoration strategies for CB resilience have been proposed in the literature [6]. Those strategies typically make decisions after a few months or a year regarding a post-extreme event.…”

Section: Introductionmentioning

confidence: 99%

Safe Reinforcement Learning-Based Resilient Proactive Scheduling for a Commercial Building Considering Correlated Demand Response

Liang

Huang

et al. 2021

IEEE Open J. Power Energy

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It is a crucial yet challenging task to ensure commercial load resilience during high-impact, low-frequency extreme events. In this paper, a novel safe reinforcement learning (SRL)-based resilient proactive scheduling strategy is proposed for commercial buildings (CBs) subject to extreme weather events. It deploys the correlation between different CB components with demand response capabilities to maximize the customer comfort levels while minimizing the energy reserve cost. It also develops an SRL-based algorithm by combining deep-Q-network and conditional-value-at-risk methods to handle the uncertainties in the extreme weather events such that the impact from extreme epochs in the learning process is greatly mitigated. As a result, an optimum control decision can be derived that targets proactive scheduling goals, where exploration and exploitation are considered simultaneously. Extensive simulation results show that the proposed SRL-based proactive scheduling decisions can ensure the resilience of a commercial building while maintaining comprehensive comfort levels for the occupants.

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“…Microgrid describes a set of loads and distributed energy resources (DERs) connected through a mediumvoltage network within a specified electrical boundary, which can feed its loads in both islanding and grid-connected modes [5]. Reference [6] has presented a mechanism for determining the resilience-oriented optimal decisions associated with an MG subject to disruptions in its power lines, intending to minimize system degradation. The method presented in [6] only used feeding the critical loads for MG resilience during the HIR event.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [6] has presented a mechanism for determining the resilience-oriented optimal decisions associated with an MG subject to disruptions in its power lines, intending to minimize system degradation. The method presented in [6] only used feeding the critical loads for MG resilience during the HIR event. In [7] the resilience of an interdependent multi-energy system is assessed in the presence of MGs.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Resilience of Operational Microgrids Through a Two-Stage Scheduling Strategy Considering the Impact of Uncertainties

et al. 2021

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This paper deals with the stochastic scheduling of a microgrid (MG) to balance the economical and resilience metrics. In the proposed model, the MG resilience indices are integrated into the economic criteria to ensure the resilience of MG operation alongside the main MG actors' profit/loss. The MG fragility index, recovery efficiency index, MG voltage index, and lost load index are considered in the proposed planning model. Further, to make the results more realistic, the uncertainties associated with energy price and wind production, alongside with planning of energy storage systems and electric vehicles parking lots are considered. To achieve a better solution for the security-constraint operation of MG, AC network equations are included in the system modelling. Finally, a large-scale MG based on the IEEE-33 bus testbed is utilized to evaluate the effectiveness of the proposed stochastic scheduling program.

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Resilient microgrid system design for disaster impact mitigation

Cited by 16 publications

References 41 publications

A novel comprehensive energy management model for multi‐microgrids considering ancillary services

A novel comprehensive energy management model for multi‐microgrids considering ancillary services

Safe Reinforcement Learning-Based Resilient Proactive Scheduling for a Commercial Building Considering Correlated Demand Response

Enhancing the Resilience of Operational Microgrids Through a Two-Stage Scheduling Strategy Considering the Impact of Uncertainties

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