scholarly journals Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices

2021 ◽  
Vol 11 (8) ◽  
pp. 3395
Author(s):  
Gianni Celli ◽  
Marco Galici ◽  
Fabrizio Pilo ◽  
Simona Ruggeri ◽  
Gian Giuseppe Soma
Keyword(s):  
Distribution System ◽  
Optimal Allocation ◽  
Distribution Network ◽  
Residual Risk ◽  
Active Management ◽  
Electricity Production ◽  
Storage Devices ◽  
Electric Distribution System ◽  
Smart Distribution Network ◽  
Battery Energy

Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure.

scholarly journals Optimal DG Unit Placement and Sizing in Radial Distribution Network for Power Loss Minimization and Voltage Stability Enhancement

2020 ◽  
Vol 64 (2) ◽  
pp. 157-169
Author(s):  
Benalia M'hamdi ◽  
Madjid Teguar ◽  
Benaissa Tahar
Keyword(s):  
Power Factor ◽  
Distribution System ◽  
Voltage Stability ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Distribution Network ◽  
Reducing Power ◽  
Optimal Size ◽  
Power Losses ◽  
Vector Method

The optimal allocation and size of decentralized generating units are essential to minimize power losses, while meeting the demand for active and reactive power in a distribution system. In other words, most of the total energy produced can be efficiently exploited by end users. In addition, if the DGs are of optimal size and location in the distribution system, the reliability, stability and efficiency of the power system are guaranteed. This paper focuses on reducing power losses and improving the voltage profile by accurately identifying the optimal location and sizing of Distributed Generation based on three indexes, namely the IVM Index Vector Method, the VDI Voltage Deviation Index and the VSI Voltage Stability Index. Two types of DGs were considered for the analysis: DGs operating with unit power factor and DGs operating with a lagging power factor. Three optimization algorithms are applied to determine the optimal sizes of decentralized generation units in a power distribution network which are GWO, WOA and PSO. The results obtained in this article show that the three algorithms give very similar values. DG at lagging power factor gives better results compared with those obtained with DGs at unity power factor. In terms of loss reduction and minimum bus voltage, the best results are obtained for the VSI index with a DG at a power factor of 0.9.

scholarly journals A Mathematical Model for the Scheduling of Virtual Microgrids Topology into an Active Distribution Network

2020 ◽  
Vol 10 (20) ◽  
pp. 7199
Author(s):  
Fernando García-Muñoz ◽  
Francisco Díaz-González ◽  
Cristina Corchero
Keyword(s):  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Distribution Network ◽  
Mathematical Optimization ◽  
Renewable Resource ◽  
Total Load ◽  
Load Response ◽  
Load Curtailment ◽  
Battery Energy

This article presents a method based on a mathematical optimization model for the scheduling operation of a distribution network (DN). The contribution of the proposed method is that it permits the configuration and operation of a DN as a set of virtual microgrids with a high penetration level of distributed generation (DG) and battery energy storage systems (BESS). The topology of such virtual microgrids are modulated in time in response to grid failures, thus minimizing load curtailment, and maximizing local renewable resource and storage utilization as well. The formulation provides the load reduced by bus to balance the system at every hour and the global probability to present energy not supplied (ENS). Furthermore, for every bus, a flexibility load response range is considered to avoid its total load curtailment for small load reductions. The model has been constructed considering a linear version of the AC optimal power flow (OPF) constraints extended for multiple periods, and it has been tested in a modified version of the IEEE 33-bus radial distribution system considering four different scenarios of 72 h, where the global energy curtailment has been 27.9% without demand-side response (DSR) and 10.4% considering a 30% of flexibility load response. Every scenario execution takes less than a minute, making it appropriate for distribution system operational planning.

scholarly journals Predictive Voltage Control: Empowering Domestic Customers With a Key Role in the Active Management of LV Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 2635
Author(s):  
Micael Simões ◽  
André G. Madureira
Keyword(s):  
Distribution System ◽  
Smart Grids ◽  
Low Voltage ◽  
Voltage Control ◽  
Distribution Networks ◽  
Active Management ◽  
Storage Devices ◽  
Home Energy Management ◽  
System Operator ◽  
Home Energy Management System

In order to avoid voltage problems derived from the connection of large amounts of renewable-based generation to the electrical distribution system, new advanced tools need to be developed that are able to exploit the presence of Distributed Energy Resources (DER). This paper describes the approach proposed for a predictive voltage control algorithm to be used in Low Voltage (LV) distribution networks in order to make use of available flexibilities from domestic consumers via their Home Energy Management System (HEMS) and more traditional resources from the Distribution System Operator (DSO), such as transformers with On-Load Tap Changer (OLTC) and storage devices. The proposed algorithm—the Low Voltage Control (LVC)—is detailed in this paper. The algorithm was tested through simulation using a real Portuguese LV network and real consumption and generation data, in order to evaluate its performance in preparation for a field-trial validation in a Portuguese smart grids pilot.

scholarly journals Optimal Siting and Sizing of Battery Energy Storage Systems for Distribution Network of Distribution System Operators

Batteries ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 56
Author(s):  
Panyawoot Boonluk ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Sirote Khunkitti
Keyword(s):  
Energy Storage ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Distribution Network ◽  
Energy Storage System ◽  
Power Losses ◽  
Battery Energy Storage ◽  
Battery Energy Storage Systems ◽  
Battery Energy

In this work, optimal siting and sizing of a battery energy storage system (BESS) in a distribution network with renewable energy sources (RESs) of distribution network operators (DNO) are presented to reduce the effect of RES fluctuations for power generation reliability and quality. The optimal siting and sizing of the BESS are found by minimizing the costs caused by the voltage deviations, power losses, and peak demands in the distribution network for improving the performance of the distribution network. The simulation results of the BESS installation were evaluated in the IEEE 33-bus distribution network. Genetic algorithm (GA) and particle swarm optimization (PSO) were adopted to solve this optimization problem, and the results obtained from these two algorithms were compared. After the BESS installation in the distribution network, the voltage deviations, power losses, and peak demands were reduced when compared to those of the case without BESS installation.

Sign in / Sign up

Export Citation Format

Share Document