Surrogate-Assisted Multi-Objective Probabilistic Optimal Power Flow for Distribution Network With Photovoltaic Generation and Electric Vehicles

Srithapon, Chitchai; Fuangfoo, Pradit; Ghosh, Prasanta Kumar; Siritaratiwat, Apirat; Chatthaworn, Rongrit

doi:10.1109/access.2021.3061471

Cited by 27 publications

(18 citation statements)

References 58 publications

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“…where Z i is the normalized data, F i is the feature vectors, F min and F max are the feature vectors having the minimum and maximum value. Therefore, the overall feature set can be obtained using Equation (4).…”

Section: Feature Vectormentioning

confidence: 99%

“…Electricity is widely regarded as a critical factor in economic growth, as increased usage is required to serve a diverse range of load types, including industrial, commercial, and residential customers [1][2][3][4]. Renewable energy sources have been widely used to improve the capacity of electrical generation, particularly in the last few decades [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

et al. 2021

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Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

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Section: Feature Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

et al. 2021

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“…For example, in references [20]- [22], authors applied the PPF using Monte Carlo Simulation (MCS) method to create the scenarios. Moreover, Srithapon et al [23] presented Zhao's Point Estimation Method combined with Nataf transformation (PEMN). Numerical results show that the PEMN can provide a high confidence level to guarantee OEM solutions.…”

Section: Introductionmentioning

confidence: 99%

Electric Distribution Network With Multi-Microgrids Management Using Surrogate-Assisted Deep Reinforcement Learning Optimization

2022

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Increasing of electric vehicle demand and uncertain electricity generation from solar photovoltaics lead to poor reliability in the Distribution Network (DN). MicroGrids (MG) connecting to the DN with suitable energy trading is an effective way to solve this issue. However, suitable energy trading considering grid constraints with a low computational burden is a challenge for solving Optimal Energy Management (OEM). Therefore, this paper proposes an OEM using a surrogate which is modeled based on a Deep Neural Network to assist the Deep Reinforcement Learning Optimization for reducing the computational burden. The proposed method is applied to a bi-level OEM for multi-MGs connected in the DN with real-time pricing consideration, represented as the proposed strategy. The surrogate is modeled to predict the power system parameters of the MG based on probabilistic power flow, whereas a deterministic power flow is applied to evaluate the parameters of DN. To validate and demonstrate the proposed method and strategy, the modified IEEE-33 test feeder and a residential distribution system which are defined as the DN and MG, respectively are used to test. Simulation results show that the proposed method can reduce computational burden by 89.23% compared with the Differential Evolution algorithm. Moreover, the proposed strategy provides the optimal purchased energy price of DN offering to each MG which can reduce the cost of energy purchased from the main grid resulting in lower operating cost of DN.

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“…Steady-state transformer models are frequently employed in power system load flow simulations and other applications in which component modeling can be carried out in quasi -stationary regime [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Different transformer models can be selected according to frequency range [ 10 , 11 , 12 ] and the required trade-off between precision and complexity.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Steady-State Power Transformer Modeling Considering Direct Current Resistance Test Measurements

Corrêa

Vieira

2021

Sensors

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Measurements obtained in transformer tests are routinely used for computing associated steady-state model parameters, which can then be used for load flow simulation and other modeling applications. The short circuit and open circuit tests are most commonly performed with this purpose, allowing estimation of series and parallel branch transformer parameters. In this study, an extended model is proposed which does not employ the usually assumed cantilever circuit approximation and explicitly accounts for transformer connection resistance. An estimation of the proposed model parameters is enabled by usage of additional measurements yielded by the direct current (DC) resistance test. The proposed approach is validated by means of an experiment carried out on a real distribution power transformer, whose results demonstrate that the proposed model and parameter computation approach effectively decompose total transformer resistance into winding and contact components. Furthermore, the numerical results show that contact resistance is not negligible especially for low voltage windings, which reinforces the usefulness of the proposed model in providing detailed modeling of transformer resistances.

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Surrogate-Assisted Multi-Objective Probabilistic Optimal Power Flow for Distribution Network With Photovoltaic Generation and Electric Vehicles

Cited by 27 publications

References 58 publications

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

Electric Distribution Network With Multi-Microgrids Management Using Surrogate-Assisted Deep Reinforcement Learning Optimization

An Approach to Steady-State Power Transformer Modeling Considering Direct Current Resistance Test Measurements

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