Two-Time-Scale Energy Management for Microgrids With Data-Based Day-Ahead Distributionally Robust Chance-Constrained Scheduling

Zhen, Yuan; Xia, Jing; Li, Peng

doi:10.1109/tsg.2021.3092371

Cited by 53 publications

(8 citation statements)

References 31 publications

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“…This increasing trend in the generation cost curve is shown in Fig. 8 for various values of the degradation severity, using (28). The more severe the degradation, the higher the cost to generate the same power.…”

Section: A Objective Functionmentioning

confidence: 88%

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Microgrid Optimal Scheduling Incorporating Remaining Useful Life and Performance Degradation of Distributed Generators

2022

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This study presents a novel microgrid optimal scheduling strategy, which considers the degradation of distributed generators (DGs). DGs affected by degradation exhibit reduced generation efficiency and capacity. Operating the microgrid without consideration of these impacts increases operating and management costs and reduces reliability and stability. To overcome this problem, we developed a new optimal scheduling strategy that considers the degradation of DGs. This study focuses on a permanent magnet synchronous generator, which is widely used as DGs recently, and on the degradation of stator insulation, which is one of the most frequently occurring forms of degradation. The reduction in performance caused by insulation degradation is analyzed using finite element analysis and is integrated into the optimal scheduling strategy. We demonstrate that the proposed strategy operates a microgrid more economically through a reduction of total operating costs, achieved by adaptively accounting for the increase in operating costs and the reduction of capacity arising from degradation. In addition, the sudden shutdown of a DG can be prevented by predicting its remaining useful life and incorporating it into the optimal scheduling strategy. The effectiveness and feasibility of the proposed strategy are confirmed using various case studies.INDEX TERMS Distributed generator, generator degradation, microgrid, optimal scheduling, remaining useful life estimation. NOMENCLATURE b ESS charging/discharging efficiencies. N g ,N r ,N b ,N l number of dispatchable DGs, renewable DGs, ESSs and loads.

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Section: A Objective Functionmentioning

confidence: 88%

“…The proposed method was implemented on a microgrid test system based on an existing study [28] to validate the proposed optimal scheduling strategy, as shown in Fig. 7.…”

Section: Case Studies a Case Study 1 1) Microgrid Test System Datamentioning

confidence: 99%

Microgrid Optimal Scheduling Incorporating Remaining Useful Life and Performance Degradation of Distributed Generators

2022

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“…However, the actual distribution is difficult to obtain in reality, and it might not perfectly follow a predefined distribution. Researchers in [70] propose using beta distribution to represent wind forecast error instead of using the normal distribution. To overcome the drawback of defining a probabilistic distribution, a family of distributions in which the uncertainty parameter 𝛿𝛿 may fall is defined in [71].…”

Section: Uncertainties Affecting the Generationmentioning

confidence: 99%

A Review of Uncertainties in Power Systems - Modelling, Impact, and Mitigation

Hu,

Yu,

Trinh

2023

Preprint

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A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, a crucial consideration for emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication link. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyberattacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control, and modern probabilistic and data-driven approaches for modelling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

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“…For example, Wang et al [11] proposed a two-stage energy management model for the sustainable wind-PV-hydrogen-storage microgrid based on receding horizon optimization. Similarly, Yuan et al [12] presented a two-time-scale microgrid energy management model for scheduling with low operational costs and high reliability against uncertainties. In [13], the authors proposed a novel multi-energy systems optimization model to maximize investment and operating synergy in the electricity, heating, and transport sectors.…”

Section: Introductionmentioning

confidence: 99%

Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

Lu,

Peng,

Sun

2024

Energies

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To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid.

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Two-Time-Scale Energy Management for Microgrids With Data-Based Day-Ahead Distributionally Robust Chance-Constrained Scheduling

Cited by 53 publications

References 31 publications

Microgrid Optimal Scheduling Incorporating Remaining Useful Life and Performance Degradation of Distributed Generators

Microgrid Optimal Scheduling Incorporating Remaining Useful Life and Performance Degradation of Distributed Generators

A Review of Uncertainties in Power Systems - Modelling, Impact, and Mitigation

Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

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