Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach

Guo, Zhongjie; Wei, Wei; Chen, Laijun; Xie, Rui; Mei, Shengwei

doi:10.1049/iet-rpg.2020.0354

Cited by 21 publications

(23 citation statements)

References 31 publications

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“…In (7), p rm kt is the available renewable power in renewable plant k in period t, which is uncertain and determined by the weather conditions; hence, the dispatched renewable power, p r kt , cannot exceed p rm kt . Meanwhile, the curtailed renewable power is defined as ∆p r kt .…”

Section: Operating Constraintsmentioning

confidence: 99%

“…Dui X. et al proposes a two-stage method to determine the optimal capacity of battery energy storage to decrease wind power curtailment in grid-connected wind farms [6]; the first stage schedules the unit commitment of thermal generators and the wind farm output, and the second stage optimizes the operational strategies of battery energy storage while penalizing the curtailment in the objective function. In Reference [7], a chance-constrained model is developed to address the energy storage sizing problem, where the constraint on total curtailment rate is satisfied given a confidence level. The optimal placement framework for ESU is proposed in Reference [8], which minimizes the hourly social cost and maximizes wind power utilization within a power system with high wind penetration.…”

Section: Introductionmentioning

confidence: 99%

“…• Continuous variable vector: x is a column vector and encloses all the remaining continuous variables in (1)- (7).…”

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confidence: 99%

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Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems

et al. 2021

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The uncertain natures of renewable energy lead to its underutilization; energy storage unit (ESU) is expected to be one of the most promising solutions to this issue. This paper evaluates the impact of ESUs on renewable energy curtailment. For any fixed renewable power output, the evaluation model minimizes the total amount of curtailment and is formulated as a mixed integer linear program (MILP) with the complementarity constraints on the charging and discharging behaviors of ESUs; by treating the power and energy capacities of ESUs as parameters, the MILP is transformed into a multi-parametric MILP (mp-MILP), whose optimal value function (OVF) explicitly maps the parameters to the renewable energy curtailment. Further, given the inexactness of uncertainty’s probability distribution, a distributionally robust mp-MILP (DR-mp-MILP) is proposed that considers the worst distribution in a neighborhood of the empirical distribution built by the representative scenarios. The DR-mp-MILP has a max–min form and is reformed as a canonical mp-MILP by duality theory. The proposed method was validated on the modified IEEE nine-bus systems; the parameterized OVFs provide insightful suggestions on storage sizing.

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Section: Operating Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems

et al. 2021

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“…Wind power is one of the primary forms of renewable energy, and its participation in the power system dispatch has received extensive attention. Some researchers use stochastic optimization (Zhu et al, 2020), robust optimization (Yu et al, 2020), distributionally robust optimization (Guo et al, 2020), and other optimization methods to try to describe the existing uncertainties in the cooperative dispatch integrated with wind power. Among them, the distributionally robust method is a data-driven optimization method, which constructs the ambiguity set of uncertain parameter probability by using the information implicit in the actual data.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. (Guo et al, 2020) compared and analyzed the adaptability of distributionally robust optimization methods based on Wasserstein divergence distribution. (Esfahani and Kuhn, 2018) demonstrated this method's performance guarantee and ease of treatment in detail.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Heat-Hydrogen Balance Characteristic of Hydrogen Energy Storage and Cooperative Dispatch of Wind-Hydrogen Hybrid System

Yang

Chen

et al. 2021

Front. Energy Res.

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The heat and hydrogen balance of the hydrogen energy storage system’s intermittent operation becomes a key factor affecting the performance of the wind-hydrogen hybrid system (W-HHS). This work designed a hydrogen energy storage system (HESS), including waste heat utilization. Then, a dual state of charge (SOC) model is established, in which hydrogen and heat storage is considered. Furthermore, based on the distributionally robust method, an optimal dispatch method of W-HHS is proposed to reduce the operation cost of conventional units in the grid and increase the revenue of the W-HHS. The previously proposed dual SOC model of heat-hydrogen balance is regarded as a constraint in this cooperative dispatch. The effectiveness and efficiency of the dual SOC model were verified on the IEEE 30-bus system with an actual wind plant data set. The results show that the hydrogen-heat dual SOC model can fully reflect the influence of heat and hydrogen balance on the operation of the W-HHS. The cooperative dispatch method improves the reliability of the W-HHS operation under the premise of ensuring the heat-hydrogen balance. When the constraints of hydrogen balance SOC and heat balance SOC are met simultaneously, the available power of the wind plant is 6–8% lower than the ideal situation. Parameter analysis indicates that reducing the heat dissipation coefficient can reduce the influence of the SOC constraint of heat balance on the dispatch strategy and increase the power output of the wind plant. When the heat dissipation coefficient is less than 1/1,200, the heat balance SOC constraint fails.

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Wasserstein‐metric‐based distributionally robust optimization method for unit commitment considering wind turbine uncertainty

Yan

Chen

et al. 2023

Engineering Reports

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The penetration of wind turbines in the power grid is increasing rapidly. Still, the wind turbine output power has uncertainty, leading to poor grid reliability, affecting the grid's dispatching plan, and increasing the total cost. Thus, a distributionally robust optimization method for thermal power unit commitment considering the uncertainty of wind power is proposed. For this method, energy storage and interruptible load are added to simulate increasingly complex electricity consumption scenarios. Furthermore, the amount of load cutting reflects the satisfaction level of electricity consumption on the user side. Based on Wasserstein metric, an ambiguity set is established to reflect the probabilistic distribution information of the wind power uncertainty. An ambiguity set preprocessing method is proposed to depict the probability distribution of ambiguity set more clearly, to minimize the operation cost under the condition that the uncertainty of wind turbine output power obeys the extreme probabilistic distribution of the ambiguity set. The test case in a modified version of the IEEE 6‐bus system shows that the proposed method can flexibly adjust the robustness and economy of optimization decisions by controlling the sample size and the confidence of Wasserstein ambiguity set radius. In addition, the proposed ambiguity set preprocessing method can obtain more economical dispatching decisions with a smaller sample size.

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Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach

Cited by 21 publications

References 31 publications

Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems

Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems

Modeling the Heat-Hydrogen Balance Characteristic of Hydrogen Energy Storage and Cooperative Dispatch of Wind-Hydrogen Hybrid System

Wasserstein‐metric‐based distributionally robust optimization method for unit commitment considering wind turbine uncertainty

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