A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

He, Shuaijia; Gao, Hongjun; Tian, Hao; Wang, Lingfeng; Liu, Youbo

doi:10.35833/mpce.2019.000174

Cited by 38 publications

(24 citation statements)

References 37 publications

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“…The three types of loads in this transformer district mentioned in this article have different proportions in the entire station area, and different types of loads have different electricity price demand balance relationships in different time periods, and there are differences in response elasticity. At present, in the study of price-based demand response models, electricity price differentiation or elastic coefficient differentiation response models are mostly used (He et al, 2021), which are obviously insufficient. However, this article comprehensively considers the load power consumption characteristics of the station area and constructs a different type of load response elasticity according to the difference in response elasticity of different types of loads.…”

Section: Differential Price-based Demand Responsementioning

confidence: 99%

Optimization for Transformer District Operation Considering Carbon Emission and Differentiated Demand Response

et al. 2022

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With the promotion of the “dual carbon” goal, a large number of distributed photovoltaic power are connected to the distribution network. Since the current operation optimization of the low-voltage transformer district is based on single objectives such as the economy and power reliability, the model is relatively simple and difficult to adapt to the large-scale access of photovoltaics. Therefore, this article comprehensively considers carbon emissions, different load characteristics, and differentiated demand response of the district. An optimization method for low-voltage transformer district operation under the dual-carbon background is proposed. First, the typical structure of a low-voltage transformer district is introduced. Second, the load types and characteristics of the low-voltage transformer district are analyzed, and differentiated demand response models are established for different types of loads. Finally, taking the minimum economic cost and carbon emission as the objective, the low-voltage transformer district operation optimization model considering carbon emission and differentiated demand response is established by considering the voltage overrun of the photovoltaic access point, substation capacity constraint, and carbon emission constraint. The simulation results show that the model can effectively reduce the economic cost and carbon emissions of the low-voltage transformer district, achieve more than 95% reasonable utilization rate of new energy in the low-voltage transformer district, improve the lateral time distribution of load in the low-voltage transformer district, and provide an effective means for low-carbon scheduling of distribution networks.

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Section: Differential Price-based Demand Responsementioning

confidence: 99%

Optimization for Transformer District Operation Considering Carbon Emission and Differentiated Demand Response

et al. 2022

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“…Uncertainty, which typically arises from the observed value, model and parameters, is prevalent in real-world applications. Although uncertainty is unavoidable in optimization problems, we can handle it through robust optimization which has been widely used in power systems [1], logistics [2], economics [3] etc. In 1973, Soyster [4] first utilized robust optimization to handle the uncertainty in linear programming.…”

Section: Introductionmentioning

confidence: 99%

A Neurodynamic Approach for Solving Robust Linear Programming under the Polyhedral Uncertainty Set

Peng

et al. 2022

Preprint

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This paper converts the robust linear programming under the polyhedral uncertainty set to standard linear programming. After eliminating the parameter uncertainty, we design a projection neural network to address the problem. The equilibrium point of the neural network model is theoretically proved to be stable in the Lyapunov sense and globally convergent to the optimal solution to robust linear programming. Two numerical examples are provided to illustrate the validity and performance of the proposed approach.

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“…However, in the research of the collaborative planning of SOPs, numerous demand-side resources with flexibility and controllability in active distribution networks did not attract enough attention. Meanwhile, the solution to the uncertainty of DGs output and DR mainly focuses on the stochastic optimization (SO) and the robust optimization (RO) [17][18][19]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [19] proposes a two-stage robust optimal allocation model of DGs considering capacity curve and real-time price. It is not difficult to discover that SO usually requires more discrete scenarios, which increases the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Distributionally robust co‐optimization of the demand‐side resources and soft open points allocation for the high penetration of renewable energy

Liu

Zhang

et al. 2021

IET Renewable Power Gen

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The new technologies, such as soft open points (SOPs) and demand response (DR), offer original approaches to stabilize the system operation and accommodate the high penetration of distributed generators (DGs). However, the applied effects of these technologies are closely affected by the uncertainties of the consumer's willingness and the outputs of DGs. To solve the problems caused by the uncertainty of the resources in active distribution system, this paper proposes a distributionally robust co-optimization model for the demand side resources and SOPs, which realizes the combination of investment economy and operation robustness. Next, using equivalent substitution and polyhedron linearization technique, this paper proposes the mathematical method which transforms the original non-linear programming model into a mixed-integer linear programming model, and obtains the solution to the distributionally robust model with column and constraint generation (CCG) algorithm. Finally, the effectiveness of the proposed model is verified with the sample from a power distribution system in northern China. The result demonstrates that the co-optimization model can realize complementary advantages of the demand side resources and SOPs, which can not only guarantee the economy of scheme, but also improve the accommodation of DGs.

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A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Cited by 38 publications

References 37 publications

Optimization for Transformer District Operation Considering Carbon Emission and Differentiated Demand Response

Optimization for Transformer District Operation Considering Carbon Emission and Differentiated Demand Response

A Neurodynamic Approach for Solving Robust Linear Programming under the Polyhedral Uncertainty Set

Distributionally robust co‐optimization of the demand‐side resources and soft open points allocation for the high penetration of renewable energy

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