scholarly journals Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network

2021 ◽  
Vol 3 (2) ◽  
pp. 409-423
Author(s):  
Ayman Awad ◽  
Hussein Abdel-Mawgoud ◽  
Salah Kamel ◽  
Abdalla A. Ibrahim ◽  
Francisco Jurado
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Optimal Allocation ◽  
Hybrid Technique ◽  
Radial Distribution System ◽  
Hybrid Optimization Algorithm ◽  
Power Quality Improvement ◽  
Sine Cosine Algorithm ◽  
Optimized Allocation ◽  
The Impact

Distributed generation (DG) is becoming a prominent key spot for research in recent years because it can be utilized in emergency/reserve plans for power systems and power quality improvement issues, besides its drastic impact on the environment as a greenhouse gas (GHG) reducer. For maximizing the benefits from such technology, it is crucial to identify the best size and location for DG that achieves the required goal of installing it. This paper presents an investigation of the optimized allocation of DG in different modes using a proposed hybrid technique, the tunicate swarm algorithm/sine-cosine algorithm (TSA/SCA). This investigation is performed on an IEEE-69 Radial Distribution System (RDS), where the impact of such allocation on the system is evaluated by NEPLAN software.

scholarly journals A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Varaprasad Janamala
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Optimal Allocation ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Voltage Profile ◽  
Pv System ◽  
Solar Photovoltaic System ◽  
The Impact

AbstractA new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

scholarly journals Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6532
Author(s):  
Vahab Rostampour ◽  
Thom S. Badings ◽  
Jacquelien M. A. Scherpen
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Distribution System ◽  
Large Scale ◽  
Wind Power Generation ◽  
Forecast Errors ◽  
Monte Carlo Simulation Study ◽  
System Operator ◽  
The Impact

We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements.

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