Calculating the locational marginal price and solving optimal power flow problem based on congestion management using GA-GSF algorithm

Dashtdar, Masoud; Najafi, Mohammad; Esmaeilbeig, Mostafa

doi:10.1007/s00202-020-00974-z

Cited by 38 publications

(17 citation statements)

References 20 publications

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“…Today, fossil fuel restrictions and air pollution have created an incentive to use distributed generation resources and expand the technology. Power generation near the place of consumption, in addition to reducing system losses, can provide more flexibility to provide diverse services to Based on Battery and MPPT Control consumers [21][22]. One of the views proposed to effectively increase the contribution of these resources is the integration of these resources with the objectives of visibility, the proper connection between these resources and the electricity network, as well as the more efficient control of these resources.…”

Section: Discussionmentioning

confidence: 99%

Voltage-Frequency Control (v-f) of Islanded Microgrid Based on Battery and MPPT Control

Dashtdar¹,

Dashtdar²

2020

AJECE

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With the development and increasing influence of the use of distributed generation resources, distribution networks have changed from passive networks to active networks. In these new networks, operation on an island will increase both the reliability of the network in the competitive market and the maximum utilization of distributed generation resources. But despite the various benefits, Microgrids with distributed generation resources also have disadvantages. One of the disadvantages is the increasing complexity of control systems, protection systems and operating systems of the global distribution network. In recent research, various methods have been proposed for controlling the Microgrids, especially voltage and frequency control. This paper presents a method for controlling a photovoltaic (PV) system with maximum power point tracking (MPPT) controller and battery storage to provide voltage-frequency (v-f) support in an islanded microgrid. It introduces a new algorithm for MPPT control that offers control strategies, effective coordinated between v-f control in inverter, MPPT control, and battery storage control. Finally, the proposed scheme is implemented on the IEEE 13-bus distribution feeder in islanded mode using MATLAB software, the results of which clearly demonstrate the efficiency of the control methods.

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Section: Discussionmentioning

confidence: 99%

Voltage-Frequency Control (v-f) of Islanded Microgrid Based on Battery and MPPT Control

Dashtdar¹,

Dashtdar²

2020

AJECE

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“…It is observing that multiple DG placement is more beneficial because the profit increases by 25.3% in two-DG placements and 33.2% in three-DG placements compared to single-DG placements. On the other hand, active and reactive losses, as seen in Table (3) and Table (4), are further reduced compared to single-DG placement (losses compared to single-DG placement, in the placement of two-DG units 16.1% and the placement of the three-DG units decreased by 28.2%). The amount of reduction in the active power node price in the buses with the connection of one DG, two DG, and three DG compared to without DG in Figure (8).…”

Section: Multiple Dg Placementmentioning

confidence: 99%

Placement and Optimal Size of DG in the Distribution Network Based on Nodal Pricing Reduction with Nonlinear Load Model using the IABC Algorithm

Dashtdar¹,

Najafi²,

Esmaeilbeig³

2021

Preprint

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The growing use of distributed generation (DG) at the distribution level has led to a change in the status of distribution networks from a passive network to an active network such as transmission systems. Therefore, transmission network pricing methods such as nodal pricing could be used in the distribution network. DG connection to the distribution network affects bus nodal pricing. If the DG presence reduces losses and congestion in the distribution network, nodal pricing will also decrease. This paper presents a method for calculating the optimal size and place of DG in the distribution network based on nodal pricing. This planning is doing to maximize the profits of distribution companies that have used DG in their network to meet several advantages. The simulation was performing using the improved artificial bee colony algorithm (IABC). In the IABC algorithm, by exchanging the received information between bees according to Newton and gravity laws, it uses all this algorithm capacity to find the ideal answer by considering the constraints applied to the system. In most DG placement articles, network loads are assuming to constant. Because loads are often sensitive to voltage and frequency, constant load analysis leads to inaccurate results. Therefore, in this paper, the proposed method is implementing on a 38-bus radial distribution system with a model of real loads sensitive to the voltage and frequency of the system, including residential, commercial, and industrial loads.

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“…In this article, the effect of UPQC presence in the network on THD will be investigated. Therefore, the value of THD based on mains voltage can be calculated by Equation (14).…”

Section: Total Harmonic Distortionmentioning

confidence: 99%

Improving voltage profile and reducing power losses based on reconfiguration and optimal placement of UPQC in the network by considering system reliability indices

Dashtdar

Bajaj

Hosseinimoghadam

et al. 2021

Int Trans Electr Energ Syst

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Distribution networks have problems such as an increase in active losses, overload on distribution substations, voltage drop, decrease in network reliability, increase in load unbalance and unbalance in line current due to variable characteristics in terms of load consumption. In this article, to reduce losses, improve the voltage profile and increase the reliability of the system, the UPQC (Unified Power Quality Controller) optimal placement and reconfiguration method have been used in the distribution network. The two objective functions of losses and the mean voltage deviation are defined by considering the reliability indices. Since the reconfiguration of distribution networks is a large-scale nonlinear hybrid optimization problem with several constraints, the genetic algorithm combines genes including key numbers and UPQC location, UPQC series power size, UPQC shunt power size used to solve the problem. The proposed method is implemented in three steps on IEEE 33-bus and 69-bus standard networks by considering the load model List of Symbols and Abbreviations: agr, agriculture load; com, commercial load; C i , number of loads connected to the ith bus; D, load coefficient; F1, F2, objective functions; fraci,k, the amount of load lost in the ith bus; gen, general load; I EAF_a , main current component of a phase; Ii, current of feeders; Ii, input current to bus i; ind, industrial load; i p , reference current phase component for active power; i q , reference current phase component for reactive power; I ref_a , current reference of the a phase; I s , source current; n, number of buses; P 0i , Q 0i , active and reactive power at a per-unit voltage of bus i; Pi, Q i , active and reactive power passing through branch i; P ref , reference active power; P rk , probability of losing the ith bus; P total,loss , total active network losses; Q a , reactive power of a phase; Q L , reactive power demand; res, residential load; ri, resistance of branch i; S, tnumber of UPQCs installed in the network; V DC_ref , treference DC voltage; V i , voltage of Bus i; V trans_a , effective value of the voltage of the a phase; V s , source voltage; Y ij , admittance matrix; Z s , source impedance; Δδ i , changes in voltage angle of bus, changes in voltage magnitude of bus i; ΔP i , changes in active power of bus i; ΔQ i , changes in reactive power of bus i; γ, τ, active and reactive charge change coefficient; ACS, ant colony search; ACIFi, average fixed number of each customer (1/a) per year; AENS, average energy not supplied per customer; AI

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Calculating the locational marginal price and solving optimal power flow problem based on congestion management using GA-GSF algorithm

Cited by 38 publications

References 20 publications

Voltage-Frequency Control (v-f) of Islanded Microgrid Based on Battery and MPPT Control

Voltage-Frequency Control (v-f) of Islanded Microgrid Based on Battery and MPPT Control

Placement and Optimal Size of DG in the Distribution Network Based on Nodal Pricing Reduction with Nonlinear Load Model using the IABC Algorithm

Improving voltage profile and reducing power losses based on reconfiguration and optimal placement of UPQC in the network by considering system reliability indices

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