Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting

Sun, Sizhou; Wang, Chenxi; Wang, Yu; Zhu, Xuehua; Lu, Huacai

doi:10.1016/j.egyr.2022.01.175

Cited by 41 publications

(10 citation statements)

References 41 publications

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“…Among them, data acquisition is the basis of collaborative scheduling, and the data of energy users are collected through smart meters, sensors, and other equipment, and transmitted to the dispatching center through the communication mode under the carbon verification data mining algorithm [5]. To improve the cooperative scheduling ability of the power grid energy side, this paper preliminarily obtains the cooperative scheduling task data of the power grid energy side, centrally adjusts the data existence mode and storage space, and carries out integrated data processing according to the relevant storage principles [6]. We organize the data information collection mode.…”

Section: Collaborative Scheduling Task Data Acquisition Of Grid Energ...mentioning

confidence: 99%

Collaborative scheduling method of power grid energy side based on carbon verification data mining algorithm

Yan,

Jiang,

Ren

2024

J. Phys.: Conf. Ser.

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In the process of the collaborative scheduling of the grid energy-consuming side, the voltage value is low, and the utilization rate of collaborative scheduling resources of the grid energy-consuming side is poor. Therefore, a collaborative scheduling method for the grid energy-consuming side based on a carbon verification data mining algorithm is proposed. Based on the structure of the data management system, the related data structure lookup formula is set to determine the collaborative scheduling task data of the power grid energy side. Under the carbon verification data mining algorithm, the power grid topology structure is constructed, the scheduling target parameters are processed, and the coordinated scheduling of the power grid energy side is realized. The experimental results show that this method can effectively improve the voltage value of each node, and the voltage value of all nodes can reach above the qualified value and improve the utilization rate and energy-saving performance, which has a certain effectiveness.

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Section: Collaborative Scheduling Task Data Acquisition Of Grid Energ...mentioning

confidence: 99%

Collaborative scheduling method of power grid energy side based on carbon verification data mining algorithm

Yan,

Jiang,

Ren

2024

J. Phys.: Conf. Ser.

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“…In the past decade, the modeling of uncertainty factors in power system has made great progress. Various statistical methods have been applied to the modeling and analysis of renewable energy generation [99], diversified load demand [100], energy market transaction price [101] and equipment operation state evaluation [102], all of which accurately reveal the random statistical behavior of uncertainty factors.…”

Section: A Characteristics and Modeling Of Uncertainmentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

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“…Market pricing (buying/selling) is taken into consideration for this power exchange. To showcase the effectiveness of the recommended algorithm, the performance of IPOA is compared to that of the original POA, the improved differential evolutionary (IDE) algorithm [17], and the bat algorithm [18]. This paper's key contributions can be succinctly outlined as follows:…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm

Jamal,

Pasupuleti,

Rahmat

et al. 2024

IEEE Access

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The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO 2 ) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO 2 emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multiobjective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG's optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO 2 emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO 2 emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO 2 emissions as against the first scenario.

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Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting

Cited by 41 publications

References 41 publications

Collaborative scheduling method of power grid energy side based on carbon verification data mining algorithm

Collaborative scheduling method of power grid energy side based on carbon verification data mining algorithm

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm

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