Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties

Yang, Lei; Wang, Dan; Jia, Hongjie; Li, Jiaxi; Chen, Jingcheng; Li, Jingru; Zhihong, Yang

doi:10.1016/j.apenergy.2021.117224

Cited by 54 publications

(20 citation statements)

References 37 publications

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“…Similar to HST, the storage status of UHS should not exceed its lower and upper limits, as shown in (27), while the hydrogen injection and withdrawal behaviour of UHS are also constrained by (28) to (30). x lwdt uhs,ch + x lwdt uhs,dis ≤ 1 (30) It should be pointed out that the hydrogen injection and withdrawal rate of UHS is significantly lower than that of HST, which is reflected by the value of 𝛾 uhs,ch and 𝛾 uhs,dis .…”

Section: 25mentioning

confidence: 99%

“…[27] established a multi‐stage planning model for equipment capacity configuration in park‐level electricity‐gas‐heat IES, which significantly reduced equipment configuration at each stage. Authors of [28] considered uncertainty and construction time sequence on a long‐term time scale, and proposed a regional IES planning method based on the multi‐stage scenario tree generation method. Ref.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐stage flexible planning of regional electricity‐HCNG‐integrated energy system considering gas pipeline retrofit and expansion

Qiu

Zhou

et al. 2022

IET Renewable Power Gen

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The development of hydrogen-enriched compressed natural gas (HCNG) can make full use of the existing natural gas infrastructure, thereby alleviating the economic and technical pressure on the development of pure hydrogen. This paper proposes a multistage flexible planning method for the regional electricity-HCNG-integrated energy system (E-HCNG-IES), which considers the medium and long-term dynamic characteristics of the E-HCNG-IES. For each sub-regional energy station, an energy-hub type model including hydrogen/HCNG-related equipment and facilities is established. Considering the influence of hydrogen blending on the energy delivery capacity and line pack of pipelines, a hydrogen-resistant retrofit model for existing natural gas pipelines between sub-regions is proposed. Meanwhile, the model for newly constructed hydrogen pipelines is also presented. Subsequently, a series of flexibility evaluation indicators are proposed to support the performance analysis of planning schemes. The lateral comparison of sub-cases in Changzhou, China demonstrates that considering the hydrogen-resistant retrofit of natural gas pipelines can reduce the construction cost of hydrogen pipelines by about 37.12%. The peak shaving capacity of underground hydrogen storage (UHS), the energy recycling ability of turbo expander (TE), and the space-time decoupling ability of hydrogen in energy supply and demand have also been confirmed.

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Section: 25mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐stage flexible planning of regional electricity‐HCNG‐integrated energy system considering gas pipeline retrofit and expansion

Qiu

Zhou

et al. 2022

IET Renewable Power Gen

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show abstract

“…GAN was proposed in 2014 (Goodfellow et al, 2014). It was first used for image recognition but has recently also shown great results in the prediction of sources and loads for integrated energy systems (Lei et al, 2021;Hu et al, 2021). used GAN to generate scenarios of real wind and photovoltaic (PV) power distributions without complex statistical assumptions and sampling.…”

Section: Introductionmentioning

confidence: 99%

Two-stage scheduling of integrated energy systems based on a two-step DCGAN-based scenario prediction approach

Liu

et al. 2023

Front. Energy Res.

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Integrated energy systems (IESs) are developing rapidly as a supporting technology for achieving carbon reduction targets. Accurate IES predictions can facilitate better scheduling strategies. Recently, a newly developed unsupervised machine learning tool, known as Generative Adversarial Networks (GAN), has been used to predict renewable energy outputs and various types of loads for its advantage in that no prior assumptions about data distribution are required. However, the structure of the traditional GAN leads to the problem of uncontrollable generations, which can be improved in deep convolutional GAN (DCGAN). We propose a two-step prediction approach that takes DCGAN to achieve higher accuracy generation results and uses a K-means clustering algorithm to achieve scenario reduction. In terms of scheduling strategies, common two-stage scheduling is generally day-ahead and intraday stages, with rolling scheduling used for the intraday stage. To account for the impacts on the prediction accuracy of scheduling results, Conditional Value at Risk (CVaR) is added to the day-ahead stage. The intra-day prediction process has also been improved to ensure that the inputs for each prediction domain are updated in real-time. The simulations on a typical IES show that the proposed two-step scenario prediction approach can better describe the load-side demands and renewable energy outputs with significantly reduced computational complexity and that the proposed two-stage scheduling strategy can improve the accuracy and economy of the IES scheduling results.

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“…Nevertheless, even the most advanced algorithm still cannot fill the gap of prediction errors. To solve the problem of low accuracy of single-point load forecasting, Lei et al (Lei et al, 2021) proposed a multi-stage scenario tree generation method based on the conditional generative adversarial network-random forest-Markov chain. However, the large number of RES scenarios directly leads to an increase in the computational cost.…”

Section: Introductionmentioning

confidence: 99%

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

Yang²,

Liang³

et al. 2023

Front. Energy Res.

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The uncertainty caused by the growing use of renewable energy sources, such as wind and solar energy, makes it difficult to forecast the operation costs of micro-energy systems, particularly those in remote rural areas. Motivated by this point, this paper analyzes the possible operational risks and then introduces Condition Value at Risk (CVaR) to quantify the cost of the operational risk. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, the operating cost, and the cost of operational risk, while considering the physical limitations of the multi-energy microgrid, is presented. Especially, scenarios of wind and solar energy output are generated using the Latin hypercube sampling method and reduced using the crowding measure-based scenario reduction method. After piecewise linearization and second-order cone relaxation, the model proposed in this paper is processed as a mixed integer linear model and solved by CPLEX. According to the achieved typical scenarios processed by the reduction method, the simulation shows that the presented configuration model can balance the investment cost and the cost of the operational risk, which effectively enhances the system’s ability to cope with uncertainties and fluctuations. Moreover, by adjusting the risk preference coefficient, the conservativeness of the planning scheme can be correspondingly adjusted.

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Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties

Cited by 54 publications

References 37 publications

Multi‐stage flexible planning of regional electricity‐HCNG‐integrated energy system considering gas pipeline retrofit and expansion

Multi‐stage flexible planning of regional electricity‐HCNG‐integrated energy system considering gas pipeline retrofit and expansion

Two-stage scheduling of integrated energy systems based on a two-step DCGAN-based scenario prediction approach

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

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