A Distributionally Robust Chance Constrained Planning Method for Integrated Energy Systems

Zhou, Jingdong; Li, Wenliang; Chen, Xiang; Sun, Mingjie; Mei, Chao; He, Shuaijia; Gao, Hongjun; Liu, Junyong

doi:10.1109/appeec45492.2019.8994548

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…[26] focuses on generation expansion planning, and the DRO method is compared with SP and RO. Integrated energy system planning is considered in [27], [28]. A multi-disasterscenario-based DRO planning model is proposed in [29] for distribution systems.…”

Section: Refmentioning

confidence: 99%

Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Xie

Wei

Shahidehpour

et al. 2022

IEEE Trans. Power Syst.

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Section: Refmentioning

confidence: 99%

Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Xie

Wei

Shahidehpour

et al. 2022

IEEE Trans. Power Syst.

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“…By leveraging the advantages of stochastic programming and robust optimization, distributionally robust optimization (DRO) has gain increasing popularity in recent integrated energy system planning and operations studies [17][18][19][20][21][22][23]. It brings important characteristics such as adjustable conservativeness level and not requiring the uncertainty source's true probability distribution [19,20].…”

Section: Reversible Voltage U Tnmentioning

confidence: 99%

“…There are different paradigms to construct the ambiguity set in the literature. The studies [9,[22][23][24] constructed their ambiguity sets based on the first and second order moment information, while in [17,21] more general moment information was adopted to achieve less conservative results. The authors in [19] built the ambiguity set based on 1-norm and inf-norm constraints, which allows a simpler solution method.…”

Section: Reversible Voltage U Tnmentioning

confidence: 99%

Distributionally Robust Chance-Constrained Flexibility Planning for Integrated Energy System

Zhan¹,

Hou²,

Yang³

2021

Preprint

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Inflexible combined heat and power (CHP) plants and uncertain wind power production result in excess power in distribution networks, which leads to inverse power flow challenging grid operations. Power-to-X facilities such as electrolysers and electric boilers can offer extra flexibility to the integrated energy system. In this regard, we aim to jointly determine the optimal Power-to-X facility sizing and integrated energy system operations in this study. To account for wind power uncertainties, a distributionally robust chance-constrained model is developed to characterize wind power uncertainties using ambiguity sets. Linear decision rules are applied to analytically express real-time recourse actions when uncertainties are exposed, which allows the propagation of wind power uncertainties to gas and heat systems. Accordingly, the developed three-stage distributionally robust chance-constrained model is converted into a computationally tractable single-stage mixed-integer conic model. A case study validates the effectiveness of introducing the electrolyser and electric boiler into the integrated energy system, with respect to the decreased system cost, expanded CHP plant flexibility and reduced inverse power flow. The developed distributionally robust optimization model exhibits better effectiveness and robustness compared to a chance-constrained optimization model assuming wind forecast errors follow Gaussian distribution. Detailed profit analysis reveals that although the overall system cost is minimized, the profit is distributed unevenly across various stakeholders in the system. The profit mainly falls with the wind power plants, which therefore are most motivated to make investments in the flexibility resources. Other parties rely on additional policies such as bilateral contracts with the wind power plants to gain incentives to invest. The findings from this study can be used to motivate policy-makers to make proper regulations to incentivize investments in flexibility resources and establish a more reliable power grid.

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“…In addition, there have been several studies in recent years on the analysis of power systems and integrated energy systems considering the higher-order uncertainty of node power. Lubin, et al [19,20] considered the uncertainty of the mean and variance of wind power in robust optimization models of power systems and combined heat and power systems, respectively. In [21], considering the uncertainty of the probability distribution type of wind power, a distributed robust coordinated scheduling model was proposed.…”

Section: W0 X0 F0mentioning

confidence: 99%

Interval Probabilistic Energy Flow Calculation of CCHP Campus Microgrid Considering Interval Uncertainties of Distribution Parameters

et al. 2020

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Due to the absence of historical data and the errors of measurement instruments, there may be uncertainties in the distribution parameters of the random variables describing the uncertain fluctuations of node power including renewable energy station output and load power in the combined cooling heating and power (CCHP) campus microgrid. In this paper, intervals are used to describe the uncertainties of distribution parameters of the random variables, and an interval probabilistic energy flow (IPEF) calculation model of the CCHP campus microgrid is established. Introducing the interval arithmetic (IA) into the cumulant method, an IA-based IPEF algorithm is proposed to obtain the analytical expressions of probability density function or cumulative distribution function intervals of the state variables. Moreover, affine arithmetic (AA) is introduced to address the interval extension problem in the calculation, and an AA&IA-based IPEF algorithm is proposed. By constructing the correlation transformation matrixes, the correlation among different node power is considered in the IPEF calculation. A case study on a CCHP campus microgrid demonstrates that the results of the AA&IA-based IPEF algorithm are more accurate than those of the IA-based IPEF algorithm by using the results of the double-layer Monte Carlo method as a reference. Moreover, the proposed algorithms are more efficient than the double-layer Monte Carlo method. INDEX TERMS CCHP campus micro-grid, interval probabilistic energy flow calculation, higher-order uncertainty, cumulant method, interval arithmetic, affine arithmetic, correlation NOMENCLATURE A． ACRONYMS

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A Distributionally Robust Chance Constrained Planning Method for Integrated Energy Systems

Cited by 8 publications

References 6 publications

Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Distributionally Robust Chance-Constrained Flexibility Planning for Integrated Energy System

Interval Probabilistic Energy Flow Calculation of CCHP Campus Microgrid Considering Interval Uncertainties of Distribution Parameters

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