Sizing and Placement of Battery-Sourced Solar Photovoltaic (B-SSPV) Plants in Distribution Networks

2018 ◽  
pp. 220-251
Author(s):  
Abid Ali ◽  
Nursyarizal Mohd Nor ◽  
Taib Ibrahim ◽  
Mohd Fakhizan Romlie ◽  
Kishore Bingi
Keyword(s):  
Total Energy ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Weather Data ◽  
Solar Photovoltaic ◽  
Integer Optimization ◽  
Battery Storage ◽  
Voltage Dependent ◽  
Loss Index ◽  
Bus Voltage

This chapter proposes a mixed-integer optimization using genetic algorithm (MIOGA) for determining the optimum sizes and placements of battery-sourced solar photovoltaic (B-SSPV) plants to reduce the total energy losses in distribution networks. Total energy loss index (TELI) is formulated as the main objective function and meanwhile bus voltage deviations and PV penetrations of B-SSPV plants are calculated. To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired for different time varying voltage dependent load models. From the results, it is known that, compared to PV only, the integration of B-SSPV plants in the distribution networks resulted in higher penetration levels in distribution networks. The proposed algorithm was very effective in terms of determining the sizes of the PV plant and the battery storage, and for the charging and discharging of the battery storage.

Sizing and Placement of Battery-Sourced Solar Photovoltaic (B-SSPV) Plants in Distribution Networks

2021 ◽  
pp. 1123-1154
Author(s):  
Abid Ali ◽  
Nursyarizal Mohd Nor ◽  
Taib Ibrahim ◽  
Mohd Fakhizan Romlie ◽  
Kishore Bingi
Keyword(s):  
Total Energy ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Weather Data ◽  
Solar Photovoltaic ◽  
Integer Optimization ◽  
Battery Storage ◽  
Voltage Dependent ◽  
Loss Index ◽  
Bus Voltage

This chapter proposes a mixed-integer optimization using genetic algorithm (MIOGA) for determining the optimum sizes and placements of battery-sourced solar photovoltaic (B-SSPV) plants to reduce the total energy losses in distribution networks. Total energy loss index (TELI) is formulated as the main objective function and meanwhile bus voltage deviations and PV penetrations of B-SSPV plants are calculated. To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired for different time varying voltage dependent load models. From the results, it is known that, compared to PV only, the integration of B-SSPV plants in the distribution networks resulted in higher penetration levels in distribution networks. The proposed algorithm was very effective in terms of determining the sizes of the PV plant and the battery storage, and for the charging and discharging of the battery storage.

Big Data Storage for the Modeling of Historical Time Series Solar Irradiations

2018 ◽  
pp. 433-463
Author(s):  
Abid Ali ◽  
Nursyarizal Mohd Nor ◽  
Taib Ibrahim ◽  
Mohd Fakhizan Romlie ◽  
Kishore Bingi
Keyword(s):  
Big Data ◽  
Energy Loss ◽  
Data Analytics ◽  
Optimization Problem ◽  
Big Data Analytics ◽  
Distribution Networks ◽  
Data Sets ◽  
Voltage Dependent ◽  
Loss Index ◽  
Bus Voltage

This chapter proposes Big Data Analytics for the sizing and locating of solar photovoltaic farms to reduce the total energy loss in distribution networks. The Big Data Analytics, which uses the advance statistical and computational tools for the handling of large data sets, has been adopted for modeling the 15 years of solar weather data. Total Power Loss Index (TPLI) is formulated as the main objective function for the optimization problem and meanwhile bus voltage deviations and penetrations of the PV farms are calculated. To solve the optimization problem, this study adopts the Mixed Integer Optimization using Genetic Algorithm (MIOGA) technique. By considering different time varying voltage dependent load models, the proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired. From the results, it is revealed that compared to single PV farm, the integration of two PV farms reduced more energy loss and reduced the total size of PV farms. Big Data Analytics is found very effective for the storing, handling, processing and the visualizing of the weather Big Data.

Nonlinear and Mixed-Integer Optimization

1995 ◽  
Author(s):  
Christodoulos A. Floudas
Keyword(s):  
Nonlinear Optimization ◽  
Systems Engineering ◽  
Chemical Engineering ◽  
Applied Mathematics ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Process Synthesis ◽  
Synthesis Process ◽  
Integer Optimization ◽  
Mixed Integer Optimization

Filling a void in chemical engineering and optimization literature, this book presents the theory and methods for nonlinear and mixed-integer optimization, and their applications in the important area of process synthesis. Other topics include modeling issues in process synthesis, and optimization-based approaches in the synthesis of heat recovery systems, distillation-based systems, and reactor-based systems. The basics of convex analysis and nonlinear optimization are also covered and the elementary concepts of mixed-integer linear optimization are introduced. All chapters have several illustrations and geometrical interpretations of the material as well as suggested problems. Nonlinear and Mixed-Integer Optimization will prove to be an invaluable source--either as a textbook or a reference--for researchers and graduate students interested in continuous and discrete nonlinear optimization issues in engineering design, process synthesis, process operations, applied mathematics, operations research, industrial management, and systems engineering.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

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