Risk-Limiting Load Restoration for Resilience Enhancement With Intermittent Energy Resources

Wang, Zhiwen; Shen, Chen; Xu, Yin; Liu, Feng; Wu, Xiangyu; Liu, Chen‐Ching

doi:10.1109/tsg.2018.2803141

Cited by 117 publications

(72 citation statements)

References 48 publications

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“…The level of load shedding is less than the load demand at each time interval as denoted by (7). This relationship is defined over all failures, scenarios, seasons and timeintervals.…”

Section: B Set Of Uncertaintiesmentioning

confidence: 99%

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Resilience-uncertainty nexus in building energy management integrated with solar system and battery storage

Mehrjerdi¹

2024

IEEE Access

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Application of local energy resources in the buildings encounters such systems with both uncertainties in the produced power and failures (events) of the resources. The energy planning excluding uncertainty and events may be economically efficient but it is not resilient and practical. This paper provides a platform for energy management in the building under a set of realistic failures and uncertainties. The energy resilience is improved and the energy cost is decreased at the same time while all events and uncertainties are modeled. A typical building integrated with solar-battery-grid is studied. The resilience index is to minimize the loss of demanded service following all possible events. Three objective functions are defined and compared including (i) resilience cost, (ii) energy cost, (ii) resilience and energy costs together. The stochastic mixed integer linear programming is implemented to minimize the objective functions. The results validate that the developed toolkit can cope with all events and uncertainties with minimum operating cost and maximum resilience. The optimal tradeoff between operating cost, resilience, and uncertainty is presented. The results demonstrate that the uncertainty and resilience increase the planning cost by 5.3% and 13.8%, respectively, and both of them together increase the cost by 19.5%.

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“…The level of load shedding is less than the load demand at each time interval as denoted by (7). This relationship is defined over all failures, scenarios, seasons and timeintervals.…”

Section: B Set Of Uncertaintiesmentioning

confidence: 99%

“…In such case, the fossil fuel restrictions and energy crises are studied and modeled [6]. The resilience aims to keep the service of load demand available during fault and post-fault [7]. As a result, the availability of service to the consumers can be a proper index to measure the system resilience.…”

Section: Introductionmentioning

confidence: 99%

Resilience-uncertainty nexus in building energy management integrated with solar system and battery storage

Mehrjerdi¹

2024

IEEE Access

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“…Moreover, the rc will not travel to the depots expect for dp(rc). Constraint (8) indicates that once a crew rc closes the manual switch at vertex a, he will move to the manual switch at the next vertex b. Constraint (9) represents that a manual switch at vertex a can be operated by one crew only. It is noted that the definitions of constraints (7) and (8) are similar to those of (1) and (2).…”

Section: / { ( )}mentioning

confidence: 99%

“…However, the interdependence of DRM, MCDM and RCDM has not been comprehensively studied in the existing research. For example, common restoration models only considered the efficient utilization infrastructure and facilities in distribution systems, e.g., microgrids [5]- [6], dispatchable DGs [7], renewable energy [8] and transportable energy storage [9]. Although these studies have significantly contributed to the service restoration of distribution systems, the interrelation of maintenance and restoration crews with MCDM and RCDM may deteriorate the feasibility and optimality of the restoration solution.…”

mentioning

confidence: 99%

Sequential Disaster Recovery Model for Distribution Systems With Co-Optimization of Maintenance and Restoration Crew Dispatch

Zhang

et al. 2020

IEEE Trans. Smart Grid

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To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixedinteger linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an "event-based" model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system. 1

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“…The blackout happened on 30 July 2012 in India and many other blackout events imply that it is very difficult, if not impossible, to prevent blackouts from the occurrence, and hence the importance of the rapid system restoration after such an event cannot be overlooked [1][2][3][4][5]. Thus, power system restoration is still an important area deserving further and in-depth studies.…”

Section: Introductionmentioning

confidence: 99%

Two‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units

Zhang¹,

Zhang²,

Liu

et al. 2020

IET Smart Grid

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With increased penetration of wind power units and plug-in electric vehicles (PEVs), their control flexibility and quick response potentially provide an alternative way to fulfil the need for rapid restoration. A two-stage restoration strategy optimisation approach is presented with the coordination of PEVs and wind power units considered. The optimisation model is to maximise the restored generation capability and minimise the fluctuation of cranking power. In the first stage, the aim is to provide reliable cranking power remotely for black start generators through coordinated dispatch of PEVs and wind power units and quadratic programming (QP) models for dispatching electric vehicle aggregators (EVAs) subject to wind power fluctuations, and for dispatching numerous PEVs within each EVA are developed. To ensure close coordination between these two dispatching procedures, bi-level programming-based hierarchical decomposition approach is used to solve the QP models in an iterative way. In the second stage, an integer linear programming model is proposed to optimise the restoration schedules through an effective transformation of the original non-linear formulation, so as to reduce the computing time and effort significantly. Finally, a case study is presented to demonstrate the effectiveness and essential features of the developed models and methods.

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Risk-Limiting Load Restoration for Resilience Enhancement With Intermittent Energy Resources

Cited by 117 publications

References 48 publications

Resilience-uncertainty nexus in building energy management integrated with solar system and battery storage

Resilience-uncertainty nexus in building energy management integrated with solar system and battery storage

Sequential Disaster Recovery Model for Distribution Systems With Co-Optimization of Maintenance and Restoration Crew Dispatch

Two‐stage restoration strategies for power systems considering coordinated dispatch between plug‐in electric vehicles and wind power units

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