scholarly journals A Probabilistic Conductor Size Selection Framework for Active Distribution Networks

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6387
Author(s):  
Lewis Waswa ◽  
Munyaradzi Justice Chihota ◽  
Bernard Bekker
Keyword(s):  
Thermal Loading ◽  
Planning Process ◽  
Low Voltage ◽  
Optimization Problems ◽  
Uncertainty Modeling ◽  
Distribution Networks ◽  
Loading Conditions ◽  
Size Selection ◽  
Selection Framework ◽  
Design Case Study

With the increasing adoption of distributed energy resources (DERs) such as wind and solar photovoltaics (PV), many distribution networks have changed from passive to active. In turn, this has led to increased technical and operational challenges such as voltage issues and thermal loading in high DER penetration scenarios. These challenges have been further increased by the uncertainties arising from DER allocation. The implication of DER allocation uncertainty in the planning process is far-reaching as it affects critical planning processes, including conductor size selection (CSS). Most reported CSS methods in the literature do not include DER allocation uncertainty modeling as they are mostly deterministic and are set out as optimization problems. The methods, therefore, lack foresight on future loading conditions and cannot be used in a CSS process for feeders with high DER penetration. This paper proposes a novel input–process–output stochastic–probabilistic CSS framework for distribution feeders with DERs. The efficacy of the proposed framework is demonstrated using a low voltage feeder design case study with varying PV penetration targets, and the performance compared to deterministic–active-based estimates from our earlier work. The proposed CSS method is well-suited to the sizing of conductors for future loading conditions considering DER allocation uncertainty and will therefore be useful to planners working on new electrification projects.

scholarly journals Laplacian Matrix-Based Power Flow Formulation for LVDC Grids with Radial and Meshed Configurations

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1866
Author(s):  
Zahid Javid ◽  
Ulas Karaagac ◽  
Ilhan Kocar ◽  
Ka Wing Chan
Keyword(s):  
Fixed Point ◽  
Power Flow ◽  
Low Voltage ◽  
Mathematical Formulation ◽  
Distribution Networks ◽  
Load Flow ◽  
Banach Fixed Point Theorem ◽  
Loading Conditions ◽  
Flow Equations ◽  
Uniqueness Of The Solution

There is an increasing interest in low voltage direct current (LVDC) distribution grids due to advancements in power electronics enabling efficient and economical electrical networks in the DC paradigm. Power flow equations in LVDC grids are non-linear and non-convex due to the presence of constant power nodes. Depending on the implementation, power flow equations may lead to more than one solution and unrealistic solutions; therefore, the uniqueness of the solution should not be taken for granted. This paper proposes a new power flow solver based on a graph theory for LVDC grids having radial or meshed configurations. The solver provides a unique solution. Two test feeders composed of 33 nodes and 69 nodes are considered to validate the effectiveness of the proposed method. The proposed method is compared with a fixed-point methodology called direct load flow (DLF) having a mathematical formulation equivalent to a backward forward sweep (BFS) class of solvers in the case of radial distribution networks but that can handle meshed networks more easily thanks to the use of connectivity matrices. In addition, the convergence and uniqueness of the solution is demonstrated using a Banach fixed-point theorem. The performance of the proposed method is tested for different loading conditions. The results show that the proposed method is robust and has fast convergence characteristics even with high loading conditions. All simulations are carried out in MATLAB 2020b software.

scholarly journals Comparison of Different References When Assessing PV HC in Distribution Networks

2021 ◽  
Vol 3 (1) ◽  
pp. 123-137
Author(s):  
Samar Fatima ◽  
Verner Püvi ◽  
Matti Lehtonen
Keyword(s):  
Low Voltage ◽  
Distribution Networks ◽  
Maximum Amount ◽  
Loading Conditions ◽  
Simulation Approach ◽  
Voltage Distribution ◽  
Network Loading ◽  
Mc Simulation ◽  
Pv Generation

The burgeoning photovoltaics’ (PVs) penetration in the low voltage distribution networks can cause operational bottlenecks if the PV integration exceeds the threshold known as hosting capacity (HC). There has been no common consensus on defining HC, and its numerical value varies depending on the reference used. Therefore, this article compared the HC values of three types of networks in rural, suburban, and urban regions for different HC reference definitions. The comparison was made under balanced and unbalanced PV deployment scenarios and also for two different network loading conditions. A Monte Carlo (MC) simulation approach was utilized to consider the intermittency of PV power and varying loading conditions. The stochastic analysis of the networks was implemented by carrying out a large number of simulation scenarios, which led towards the determination of the maximum amount of PV generation in each network case.

scholarly journals D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2754
Author(s):  
Mengmeng Xiao ◽  
Shaorong Wang ◽  
Zia Ullah
Keyword(s):  
Mobile Networks ◽  
Control Method ◽  
Low Voltage ◽  
Control Unit ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Positive Sequence ◽  
Measurement Unit ◽  
Control Units ◽  
Three Phase

Three-phase imbalance is a long-term issue existing in low-voltage distribution networks (LVDNs), which consequently has an inverse impact on the safe and optimal operation of LVDNs. Recently, the increasing integration of single-phase distributed generations (DGs) and flexible loads has increased the probability of imbalance occurrence in LVDNs. To overcome the above challenges, this paper proposes a novel methodology based on the concept of "Active Asymmetry Energy-Absorbing (AAEA)" utilizing loads with a back-to-back converter, denoted as “AAEA Unit” in this paper. AAEA Units are deployed and coordinated to actively absorb asymmetry power among three phases for imbalance mitigation in LVDNs based on the high-precision, high-accuracy, and real-time distribution-level phasor measurement unit (D-PMU) data acquisition system and the 5th generation mobile networks (5G) communication channels. Furthermore, the control scheme of the proposed method includes three control units. Specifically, the positive-sequence control unit is designed to maintain the voltage of the DC-capacitor of the back-to-back converter. Likewise, the negative-sequence and zero-sequence control units are expected to mitigate the imbalanced current components. A simple imbalanced LVDN is modeled and tested in Simulink/Matlab (MathWorks, US). The obtained results demonstrate the effectiveness of the proposed methodology.

scholarly journals Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4133
Author(s):  
Alessandro Bosisio ◽  
Matteo Moncecchi ◽  
Andrea Morotti ◽  
Marco Merlo
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Low Voltage ◽  
Regression Tree ◽  
Distribution Networks ◽  
Machine Learning Algorithms ◽  
Extreme Weather Events ◽  
Medium Voltage ◽  
Weather Events ◽  
Building Area

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

scholarly journals Service Restoring Reconfiguration for Distribution NetworksConsidering Uncertainty in Available Information

2021 ◽  
Vol 11 (9) ◽  
pp. 4169
Author(s):  
Hirotaka Takano ◽  
Junichi Murata ◽  
Kazuki Morishita ◽  
Hiroshi Asano
Keyword(s):  
Power Distribution ◽  
Solution Method ◽  
Optimization Problems ◽  
Problem Formulation ◽  
Distribution Networks ◽  
Accurate Information ◽  
Power Distribution Networks ◽  
Combinatorial Optimization Problems ◽  
Service Restoration ◽  
Available Information

The recent growth in the penetration of photovoltaic generation systems (PVs) has brought new difficulties in the operating and planning of electric power distribution networks. This is because operators of the distribution networks normally cannot monitor or control the output of the PVs, which introduces additional uncertainty into the available information that operations must rely on. This paper focuses on the service restoration of the distribution networks, and the authors propose a problem framework and its solution method that finds the optimal restoration configuration under extensive PV installation. The service restoration problems have been formulated as combinatorial optimization problems. They do, however, require accurate information on load sections, which is impractical in distribution networks with extensively installed PVs. A combined framework of robust optimization and two-stage stochastic programming adopted in the proposed problem formulation enables us to deal with the PV-originated uncertainty using readily available information only. In addition, this problem framework can be treated by a traditional solution method with slight extensions. The validity of the authors’ proposal is verified through numerical simulations on a real-scale distribution network model and includes a discussion of their results.

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