Robust optimization with box set for reactive power optimization in wind power integrated system

Yang, Yuwei; Zhou, Ri-Gui; Ran, Xiaohong

doi:10.1109/pesgm.2012.6345174

Cited by 16 publications

(2 citation statements)

References 14 publications

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“…In this paper, the aggregation units of prosumers include customer-side wind power generation, ESS, and fixed load. Then, a box uncertainty set is introduced to characterize multiple uncertainties [32][33][34]. Finally, this paper develops a TSRO model considering multiple uncertainties using the optimization objective of minimizing the overall operating cost of prosumers.…”

Section: A Tsro Model For Prosumers Considering Multiple Uncertaintiesmentioning

confidence: 99%

Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

Zhang¹,

Zhang²,

Gao³

et al. 2023

Sustainability

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This paper develops a two-stage robust optimization (TSRO) model for prosumers considering multiple uncertainties from the sustainable energy of wind power generation and load demand and extends the existing nested column-and-constraint generation (C&CG) algorithm to solve the corresponding optimization problem. First, considering the impact of these uncertainties on market trading strategies of prosumers, a box uncertainty set is introduced to characterize the multiple uncertainties; a TSRO model for prosumers considering multiple uncertainties is then constructed. Second, the existing nested C&CG algorithm is extended to solve the corresponding optimization problem of which the second-stage optimization is a bi-level one and the inner level is a non-convex optimization problem containing 0–1 decision variables. Finally, a case study is solved. The optimized final overall operating cost of prosumers under the proposed model is CNY 3201.03; the extended algorithm requires only four iterations to converge to the final solution. If a convergence accuracy of 10−6 is used, the final solution time of the extended algorithm is only 9.75 s. The case study result shows that prosumers dispatch the ESS to store surplus wind power generated during the nighttime period and release the stored electricity when the wind power generation is insufficient during the daytime period. It can contribute to promoting the local accommodation of renewable energy and improving the efficiency of renewable energy utilization. The market trading strategy and scheduling results of the energy storage system (ESS) are affected by multiple uncertainties. Moreover, the extended nested C&CG algorithm has a high convergence accuracy and a fast convergence speed.

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Section: A Tsro Model For Prosumers Considering Multiple Uncertaintiesmentioning

confidence: 99%

Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

Zhang¹,

Zhang²,

Gao³

et al. 2023

Sustainability

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show abstract

“…In (1), x is the state variable, refers to the node voltage, u There are several ways to solve (1), including linear programming [1], [2], nonlinear programming (including quadratic programming method [3], [4], interior point methods [5]- [8], evolutionary programming [9]- [11], etc.) and intelligent algorithms [12]- [17].…”

Section: Introductionmentioning

confidence: 99%

A New Method to Solve Reactive Power Optimization Problems of Power System by Introducing the Branch Current

Tao¹,

Wang

Xie

et al. 2013

ElAEE

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The mathematical model used in this paper is not only the traditional node voltage equation but also the introduction of the branch current equation when research the power system reactive power optimization, and so establishes a mathematical model of hybrid power network node voltages and branch currents. The state variables in this model are the node voltages and branch currents, the network flow is explicitly expressed, and they play a key role for the simplification of the solving model. Solving the model will be broken down into two sub-problems, one is a network loss minimization objective augmented Lagrange function, forming the Kuhn-Tucker conditions, and the other is a linear equation. IEEE 30-bus system example shows that the complexity and high dimension of the model solution have been significantly improved, the solving process becomes easier, and the solution is close to the global optimal solution. Compared with traditional optimal power flow algorithm, this algorithm can improve the computational efficiency of reactive power optimization.

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Introduction

Ding¹

2016

Power System Operation With Large Scale Stochastic Wind Power Integration

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Robust optimization with box set for reactive power optimization in wind power integrated system

Cited by 16 publications

References 14 publications

Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

A New Method to Solve Reactive Power Optimization Problems of Power System by Introducing the Branch Current

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