scholarly journals Baselining Flexibility from PV on the DSO-Aggregator Interface

2021 ◽  
Vol 11 (5) ◽  
pp. 2191
Author(s):  
Rik Fonteijn ◽  
Phuong H. Nguyen ◽  
Johan Morren ◽  
J. G. (Han) Slootweg
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Distribution Network ◽  
Weather Data ◽  
Small Scale ◽  
Proof Of Concept ◽  
Power Profile ◽  
Photo Voltaic

Flexibility can be used to mitigate distribution network overloading. Distribution system operators (DSOs) can obtain this flexibility from market parties connected to the distribution network. After flexibility has been delivered to the DSO, it needs to be settled. This is typically done by comparing load measurements with a baseline. This baseline describes an asset’s power profile in case no flexibility would have been delivered. Until recently, baselining research mainly focused on large-scale, predictable and controllable assets. The flexibility used by DSOs however typically comes from small-scale, less predictable and less controllable assets. This paper addresses the baselining problem for photo-voltaic systems. Three existing baselining methods are selected based on their simplicity and transparency and their limitations with respect to application towards photo-voltaic systems are evaluated. Based on this, a proof-of-concept for a new, fourth method is provided. It overcomes some of the limitations of the three existing ones, while still ensuring simplicity and transparency in order to promote market acceptance and practical applicability. All four methods are subjected to two different curtailment strategies: curtailing all peaks above a threshold and curtailing based on a day-ahead flexibility request. Using weather data from three summer weeks in 2019, it is shown that the newly developed method is able to provide a more accurate baseline than the existing methods.

scholarly journals Massive Generation of Customer Load Profiles for Large Scale State Estimation Deployment: An Approach to Exploit AMI Limited Data

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1277
Author(s):  
Davide Della Giustina ◽  
Stefano Rinaldi ◽  
Stefano Robustelli ◽  
Andrea Angioni
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Distribution System ◽  
Large Scale ◽  
Distribution Network ◽  
Worst Case ◽  
Large Variability ◽  
Power Profile ◽  
Wide Range ◽  
Full Power

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.

Fast Service Restoration of Multi-Source Active Distribution Network with Microgrids and Electric Vehicles

2014 ◽  
Vol 672-674 ◽  
pp. 1175-1178
Author(s):  
Guang Min Fan ◽  
Ling Xu Guo ◽  
Wei Liang ◽  
Hong Tao Qie
Keyword(s):  
Electric Vehicles ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Distribution Network ◽  
Active Distribution Network ◽  
Optimal Power ◽  
Service Restoration ◽  
Restoration Method

The increasingly serious energy crisis and environmental pollution problems promote the large-scale application of microgrids (MGs) and electric vehicles (EVs). As the main carrier of MGs and EVs, distribution network is gradually presenting multi-source and active characteristics. A fast service restoration method of multi-source active distribution network with MGs and EVs is proposed in this paper for service restoration of distribution network, which takes effectiveness, rapidity, economy and reliability into consideration. Then, different optimal power flow (OPF) models for the service restoration strategy are constructed separately to minimize the network loss after service restoration. In addition, a genetic algorithm was introduced to solve the OPF model. The analysis of the service restoration strategy is carried out on an IEEE distribution system with three-feeder and eighteen nodes containing MGs and EVs, and the feasibility and effectiveness are verified

scholarly journals The Technical Challenges Facing the Integration of Small-Scale and Large-scale PV Systems into the Grid: A Critical Review

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1443 ◽  
Author(s):  
Abdullah Alshahrani ◽  
Siddig Omer ◽  
Yuehong Su ◽  
Elamin Mohamed ◽  
Saleh Alotaibi
Keyword(s):  
Large Scale ◽  
Distribution Network ◽  
Small Scale ◽  
Energy Access ◽  
Solar Pv ◽  
Pv System ◽  
Pv Systems ◽  
Technical Challenges ◽  
Technical Solutions ◽  
The Impact

Decarbonisation, energy security and expanding energy access are the main driving forces behind the worldwide increasing attention in renewable energy. This paper focuses on the solar photovoltaic (PV) technology because, currently, it has the most attention in the energy sector due to the sharp drop in the solar PV system cost, which was one of the main barriers of PV large-scale deployment. Firstly, this paper extensively reviews the technical challenges, potential technical solutions and the research carried out in integrating high shares of small-scale PV systems into the distribution network of the grid in order to give a clearer picture of the impact since most of the PV systems installations were at small scales and connected into the distribution network. The paper reviews the localised technical challenges, grid stability challenges and technical solutions on integrating large-scale PV systems into the transmission network of the grid. In addition, the current practices for managing the variability of large-scale PV systems by the grid operators are discussed. Finally, this paper concludes by summarising the critical technical aspects facing the integration of the PV system depending on their size into the grid, in which it provides a strong point of reference and a useful framework for the researchers planning to exploit this field further on.

scholarly journals Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

2019 ◽  
Vol 8 (4) ◽  
pp. 6357-6363
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Optimal Placement ◽  
Small Scale ◽  
Voltage Profile ◽  
Distribution Grid ◽  
Step Size ◽  
System Power

The reliability of distribution network can be improved with the penetration of small scale distributed generation (DG) unit to the distribution grid. Nevertheless, the location and sizing of the DG in the distribution network have always become a topic of debate. This problem arises as different capacity of DG at various location can affect the performance of the entire system. The main objective of this study is to recommend a suitable size of DG to be placed at the most appropriate location for better voltage profile and minimum power loss. Therefore, this paper presents an analytical approach with a fixed DG step size of 500 kW up to 4500 kW DG to analyses the effect of a single P-type DG in IEEE 33 bus system with consideration of system power loss and voltage profile. Four scenarios have been selected for discussions where Scenario 1: 3500 kW DG placed at node 3; Scenario 2: 2500 kW DG placed at node 6; Scenario 3: 1000 kW DG placed at node 18 and Scenario 4: 3000 kW DG placed at node 7. Results show that all the four scenarios are able to reduce the power loss and improve the voltage profile however Scenario 4 has better performance where it complies with minimum voltage requirement and minimizing the system power loss.

scholarly journals Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3208 ◽  
Author(s):  
Xiangyu Li ◽  
Dongmei Zhao ◽  
Baicang Guo
Keyword(s):  
Distribution System ◽  
Gas Turbines ◽  
Power Plants ◽  
Large Scale ◽  
Distribution Network ◽  
Virtual Power ◽  
Distributed Energy ◽  
Active Distribution Network ◽  
Active Distribution System ◽  
Virtual Power Plants

In order to build an active distribution system with multi virtual power plants (VPP), a decentralized two-stage stochastic dispatching model based on synchronous alternating direction multiplier method (SADMM) was proposed in this paper. Through the integration of distributed energy and large-scale electric vehicles (EV) in the distribution network by VPP group, coordinative complementarity, and global optimization were realized. On the premise of energy autonomy management of active distribution network (AND) and VPP, after ensuring the privacy of stakeholders, the power of tie-line was taken as decoupling variable based on SADMM. Furthermore, without the participation of central coordinators, the optimization models of VPPs and distribution networks were decoupled to achieve fully decentralized optimization. Aiming at minimizing their own operating costs, the VPPs aggregate distributed energy and large-scale EVs within their jurisdiction to interact with the upper distribution network. On the premise of keeping operation safe, the upper distribution network formulated the energy interaction plan with each VPP, and then, the global energy optimization management of the entire distribution system and the decentralized autonomy of each VPP were achieved. In order to improve the stochastic uncertainty of distributed renewable energy output, a two-stage stochastic optimization method including pre-scheduling stage and rescheduling stage was adopted. The pre-scheduling stage was used to arrange charging and discharging plans of EV agents and output plans of micro gas turbines. The rescheduling stage was used to adjust the spare resources of micro gas turbines to deal with the uncertainty of distributed wind and light. An example of active distribution system with multi-VPPs was constructed by using the improved IEEE 33-bus system, then the validity of the model was verified.

scholarly journals Optimization of Placement and Size of Distribution Generators Using Quantum Genetic Algorithms to Improve Power Quality in Bali Distribution Networks

2019 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Yanu Prapto Sudarmojo
Keyword(s):  
Electric Power ◽  
Power Quality ◽  
Power Distribution ◽  
Distribution System ◽  
Large Scale ◽  
Energy Requirement ◽  
Distribution Networks ◽  
Small Scale ◽  
Voltage Profile ◽  
Distributed Generator

World energy requirement increased significantly, the main energy source from an oil is very limited. This problem drive an enhancement develop which support small scale generator to be connected near distributed network or near load center. Distributed Generator (DG) is a power plant which have a little capacity range between 15 kW to 10 MW. Basically, DG instalation is one way to fix a voltage profile where an installed DG would inject voltage to a transmission system or electric power distribution. Bali is a tourism area which it’s electric power source got a supply from Java and some large scale plant which use fuel of oil and gas, which until now still needed more of electric energy. An addition small scale generator for Bali is very helpful where economic profit is distribution cost and transmission cost’s reduction, electric cost and saving fuel energy. Technically a distributor of DG must be done correctly and optimal from it’s size or location so that give a maximum result from economic side, minimalizing electricity loss and increase voltage profile which result an electric power quality is improved. For that, in this research will use heuristic optimation with use Quantum Genetic Alghorithm method to placing distributed generator to Bali Electricity Network. To counting electicity loss and voltage profile, a method which used to solve it is Newton Raphson method. The result of this research, DG is installed to feeder which plaed in Abang Sub-District, Karangasem District where Abang Feeder had a total 43a bus which is a part from Bali Distribution System. With using QGA, DG is installed to bus 1, 5, 7, and 302 with each DG capacity is 0,374 MW, 1,894 MW, 1,988 MW and 0,500 MW, after installment of DG, voltage profile can be fixed. Voltage profile for some bus to Abang Feeder could be fixed from 0,83 pu to 0,98 pu. Electricity loss from 1,105 MW become 0,234 MW.

scholarly journals μDFL: A Secure Microchained Decentralized Federated Learning Fabric atop IoT Networks

2021 ◽  
Author(s):  
Ronghua Xu ◽  
Yu Chen
Keyword(s):  
Large Scale ◽  
Privacy Preservation ◽  
Security And Privacy ◽  
Small Scale ◽  
Proof Of Concept ◽  
Byzantine Fault Tolerance ◽  
Consensus Protocol ◽  
High Level ◽  
Collaborative Training ◽  
And Storage

<div>Federated Learning (FL) has been recognized as a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on the aggregation of distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized aggregation manner. Possessing attractive features like decentralization, immutability and auditability, Blockchain is promising to enable a tamper-proof and trust-free framework to enhance performance and security in IoT based FL systems. However, directly integrating blockchains into the large scale IoT-based FL scenarios still faces many limitations, such as high computation and storage demands, low transactions throughput, poor scalability and challenges in privacy preservation. This paper proposes uDFL, a novel hierarchical IoT network fabric for decentralized federated learning (DFL) atop of a lightweight blockchain called microchain. Following the hierarchical infrastructure of FL, participants in uDFL are fragmented into multiple small scale microchains. Each microchain network relies on a hybrid Proof of Credit (PoC) block generation and Voting-based Chain Finality (VCF) consensus protocol to ensure efficiency and privacy-preservation at the network of edge. Meanwhile, microchains are federated vie a high-level inter-chain network, which adopts an efficient Byzantine Fault Tolerance (BFT) consensus protocol to achieve scalability and security.</div><div>A proof-of-concept prototype is implemented, and the experimental results verify the feasibility of the proposed uDFL solution in cross-devices FL settings with efficiency, security and privacy guarantees.</div>

scholarly journals Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 109 ◽  
Author(s):  
Jingjing Tu ◽  
Yonghai Xu ◽  
Zhongdong Yin
Keyword(s):  
Power Generation ◽  
Distribution System ◽  
Large Scale ◽  
Voltage Stability ◽  
Distribution Network ◽  
Peak Load ◽  
Data Driven ◽  
Support Vector ◽  
Machine Method ◽  
Photovoltaic Power

For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified.

scholarly journals A Novel Methodology for the Scalability Analysis of ICT Systems for Smart Grids Based on SGAM: The InteGrid Project Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3818
Author(s):  
Sergio Potenciano Menci ◽  
Julien Le Baut ◽  
Javier Matanza Domingo ◽  
Gregorio López López ◽  
Rafael Cossent Arín ◽  
...  
Keyword(s):  
Smart Grid ◽  
Distribution System ◽  
Smart Grids ◽  
Large Scale ◽  
Small Scale ◽  
Traffic Light ◽  
Architecture Model ◽  
Indirect Transmission ◽  
Scalability Analysis ◽  
System Operator

Information and Communication Technology (ICT) infrastructures are at the heart of emerging Smart Grid scenarios with high penetration of Distributed Energy Resources (DER). The scalability of such ICT infrastructures is a key factor for the large scale deployment of the aforementioned Smart Grid solutions, which could not be ensured by small-scale pilot demonstrations. This paper presents a novel methodology that has been developed in the scope of the H2020 project InteGrid, which enables the scalability analysis of ICT infrastructures for Smart Grids. It is based on the Smart Grid Architecture Model (SGAM) framework, which enables a standardized and replicable approach. This approach consists of two consecutive steps: a qualitative analysis that aims at identifying potential bottlenecks in an ICT infrastructure; and a quantitative analysis of the identified critical links under stress conditions by means of simulations with the aim of evaluating their operational limits. In this work the proposed methodology is applied to a cluster of solutions demonstrated in the InteGrid Slovenian pilot. This pilot consists of a Large Customer Commercial Virtual Power Plant (VPP) that provides flexibility in medium voltage for tertiary reserve and a Traffic Light System (TLS) to validate such flexibility offers. This approach creates an indirect Transmission System Operator (TSO)—Distribution System Operator (DSO) coordination scheme.

The Impact of Fast Charging for EVs on Distribution System

2014 ◽  
Vol 1070-1072 ◽  
pp. 1664-1667 ◽  
Author(s):  
Wen Chen ◽  
Chun Lin Guo
Keyword(s):  
Electric Vehicle ◽  
Distribution System ◽  
Large Scale ◽  
Distribution Network ◽  
Chinese Government ◽  
Voltage Loss ◽  
Fast Charging ◽  
New Energy ◽  
The Impact ◽  
New Energy Vehicles

With the implementation of incentive policies for new energy vehicles of Chinese government, the development of new energy vehicles in China has made considerable progress, and in the field of electric vehicle charging facilities can also appeared in a number of demonstration projects. With the support of the National Grid, the establishment of a domestic Chongqing's first commercial fast-charging stations was done in 2012. Research on Electric vehicle fast charging caused to the distribution network with future large-scale deployment of electric vehicles will be of great value in guiding for the planning of distribution of the networks. In this work, we used a typical distribution network model to simulate the real conditions. The voltage loss caused by EVs’ fast charging on the transmission line was studied under different implementation scenarios.

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