A novel approach towards uncertainty modeling in multiobjective optimal power flow with renewable integration

Saha, Anulekha; Bhattacharya, Aniruddha; Das, Priyanath; Chakraborty, Ajoy Kumar

doi:10.1002/2050-7038.12136

Cited by 26 publications

(17 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MCS has been used in [67,68] to handle the uncertainties of Plug-in Hybrid Electric Vehicle (PHEV). However, most of these techniques have drawbacks with the computational time and complexity to handle large number of uncertain variable, MCS takes huge time for calculation; difficulties to predict K-value in K-mean algorithm and complexity in chance constraints [69]. The 2m Point Estimation Method (2m PEM) has advantages over them to model the uncertainties in simple way with less computational time and high accuracy [70].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Placement of electric vehicle charging station and solar distributed generation in distribution system considering uncertainties

2023

Self Cite

View full text Add to dashboard Cite

Electric vehicle (EV) penetration in the transport section is increasing and replacing the conventional fossil fuel based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location in the electrical network affects the energy loss and voltage deviation. Therefore, the test system considered here, is a road network of urban area overlapped with a 33-bus radial network. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation have been attempted simultaneously using 2-layer optimization. Differential Evolution and Harris Hawks Optimization are the two tools have been used to solve the problem and the final results have been validated using eight other established optimization techniques. 2m point estimation method has been used to take care of uncertainties related to EV and PV. Monte-carlo simulation is also applied to cross verify the performance. The land cost, customer accessibility to charging station have been taken into account to allocate it at proper places. The whole work has been performed based on the 24-hrs dynamically varying EV flows and PV outputs.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Most suitable uncertainty handling tool 2m PEM [70,71] is adopted to take care of uncertainties in a better manner with different probable scenarios. The final solution is cross verified with MCS [69] and real data to check the performances. iv) EV flow is a 24 hours event which has not been taken in many of the research work.…”

Section: Introductionmentioning

confidence: 99%

Placement of electric vehicle charging station and solar distributed generation in distribution system considering uncertainties

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the probabilistic load flow (PLF) method is the most widely used technique to accommodate the uncertain parameters, like random load demand and volatile wind power, in power system planning. [39][40][41][42][43] Simulation-based methods, such as Monte Carlo simulation (MCS), 39 analytical method, 40 and approximate method 41,42 , are commonly used techniques to solve PLF. The MCS approach is the most straightforward method, which calculates the uncertainty by simulating the defined process.…”

mentioning

confidence: 99%

“…40 The point estimate method (PEM) is an example of an approximate method, which provides a rough estimate of the statistical properties of random output variables. [41][42][43] Here, the statistical information (ie, mean, variance, and skewness) of the output variables is calculated by a set of deterministic solution. 43 The most significant advantage of PEM is its stability between accuracy and speed.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Estimating the impact of uncertainty on optimum capacitor placement in wind‐integrated radial distribution system

Das

Malakar

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

The unpredictable load variations and raising presence of wind farms have caused modern-day distribution system operation and planning extremely complex. This paper puts an earliest effort toward the optimal allocation of the shunt capacitors in radial distribution system (RDS) under uncertain load and wind power generation. The authors aim to minimize the annual operating cost (AOC) for RDS by deciding the optimum placements of shunt capacitors. Here, three-point estimate method is applied to estimate the uncertainties in the load demand and power output from the wind generator. The stochastic behavior of wind generators and load demands are modeled by using Weibull and normal distribution functions, respectively. An opposition-based competitive swarm optimizer (OCSO) algorithm is applied to minimize the AOC, and the optimization problem is solved for both probabilistic and deterministic approach. The Cornish-Fisher expansion is used in this work to provide more accurate probability distribution functions of AOC. Furthermore, the impact of VAr injection on AOC values and power losses are also studied in this work. Moreover, the results obtained from the OCSO algorithm are compared with the original competitive swarm optimizer results. The proposed problem is tested on 69 bus RDS with different loading conditions, and results are analyzed for each scenario.competitive swarm optimizer, load uncertainty, optimum capacitor allocation, point estimate method, wind generation uncertainty | INTRODUCTIONShunt capacitors (SC) are being used in power distribution system (DS) conventionally to reduce power loss, improve the system voltage, and power factor. The power loss in DS is higher than that of the transmission system because of its List of Symbols and Abbreviations: V W , wind speed; P WT , power output of WGS; Pr, rated power output of WGS; Vr, rated wind speed; Vci, cut-in wind speed; Vco, cut-out wind speed;V w , mean of the wind speed; σ w , SD of the wind speed; P Li , real power demand at the bus "i"; Q Li , reactive power demand at the bus "i"; μ PLi , mean of real power demand; μ QLi , mean of reactive power demand; σ PLi , SD of real power demand; σ QLi , SD of reactive power demand; P μ loss , mean active power loss; Q μ ci, , mean VAr supply cost; nl, number of branches; cb, number of candidate bus; nb, number of load bus; P ss , real power of the substation bus; Q ss , reactive power of the substation bus; P μ w , mean power output from WGS; Q μ ci , mean VAr injection at bus "i"; P μ di , mean demands for real power at bus "i"; Q μ di , mean demands for reactive power at bus "i"; P μ lossj , mean real power loss at branch "j"; Q μ lossj , mean reactive power loss at branch "j"; Q cn , VAr injection at bus "n"; Q cn min , minimum values of Q cn ; Q cn max , maximum values of Q cn ; S li , complex power flow through line "i"; S li max , maximum line limit through line "i".

show abstract

Planning of Electric Vehicle Charging Station with Integration of Renewables in Distribution Network

Pal,

Bhattacharya,

Chakraborty

2022

Energy Systems in Electrical Engineering

View full text Add to dashboard Cite

A novel approach towards uncertainty modeling in multiobjective optimal power flow with renewable integration

Cited by 26 publications

References 47 publications

Placement of electric vehicle charging station and solar distributed generation in distribution system considering uncertainties

Placement of electric vehicle charging station and solar distributed generation in distribution system considering uncertainties

Estimating the impact of uncertainty on optimum capacitor placement in wind‐integrated radial distribution system

Planning of Electric Vehicle Charging Station with Integration of Renewables in Distribution Network

Contact Info

Product

Resources

About