scholarly journals Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2981 ◽  
Author(s):  
Mohammad Seydali Seyf Abad ◽  
Jin Ma ◽  
Ahmad Ahmadyar ◽  
Hesamoddin Marzooghi
Keyword(s):  
Robust Optimization ◽  
Distributed Generation ◽  
Optimization Model ◽  
Distribution Systems ◽  
Historical Data ◽  
Distribution Networks ◽  
Distributionally Robust Optimization ◽  
Distributed Generations ◽  
Distributionally Robust ◽  
The Impact

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

2021 ◽  
Author(s):  
Lei Xu ◽  
Tsan Sheng (Adam) Ng ◽  
Alberto Costa
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Rail Transit ◽  
Distributionally Robust Optimization ◽  
Performance Function ◽  
Transit Networks ◽  
Disruption Tolerance ◽  
Distributionally Robust

In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.

scholarly journals Impacts of the Inclusion of Distributed Generation on Congestion of Distribution Networks and in the Islanding Operation Capability

2021 ◽  
Vol 2 (2) ◽  
pp. 15-22
Author(s):  
Jose David Beltrán Gallego ◽  
Leidy Daniela Castro Montilla ◽  
Alexandra Castro Valencia ◽  
Camilo Augusto Giraldo Muñoz ◽  
Dahiana López García
Keyword(s):  
Power Systems ◽  
Distributed Generation ◽  
Distribution Systems ◽  
Distribution Networks ◽  
Support Service ◽  
Transport Systems ◽  
Ancillary Service ◽  
Islanding Operation ◽  
Generation Capacity ◽  
The Impact

The growing demand for electricity in the world has led to power systems having to constantly increase their generation capacity and expand their transmission and distribution systems. Consequently, distributed generation has positioned as a technology able to integrate generation close to consumption centers, freeing up capacity in the transport systems, which can be translated into a deferral of investments in network expansion. Therefore, this paper analyzes the impact of the inclusion of distributed generation in the congestion of a typical distribution network and evaluates the potential of providing the island operation capability ancillary service in a section of the system to identify the possible challenges and benefits that the development of this technical support service could have in typical Colombian distribution networks.

Investment Analysis of Network Reinforcements Mix for Distribution Systems with Distributed Generations

2012 ◽  
Vol 433-440 ◽  
pp. 1730-1734
Author(s):  
Chun Lien Su
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Distribution Network ◽  
Cost Effective ◽  
Distribution Networks ◽  
Investment Analysis ◽  
Distributed Generations ◽  
Network Connection ◽  
Effective Network ◽  
The Impact

New trends of environmental, regulation, and economical aspects cause an increasingly distributed generation (DG) connected to distribution networks. Existing distribution networks, however, are not designed to accept extensive DG. Many novel network reinforcement approaches have been proposed for solving the network connection issues. For economic viewpoints, it is important for determining cost-effective network reinforcement solutions for facilitating the meeting of DG growth goals while maintaining the network supply quality and operation security. This paper aims to address network reinforcements investment analysis for accommodating DG capacity growth. A methodology is proposed in this approach to quantify the impact of future increases in DG through the undertaking of a case study based on a generic distribution network model and different projected DG scenarios. Some available network reinforcement options used for managing fault level, voltage level, and power flows are presented and their costs required for meeting supply quality requirements of different types of customers are analyzed. Test results of applications of the proposed method to a practical distribution system are presented. Analysis results can assist distribution network operators in determining proper reinforcement options for managing distributed energy resources.

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