A novel method based on coyote algorithm for simultaneous network reconfiguration and distribution generation placement

Nguyen, Thuan Thanh; Nguyen, Thang Trung; Nguyen, Ngoc Au; Duong, Thanh Long

doi:10.1016/j.asej.2020.06.005

Cited by 42 publications

(24 citation statements)

References 36 publications

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“…Case 5: Feeder performance improvement via simultaneous optimal allocation of DGs and EV fleet considering loss minimization and voltage stability maximization: In this case, the increased loading condition across the network is taken as total power rating of a single EV fleet and optimized its location along with DG locations and sizes. FSA resulted lowest objective function by EV fleet location at 2 and DGs sizes in kW and locations are: 1179(7), 1317 (17), 427 (30), correspondingly the losses (3.6418 kW + j 2.6495 kVAr), lowest voltage at 11th bus is 0.9981 and VSI is 0.9923.…”

Section: Practical 36-bus Residential Feedermentioning

confidence: 99%

“…The study revealed that the DGs with non-unity power factor can improve RDNs performance significantly than the DGs with unity power factor. Coyote optimization algorithm (COA) [30] is proposed for optimal integration of DGs in RDNs considering multi-objective function, formed using real power loss, operating cost and voltage stability. In [31], an improved variant of COA as enhanced coyote optimization algorithm (ECOA) is proposed for solving simultaneous optimal network reconfiguration and DG allocation problem.…”

Section: Introductionmentioning

confidence: 99%

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Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects

Janamala

Kumar²,

Pandraju³

2021

SN Appl. Sci.

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In this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms.

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Section: Practical 36-bus Residential Feedermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects

Janamala

Kumar²,

Pandraju³

2021

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…This algorithm has recently been applied in several applications, especially to feature selection [54], tune heavy-duty gas turbine hyperparameters [55], optimal power flow for transmission power networks [56] define networks reconfiguration [57], and for optimal parameter estimation of a proton exchange membrane fuel cell [58]. Due to the promising potentials results, a search of the literature reveals that the COA has not yet been applied for the CEEMD's hyperparameters definition, then it is adopted.…”

Section: Coyote Optimization Algorithmmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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“…The DSR and CBs placement were concurrently presented in literature based on fuzzy binary gravitational search algorithm [30]; ant colony algorithm [31]. Also, DSR functionality and DGs allocation were concurrently presented using various methods, for example, dataset approach with marine predators algorithm [32]; coyote algorithm [33]; hybrid genetic algorithm, particle swarm optimization and blue whale optimization [34]. In addition, optimal placement of CBs and DGs have been solved using particle swarm optimization [3], water cycle algorithm [35], enhanced grey wolf algorithm [36].…”

Section: Introductionmentioning

confidence: 99%

Effective Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering Reconfiguration Capability by Jellyfish Search Algorithm

et al. 2021

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Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem's complexity, the proposed technique JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.

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A novel method based on coyote algorithm for simultaneous network reconfiguration and distribution generation placement

Cited by 42 publications

References 36 publications

Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects

Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Effective Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering Reconfiguration Capability by Jellyfish Search Algorithm

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