Fast Cumulant Method for Probabilistic Power Flow Considering the Nonlinear Relationship of Wind Power Generation

Lin, Chaofan; Bie, Zhaohong; Pan, Chaoqiong; Liu, Shiyu

doi:10.1109/tpwrs.2019.2959404

Cited by 57 publications

(16 citation statements)

References 28 publications

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“…The solutions available to the POPF problem can be classified as analytical [20,21], approximate [22], numerical [23], and heuristic approaches [24]. Error!…”

Section: Introductionmentioning

confidence: 99%

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Shaheen

Ullah

Qais³

et al. 2022

Energies

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Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

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“…The solutions available to the POPF problem can be classified as analytical [20,21], approximate [22], numerical [23], and heuristic approaches [24]. Error!…”

Section: Introductionmentioning

confidence: 99%

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Shaheen

Ullah

Qais³

et al. 2022

Energies

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show abstract

“…For designing a suitable and efficient emulator for wind turbine, a complete study and identification of the real wind turbine is required considering the linear and nonlinear relations of wind power systems [24][25][26]. The wind turbine design, types and environmental conditions affecting the turbine modelling and simulation must be considered [27,28]. In [29], emulation of high rating and inertia wind turbines using smaller machines of lower rates and inertia, the simulation of wind turbines and generators inertia and their effects on power system stability have been presented.…”

Section: Introductionmentioning

confidence: 99%

Design of Optimal Real Time Emulators for Steam and Wind Turbines for Avoiding Risky Severe Conditions

Zoghby

Ramadan

Alhelou

2022

Preprint

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Modern energy infrastructures may face critical impacts on distributed generation and microgrids in presence of renewable and conventional energy sources. Fast restorations for these networks through proposing convenient proactive protection systems become mandatory for securing energy particularly after severe faults. This paper deals with presenting a descriptive modelling and comprehensive analysis of both steam and wind turbines using optimal real time emulators with unique testbench. Based on the dynamics of each turbine, both emulators are performed using 4kW, 180V, 1500r.p.m separately exited DC motor coupled to 2kW, 380V, 50Hz, 1500r.p.m three-phase synchronous generator. For real-time interface implementation, the mathematical models of steam and wind turbines are realized using LabVIEWTM software. The characterization and verification of both emulated steam and wind turbines are examined at different normal operating conditions in terms of steam valve position and wind speed, respectively. To regulate the current for both systems despite their diverse dynamics, a simple industrial proportional-integral (PI) controller is considered. Unlike other artificial intelligence-based controllers, the offline-controller gains are scheduled using genetic algorithm (GA) via MatlabTM software to ensure the due fast response to cope with unexpected faults. The experimental validity of both emulators is tested at the most severe abnormal operating conditions. The three-phase short circuit is considered at the generator terminals with different fault periods until reaching out-of-step conditions. From numerical analysis and experimental results, the characterization of both emulated steam and wind turbines explicitly mimics their real large-scale turbines in normal conditions. The emulators’ fast responses using the proposed GA-PI control approach are verified. Besides, the experimental dynamic behavior convergence and interoperability between the emulated and real systems for both steam and wind turbines are validated under severe conditions. The practical results confirm the fast-nature performance of the GA in avoid risky instability conditions.

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“…For this reason, refs. [7] and [8] build Gumbel copula and mixed-copula models based on the edge probability distribution of variables directly to model the probability of long-time scale spatial correlation of renewable energy output, and capture the tail correlation between variables with strong randomness. However, the copula models above are static, which lack consideration of the changes of correlation structure between variables, and have a great impact on the accuracy of correlation model.…”

Section: Introductionmentioning

confidence: 99%

Variable‐structure copula model for multi‐scenario bi‐level transmission and generation expansion planning

Wang

Yang

et al. 2022

IET Generation Trans & Dist

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With the large-scale integration of renewable energy into power systems, the strong correlation of wind power output as an important feature will inevitably bring huge impact on power grid planning and operation. However, due to the proliferation of planning and operation scenarios, the traditional power grid planning method can no longer guarantee the operation flexibility and stability under the fluctuation of new energy. In this work, a bi-level transmission and generation expansion planning (TGEP) model considering the correlation of wind power based on mixed integer linear programming (MILP) is proposed. Firstly, a variable-structure point diagnosis method is proposed to improve the fitting accuracy of the correlation. Secondly, a scenario reduction method considering the correlation, joint probability distribution and geometric characteristics is proposed, which effectively provides a more matching operating scenarios basis for planning. Thirdly, a multi-scenario bi-level TGEP model is proposed. The upper-level investment decision model aims at the economical goal, while the lower-level multi-scenario coordinated optimization operation model aims to minimize the weighted operation index. The impact of the correlation on the results of TGEP is examined and verified in case study.

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Fast Cumulant Method for Probabilistic Power Flow Considering the Nonlinear Relationship of Wind Power Generation

Cited by 57 publications

References 28 publications

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Design of Optimal Real Time Emulators for Steam and Wind Turbines for Avoiding Risky Severe Conditions

Variable‐structure copula model for multi‐scenario bi‐level transmission and generation expansion planning

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