Smart Distribution Grid: Optimal Day-Ahead Scheduling With Reconfigurable Topology

Golshannavaz, Sajjad; Afsharnia, Saeed; Aminifar, Farrokh

doi:10.1109/tsg.2014.2335815

Cited by 148 publications

(112 citation statements)

References 23 publications

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…Network data and other technical information related to this system can be found in [32]. The five dashed lines represent the existing tie-lines in the original configuration.…”

Section: Tests and Resultsmentioning

confidence: 99%

“…The five dashed lines represent the existing tie-lines in the original configuration. In order to reach all of the possible topologies, it is assumed that all of the branches are equipped with RCSs [32,33]. For the sake of simplicity, it has been assumed that all branches have an equal length and the distance of each RCS from critical points are calculated based on the related per unit amounts.…”

Section: Tests and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

et al. 2018

View full text Add to dashboard Cite

Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

show abstract

“…Network data and other technical information related to this system can be found in [32]. The five dashed lines represent the existing tie-lines in the original configuration.…”

Section: Tests and Resultsmentioning

confidence: 99%

Section: Tests and Resultsmentioning

confidence: 99%

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The equations are shown in (14) and (15). where x t and x t?1 are the upper and lower bounds of the t th time interval, respectively; w t is the cumulative probability of the t th time interval; F(Á) and f(Á) are the cumulative probability density function and the probability density function, respectively.…”

Section: State Analysis For Wind Power and Pv Arraymentioning

confidence: 99%

“…Its research design and analysis is mainly based on previous studies guided by local circumstances. The foundation of the research covers several areas including microgrids [11], combined heat and power (CHP) generation, combined cooling heating and power (CCHP) systems [5,12], smart grids [13][14][15], energy internet [10], and other distributed energy technologies. At present, technical studies and design theory for CHP and CCHP are relatively comprehensive and mature, while the researches on smart grids, microgrids and energy internet have made substantial progress.…”

Section: Introductionmentioning

confidence: 99%

Economic optimization for configuration and sizing of micro integrated energy systems

Zhang

Deng

et al. 2017

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

Based on analysis of construction and operation of micro integrated energy systems (MIES), this paper presents economic optimization for their configuration and sizing. After presenting typical models for MIES, a residential community MIES is developed by analyzing residential direct energy consumption within a general design procedure. Integrating with available current technologies and local resources, the systematic design considers a prime mover, fed by natural gas, with wind power, photovoltaic generation, and two storage devices serving thermal energy and power to satisfy cooling, heating and electricity demands. Control strategies for MIES also are presented in this study. Multi-objective formulas are obtained by analyzing annual cost and dumped renewable energy to achieve optimal coordination of energy supply and demand. According to historical load data and the probability distribution of distributed generation output, clustering methods based on K-means and discretization methods are employed to obtain typical scenarios representative of uncertainties. The modified non-dominated sorting genetic algorithm is applied to find the Pareto frontier of the constructed multi-objective formulas. In addition, aiming to explore the Pareto frontier, the dumped energy cost ratio is defined to check the energy balance in different MIES designs and provide decision support for the investors. Finally, simulations and comparision show the appropriateness of the developed model and the applicability of the adopted optimization algorithm.

show abstract

“…On the other hand, there is now a common sense between the industry and academia on the ongoing transition changing the passive distribution network to a more intelligent and efficient one known as active distribution networks (ADNs) [7][8][9]. ADNs are mainly benefitting from the rapid evolution in the information and communication technology (ICT).…”

Section: Introductionmentioning

confidence: 99%

An Optimal Procedure for Sizing and Siting of DGs and Smart Meters in Active Distribution Networks Considering Loss Reduction

Sattarpour¹,

Nazarpour²,

Golshannavaz³

et al. 2015

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-The presence of responsive loads in the promising active distribution networks (ADNs) would definitely affect the power system problems such as distributed generations (DGs) studies. Hence, an optimal procedure is proposed herein which takes into account the simultaneous placement of DGs and smart meters (SMs) in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Seeking to power loss minimization, the optimization procedure is tackled with genetic algorithm (GA) and tested thoroughly on 69-bus distribution test system. Different scenarios including variations in the number of DG units, adaptive power factor (APF) mode for DGs to support reactive power, and individual or simultaneous placing of DGs and SMs have been established and interrogated in depth. The obtained results certify the considerable effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the lowest value of power losses as well.

show abstract

Smart Distribution Grid: Optimal Day-Ahead Scheduling With Reconfigurable Topology

Cited by 148 publications

References 23 publications

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

Economic optimization for configuration and sizing of micro integrated energy systems

An Optimal Procedure for Sizing and Siting of DGs and Smart Meters in Active Distribution Networks Considering Loss Reduction

Contact Info

Product

Resources

About