Optimal Siting of Distributed Generation Unit in Power Distribution System considering Voltage Profile and Power Losses

Many underdeveloped countries are facing acute shortage of electric power and short term measures are important to consider to address the problems of power outage, power plant failures, and disaster areas. Distributed generation (DG) is a promising approach for such cases as it allows quick on-site installation and generation of electric power. Injection of DG can improve the system voltage profile and also reduce the system's total power losses. However, the placement and sizing of the DG unit is an optimization problem in the radial distribution system. As a test case, this study examines voltage profile improvement and system power losses for an 11 KV residential feeder at the Abdul Rehman Baba grid station in Pakistan, which is modelled using the Electrical Transient Analyzer Program (ETAP). For various scenarios, several tests are conducted to assess the effects of DG on the distribution system. The results show that proper design considerations of size and location of a DG, to be inserted in to the system, lead to significant reduction in power losses and improvement in voltage profile and thus improvement in the overall efficiency of the power system. The projections of this work can be used to optimize the expansion of a power system and tackling different issues related to voltage profile in distribution sector worldwide.

Expansion Planning in Distribution Network with DSTATCOM using Distance Oriented Grasshopper Optimization Algorithm An Optimal Model

This paper intends to consider a multi-objective problem for expansion planning in Power Distribution System (PDS) by focusing on (i) expansion strategy (ii) allocation of Circuit Breaker (CB), (iii) allocation of Distribution Static Compensator (DSTATCOM), (iv) Contingency Load Loss Index (CLLI), and power loss. Accordingly, the encoding parameters decide for expansion, Circuit Breaker (CB) placement, DSTATCOM placement, load of real and reactive powers of expanded bus or node are optimized using Grasshopper Optimization Algorithm (GOA) based on its distance and hence, the proposed algorithm is termed as Distance Oriented Grasshopper Optimization Algorithm (DGOA). The proposed expansion planning model is carried out in IEEE 33 test bus system. Moreover, the adopted scheme is compared with conventional algorithms and the optimal results are obtained.

Optimization of Energy Efficiency for Electric Power Distribution System Losses

This study evaluated the existing electric power network of Mile 2 Diobu zone, Port Harcourt distribution network which consists of four (4) 11kV distribution feeders namely; Ojoto, Nsukka, Udi and Silverbird. This work considered Ojoto and Nsukka Street distribution network for improved power quality. The three (3) 33/11kv injection substations are fed from 165 MVA transmission station (PH Town) at Amadi junction by Nzimiro. Collection and analysis of data collected from the injection substations that supply electricity to mile 2 Diobu, Port Harcourt was the first consideration. The distribution network was modeled in Electrical Transient Analyzer Program (ETAP) using Newton-Raphson Load Flow equations. The simulation result of the existing condition network shows that the network has low voltage profile problem on Nsukka network and overloading of distribution transformers on Ojoto networks. The following optimization techniques are applied: up-gradation of distribution transformers, and transformer load tap changer to improve the distribution network for Mile 2 Diobu, Port Harcourt electrical power network. The simulation result of the improved distribution network for Mile 2 Diobu, Port Harcourt power network shows that the voltage profile Nsukka network has improved within the statutory limit which is between 95.0 -105.0% and the loading of the distribution transformers on Ojoto and Nsukka networks are all below 70% required capacity. Keywords: Optimization, Energy Efficiency Distribution

Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System with IoTs

<div>The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoTs) based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable and resilient operation. At the same time, IoT based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power Distribution System Resiliency (DSR) metric, the IoT Trustability Score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using Fuzzy Multiple-Criteria Decision Making (F-MCDM) to compute Primary level Node Resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic Data Envelopment Analysis (DEA) based optimization. The developed metrics will be valuable for i) monitoring the distribution system resiliency considering a holistic cyber-power model; ii) enabling data privacy by not utilizing the raw user data; and iii) enabling better decision-making to select the best possible mitigation strategies towards resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.</div>

Probability-Based Customizable Modeling and Simulation of Protective Devices in Power Distribution Systems

Fused contactors and thermal magnetic circuit breakers are commonly applied protective devices in power distribution systems to protect the circuits when short-circuit faults occur. A power distribution system may contain various makes and models of protective devices, as a result, customizable simulation models for protective devices are demanded to effectively conduct system-level reliable analyses. To build the models, thermal energy-based data analysis methodologies are first applied to the protective devices’ physical properties, based on the manufacturer’s time/current data sheet. The models are further enhanced by integrating probability tools to simulate uncertainties in real-world application facts, for example, fortuity, variance, and failure rate. The customizable models are expected to aid the system-level reliability analysis, especially for the microgrid power systems.

Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System with IoTs

<div>The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoTs) based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable and resilient operation. At the same time, IoT based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power Distribution System Resiliency (DSR) metric, the IoT Trustability Score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using Fuzzy Multiple-Criteria Decision Making (F-MCDM) to compute Primary level Node Resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic Data Envelopment Analysis (DEA) based optimization. The developed metrics will be valuable for i) monitoring the distribution system resiliency considering a holistic cyber-power model; ii) enabling data privacy by not utilizing the raw user data; and iii) enabling better decision-making to select the best possible mitigation strategies towards resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.</div>