skip to main content

Title: Multi-Objective Approach for Optimal Size and Location of DGs in Distribution Systems
In the recent years, due to the economic and environmental requirements, the use of distributed generations (DGs) has increased. If DGs have the optimal size and are located at the optimal locations, they are capable of enhancing the voltage profile and reducing the power loss. This paper proposes a new approach to obtain the optimal location and size of DGs. To this end, exchange market algorithm (EMA) is offered to find the optimal size and location of DGs subject to minimizing loss, increasing voltage profile, and improving voltage stability in the distribution systems. The effectiveness of the proposed approach is verified on both 33- and 69-bus IEEE standard systems.
; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
2020 IEEE Green Energy and Smart Systems Conference (IGESSC)
Page Range or eLocation-ID:
1 to 6
Sponsoring Org:
National Science Foundation
More Like this
  1. This paper presents a droop-free distributed secondary control for DC microgrids with admissible voltage profile guarantees. The control objectives are achieved through an average voltage regulator, voltage variance regulator, and a relaxed current sharing regulator. Regulations of the global average voltage to the microgrid rated voltage is ensured by the average voltage regulator and regulations of the global voltage variance to a predetermined reference is enabled by the voltage variance regulator. In order to achieve the objectives of voltage regulation, the current sharing from one of the DGs which may be owned by the microgrid community is relaxed. The globalmore »dynamic model of the DC microgrid with the proposed control is derived. Besides, steady-state analysis is performed to show that all objectives can be achieved. Finally, simulations on a 4-DG DC microgrid test system are performed to validate the efficacy of the proposed control.« less
  2. As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek tomore »find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB. These simulations aim to develop a better understanding of the potential impacts of electric vehicles in smart grids, such as power quality and monetary benefits for utility companies and electric vehicle users« less
  3. Uwe Sauer, Dirk (Ed.)
    A B S T R A C T The probabilistic and intermittent output power of Wind Turbines (WT) is one major inconsistency of these Renewable Energy Sources (RES). Battery Energy Storage Systems (BESS) are a suitable solution to mitigate this intermittency by smoothening WT’s output power. Although the main benefit of BESSs mentions as peak shaving and load-shifting, but in this research, it will verify that optimal placement and sizing them jointly with WTs can lead to more benefits like compensating the required system’s reactive power support from WTs. The reactive power size of WTs and BESSs will be derivedmore »from the result of the joint sizing and placement in this study, as well as their active power output to meet the load demand. This can facilitate WTs and BESSs contribution to cover the system’s required reactive power and their participation in the reactive power market and ancillary services. This paper also proposes new cost functions for both WTs and BESSs and minimizes their cost while ensuring minimal total loss (active and reactive) in the power distribution system. This can benefit both WTs’ and BESSs’ owners as well as system operators. Suitable placement and sizing of the WTs and BESSs can also improve the load bus voltage profiles, which can benefit the end-users, and will verify using the proposed optimization by different case studies on the 33 bus distribution system. The results of case studies ascertain the consistency of the proposed formulation for placement and sizing BESSs and WTs jointly, as well as other benefits to the power system, the power plant owners, and system operators.« less
  4. Water distribution systems (WDSs) face a significant challenge in the form of pipe leaks. Pipe leaks can cause loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. Locating pipe leaks has been a constant challenge for water utilities and stakeholders due to the underground location of the pipes. Physical methods to detect leaks are expensive, intrusive, and heavily localized. Computational approaches provide an economical alternative to physical methods. Data-driven machine learning-based computational approaches have garnered growing interest in recent years to address the challenge of detecting pipe leaks in WDSs. Whilemore »several studies have applied machine learning models for leak detection on single pipes and small test networks, their applicability to the real-world WDSs is unclear. Most of these studies simplify the leak characteristics and ignore modeling and measuring device uncertainties, which makes the scalability of their approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases that account for the realistic leak characteristics (multiple, multi-size, and randomly located leaks) and incorporating noise in the input data to account for the model- and measuring device- related uncertainties. A machine learning-based approach that uses simulated pressure as input to predict both location and size of leaks is proposed. Two different machine learning models: Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), are trained and tested for the four study cases, and their performances are compared. The precision and recall results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN generally outperforming MLP.« less
  5. We propose a multiphase distribution locational marginal price (DLMP) model. Compared to existing DLMP models in the literature, the proposed model has three distinctive features: i) It provides linear approximation of relevant DLMP components which captures global behavior of nonlinear functions; ii) it decomposes into most general components, i.e., energy, loss, congestion, voltage violations; and iii) it incorporates both wye and delta grid connections along with unbalanced loadings. The developed model is tested on a benchmark IEEE 13-bus unbalanced distribution system with the inclusion of distributed generators (DGs).