Multi-Objective Optimization for Coordinated Day-Ahead Scheduling Problem of Integrated Electricity-Natural Gas System With Microgrid

Zheng, Jiehui; Wu, Chuanlong; Huang, Jinbo; Liu, Y.

doi:10.1109/access.2020.2993263

Cited by 14 publications

(14 citation statements)

References 26 publications

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“…Nonetheless, the PM strategy leads to considerable maintenance costs with respect to the maintenance resources and component costs. Based on Figures 2 and 4, where the dashed lines indicate probable maintenance activities, Markov reward models [34] were established with reward matrices accounting for the cost-induced maintenance activities conducted on the main power system and redundant solar system in a smart grid, to calculate the total maintenance cost with different PM strategies. The summation over these two rewards then determines the total maintenance cost.…”

Section: Total Maintenance Costmentioning

confidence: 99%

Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand

Wang

Huang²,

You

2022

Sustainability

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This study established power-related efficiency measures from the perspective of reliability, namely, power system availability, expected power deficiency, accumulated power deficiency, instantaneous power capacity, and accumulated power capacity for a hybrid power system (HPS) in a generic smart grid. Methodologically, a power supply–demand stochastic model that simultaneously considers the inherently stochastic nature of power supply and demand was developed to quantify their interrelationship and characterize the dynamic behavior of an HPS in a continuous-time Markov chain. Preventive maintenance (PM) models were also constructed to determine the optimal PM strategy in alignment with specific scenarios that reflect the power performance requirements and resource limitations. A sensitivity analysis was conducted using the design of experiments (DOE) scheme that simulated climate change and revealed that extreme climate worsens power-related efficiency measures. This analysis provides further insight into the extent to which extreme climate conditions diminish the engineers and designers of smart grid systems’ power-related efficiency measures. The proposed approach will potentially contribute to sustainability and maintainability in the clean energy industry.

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Section: Total Maintenance Costmentioning

confidence: 99%

Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand

Wang

Huang²,

You

2022

Sustainability

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“…The proposed model is solved on the Yalmip platform in Matlab 2019b with the CPLEX solver. The multiple energy demand curves of IESs are shown in Figure 5 [15,39]. The RESs in three IESs consist of two photovoltaics (PVs) and one wind turbine (WT), whose output curves are shown in Figure 6 [17,21].…”

Section: Case Studymentioning

confidence: 99%

Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage

Chen

Deng

Zhi

et al. 2021

IET Generation Trans & Dist

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As the physical carrier of the Energy Internet, integrated energy system (IES) is a future development trend in the energy field, and the optimal scheduling of IES for improving energy utilisation efficiency has become a hot topic. An optimal day‐ahead scheduling model of multiple IESs considering integrated demand response (IDR), cooperative game and virtual energy storage (VES) is proposed innovatively in this study to maximise the overall benefits of the cooperative alliance. IDR and VES are considered together for the first time to optimise the internal scheduling of each IES, where IDR can enhance the response potential on the demand side and VES can improve the scheduling flexibility of IES. Cooperative game theory is utilised to process the energy trading mechanism among multiple IESs and the Nash bargaining method is utilised to solve the cooperative game problem and obtain a fair and Pareto‐optimal energy trading strategy. The case study shows that the proposed model effectively improves the operating benefits and the renewable energy penetration levels of each IES.

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“…The nondominated sorting genetic algorithm III (NSGA-III) [21][22][23] has been successfully adopted to solve various multiobjective optimisation problems, and variants have been developed for specific problems [24][25][26][27][28]. Bhesdadiya et al [21] applied NSGA-III to lower the emission value and fuel cost of fossil fuel power plants while providing a sustainable and reliable power supply.…”

Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 99%

“…Liu et al [27] improved NSGA-III using a genetic K-means (GKM) clustering algorithm to improve convergence. Zheng et al [28] introduced the acceleration of the differential evolution mechanism for improving NSGA-III to accelerate convergence and diversity exploration. Chao [29] showed that the random search algorithm outperforms the gradient-based counterparts including distorted and stochastic distorted greedy algorithms and resolves monotonic approximation with size constraints to maximise the submodular submodulus function.…”

Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 99%

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Huang

Wang

Chen

2021

International Journal of Aerospace Engineering

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Modern equipment is designed to operate under deteriorating performance conditions resulting from internal ageing and/or external environmental impacts influencing downstream maintenance. This study focuses on the development of a multistate system (MSS) that considers a human reliability factor associated with maintenance personnel—a condition-based multiobjective MSS preventive maintenance model (MSSPMM). The study assumes that no more than one maintenance activity is performed to achieve the most appropriate preventive maintenance (PM) strategy and easy implementation and to reduce maintenance error due to human reliability. The MSS performance based on mean system unavailability and total maintenance cost is evaluated using a stochastic model approach, and then, the MSSPMM is used for optimisation. A customised version of the nondominated sorting genetic algorithm III is employed to ensure efficient solution of the PM model with human reliability—which is considered a constrained multiobjective combinatorial optimisation problem. The optimised solutions are determined from the nondominated Pareto frontier comprising the diversified PM alternatives. A helicopter power transmission system is used as an example to illustrate the efficacy and applicability of the proposed approach through sensitivity analyses with relevant parameters.

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Multi-Objective Optimization for Coordinated Day-Ahead Scheduling Problem of Integrated Electricity-Natural Gas System With Microgrid

Cited by 14 publications

References 26 publications

Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand

Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand

Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

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