Joint expansion planning of distribution network with uncertainty of demand load and renewable energy

Zhou, Siyu; Han, Yi; Chen, Shu‐Heng; Yang, Ping; Zalhaf, Amr S.

doi:10.1016/j.egyr.2022.05.203

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…e proposed approach combines Delaunay Triangulation with a MILP model. In [18], a new approach based on spanning tree to solve the expansion of lines and allocation of DGs considering the uncertainty of load demand and RESs is proposed for the MV distribution network. In [19], an improved particle swarm optimization algorithm based on particle swarm optimization for adaptive improvement is proposed for the DNEP problem in the MV part, in which the feasibility and superiority of the algorithm are illustrated.…”

Section: Motivation and Aimmentioning

confidence: 99%

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Rastgou

Hosseini-Hemati

2022

International Transactions on Electrical Energy Systems

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Distribution network expansion planning (DNEP) is one of the important matters in the field of planning and operation of electrical power systems. Since many costs and losses have occurred in the distribution networks, it has increased attention towards this network. The electrical energy distribution network is divided into two parts: the medium voltage (MV) network and the low voltage (LV) network. The main problem in this field is that planning is done either only on the MV network or only on the LV network. While planning in each of these networks has a significant effect on the other networks, this important case has not been considered in most research studies. Therefore, this study has tried to do integrated planning in the form of a bi-level model in the presence of different types of distributed generations (DGs) and consider the uncertainties of renewable sources and load demand in both MV and LV networks so that the planning and operation costs are minimized. In the proposed bi-level model, the upper-level section aims to minimize the investment and the operation cost of the MV network, and the lower-level problem minimizes the investment and the operation cost of the LV network considering the DGs and pollution emission. The obtained results show the effectiveness of the proposed model.

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Section: Motivation and Aimmentioning

confidence: 99%

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Rastgou

Hosseini-Hemati

2022

International Transactions on Electrical Energy Systems

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“…The results show that the increase in the proportion of structural coupling lines decreases the threshold for the first-order phase transition threshold, and the irrational distribution of service routings exacerbates the impact of fault cascading caused by functional coupling. Collaborative planning can comprehensively improve the security and reliability of the system from the overall perspective of the system [9][10][11]. In this paper, we formulate the collaborative expansion planning of CPS considering resilience as: under the premise that the location of new power/communication sites is determined, the system planner collaboratively optimizes transmission lines, communication lines, and service routings to minimize investment cost and improve the CPS resilience.…”

Section: Introductionmentioning

confidence: 99%

Collaborative stochastic expansion planning of cyber‐physical system considering extreme scenarios

Zhang

Wan

et al. 2023

IET Generation Trans & Dist

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In this paper, a collaborative stochastic expansion planning model of cyber‐physical system (CPS) with resilience constraints is proposed. The model can reduce the coupling risk and enhance the resilience under extreme scenarios, from the perspective of the structural/functional coupling between the physical and cyber systems. The model is to collaboratively optimize expansion transmission line siting, expansion communication fibre siting, and service routing distribution to minimize total investment cost. Constraints are divided into conventional constraints and resilience constraints. In the resilience constraints, the impact of cyber failures on the dispatch strategy is analyzed, and the load shedding in stochastic scenarios is limited to guarantee the resilience of the planning scheme. Particularly, to reach a stable solution for the stochastic planning, a mixed scenario set is proposed based on the probabilistic typhoon model and the complex network theory. Finally, the model is solved by the progressive hedging (PH) algorithm. The case studies of the IEEE RTS‐79 test system demonstrate that, compared with the traditional independent planning model, the model proposed in this paper performs better in limiting load loss and enhancing resilience under extreme scenarios.

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“…Similarly, the electricity demand from consumers can fluctuate unpredictably. 6,7 This results in some problems, such as harmonic pollution, 8 substandard voltage quality, 9 and equipment damage, 10 which affect the safe and stable operation of distribution networks. Secondly, the random and intermittent nature of DG and EV affect the distribution network operation.…”

Section: Introductionmentioning

confidence: 99%

“…20 Therefore, it is important to study the optimization of DG in modern power electronic distribution networks, considering the uncertainty of DG and load. 21 Currently, research dedicated to optimizing power quality and decreasing line losses is relatively mature. The majority of studies started from the perspective of optimizing algorithms or adding planning objects, aiming to improve computational efficiency while obtaining better optimization results.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike conventional power plants, the output of renewable energy sources like solar and wind depends on weather conditions, which can be variable and difficult to predict accurately. Similarly, the electricity demand from consumers can fluctuate unpredictably 6,7 . This results in some problems, such as harmonic pollution, 8 substandard voltage quality, 9 and equipment damage, 10 which affect the safe and stable operation of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

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A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

Jiang,

Han,

et al. 2023

Circuit Theory & Apps

Self Cite

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SummaryThe large integration of distributed generation (DG) and electric vehicles (EVs) facilitates daily life and reduces carbon emissions. However, it brings problems such as increased uncertainty and power electronic permeability to the distribution network. In this context, it is important to solve the line loss optimization problem as closely as possible to the real situation. Therefore, this paper solves the line loss optimization problem based on a probabilistic model of three‐phase unbalanced modern power electrical distribution network. Firstly, the paper proposes a frequency domain model of the distribution network and the optimization problem model. Then, to deal with the uncertainty of the system, the point estimation method (PEM) and Monte Carlo Simulation (MCS) are used. Finally, this paper proposes two optimization schemes considering the power quality when single and multiple DGs are integrated. The simulation results demonstrate that the PEM using the Gram–Charlier series expansion of the fourth‐order statistical moments to estimate the probability density function (PDF) is significantly less time consuming than the traditional MCS method while maintaining accuracy. The comparative analysis reveals that under the optimization scheme with single DG integration, the expected value of system line loss and expected line loss rate are reduced from 283.95 kW to 218.96 kW (22.89% reduction) and from 13.44% to 10.68%, respectively. Under the dual DG integrated optimization scheme, the expected value of line losses and expected line loss rate are reduced from 283.95 kW to 208.88 kW (26.4% reduction) and from 13.44% to 10.25%, respectively. However, after taking the uncertainty into account, the operational reliability of the system has been enhanced under different scenarios. In addition, it is found that the optimization approach incorporating multiple DG units can effectively mitigate the system line loss under diverse operational scenarios while enhancing the system's DG integration capability.

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Joint expansion planning of distribution network with uncertainty of demand load and renewable energy

Cited by 20 publications

References 10 publications

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Collaborative stochastic expansion planning of cyber‐physical system considering extreme scenarios

A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

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