A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jitendra Singh Bhadoriya ◽  
Atma Ram Gupta
Keyword(s):  
Energy Storage ◽  
Objective Function ◽  
Distributed Generation ◽  
Carbon Emissions ◽  
Distribution Network ◽  
Clean Energy ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Novel Approach ◽  
Demand Models

Abstract In recent times, producing electricity with lower carbon emissions has resulted in strong clean energy incorporation into the distribution network. The technical development of weather-driven renewable distributed generation units, the global approach to reducing pollution emissions, and the potential for independent power producers to engage in distribution network planning (DNP) based on the participation in the increasing share of renewable purchasing obligation (RPO) are some of the essential reasons for including renewable-based distributed generation (RBDG) as an expansion investment. The Grid-Scale Energy Storage System (GSESS) is proposed as a promising solution in the literature to boost the energy storage accompanied by RBDG and also to increase power generation. In this respect, the technological, economic, and environmental evaluation of the expansion of RBDG concerning the RPO is formulated in the objective function. Therefore, a novel approach to modeling the composite DNP problem in the regulated power system is proposed in this paper. The goal is to increase the allocation of PVDG, WTDG, and GSESS in DNP to improve the quicker retirement of the fossil fuel-based power plant to increase total profits for the distribution network operator (DNO), and improve the voltage deviation, reduce carbon emissions over a defined planning period. The increment in RPO and decrement in the power purchase agreement will help DNO to fulfill round-the-clock supply for all classes of consumers. A recently developed new metaheuristic transient search optimization (TSO) based on electrical storage elements’ stimulation behavior is implemented to find the optimal solution for multi-objective function. The balance between the exploration and exploitation capability makes the TSO suitable for the proposed power flow problem with PVDG, WTDG, and GSESS. For this research, the IEEE-33 and IEEE-69 low and medium bus distribution networks are considered under a defined load growth for planning duration with the distinct load demand models’ aggregation. The findings of the results after comparing with well-known optimization techniques DE and PSO confirm the feasibility of the method suggested.

Research on the Voltage Influence of Active Distribution Network with Distributed Generation Access

2014 ◽  
Vol 668-669 ◽  
pp. 749-752 ◽  
Author(s):  
Xiao Yi Zhou ◽  
Ling Yun Wang ◽  
Wen Yue Liang ◽  
Li Zhou
Keyword(s):  
Distributed Generation ◽  
Power Distribution ◽  
Distribution System ◽  
Power Flow ◽  
Distribution Network ◽  
Distribution Networks ◽  
Power Distribution Network ◽  
Active Distribution Network ◽  
Novel Approach ◽  
Active Distribution Networks

Distributed generation (DG) has an important influence on the voltage of active distribution networks. A unidirectional power distribution network will be transformed into a bidirectional, multiple power supply distribution network after DGs access to the distribution network and the direction of power flow is also changed. Considering the traditional forward and backward substitution algorithm can only deal with the equilibrium node and PQ nodes, so the other types of DGs should be transformed into PQ nodes, then its impact on active distribution network can be analyzed via the forward and backward substitution algorithm. In this paper, the characteristics of active distribution networks are analyzed firstly and a novel approach is proposed to convert PI nodes into PQ nodes. Finally, a novel forward and backward substitution algorithm is adopted to calculate the power flow of the active distribution network with DGs. Extensive validation of IEEE 18 and 33 nodes distribution system indicates that this method is feasible. Numerical results show that when DG is accessed to the appropriate location with proper capacity, it has a significant capability to support the voltages level of distribution system.

scholarly journals Adaptive Day-Ahead Prediction of Resilient Power Distribution Network Partitions

2021 ◽  
Author(s):  
Chinmay Shah ◽  
Richard Wies
Keyword(s):  
Power Distribution ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Power Distribution Networks ◽  
Power Distribution Network ◽  
High Penetration ◽  
Islanded Mode

The conventional power distribution network is being transformed drastically due to high penetration of renewable energy sources (RES) and energy storage. The optimal scheduling and dispatch is important to better harness the energy from intermittent RES. Traditional centralized optimization techniques limit the size of the problem and hence distributed techniques are adopted. The distributed optimization technique partitions the power distribution network into sub-networks which solves the local sub problem and exchanges information with the neighboring sub-networks for the global update. This paper presents an adaptive spectral graph partitioning algorithm based on vertex migration while maintaining computational load balanced for synchronization, active power balance and sub-network resiliency. The parameters that define the resiliency metrics of power distribution networks are discussed and leveraged for better operation of sub-networks in grid connected mode as well as islanded mode. The adaptive partition of the IEEE 123-bus network into resilient sub-networks is demonstrated in this paper.

scholarly journals Proposal of optimal operation strategy applied to water distribution network with statistical approach

2020 ◽  
Vol 15 (2) ◽  
pp. 1
Author(s):  
Alex Takeo Yasumura Lima Silva ◽  
Fernando Das Graças Braga da Silva ◽  
André Carlos da Silva ◽  
José Antonio Tosta dos Reis ◽  
Claudio Lindemberg de Freitas ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Optimization Techniques ◽  
Experimental Techniques ◽  
Computational Optimization ◽  
Depth Analysis

 Inefficiency of sanitation companies’ operation procedures threatens the population’s future supplies. Thus, it is essential to increase water and energy efficiency in order to meet future demand. Optimization techniques are important tools for the analysis of complex problems, as in distribution networks for supply. Currently, genetic algorithms are recognized by their application in literature. In this regard, an optimization model of water distribution network is proposed, using genetic algorithms. The difference in this research is a methodology based on in-depth analysis of results, using statistics and the design of experimental tools and software. The proposed technique was applied to a theoretical network developed for the study. Preliminary simulations were accomplished using EPANET, representing the main causes of water and energy inefficiency in Brazilian sanitation companies. Some parameters were changed in applying this model, such as reservoir level, pipe diameter, pumping pressures, and valve-closing percentage. These values were established by the design of experimental techniques. As output, we obtained the equation of response surface, optimized, which resulted in values of established hydraulic parameters. From these data, the obtained parameters in computational optimization algorithms were applied, resulting in losses of 26.61%, improvement of 16.19 p.p. with regard to the network without optimization, establishing an operational strategy involving three pumps and a pressure-reducing valve.  We conclude that the association of optimization and the planning of experimental techniques constitutes an encouraging method to deal with the complexity of water-distribution network optimization.

Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network

2018 ◽  
pp. 29-48 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Luqman Hakim Rahman
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Voltage Stability ◽  
Reactive Power ◽  
Distribution Network ◽  
Stability Index ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Optimal Placement

In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG integration. A specific analysis has been applied on IEEE 33 bus system radial distribution networks using MATLAB 2015a software.

scholarly journals Security-Constrained Day-Ahead Operational Planning for Flexible Hybrid AC/DC Distribution Networks

2019 ◽  
Vol 9 (21) ◽  
pp. 4685 ◽  
Author(s):  
Ahmad Asrul Ibrahim ◽  
Behzad Kazemtabrizi ◽  
Javier Renedo
Keyword(s):  
Distributed Generation ◽  
Optimal Power Flow ◽  
Distribution Network ◽  
Distribution Networks ◽  
Operational Planning ◽  
Hybrid Network ◽  
Management Framework ◽  
Medium Voltage ◽  
Operational Costs ◽  
Network Losses

A new active network management framework is presented based on a multi-period optimal power flow problem that is bounded by security constraints at the distribution level for upholding the security of supply. This can be achieved through active engagement with flexible demand and distributed generation to prepare for contingency events in day-ahead operational planning. This framework is coupled with a flexible hybrid AC/DC medium voltage (MV) distribution network topology. It contains an integrated multi-terminal medium voltage DC (MVDC) interface for a seamless interaction and integration of the flexible demand and generation on both AC and DC sides of the hybrid network. The active energy management framework when coupled with a flexible hybrid AC/DC topology provides unprecedented degrees of flexibility as well as security of operation under a variety of conditions. To this end, the 75-bus UK generic distribution network has been modified and converted into a hybrid AC/DC network using the integrated MVDC interface. This framework is then deployed to minimise operational costs to the network operator, considering costs of schemes such as distributed generation curtailment and flexible demand shifting, as well as network losses. Results show a significant improvement in operational costs when the network operates as a flexible hybrid when compared to a pure AC or a more conventional AC/DC hybrid.

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 Research on Active Distribution Network Planning with Electric Vehicles and Distributed Generation

2021 ◽  
Vol 257 ◽  
pp. 01010
Author(s):  
Lingyan Wei ◽  
Bing Wang ◽  
Xiaoyue Wu ◽  
Fumian Wang ◽  
Peng Chen
Keyword(s):  
Time Series ◽  
Distributed Generation ◽  
Distribution Network ◽  
Network Planning ◽  
Clean Energy ◽  
Space Time ◽  
Maintenance Cost ◽  
Active Distribution Network ◽  
Distribution Network Planning ◽  
Ev Charging

With the increasing number of Electric Vehicle (EV) and clean energy generation year by year, EV and distributed generation (DG) have become issues that have to be considered in active distribution network planning. Firstly, considering the time series characteristics of DG, the output time series model of DG is established; Secondly, the parking demand and space-time movement model of EV is established, and the Monta Carlo method is used to simulate the space-time distribution of EV charging load in different planning areas; Finally, taking the system investment and annual operation and maintenance cost, voltage index and environmental index as the objective function, and considering the node voltage, node current and DG installation capacity as constraints. The improved particle swarm optimization algorithm is used to solve the planning model, and the access location and capacity of EV charging station and DG are obtained. Taking a distribution network as an example, the rationality and effectiveness of the proposed model and algorithm are verified.

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