Skip to main content

OPINION article

Front. Energy Res., 14 September 2022
Sec. Smart Grids
This article is part of the Research Topic Flexibility Analysis and Regulation Technology of Clean Energy System View all 17 articles

Flexibility evaluation and index analysis of distributed generation planning for grid-source coordination

Xiaowei Yu
Xiaowei Yu1*Xu LingXu Ling1Xiaogang ZhouXiaogang Zhou1Kui Li
Kui Li2*Xiaoxia FengXiaoxia Feng2
  • 1Central China Branch of State Grid Corporation of China, Wuhan, China
  • 2Central Southern China Electric Power Design Institute Co.,Ltd., Wuhan, China

1 Introduction

Electric energy has a critical impact on the consumer’s life, which is mostly based on centralized generation at present. However, the centralized generation has caused high power transmission and distribution losses. Meanwhile, the high combustion cost of fossil fuels and the environmental pollution caused by the greenhouse effect has become the problems that have to face. Reasonable planning sizing and location of distributed generations (DGs) to the distribution network (DN) may overcome the above limitations such as reducing power loss and environmental pollution. Therefore, access to DGs at the DN has been widely adopted (Rana et al., 2017; Ali and Qiang, 2018). Nowadays, due to the ability to maximize the use of renewable energy such as wind, solar, wave, hydro, and hydrogen the penetration rate of DGs has increased.

The optimal location and capacity of DGs properly may reduce power loss, cost and enhance the reliability of the power grid. A large number of DG connections have a great impact on DN such as power flow, voltage profile, power loss, and power grid stability of the DN. Therefore, it is valuable to quantitatively evaluate the comprehensive impact of DG connections on the DN (Francisco et al., 2020; Li et al., 2021). In order to evaluate whether the location and capacity of the DGs connected to the power grid are correct, various evaluation indexes are proposed mainly including economy, technology, and environment. Reference (Wang et al., 2019) has defined the voltage stability index based on the power flow calculation results of the DN and studied the influence of the DG connection on the voltage stability of the DN. Literature (Su, 2010) has considered the uncertainty of the operation of the distribution system, including the daily variable load, the random DG generation, the grid configuration, and the voltage control device, and analyzes the influence of the distributed power supply connected to the distribution network on the voltage deviation. Currently, various literatures proposed several methods for optimal location and capacity of DGs, literature (Koutsoukis et al., 2014; Magadum and Kulkarni, 2015) separately proposed tabu search (TS), and fuzzy logic to minimize power loss and cost of DGs. Moreover, artificial neural networks (ANN) have either been designed to minimize energy loss, and it no need for flow calculations (Semic et al., 2019).

However, the above literature both ignores the users’ demand response (DR) about the DGs accessing, and just consider the DGs’ impact on the grid. Meanwhile, there is no review paper evaluating and analyzing the index of DGs. Hence, this paper has mainly evaluated and analyzed the economic index, and technology index of DGs. Furthermore, the advantages/limitations, applications, and objective function of each index have been proposed. Finally, valuable perspectives and challenges for future researchers are proposed.

2 Analysis of economic indexes

Generally, to decrease the environmental pollution and power loss caused by the traditional centralized generators, installing DGs in DN as far as possible is a conventional method to solve. However, how to not only decrease the investment, operation, and maintenance cost but enhance the reliability of the power grid is a critical challenge for the current researchers. Currently, several approaches have been proposed to solve this problem, as follows:

● By optimizing the location and capacity, the voltage quality can be improved and the loss of the grid will be reduced with less investment cost. Moreover, investment costs can be recovered by actively participating in DN management based on a price control mechanism, and investment costs can be recovered by appropriately increasing electricity prices. Meanwhile, by adding robots to make equipment intelligent to reduce operation and maintenance costs;

● Establish an incentive scheme to help the company and government to install DGs. These incentive plans may include government subsidies on the generator side and taxes on customers (e.g., environmental taxes);

● Active DN (ADN) mechanism should be more widely used in the current new power system. Based on the resident DR, combine the resident and generation to create more ADN.

The cost of the DG is a complex problem, which includes several uncertain paraments such as the price of the fuel cell, labor cost, inflation rate, tax, raw material cost, government subsidy, and DGs types. Literature (Yang et al., 2020) has comprehensively summarized the types of DGs which are divided into four types including only active power DGs (type-I), reactive power generators (type-II), which generate both active and reactive power DGs (type-III), and generate active power and consume reactive power (type-VI). A variety of uncertain parameters will lead to a very complex problem in calculating the investment and operation cost of the system. In addition to the investment and operation costs, the environmental benefits generated by installing DGs on the DN side should also be considered, which not only alleviates the pollution but better use of renewable energy (Priyanka et al., 2014). Because of its benefit, several researchers have chosen the minimized cost of DGs as the objective to optime the capacity and location. Table 1 comprehensively summarizes the typical economic indexes and analyzes their application, benefits, and limitations.

TABLE 1
www.frontiersin.org

TABLE 1. Summary and analyze the evaluation indexes of DGs.

Drawn from Table 1, typical economic indexes have been adopted for various types of DGs, and most of them are tested in experimental models. However, many researchers ignore the labor cost, tax, and fuel costs at present. Meanwhile, DGs planning is based on experimental models and has not been applied to the real model. Besides, in future research, DGs should be planned based on user experience. The demand side is extremely important. Future researchers should consider how to not only meet the users’ power consumption feeling but also reduce the investment and operation costs. In the small-scale power grid, the load on the user side and the daily load fluctuation are relatively low, while in the large-scale power grid, the daily load fluctuation is large and more DGs are required, so it is difficult to ensure the economy. Hence, in the large-scale power grid, the reliability index such as load fluctuation and voltage profile are more important. Moreover, as the increasing penetration of electric vehicles (EVs) has increased, it will cause load fluctuation. The design and selection of economic indexes will face difficulties.

3 Analysis of technology indexes

3.1 Power loss

Before the DGs are connected to DN, the power flow of the DN simply flows from the substation side to the load side. However, with the incorporation of the DGs, the direction and magnitude of the system power flow will be affected, thus changing the power loss of the DN. To minimize the power loss, the most ideal method is to install DGs locally at each power node. However, due to the investment cost and the limitation of the number of generators installed, this method is not feasible. In general, the value of DGs power factor is equal to the total load of the electric power system network, but there are equality constraints to prevent power flow reversal. In practice, the power factor of most DGs operates according to their rated power value, reducing power loss on the premise of maintaining voltage stability. The value of DGs power factor will be different in different periods (Quezada et al., 2006; Hengsritawat and Tayjasanant, 2012). During the peak off hour, to limit voltage fluctuation and reduce active power loss, the value of DGs power factor is generally chosen as the leading power factor. Besides, during the peak hour, to compensate for voltage drop and enhance reactive power, the value of DGs power factor is supposed to be chosen for the lagging power factor.

The access of DGs makes the DN change from a simple radiation network to a multi-power supply system. Before DGs are connected, the direction of feeder power flow is always unidirectional. When DN is connected to the DGs, the unidirectional power flow mode of the system is changed. The power flow of the system is not necessarily unidirectional, and the reverse flow of the power flow may occur. The access to DGs may lead to the increase or decrease of power flow along the feeder line. The power flow of DN will change, and the power loss of DN will change accordingly. The power loss may increase or decrease, depending on various factors. Furthermore, in the large-scale grid, due to the long transmission distance, the installation of DGs with improper capacity at the end of the line will reduce the power loss. Generally, the power loss may be reduced by the penetration level of DGs. Meanwhile, note that the location of DGs installed on the DN must cause a different effect on the power loss. The equations of the power loss with DGs and without DGs are as follows:

Sloss=SGSL=(UGUL)Ilink(1)
SlossDG=SGSL+SDG=(UGUL)(IlinkIDG)(2)

where SG, SL and SDG separately mean the power of the main grid, load, and DG; UG and UL denote the voltage of the main grid and load; Ilink and IDG are the current through the link and DG. The power loss reduced by DG is as follows:

SlossSlossDG=(UGUL)IDG(3)

Furthermore, as concluded from the literature (Ufa et al., 2022), all types of DGs can reduce power loss. However, the power loss reduced by different types of DGs connected to DN is different. The installation type of type-III has the most obvious effect on the reduction of power loss. The types of DGs that reduce power loss are sorted in the order of type-III, type-I, type-II, and type-IV, respectively. When DGs can deliver both active and reactive power, which have the most obvious effect on power loss reduction. In addition, DGs type-III and type-II can generate reactive power (improve voltage profile), which also has a great impact on the reduced power loss. Finally, when there is only type-IV installed on the DN, the high penetration level of the DGs will cause greater power loss than that without DGs (Prakash and Lakshminarayana, 2018).

3.2 Voltage profile

The allowable deviation of the three-phase power supply voltage of 10 kV and below is ± 7% of the nominal voltage (Li et al., 2021), and the smaller the absolute value, the better. Therefore, the system voltage profile index is defined as the average value of the absolute value of the voltage profile of each node to represent the overall voltage deviation level of the system. The calculation formula is as follows:

Us=i=1K|UiUR|URK(4)

where UR means the reference voltage of the electric power system; Ui is the ith node voltage; K denotes the number of the node.

Similar to the power loss, the voltage profile is related to the power factor, location, capacity, and penetration level of DGs. However, for small-scale load systems, DG integration may lead to voltage overrun and other problems. Therefore, before the DGs have been connected to the power grid, a large number of experimental analyses should be conducted.

4 Discussion and conclusion

To research the comprehensive impact of DGs access on DN, this paper comprehensively evaluated and analyzed the economic indexes and technical indexes of DG, and summarized their advantages and disadvantages and applicable scenarios in Table 1. Besides, two valuable conclusions about the DGs evaluation index have been proposed, as follows:

a) Economic indexes are generally applicable to small-scale grids because the load fluctuation of residents with low load in small-scale grids will not be large. Large-scale grids, not only consider the benefits and investment costs of the power grid but also consider the demand side response. Meanwhile, in the large-scale power grid, the load fluctuation is high. The most significant to configuring DGs is to maintain the stability of the power grid, so minimizing the cost and maximizing the benefits are suitable for small-scale power grids.

b) Reasonable access to DGs will reduce the power loss and improve the voltage profile of the power grid, especially in the large-scale power grid. The power loss and voltage profile are related to the power factor and location penetration level of DGs. In small-scale power grids, it is better not to connect DGs under light load conditions, otherwise voltage quality may reduce.

Generally speaking, according to the scale of the power grid, it is very important to flexibly coordinate the grid-source and reasonably select the evaluation indexes to maintain the stability of the power grid and ensure the economy.

Author contributions

XY: writing the manuscript; XL: discussion of the economic index; XZ: discussion of the technology index; KL: editing; XF: discussion of the topic.

Funding

Grid-source coordination development-based power sources planning evaluation index system research (5214GH210011).

Conflict of interest

XY, XL, XZ, KL, XF was employed by Central China Branch of State Grid Corporation of China, Central Southern China Electric Power Design Institute Co., Ltd.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Ali, E., and Qiang, Y. (2018). Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques. Appl. Energy 210, 44–59. doi:10.1016/j.apenergy.2017.10.106

CrossRef Full Text | Google Scholar

Francisco, C. R. C., Wesley, P., Ivo, C., and Bruno, H. (2020). Empirical continuous metaheuristic for multiple distributed generation scheduling considering energy loss minimization, voltage and unbalance regulatory limits. IET Generation,Transmission Distribution 14 (16), 3301–3309. doi:10.1049/iet-gtd.2019.1860

CrossRef Full Text | Google Scholar

He, S., Gao, H., Liu, J., and Liu, J. (2019)“Distributionally robust optimal DG allocation model considering flexible adjustment of demand response,” in Paper presented at: 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, August 4-8, 2019, 1–5. doi:10.1109/PESGM40551.2019.8973569

CrossRef Full Text | Google Scholar

Hengsritawat, V., and Tayjasanant, T. (2012). Impacts of load models and power factor control on optimal sizing of photovoltaic distributed generators in a distribution system. IEEJ Trans. Elec. Electron. Eng. 7 (6), 567–573. doi:10.1002/tee.21774

CrossRef Full Text | Google Scholar

Jabr, R., and Pal, B. (2009). Ordinal optimisation approach for locating and sizing of distributed generation. IET Generation Transm. Distribution 3 (8), 713–723. doi:10.1049/iet-gtd.2009.0019

CrossRef Full Text | Google Scholar

Koutsoukis, N. C., Georgilakis, P. S., and Hatziargyriou, N. D. (2014)“A Tabu search method for distribution network planning considering distributed generation and uncertainties,” in Paper presented at: 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Durham, England, July 7-10, 2014, 1–6. doi:10.1109/PMAPS.2014.6960627

CrossRef Full Text | Google Scholar

Li, G. D., Wang, Z., Shuai, H., Zhao, F., Zhang, Q., Liu, C., et al. (2021). Evaluation of the impact of distributed generation access on distribution network operation index. Electr. Energy Manag. Technol. 6, 79–85. doi:10.16628/j.cnki.2095-8188.2021.06.014

CrossRef Full Text | Google Scholar

Magadum, R. B., and Kulkarni, D. (2015)“Power loss reduction by optimal location of DG using fuzzy logic,” in Paper presented at: 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, India, May 6-8, 2015, 338–343. doi:10.1109/ICSTM.2015.7225438

CrossRef Full Text | Google Scholar

Porkar, S., Poure, P., Abbaspour-Tehrani-fard, A., and Saadate, S. (2011). Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming. Euro. Trans. Electr. Power 21 (1), 1072–1087. doi:10.1002/etep.497

CrossRef Full Text | Google Scholar

Prakash, D. B., and Lakshminarayana, C. (2018). Multiple DG placements in radial distribution system for multi objectives using whale optimization algorithm. Alexandria Eng. J. 57, 2797–2806. doi:10.1016/j.aej.2017.11.003

CrossRef Full Text | Google Scholar

Priyanka, P., Patidar, N. P., and Nema, R. K. (2014). Planning of grid integrated distributed generators: A review of technology, objectives and techniques. Renew. Sustain. Energy Rev. 40 (2014), 557–570. doi:10.1016/j.rser.2014.07.200

CrossRef Full Text | Google Scholar

Quezada, V. H. M., Abbad, J. R., and Roman, T. G. S. (2006). Assessment of energy distribution losses for increasing penetration of distributed generation. IEEE Trans. Power Syst. 21, 533–540. doi:10.1109/TPWRS.2006.873115

CrossRef Full Text | Google Scholar

Rana, H. A. Z., Geev, M., Haile-Selassie, R., Aghaei, J., Niknam, T., and Pillai, P. (2017). Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review. Renew. Sustain. Energy Rev. 72 (2017), 1177–1198. doi:10.1016/j.rser.2016.10.036

CrossRef Full Text | Google Scholar

Semic, E., Hubana, T., and Šaric, M. (2019)“Distributed generation allocation in low voltage distribution network using artificial neural network,” in Paper presented at: IEEE Eurocon 2019-18th International Conference on Smart Technologies, Novi Sad, Serbia, July 1-4, 2019, 1–6. doi:10.1109/EUROCON.2019.8861816

CrossRef Full Text | Google Scholar

Su, C. (2010). Stochastic evaluation of voltages in distribution networks with distributed generation using detailed distribution operation models. IEEE Trans. Power Syst. 25 (2), 786–795. doi:10.1109/TPWRS.2009.2034968

CrossRef Full Text | Google Scholar

Ufa, R. A., Malkova, Y. Y., Rudnik, V. E., Boriov, V. A., and Borisov, V. (2022). A review on distributed generation impacts on electric power system. Int. J. Hydrogen Energy 47, 20347–20361. doi:10.1016/j.ijhydene.2022.04.142

CrossRef Full Text | Google Scholar

Wang, X. L., Hao, C. C., Li, X. M., Sun, S. X., and Shi, W. C. (2019). The vulnerability analysis of distribution network with distributed generation. Electr. Meas. Instrum. 56 (6), 38–43. doi:10.19753/j.issn1001-1390.2019.06.007

CrossRef Full Text | Google Scholar

Yang, B., Yu, L., Chen, Y. X., Ye, H., Shao, R., Shu, H., et al. (2020). Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state-of-the-art survey. Int. J. Energy Res. 45 (3), 3615–3642. doi:10.1002/er.6104

CrossRef Full Text | Google Scholar

Keywords: flexibility evaluationl, distributed generation, demand response, technology index, economic index

Citation: Yu X, Ling X, Zhou X, Li K and Feng X (2022) Flexibility evaluation and index analysis of distributed generation planning for grid-source coordination. Front. Energy Res. 10:1019352. doi: 10.3389/fenrg.2022.1019352

Received: 15 August 2022; Accepted: 23 August 2022;
Published: 14 September 2022.

Edited by:

Nantian Huang, Northeast Electric Power University, China

Reviewed by:

Xiaoshuna Zhang, Northeastern University, China
Yiyan Sang, Shanghai University of Electric Power, China

Copyright © 2022 Yu, Ling, Zhou, Li and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiaowei Yu, 1261481464@qq.com; Kui Li, 61145986@qq.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.