Bayesian Probabilistic Power Flow Analysis Using Jacobian Approximate Bayesian Computation

Zuluaga, Carlos D.; Álvarez, Mauricio A.

doi:10.1109/tpwrs.2018.2810641

Cited by 17 publications

(6 citation statements)

References 24 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PDF curves of generated power and bus loads are symmetric on all buses in our case, as shown by the solid lines in Figure 3. The curves have the same form as the IEEE 118 bus test in reference [2] and the IEEE 39 bus test in reference [24].…”

Section: Probabilistic Power Flowmentioning

confidence: 99%

“…PLF has been considerably developed by utilizing stochastics [12], [13], [21]- [24], and machine learning [25] methods to achieve accurate random values. Recent research on PLF, i.e., Nosratabadi et al [26] combined the Monte Carlo simulation (MCS) method with the Halton Quasi to get random values that vary with the same level of precision as the Latin hypercube.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic load flow based voltage stability assessment of solar power integration into power grids

Rehiara,

Bawan,

Palintin

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

The interconnection of renewable energies increases the complexity of modern power systems. Hence, stability assessments should be made to ensure the system’s stability after penetration. Solar power is a type of renewable energy that has become a widespread energy source among renewable energy sources. About 1 MWp of the solar power plant has been prepared to be interconnected to the IEEE 8 bus of Manokwari grid, and this paper investigates the voltage profile, power losses, and stability of the solar power plant penetration using an adaptive kernel density estimator (AKDE) and compares it to a Monte Carlo simulation (MCS)-based probabilistic load flow (PLF). About 5000 samples have been used to test the grid after the connection. Results of simulations show that the solar penetration can reduce power losses from 0.4084 MW to 0.3080 MW and 0.3045 MW by the proposed method and MCS method, respectively, and further increase the bus voltage profile. The power network has the stability to be connected to solar power, as indicated by the small stability index values of each bus. The proposed method using the AKDE method has a more accurate result in stability indices indicated by small fast voltage stability index (FVSI), line stability index (Lmn), and line stability factor (LQP) indices.

show abstract

Section: Probabilistic Power Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic load flow based voltage stability assessment of solar power integration into power grids

Rehiara,

Bawan,

Palintin

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Error! Reference source not found.For example, the authors of [25] treat the POPF problem as a probabilistic inference model using Bayesian inference. Elsewhere, the authors of [26] provide a novel POPF model that copes with uncertainties, considering electrical power generation from a wind turbine.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…At present, there are mainly two kinds of approaches for handling uncertainty of wind power in power flow equations: the probabilistic power flow [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] and the interval power flow [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. As for probabilistic power flow, the uncertainty of output generation from a wind farm can be defined as random with description of some probabilistic distributions, which will be utilized to build the probabilistic power flow model of power grids.…”

Section: Introductionmentioning

confidence: 99%

“…As for probabilistic power flow, the uncertainty of output generation from a wind farm can be defined as random with description of some probabilistic distributions, which will be utilized to build the probabilistic power flow model of power grids. Three methods are generally used for dealing with the probabilistic power flow model, i.e., the analytical method [2][3][4][5][6], the point estimate method [7][8][9][10][11][12], and Monte Carlo simulation (MCS) [13][14][15][16]. Monte Carlo simulation produces a series of samples and obtains the load flow results under every single sample.…”

Section: Introductionmentioning

confidence: 99%

Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method

Cheng

Shi

et al. 2018

Energies

View full text Add to dashboard Cite

Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly all the time. The uncertainty of wind power causes variation of the variables of power grids, which threatens the power grids’ operating security. Therefore, it is significant to provide the accurate ranges of power grids’ variables, which can be used by the operators to guarantee the power grid’s operating security. To achieve this goal, the present paper puts forward the interval power flow with wind farms model, where the generation power outputs of wind farms are expressed by intervals and three types of control modes are considered for imitating the operation features of wind farms. To solve the proposed model, the affine arithmetic-based method and optimizing-scenarios method are modified and employed, where three types of constraints of wind control modes are considered in their solution process. The former expresses the interval variables as affine arithmetic forms, and constructs optimization models to contract the affine arithmetic forms to obtain the accurate intervals of power flow variables. The latter regards active power outputs of the wind farms as variables, which vary in their corresponding intervals, and accordingly builds the minimum and maximum programming models for estimating the intervals of the power flow variables. The proposed methods are applied to two case studies, where the acquired results are compared with those acquired by the Monte Carlo simulation, which is a traditional method for handling interval uncertainty. The simulation results validate the advantages, effectiveness and the applicability of the two methods.

show abstract

Bayesian Probabilistic Power Flow Analysis Using Jacobian Approximate Bayesian Computation

Cited by 17 publications

References 24 publications

Probabilistic load flow based voltage stability assessment of solar power integration into power grids

Probabilistic load flow based voltage stability assessment of solar power integration into power grids

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

Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method

Contact Info

Product

Resources

About