Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits

Hassan, Ahmed S.; Othman, ElSaeed A.; Bendary, Fahmy; Ebrahim, Mohamed A.

doi:10.1016/j.egyr.2020.12.004

Cited by 58 publications

(21 citation statements)

References 25 publications

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“…Here, we adopt an optimization methodology composed of three steps to present the optimal solution of the optimization problem defined in (1) to (9). The three steps are as follows: (i) solving the optimization model with the aid of GAMS software; (ii) validating the solution using a recursive solution of the power flow problem by increasing the load consumption using loop cycles; and (iii) validating the results in DigSILENT software by implementing a program in DigSILENT Programming Language (DPL).…”

Section: Solution Methodologymentioning

confidence: 99%

“…The optimization model defined in (1) to ( 9) can be interpreted as follows: Equation (1) corresponds to the objective function regarding the simultaneous maximization of the active power consumption in all the nodes in the network; Equations ( 2) and (3) outline the application of Tellegen's theorem to all the nodes in the network (i.e., the apparent power definition to each node) [36], which generates the classical active and reactive power balance constraints; inequality constraint (4) defines the voltage regulation limits in all the nodes of the grid (this constraint is generally defined by regulatory policies and utility operative practices); inequality constraint (5) limits the maximum size of the dispersed generator that can be connected at node i; inequality constraint (6) defines the maximum number of dispersed generators that can be installed along the distribution network; inequality constraint (7) defines the maximum percentage of dispersed generation penetration in the entire distribution network; restriction (8) indicates the binary nature of the decision variable x i ; and inequality constraint (9) represents the positiveness definition of the loadability coefficient.…”

Section: Model Interpretationmentioning

confidence: 99%

“…The extant literature presents several methodologies to address the energy losses based on the optimal location of shunt devices and grid topology variations. These methods include optimal location of dispersed generators [5,6], optimal location of capacitor banks [7,8], optimal location of distribution static compensators [9,10], optimal integration of battery energy storage systems [11,12], and grid reconfiguration [13,14], among other strategies. The distinctive characteristic of these approaches is the usage of advanced optimization techniques to improve the grid performance, most of which are from the family of metaheuristic optimization techniques because of their ease of implementation with a master-slave structure using simple power flow formulations [15].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Integration of Dispersed Generation in Medium-Voltage Distribution Networks for Voltage Stability Enhancement

et al. 2022

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This study addresses the problem of the maximization of the voltage stability index (λ-coefficient) in medium-voltage distribution networks considering the optimal placement and sizing of dispersed generators. The problem is formulated through a mixed-integer nonlinear programming model (MINLP), which is solved using General Algebraic Modeling System (GAMS) software. A numerical example with a 7-bus radial distribution network is employed to introduce the usage of GAMS software to solve the proposed MINLP model. A new validation methodology to verify the numerical results provided for the λ-coefficient is proposed by using recursive power flow evaluations in MATLAB and DigSILENT software. The recursive evaluations allow the determination of the λ-coefficient through the implementation of the successive approximation power flow method and the Newton–Raphson approach, respectively. It is effected by fixing the sizes and locations of the dispersed sources using the optimal solution obtained with GAMS software. Numerical simulations in the IEEE 33- and 69-bus systems with different generation penetration levels and the possibility of installing one to three dispersed generators demonstrate that the GAMS and the recursive approaches determine the same loadability index. Moreover, the numerical results indicate that, depending on the number of dispersed generators allocated, it is possible to improve the λ-coefficient between 20.96% and 37.43% for the IEEE 33-bus system, and between 18.41% and 41.98% for the IEEE 69-bus system.

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Section: Solution Methodologymentioning

confidence: 99%

Section: Model Interpretationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Integration of Dispersed Generation in Medium-Voltage Distribution Networks for Voltage Stability Enhancement

et al. 2022

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“…In both conventional and current electrical systems, different technical and economic aspects that directly affect network operators and users must be enhanced, including their high investment and operating costs, high levels of energy loss associated with energy transportation, and high levels of pollution due to the implementation of power generation sources based on fossil fuels [1]. To address these issues, the electrical sector and several authors have recently focused on developing and promoting various strategies [2]. Some of these strategies are the reconfiguration of electrical networks [3] and the integration of new technologies such as reactive compensators (e.g., ultracapacitors and super inductors) [4,5], energy storage elements [6], and Distributed Generation (DG) sources based on renewable energy resources [7].…”

Section: General Contextmentioning

confidence: 99%

Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

et al. 2021

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In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

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“…Owing to the high energy losses in distribution networks, utilities implement different methodologies to reduce them as much as possible. Some recurrent strategies are the optimal location of fixed-step capacitor banks [6][7][8]; optimal grid reconfiguration [9,10]; optimal siting and sizing of dispersed generators [11,12]; optimal siting and sizing of battery energy storage systems [13,14]; and reactive power compensation via distribution static compensators, i.e., STATCOMs [15][16][17]. In the case of fixed-step capacitor banks, these are attractive technologies due to their low cost and high reliability; however, these devices inject reactive power in discrete steps, which implies that, due to the daily load variability, the total grid power losses are not completely minimized.…”

Section: Introductionmentioning

confidence: 99%

An Approximate Mixed-Integer Convex Model to Reduce Annual Operating Costs in Radial Distribution Networks Using STATCOMs

2021

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The problem of optimal siting and sizing of distribution static compensators (STATCOMs) is addressed in this research from the point of view of exact mathematical optimization. The exact mixed-integer nonlinear programming model (MINLP) is decoupled into two convex optimization sub-problems, named the location problem and the sizing problem. The location problem is addressed by relaxing the exact MINLP model, assuming that all the voltages are equal to 1∠0∘, which allows obtaining a mixed-integer quadratic programming model as a function of the active and reactive power flows. The solution of this model provides the best set of nodes to locate all the STATCOMs. When all the nodes are selected, it solves the optimal reactive power problem through a second-order cone programming relaxation of the exact optimal power flow problem; the solution of the SOCP model provides the optimal sizes of the STATCOMs. Finally, it refines the exact objective function value due to the intrinsic non-convexities associated with the costs of the STATCOMs that were relaxed through the application of Taylor’s series expansion in the location and sizing stages. The numerical results in the IEEE 33- and 69-bus systems demonstrate the effectiveness and robustness of the proposed optimization problem when compared with large-scale MINLP solvers in GAMS and the discrete-continuous version of the vortex search algorithm (DCVSA) recently reported in the current literature. With respect to the benchmark cases of the test feeders, the proposed approach reaches the best reductions with 14.17% and 15.79% in the annual operative costs, which improves the solutions of the DCVSA, which are 13.71% and 15.30%, respectively.

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Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits

Cited by 58 publications

References 25 publications

Optimal Integration of Dispersed Generation in Medium-Voltage Distribution Networks for Voltage Stability Enhancement

Optimal Integration of Dispersed Generation in Medium-Voltage Distribution Networks for Voltage Stability Enhancement

Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

An Approximate Mixed-Integer Convex Model to Reduce Annual Operating Costs in Radial Distribution Networks Using STATCOMs

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