scholarly journals Scenario-Based Network Reconfiguration and Renewable Energy Resources Integration in Large-Scale Distribution Systems Considering Parameters Uncertainty

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 26
Author(s):  
Ziad M. Ali ◽  
Ibrahim Mohamed Diaaeldin ◽  
Shady H. E. Abdel Aleem ◽  
Ahmed El-Rafei ◽  
Almoataz Y. Abdelaziz ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Optimization Approach ◽  
Network Reconfiguration ◽  
Distributed Generations ◽  
Node Distribution ◽  
Power Loss Reduction

Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and load uncertainties are considered in this paper to provide a more realistic mathematical programming model for the optimization problem under study. Case studies are conducted on the 16-, 59-, 69-, 83-, 415-, and 880-node distribution networks, where the 59- and 83-node distribution networks are real distribution networks in Cairo and Taiwan, respectively. The obtained results validate the effectiveness of the proposed optimization approach in maximizing the hosting capacity of DGs and power loss reduction by greater than 17% and 74%, respectively, for the studied distribution networks.

scholarly journals Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1648
Author(s):  
Marinko Barukčić ◽  
Toni Varga ◽  
Vedrana Jerković Jerković Štil ◽  
Tin Benšić
Keyword(s):  
Power System ◽  
Power Management ◽  
Computational Intelligence ◽  
Input Data ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Distributed Generations ◽  
Data Resolution ◽  
The Impact

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

scholarly journals Simultaneous optimal allocation and sizing of distributed generations and shunt capacitors in distribution networks using fuzzy GA methodology

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Srinivasa Rao Gampa ◽  
Debapriya Das
Keyword(s):  
Genetic Algorithm ◽  
Power Factor ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Distributed Generations ◽  
Shunt Capacitors ◽  
Fuzzy Genetic

AbstractThis paper presents a combination of fuzzy and genetic algorithm (GA)-based methodology for simultaneous optimum allocation and sizing of distributed generations (DGs) and shunt capacitors (SCs) together in distribution systems. The objectives of reduction of active power and reactive power supply, reduction of real power loss and improvement of branch current capacity, voltage profile and voltage stability are considered. The combination of shunt capacitors with both unity power factor DGs and lagging power factor DGs also considered for analyzing the performance of the distribution systems. Simulation results are demonstrated to show the advantage of proposed fuzzy genetic algorithm-based technique over conventional multiobjective approach and loss sensitivity-based optimization techniques reported in the literature.

scholarly journals Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4777 ◽  
Author(s):  
Olusayo A. Ajeigbe ◽  
Josiah L. Munda ◽  
Yskandar Hamam
Keyword(s):  
Renewable Energy ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Mixed Integer ◽  
Small Signal ◽  
Small Signal Stability ◽  
Distributed Generations ◽  
Signal Stability ◽  
Dynamic Voltage ◽  
The Impact

This paper solves the allocation planning problem of integrating large scale renewable energy hybrid distributed generations and capacitor banks into the distribution systems. Extraordinarily, the integration of renewable energy hybrid distributed generations such as solar photovoltaic, wind, and biomass takes into consideration the impact assessment of variable generations from PV and wind on the distribution networks’ long term dynamic voltage and small-signal stabilities. Unlike other renewable distributed generations, the variability of power from solar PV and wind generations causes small-signal instabilities if they are sub-optimally allocated in the distribution network. Hence, the variables related to small-signal stability are included and constrained in the model, unlike what is obtainable in the current works on the planning of optimal allocation of renewable distributed generations. Thus, the model is motivated to maximize the penetration of renewable powers by minimizing the net present value of total cost, which includes investment, maintenance, energy, and emission costs. Consequently, the optimization problem is formulated as a stochastic mixed integer linear program, which ensures limited convergence to optimality. Numerical results of the proposed model demonstrate a significant reduction in electricity and emission costs, enhancement of system dynamic voltage and small-signal stabilities, as well as improvement in welfare costs and environmental goodness.

Optimal Allocation of Energy Storage System in Distribution Systems with Intermittent Renewable Energy

2021 ◽  
Vol 19 (02) ◽  
pp. 288-296
Author(s):  
Luiz Renato Braz Pontes ◽  
Yuri Percy Molina Rodriguez ◽  
Jaime Luyo Kuong ◽  
Hugo Rojas Espinoza
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Storage System ◽  
Energy Storage System

scholarly journals Optimal Energy Storage System Positioning and Sizing with Robust Optimization

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 512 ◽  
Author(s):  
Nayeem Chowdhury ◽  
Fabrizio Pilo ◽  
Giuditta Pisano
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Robust Optimization ◽  
Distribution Systems ◽  
Optimal Power Flow ◽  
Distributed Storage ◽  
Distribution Networks ◽  
Fine Tuning ◽  
Optimal Placement ◽  
Storage Devices

Energy storage systems can improve the uncertainty and variability related to renewable energy sources such as wind and solar create in power systems. Aside from applications such as frequency regulation, time-based arbitrage, or the provision of the reserve, where the placement of storage devices is not particularly significant, distributed storage could also be used to improve congestions in the distribution networks. In such cases, the optimal placement of this distributed storage is vital for making a cost-effective investment. Furthermore, the now reached massive spread of distributed renewable energy resources in distribution systems, intrinsically uncertain and non-programmable, together with the new trends in the electric demand, often unpredictable, require a paradigm change in grid planning for properly lead with the uncertainty sources and the distribution system operators (DSO) should learn to support such change. This paper considers the DSO perspective by proposing a methodology for energy storage placement in the distribution networks in which robust optimization accommodates system uncertainty. The proposed method calls for the use of a multi-period convex AC-optimal power flow (AC-OPF), ensuring a reliable planning solution. Wind, photovoltaic (PV), and load uncertainties are modeled as symmetric and bounded variables with the flexibility to modulate the robustness of the model. A case study based on real distribution network information allows the illustration and discussion of the properties of the model. An important observation is that the method enables the system operator to integrate energy storage devices by fine-tuning the level of robustness it willing to consider, and that is incremental with the level of protection. However, the algorithm grows more complex as the system robustness increases and, thus, it requires higher computational effort.

scholarly journals Symbiotic Organism Search Algorithm for Power Loss Minimization in Radial Distribution Systems by Network Reconfiguration and Distributed Generation Placement

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Tung Tran The ◽  
Sy Nguyen Quoc ◽  
Dieu Vo Ngoc
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Symbiotic Organism Search

This paper proposes the Symbiotic Organism Search (SOS) algorithm to find the optimal network configuration and the placement of distributed generation (DG) units that minimize the real power loss in radial distribution networks. The proposed algorithm simulates symbiotic relationships such as mutualism, commensalism, and parasitism for solving the optimization problems. In the optimization process, the reconfiguration problem produces a large number of infeasible network configurations. To reduce these infeasible individuals and ensure the radial topology of the network, the graph theory was applied during the power flow. The implementation of the proposed SOS algorithm was carried out on 33-bus, 69-bus, 84-bus, and 119-bus distribution networks considering seven different scenarios. Simulation results and performance comparison with other optimization methods showed that the SOS-based approach was very effective in solving the network reconfiguration and DG placement problems, especially for complex and large-scale distribution networks.

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