The increasing penetration of new energy sources in power systems has significantly heightened uncertainty factors within distribution networks, thereby imposing elevated demands on their planning, operation, and control. This paper presents a comprehensive methodology to address these challenges. Initially, quantitative modeling of uncertainty factors within distribution networks is conducted, establishing a source-load output model. Subsequently, the correlation between photovoltaic generation and electrical loads is investigated, leading to the development of a probabilistic power flow calculation method that accounts for this correlation. Finally, an optimization framework is constructed with the objective function of minimizing planning costs. The results validate the effectiveness of the proposed methodology in reducing network losses and minimizing network planning expenses. This research contributes to enhancing the reliability and cost-effectiveness of distribution network operations in the face of increased renewable energy penetration and uncertainty.
SæleHSperstadIBWang HoiemK, et al.Understanding barriers to utilising flexibility in operation and planning of the electricity distribution system – classification frameworks with applications to Norway. Energy Pol2023; 180: 113618. DOI: 10.1016/j.enpol.2023.113618.
2.
ZacchiniLRidolfiAAllottaB. Informed expansion for informative path planning via online distribution learning. Robot Autonom Syst2023; 166: 104449. DOI: 10.1016/j.robot.2023.104449.
3.
GattaFMGeriAMaccioniM, et al.Low voltage electric distribution network planning with demand control. Elec Power Syst Res2024; 226: 109950. DOI: 10.1016/j.epsr.2023.109950.
4.
ZhouSHanYChenS, et al.A multiple uncertainty-based Bi-level expansion planning paradigm for distribution networks complying with energy storage system functionalities. Energy2023; 275: 127511. DOI: 10.1016/j.energy.2023.127511.
5.
PaulSPoudyalAPoudelS, et al.Resilience assessment and planning in power distribution systems: past and future considerations. Renew Sustain Energy Rev2024; 189: 113991. DOI: 10.1016/j.rser.2023.113991.
6.
Al-Ja’AfrehMAAAmjadBRoweK, et al.Optimal planning and forecasting of active distribution networks using a multi-stage deep learning based technique. Energy Rep2023; 10: 686–705. DOI: 10.1016/j.egyr.2023.07.014.
7.
DongHWangLZhangX, et al.A two-stage stochastic collaborative planning approach for data centers and distribution network incorporating demand response and multivariate uncertainties. J Clean Prod2024; 451: 141482. DOI: 10.1016/j.jclepro.2024.141482.
8.
AhmedHMASindiHFAzzouzMA, et al.Stochastic multi-benefit planning of mobile energy storage in reconfigurable active distribution systems. Sustain. Energy Grids Netw. 2023; 36: 101190. doi:10.1016/j.segan.2023.101190.
9.
YangSWangXYangY, et al.Bi-level planning model of distributed PV-energy storage system connected to distribution network under the coordinated operation of electricity-carbon market. Sustain Cities Soc2023; 89: 104347. DOI: 10.1016/j.scs.2022.104347.
10.
XuXWangMXuZ, et al.Generative adversarial network assisted stochastic photovoltaic system planning considering coordinated multi-timescale volt-var optimization in distribution grids. Int J Electr Power Energy Syst2023; 153: 109307. DOI: 10.1016/j.ijepes.2023.109307.
11.
Moradi-SepahvandMAmraeeTAminifarF, et al.Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies. Int J Electr Power Energy Syst2023; 147: 108859. DOI: 10.1016/j.ijepes.2022.108859.
12.
MoradiMHAbediniM. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst2012; 34(1): 66–74. DOI: 10.1016/j.ijepes.2011.08.023.
13.
PenangsangOPutraDFUKurniawanT. Optimal placement and sizing of distributed generation in radial distribution system using K-means clustering method. In: 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, 28–29 August 2017, pp. 98–103. DOI: 10.1109/ISITIA.2017.8124062.
14.
HerdingLPérez-BravoMBarrellaR, et al.Large-scale estimation of electricity distribution grid reinforcement requirements for the energy transition – a 2030 Spanish case study. Energy Rep2024; 12: 5432–5444. DOI: 10.1016/j.egyr.2024.11.026.
15.
MoreiraAMolinaYAquinoR, et al.Allocation and sizing of photovoltaic systems to reduce power losses and economic aspects using a new PSO approach. IEEE Latin Am Trans2022; 20(6): 977–985. DOI: 10.1109/TLA.2022.9757741.
16.
AlajmiBNAlHajriMFAhmedNA, et al.Multi-objective optimization of optimal placement and sizing of distributed generators in distribution networks. IEEJ Transactions Elec Engng2023; 18(6): 817–833. DOI: 10.1002/tee.23784.
17.
ZhangLSunCCaiG, et al.Charging and discharging optimization strategy for electric vehicles considering elasticity demand response. eTransportation2023; 18: 100262. DOI: 10.1016/j.etran.2023.100262.
18.
HemmatiR. Dynamic expansion planning in active distribution grid integrated with seasonally transferred battery swapping station and solar energy. Energy2023; 277: 127719. DOI: 10.1016/j.energy.2023.127719.
19.
DwivediDYemulaPKPalM. Evaluating the planning and operational resilience of electrical distribution systems with distributed energy resources using complex network theory. Renewable Energy Focus2023; 46: 156–169. DOI: 10.1016/j.ref.2023.06.007.
20.
WangPLiH. Coordinated planning of soft open point and energy store system in active distribution networks under source-load imbalance. Elec Power Syst Res2024; 231: 110324. DOI: 10.1016/j.epsr.2024.110324.
21.
BehzadiSBagheriA. A convex micro-grid-based optimization model for planning of resilient and sustainable distribution systems considering feeders routing and siting/sizing of substations and DG units. Sustain Cities Soc2023; 97: 104787. DOI: 10.1016/j.scs.2023.104787.
