A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties

Yan, Rujing; Wang, Jiangjiang; Wang, Jiahao; Tian, Long; Tang, Saiqiu; Wang, Yuwei; Zhang, Jing; Cheng, Yi; Li, Yuan

doi:10.1016/j.energy.2022.123498

Cited by 68 publications

(10 citation statements)

References 48 publications

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“…In addition, 1) 365 groups of historical actual data are collected, and each group contains the actual values of EM price, PV output, and thermal load for each time period on a particular day in history. Among them, the EM price is collected from North American Electricity Market (https://www.spp.org/) and thermal load data is derived from another study, [ 23 ] respectively, and the PV output data are obtained by inputting the actual irradiation intensity from another study [ 23 ] into the irradiation‐to‐power conversion equations, [ 40 ] corresponding to the PV capacity with 20 MW. 2) Based on the historical actual data, the forecasting values of EM price, PV output, and thermal load at each time period of the scheduling day are obtained, which are shown in Figure 3 and 4 , respectively.…”

Section: Simulation and Results Discussionmentioning

confidence: 99%

“…[21] However, the "worst-case" scenario is less likely to occur, which makes the RO model too conservative to achieve the desirable optimization effect. [22,23] In addition to the worst-case RO discussed, robust design optimization (RDO) considers probabilistic uncertainties at the model inputs and optimizes both the expected performance and the variability of that performance. Diederik et al applied RDO on a wind-and solar-powered hydrogen refueling system and a hydrogen-and diesel-powered bus fleet, to optimize the levelized cost Of driving and carbon intensity.…”

Section: Literature Surveymentioning

confidence: 99%

“…[ 21 ] However, the “worst‐case” scenario is less likely to occur, which makes the RO model too conservative to achieve the desirable optimization effect. [ 22,23 ]…”

Section: Introductionmentioning

confidence: 99%

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Temporal Covariance‐Involved Distributionally Robust Optimization for Photovoltaic–Hydrogen Integrated Energy System Scheduling Considering Source, Load Sides, and Market Uncertainties

Wang,

Jia,

Song

et al. 2024

Energy Tech

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Under the pressures of environmental pollution, solar energy and hydrogen have attracted widespread attention, thus promoting the development of photovoltaic–hydrogen integrated energy system. However, multiple uncertainties seriously interfere with the system's economic and stable operation. Therefore, this article proposes a temporal covariance‐involved distributionally robust optimization model for system scheduling considering multiple uncertainties from source, load sides, and market. First, the possible range of the uncertainties distribution is described by a novel moment‐based ambiguity set modified with temporal covariances. Second, the objective function is constructed as a min{sup} form to minimize the expected operation cost under the “worst‐case” distribution in the ambiguity set. Then, the constraints present a two‐stage structure. The first stage guarantees the operation feasibility under the forecasting scenario, and the second stage accounts for forecasting errors to formulate the rescheduling space for resisting uncertainties. Finally, the model is transformed into an easy‐to‐solve semidefinite programming. Simulation finds that the model 1) significantly reduces the average and standard deviation of system operation cost by 22.35% and 27.41%, respectively; 2) ensures 100% photovoltaic accommodation; and 3) maintains low conservativeness and complexity simultaneously. Above all, this study provides an effective approach for comprehensive ultilization of clean energies under multiple uncertainties.

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Section: Simulation and Results Discussionmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Temporal Covariance‐Involved Distributionally Robust Optimization for Photovoltaic–Hydrogen Integrated Energy System Scheduling Considering Source, Load Sides, and Market Uncertainties

Wang,

Jia,

Song

et al. 2024

Energy Tech

Self Cite

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“…In addition, due to changes in boundary conditions, the day-ahead clearing results may not meet the demand for an effective reserve of the system in the real-time market, especially in the new power system, where the effect of the new energy forecast is particularly significant. In the new power system, relevant research on the difficulty of reserve evaluation caused by the deviation of new energy prediction includes the probability evaluation method (Chen et al, 2022;Liu et al, 2023), random optimization (Xu et al, 2023), and robust optimization (Ran et al, 2022;Wang et al, 2022). The probabilistic evaluation method and stochastic optimization method are too dependent on the probabilistic accuracy of boundary data prediction in the day-ahead electricity spot market and cannot meet the actual scheduling demand in the day-ahead and real-time electricity spot market.…”

Section: Introductionmentioning

confidence: 99%

A safety check method to maximize the effective reserve by optimizing the power of the tie-line in the power market

Wanyan

et al. 2023

Front. Energy Res.

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To ensure the stability of the electricity spot market and the safety of the provincial and regional power systems, a safety check method is proposed to maximize the effective reserve resources in the power system by optimizing the power of each tie-line. This safety check method accurately models the tie-line equipment and the effective reserve resources and is coupled with each constraint of the electricity spot market clearing model to form a safety check algorithm to optimize the power of tie-line power. The model involved in this paper is a linear model, which has a clear implementation method in practical dispatching applications. Through this method, the power configuration scheme of each tie-line to meet the electricity spot market constraints can be obtained, and the safety check results have the executability of the power market. The rationality and feasibility of the safety check algorithm results are verified by simulating the provincial-scale electricity spot market. According to the simulation results, this method can release effective reserve resources and provide more guarantees for the safe operation of the power grid. In addition, this method can save up to 4.9% of the total operation cost of the power system and improve the dispatching economy of the power system. This method is of great significance to ensure the safe operation of the power system and the day-ahead market and real-time market scheduling in the actual power spot system. In addition, this method also has great guiding significance for the analysis of the actual reserve situation of the power market after the event.

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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]. A robust optimisation method has been proposed to reduce the conservativeness of the conventional model utilising conditional value-at-risk [18].…”

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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A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties

Cited by 68 publications

References 48 publications

Temporal Covariance‐Involved Distributionally Robust Optimization for Photovoltaic–Hydrogen Integrated Energy System Scheduling Considering Source, Load Sides, and Market Uncertainties

Temporal Covariance‐Involved Distributionally Robust Optimization for Photovoltaic–Hydrogen Integrated Energy System Scheduling Considering Source, Load Sides, and Market Uncertainties

A safety check method to maximize the effective reserve by optimizing the power of the tie-line in the power market

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

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