Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy

Lu, Jun; Liu, Tianqi; He, Chuan; Nan, Lu; Hu, Xiaotong

doi:10.1016/j.renene.2021.05.164

Cited by 53 publications

(10 citation statements)

References 42 publications

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“…Maximize the total profits of the whole system + Maximizing the social benefits of the system consumers + Minimizing the total negative environmental impacts [110] Energy price Robust optimization Minimizing the net costs of a smart home [104] Wind and PV generations Adaptive robust optimization Minimizing the operating costs of an isolated microgrid Minimizing the costs of an energy hub participating in energy and reserve markets + Minimizing the emissions of pollution [105] Wind and photovoltaic generations Two-stage adjustable robust optimization…”

Section: Robust Fuzzy Multi-objective Optimization Programmingmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

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This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.

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Section: Robust Fuzzy Multi-objective Optimization Programmingmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

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“…Due to the stochastic of renewable energy, the hybrid energy system needs to have higher flexibility than the traditional energy system to ensure the energy supply and demand balance of the system. The higher the flexibility of a hybrid energy system, the greater its ability to utilize renewable energy (Liu et al, 2019). Wang et al (2022a) combines the power grid with the hydrogen grid by adding electricity-to-hydrogen equipment in the hybrid energy system and improve the flexibility of the system, helping the grid to absorb the uncertainty of renewable energy generation, smoothing the output power of renewable energy.…”

Section: Flexibilitymentioning

confidence: 99%

A review on configuration optimization of hybrid energy system based on renewable energy

Zhang

Wei

2022

Front. Energy Res.

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Hybrid energy system based on renewable energy is an important way to solve current energy and environmental problems. However, its achievable benefits need to be realized through an integrated optimization that considers optimization objectives, optimization models, and optimization algorithms. In this paper, the integrated structure and configuration optimization techniques of the hybrid energy system based on renewable energy (RE-HES) is summarized. The performance indicators, system characteristics, model construction, and solutions of RE-HES optimized configuration are comprehensively reviewed. Current review highlights the diversification of performance evaluation index, consideration of uncertainty and dynamic properties, superstructure approaches and intelligent algorithms.

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“…Robust optimisation describes uncertain variables without the knowledge of true PDF [14,15]. Considering the uncertainties of renewable generation in IES, robust optimisation models that combine scenario generation and robustness evaluation have been developed [16,17].…”

Section: Introductionmentioning

confidence: 99%

Robust optimisation of electricity‐heating integrated energy system based on data‐driven PV interval estimation

Xu,

Liu,

Guo

et al. 2023

IET Energy Syst Integration

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Short‐term interval estimation can effectively and precisely quantify the uncertainties of renewable energy, accurately represent the range of fluctuations of uncertain variables in robust optimisation of electricity‐heating integrated energy system (EHIES) and it is getting crucial for reliable and flexible operation of renewable dominated new energy systems. The authors present a multivariate data‐driven short‐term PV power interval prediction model that consists of multiple layers, including one‐dimensional convolutional layer, ultra‐lightweight subspace attention mechanism (ULSAM), bidirectional long and short‐term memory (BiLSTM), quantile regression (QR) and kernel density estimation (KDE). The one‐dimensional convolutional layer and ULSAM can extract sequential features and highlight key information from the data; the BiLSTM processes time series data in both directions and conveys historical information; the QR and KDE models generate interval prediction with a given confidence level. Based on the proposed interval estimation, a refined PV uncertainty set can be established and adopted by robust optimal scheduling of EHIES utilising min‐max‐min algorithm. The simulation results have demonstrated the estimation accuracy and adaptability to various weather scenarios.

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Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy

Cited by 53 publications

References 42 publications

Applications of Probabilistic Forecasting in Smart Grids: A Review

Applications of Probabilistic Forecasting in Smart Grids: A Review

A review on configuration optimization of hybrid energy system based on renewable energy

Robust optimisation of electricity‐heating integrated energy system based on data‐driven PV interval estimation

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