Probabilistic multiple distribution static compensator placement and sizing based on the two-point estimate method

Kavousi‐Fard, Abdollah; Akbari-Zadeh, Mohammad-Reza

doi:10.1080/14786451.2013.799470

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…In [16], Monte Carlo‐based genetic algorithm is discussed for uncertainty‐based DG siting and sizing. Although MCS is reliable for an extensive system, due to the high computational burden and high time consumption, analytical methods such as convolution method [15], cumulant‐based method [17], and PEM [18] are popular among the researchers. Among these, the PEM method is fast, accurate, and easy to implement [15] and it can also be able to incorporate the correlation between the random variables.…”

Section: Introductionmentioning

confidence: 99%

Impact of FFC distributed generations in a DNR in the presence of renewable and load uncertainties by mixed‐discrete particle swarm‐based point estimation method

Barik

Das

2019

IET Renewable Power Generation

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This study presents the uncertainty analysis of a distribution network (DNR) caused by unit power output control (UPC) distributed generations (DGs) (solar, wind) and load demand by point estimate method (PEM) based on mixed-discretebased particle swarm optimisation (MDPSO) technique. The uncertainties are taken care by the feeder flow control (FFC) DGs which make the DNR power independent of the main grid, which means the DNR does not exchange any power with the main grid at any load level. To analyse the situation, the load flow technique is modified with introducing PQVδ and zero bus in the system. 2m + 1 and the higher-order PEM methods are applied in this study for uncertainty analysis. The FFC and the UPC DGs are placed and sized by the MDPSO algorithm. The uncertainty analysis of the system is done based on different objective functions and test cases which are the combinations of active power loss, voltage deviation, and the DG operation cost. The proposed method is applied to the 69-bus DNR, and the results are compared with teaching learning-based meta-heuristic optimisation method. The cumulative distribution function and probability density function of the output random variable are approximate with Gram-Charlier expansion method.

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Section: Introductionmentioning

confidence: 99%

Impact of FFC distributed generations in a DNR in the presence of renewable and load uncertainties by mixed‐discrete particle swarm‐based point estimation method

Barik

Das

2019

IET Renewable Power Generation

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“…In Karami et al an optimal placement of DG and D‐STATCOM in distribution systems was presented using GA with multiobjective function of loadability, total investment costs, and network loss. D‐STATCOM placement problem was solved considering uncertainties using modified BAT algorithm. Multiobjective seeker optimization algorithm was proposed in Kumar and Samantaray for designing an advanced power distribution system with D‐STATCOM.…”

Section: Introductionmentioning

confidence: 99%

Optimal coordinate control of OLTC, DG, D-STATCOM, and reconfiguration in distribution system for voltage control and loss minimization

Murty

Sharma

2018

Int Trans Electr Energ Syst

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Summary This paper presents optimal coordination and management of on‐load tap changer (OLTC), distribution static compensator (D‐STATCOM), distribution generation (DG), and reconfiguration to maximize annual cost savings and improve voltage profile using hybrid optimization. An analytical approach is developed to find optimal setting of OLTC. Optimal reconfiguration of the network is derived using genetic algorithm. Optimal location of DG and D‐STATCOM is determined using gravitational search algorithm, and optimal ratings are obtained using interface with MATLAB and GAMS environment. Fourteen realistic case studies are investigated in this paper work. In the first scenario, regulating devices are disabled to monitor the voltage profile and energy loss of the test system. In the remaining cases, influence of OLTC, DG, D‐STATCOM, and reconfiguration is independently and simultaneously studied. In the last scenario, OLTC, DG, D‐STATCOM, and reconfiguration are enabled to maintain voltage security and minimize power losses. Simulation results are obtained for IEEE 69‐bus test system and compared with other existing methods available in the literature. The proposed technique is reliable and efficient to obtain global optimal solution. Simulation results clearly indicate that optimal coordination and management among OLTC, DG, D‐STATCOM, and reconfiguration is required to maximize annual cost savings and maintain voltage at each node as per statutory requirements. Moreover, the analysis has been carried out for time‐varying distribution system with realistic load model.

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“…In Karami et al, an optimal placement of DG and D‐STATCOM in radial distribution systems was presented by using GA with multiobjective function of loadability, total investment costs, and network loss. Distribution Static Synchronous Compensator placement problem solved for radial distribution system considering uncertainties . Two‐point estimate method was applied to forecast uncertainty of the active and reactive loads in the load flow equations.…”

Section: Introductionmentioning

confidence: 99%

Impact of D-STATCOM in distribution systems with load growth on stability margin enhancement and energy savings using PSO and GAMS

Murty

Kumar

2018

Int Trans Electr Energ Syst

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Summary Optimal reactive power deployment as per the statutory provisions of Indian Electricity Grid Codes is essential for better operation of the system. In this paper, a multiobjective optimization problem is solved for sizing and siting of the reactive compensation device. The proposed scheme comprises combination of particle swarm optimization and General Algebraic Modeling System to obtain location and rating of Distribution Static Synchronous Compensator (D‐STATCOM). The main highlights of the proposed work are (i) optimal sizing and siting of D‐STATCOM under load growth and load variations scenario; (ii) voltage stability margin enhancement with D‐STATCOM in a stressed distribution system with load variations and growth; (iii) impact of single and multiple D‐STATCOM units on the losses, voltage profile, and voltage stability margin improvement; and (iv) cost of energy loss savings and annual energy savings. The proposed method is tested on 33‐bus, 69‐bus, and 30‐bus real‐time distribution system of the East Delta Network of the Unified Egyptian network. Benefits to distribution network operator are assessed in reduction in cost of energy loss, annual cost savings, and enhanced voltage stability margin. The results are compared with other methods reported in literature to verify the competency of the proposed multiobjective optimization problem using particle swarm optimization and General Algebraic Modeling System. Annual cost savings of $27 776.97 and $33 297.17 are obtained with proposed technique for 33‐bus and 69‐bus, respectively, which is higher than existing methods. After installation of D‐STATCOM, voltage stability margin enhanced from 0.6956 to 0.75845 for 33‐bus RDS, from 0.68339 to 0.74555 for 69‐bus RDS, and from 0.80223 to 0.83001 for 30‐bus RDS.

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Probabilistic multiple distribution static compensator placement and sizing based on the two-point estimate method

Cited by 10 publications

References 18 publications

Impact of FFC distributed generations in a DNR in the presence of renewable and load uncertainties by mixed‐discrete particle swarm‐based point estimation method

Impact of FFC distributed generations in a DNR in the presence of renewable and load uncertainties by mixed‐discrete particle swarm‐based point estimation method

Optimal coordinate control of OLTC, DG, D-STATCOM, and reconfiguration in distribution system for voltage control and loss minimization

Impact of D-STATCOM in distribution systems with load growth on stability margin enhancement and energy savings using PSO and GAMS

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