Multi-objective robust optimization of active distribution networks considering uncertainties of photovoltaic

Xu, Tao; Ren, Yi; Guo, Lingxu; Wang, Xiaoxue; Liang, Lemeng; Wu, Yuhan

doi:10.1016/j.ijepes.2021.107197

Cited by 44 publications

(10 citation statements)

References 35 publications

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“…As a result, the model of the considered uncertainty set is obtained from the sharing of relations (15)(16)(17)(18).…”

Section: A Mg Islanding Event Uncertainty Setmentioning

confidence: 99%

“…The stochastic programming approach is scenario-centric and considers probability distribution function (PDF) for uncertain parameters [17]. Then, the Monte Carlo method is used to simulate scenarios based on the distribution function and the probability of each scenario is determined to calculate the probability of occurrence of each scenario [18]. Robust optimization is practical when the uncertainty sets of uncertain parameters/variables are available.…”

Section: Introductionmentioning

confidence: 99%

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Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization

et al. 2022

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A two-stage adaptive robust optimization is developed for pre-disturbance scheduling in microgrids (MGs) for handling uncertainties associated with electricity market prices, renewable generation, demand forecasts, and islanding events. The objective is to produce a reliable and optimal solution for MG operation that minimizes operational costs and the risk/failure in islanding events. In the literature, the uncertainty sets associated with islanding events cover a full scheduling period which results in a sub-optimal solution. In this paper, uncertainty sets corresponding to islanding events are modeled based on reliability/resiliency-oriented indexes of the MG/grid to achieve a more accurate/reliable solution. Besides, the Benders decomposition algorithm which is used to handle uncertainties in solving the optimization problem is time-consuming. Therefore, the column-and-constraint generation (C&CG) decomposition strategy is adopted to make the problem computationally tractable. Further, the uncertainty budget parameters are clarified to balance the conservatism and optimality (cost minimization) of the robust solution in uncertainty sets. The effectiveness of the proposed framework is evaluated and discussed by using a set of numerical studies with different scenarios in an MG. The simulations show that the proposed framework reduces operational costs by using the precise analysis of uncertainty budgets and a change in scheduling periods.

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“…As a result, the model of the considered uncertainty set is obtained from the sharing of relations (15)(16)(17)(18).…”

Section: A Mg Islanding Event Uncertainty Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization

et al. 2022

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show abstract

“…Similar to [30] a combination of robust and randomized optimization algorithms is utilized to obtain a less conservative solution [31]. In robust optimization, worst‐case scenarios are used to make the solution more conservative and it leads to high cost [3, 32]. However, in [25] the IGDT is proposed to consider both aforementioned policies.…”

Section: Introductionmentioning

confidence: 99%

A novel robust Volt/Var optimization method based on worst‐case scenarios for distribution network operation

Ahmadi

Salami

Alavi

2023

IET Generation Trans & Dist

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The Volt/Var optimization (VVO) problem is used for scheduling the voltage regulation and reactive power compensation equipment in distribution networks to minimize power loss and voltage violation. In order to solve the VVO problem for a forthcoming time horizon, it is necessary to predict some parameters such as load demand and renewable energy production. The prediction of these parameters is always accompanied by uncertainty that robust optimization can be used to solve this concern. This paper presents a scenario-based robust Volt/Var optimization (RVO) method that significantly reduces the number of scenarios required for the worst-case approach. Solving the VVO problem with the worst-case scenarios reduces the computational burden and maintains the voltage security of the distribution network against the severe events. The proposed RVO is formulated based on a mixed-integer second-order cone programming (MISOCP) model in which Volt/Var control (VVC) equipment is scheduled over a two-stage strategy. The proposed method is validated using modified 33-bus and 69-bus IEEE test systems. The results demonstrate that the proposed RVO method maintains the network's voltage profile within the acceptable range against uncertainties.

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“…The output of DG has strong randomness and intermittency, which increases the complexity and uncertainty of the system. It will cause great changes in node voltage, power flow direction, network loss and branch power, and severely impact the economic and safe operation of the system (Xu et al, 2021). Nick et al (2014) argues that technologies such as network reconfiguration and reactive power optimization can effectively optimize the distribution network operation.…”

Section: Introductionmentioning

confidence: 99%

Using multi-objective sparrow search algorithm to establish active distribution network dynamic reconfiguration integrated optimization

Xiong

Tseng

Zhou

et al. 2022

Expert Systems with Applications

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Multi-objective robust optimization of active distribution networks considering uncertainties of photovoltaic

Cited by 44 publications

References 35 publications

Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization

Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization

A novel robust Volt/Var optimization method based on worst‐case scenarios for distribution network operation

Using multi-objective sparrow search algorithm to establish active distribution network dynamic reconfiguration integrated optimization

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