scholarly journals Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks

2021 ◽  
Vol 11 (19) ◽  
pp. 8916
Author(s):  
Zhiwen Xu ◽  
Changsong Chen ◽  
Mingyang Dong ◽  
Jingyue Zhang ◽  
Dongtong Han ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Operating Cost ◽  
Distribution Networks ◽  
Double Loop ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Ac Converters

By constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduce the network loss of the distribution network, a cooperative double-loop optimization strategy is proposed. The inner-loop economic dispatching reduces the daily operating cost of MMGS by optimizing the active power output of RESs, EVs, and DC/AC converters in MMGS. The outer-loop reactive power optimization reduces the network loss of the distribution network by optimizing the reactive power of the bidirectional DC/AC converters. The double-loop, which synergistically optimizes the economic cost and carbon emissions of MMGS, not only improves the economy of MMGS and operational effectiveness of the distribution network but also realizes the low-carbon emissions. The Across-time-and-space energy transmission (ATSET) of the EVs is considered, whose impact on economic dispatching is analyzed. Particle Swarm Optimization (PSO) is applied to iterative solutions. Finally, the rationality and feasibility of the cooperative multi-objective optimization model are proved by a revised IEEE 33-node system.

Multi-objective optimization of optimal capacitor allocation in radial distribution systems

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Sayed Mir Shah Danish ◽  
Mikaeel Ahmadi ◽  
Atsushi Yona ◽  
Tomonobu Senjyu ◽  
Narayanan Krishna ◽  
...  
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Reactive Power ◽  
Stability Index ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Size ◽  
Multi Objective Optimization ◽  
Multi Objective

AbstractThe optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

scholarly journals Reactive Power Management Considering Stochastic Optimization under the Portuguese Reactive Power Policy Applied to DER in Distribution Networks

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4028 ◽  
Author(s):  
Abreu ◽  
Soares ◽  
Carvalho ◽  
Morais ◽  
Simão ◽  
...  
Keyword(s):  
Power Management ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Real Data ◽  
Distribution Networks ◽  
System Operator

Challenges in the coordination between the transmission system operator (TSO) and the distribution system operator (DSO) have risen continuously with the integration of distributed energy resources (DER). These technologies have the possibility to provide reactive power support for system operators. Considering the Portuguese reactive power policy as an example of the regulatory framework, this paper proposes a methodology for proactive reactive power management of the DSO using the renewable energy sources (RES) considering forecast uncertainty available in the distribution system. The proposed method applies a stochastic sequential alternative current (AC)-optimal power flow (SOPF) that returns trustworthy solutions for the DSO and optimizes the use of reactive power between the DSO and DER. The method is validated using a 37-bus distribution network considering real data. Results proved that the method improves the reactive power management by taking advantage of the full capabilities of the DER and by reducing the injection of reactive power by the TSO in the distribution network and, therefore, reducing losses.

scholarly journals Multi-objective Optimization of Accommodation Capacity for Distributed Generation Based on Mixed Strategy Nash Equilibrium, Considering Distribution Network Flexibility

2019 ◽  
Vol 9 (20) ◽  
pp. 4395 ◽  
Author(s):  
Weisheng Liu ◽  
Jian Wu ◽  
Fei Wang ◽  
Yixin Huang ◽  
Qiongdan Dai ◽  
...  
Keyword(s):  
Nash Equilibrium ◽  
Distributed Generation ◽  
Mixed Strategy ◽  
Distribution Network ◽  
Distribution Networks ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mixed Strategy Nash Equilibrium ◽  
Network Flexibility ◽  
Strategy Nash Equilibrium

The increasing penetration of distributed generation (DG) brings about great fluctuation and uncertainty in distribution networks. In order to improve the ability of distribution networks to cope with disturbances caused by uncertainties and to evaluate the maximum accommodation capacity of DG, a multi-objective programming method for evaluation of the accommodation capacity of distribution networks for DG is proposed, considering the flexibility of distribution networks in this paper. Firstly, a multi-objective optimization model for determining the maximum accommodation of DG by considering the flexibility of distribution networks is constructed, aiming at maximizing the daily energy consumption, minimizing the voltage amplitude deviation, and maximizing the line capacity margin. Secondly, the comprehensive learning particle swarm optimization (CLPSO) algorithm is used to solve the multi-objective optimization model. Then, the mixed strategy Nash equilibrium is introduced to obtain the frontier solution with the optimal joint equilibrium value in the Pareto solution set. Finally, the effectiveness of the proposed method is demonstrated with an actual distribution network in China. The simulation results show that the proposed planning method can effectively find the Pareto optimal solution set by considering multiple objectives, and can obtain the optimal equilibrium solution for DG accommodation capacity and distribution network flexibility.

scholarly journals Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan

2020 ◽  
Vol 12 (2) ◽  
pp. 57-71
Author(s):  
Ramadoni Syahputra ◽  
Indah Soesanti
Keyword(s):  
Network Optimization ◽  
Load Balance ◽  
Distribution Network ◽  
Reducing Power ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Network Reconfiguration ◽  
Multi Objective Optimization ◽  
Power Losses ◽  
Multi Objective

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

scholarly journals Multi-objective optimization based optimal sizing & placement of multiple distributed generators for distribution network performance improvement

2021 ◽  
Author(s):  
Anilkumar Trikambhai Markana ◽  
Gargi Trivedi ◽  
Dr. Praghnesh Bhatt
Keyword(s):  
Load Balancing ◽  
Network Performance ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Multi Objective Optimization ◽  
Optimal Sizing ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Voltage Deviation ◽  
Teaching Learning

Integration of Distributed Generations (DGs) into radial distribution network (RDN) is an emerging need to explore the benefits of renewable energy sources (RES). Increasing penetration of RES based DGs in RDN without proper planning leads to several operational problems such as excessive energy losses, poor voltage quality and load balancing. Hence, in this work, multi-objective optimization (MOO) problem is formulated by carefully chosen three conflicting objectives such as power loss minimization, enhancement of load balancing index (LBI) and aggregate voltage deviation index (AVDI). Teaching- Learning-Based-Optimization (TLBO) is used to optimize MOO problem considering placement of DGs at multiple locations in RDN satisfying the constraints on bus voltage magnitude, branch flows and DG size. Comprehensive simulation studies have been carried out to obtain optimal performance for 69- nodes RDN with the increasing penetration of DGs at multiple locations. It is shown that determination of optimal sizing of DGs at multiple locations in RDN with MOO results in lesser power losses, improved voltage profiles and better load balancing as compared to placement of single DG in RDN. Performance measures such as spacing and spread indicators are used for characterizing Pareto solutions for MOO problem. Such set of non-dominated solutions obtained from Pareto front during multi-objective TLBO gives proper guidelines to the utility operator about sizing and placement of DGs based on the assigned priorities to the objectives.

scholarly journals Combining Machine Learning Analysis and Incentive-Based Genetic Algorithms to Optimise Energy District Renewable Self-Consumption in Demand-Response Programs

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 945
Author(s):  
Vincenzo Croce ◽  
Giuseppe Raveduto ◽  
Matteo Verber ◽  
Denisa Ziu
Keyword(s):  
Machine Learning ◽  
Power Flow ◽  
Renewable Energy Sources ◽  
Substantial Reduction ◽  
Distribution Networks ◽  
Third Party ◽  
Multi Objective Optimization ◽  
Smart Meters ◽  
Multi Objective ◽  
Energy Management Systems

The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district’s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district’s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District’s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator’s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption.

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