Joint Optimization of Operation and Maintenance Policies for Solar-Powered Microgrids

Mahani, Khashayar; Liang, Zhenglin; Parlikad, Ajith Kumar; Jafari, Mohsen A.

doi:10.1109/tste.2018.2849318

Cited by 23 publications

(8 citation statements)

References 17 publications

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“…We represent the microgrid purchased power from the main grid and sold power to the main grid with y gp,ω m,t,h , and y gs,ω m,t,h , respectively. Purchasing and selling limits, F gp m and F gs m , are enforced through constraint (16). MMG in locally connected mode can be modeled by setting g p,ω m,t,h and g s,ω m,t,h to zero.…”

Section: B Adaptive Predictive Operations and Maintenance Of Mmgmentioning

confidence: 99%

“…These risk predictions rely on condition monitoring techniques that monitor indicators of asset degradation from raw sensor inputs, such as vibration, temperature, and performance [14], [15]. Condition monitoring systems are actively used in wind turbines (WT), photovoltaics (PV), and conventional generators [16]- [19]. For a comprehensive review of condition monitoring methods in power generation, readers are referred to [20].…”

mentioning

confidence: 99%

“…Majority of prognostic-based approaches focus on single asset systems [17]. Markov chain models are used to characterize degradation and derive optimal maintenance decisions [16]. This literature is extended by more detailed approaches that jointly consider maintenance and environmental factors [21].…”

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confidence: 99%

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Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

Fallahi

Yildirim

Lin

et al. 2021

IEEE Trans. Power Syst.

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Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers, and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience.

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Section: B Adaptive Predictive Operations and Maintenance Of Mmgmentioning

confidence: 99%

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confidence: 99%

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Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

Fallahi

Yildirim

Lin

et al. 2021

IEEE Trans. Power Syst.

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“…The authors suggested that PM policy performs best when the utilization time (horizon) is long. A grid-connected solar PV system maintenance optimization policy was reported by [26]. The authors developed the maintenance cost minimization optimization, based on a twolayered approach, by first calculating the aging cost penalty, which was subsequently used as an input to calculate the optimal maintenance cost.…”

Section: Introductionmentioning

confidence: 99%

Improved Preventive Maintenance Scheduling for a Photovoltaic Plant under Environmental Constraints

2021

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The supply of PV power that satisfies the needs of customers is heavily dependent on the reliability of the generating plants. However, irrespective of the robustness of the design of such physical industrial assets, they tend to depreciate with usage and/or age which, in turn, increases the allowance between the design and the operational capabilities. Therefore, to ameliorate the reliability of the system, a combination of selective and preventive maintenance actions were planned by determining the best combination (optimal preventive maintenance intervals, optimal replaced components). In this work, we developed an optimal preventive maintenance strategy with minimal repair using the iterative numerical technique for a PV plant, with and without considering the influence of environmental conditions on the system. An algorithm was developed on MATLAB to determine the optimal number of preventive maintenance actions that yields the maximum availability by selecting the components to be maintained based on the reliability threshold, without considering the environmental impact on the components. The environmental elements’ criticality was introduced, and the reliability reiterated based on the new technique. Finally, by maximizing the availability of the system, an optimal preventive maintenance for a finite horizon was established.

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“…Microgrids, based on the needs of local network, can be operated in either grid-connected mode or islanded mode. They are seen to be increasingly important to achieve a reliable, flexible, and sustainable electricity network [7]. Every microgrid can include different electrical and thermal assets such as CHP systems, natural gas or diesel generators, PV systems with or without battery storage systems [8], boilers and chillers, etc.…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

Nazemi

Mahani

Ghofrani

et al. 2020

2020 IEEE Texas Power and Energy Conference (TPEC)

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The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.

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Joint Optimization of Operation and Maintenance Policies for Solar-Powered Microgrids

Cited by 23 publications

References 17 publications

Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

Improved Preventive Maintenance Scheduling for a Photovoltaic Plant under Environmental Constraints

Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

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