Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

Huang, Wujing; Zhang, Ning; Yang, Jingwei; Wang, Yi; Chen, Kang

doi:10.1109/tsg.2017.2767860

Cited by 276 publications

(54 citation statements)

References 35 publications

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“…The upper model is targeted at the annual total cost, the energy component parameters are the decision-making amount, the lower model is the annual operating cost, and the energy component scheduling is the decisionmaking amount [8].…”

Section: Bi-level Planning Modelmentioning

confidence: 99%

Bi-level Planning Model for Integrated Energy Network in Solid Waste Treatment Park

Zhang

Chen

et al. 2019

E3S Web Conf.

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In this paper, a bi-level planning model was proposed for the integrated energy network in solid waste treatment park to achieve optimal energy planning in the park. Firstly, the factors affecting planning were analyzed from the aspects of energy and environment. Then, the physical architecture of the integrated energy network planning of the solid waste treatment park was proposed, and the typical energy component models of the solid waste treatment park were proposed. This paper took the annual total cost as the first-level target and the annual operating cost as the second-level target, then proposed the bi-level planning model of the integrated energy network. Finally, the advantages of the model were verified by example. The example shows that under the action of the bi-level planning model, the energy, environment and economic benefits of the solid waste treatment park can be significantly improved.

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Section: Bi-level Planning Modelmentioning

confidence: 99%

Bi-level Planning Model for Integrated Energy Network in Solid Waste Treatment Park

Zhang

Chen

et al. 2019

E3S Web Conf.

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“…Qazi et al [24], presented a review of renewable energy sources management in the power generation system, and highlighted the influence of public opinion. Huang et al [25], investigated the optimal configuration for multi-renewable energy systems and considered load profiles, energy prices, and equipment parameters to create optimal consideration systems; the proposed approach can optimize both the equipment selection and the configuration of the multi-energy system. Cao et al [26] studied polypropylene ferroelectric (PPFE) as nanogenerators that can convert mechanical energy to electrical energy for self-powering devices and energy storage systems; the maximum power output obtained around 0.902 µW.…”

Section: Introductionmentioning

confidence: 99%

Development of Micro-Mobility Based on Piezoelectric Energy Harvesting for Smart City Applications

2020

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This study investigates the use of an alternative energy source in the production of electric energy to meet the increasing energy requirements, encourage the use of clean energy, and thus reduce the effects of global warming. The alternative energy source used is a mechanical energy by piezoelectric material, which can convert mechanical energy into electrical energy, that can convert mechanical energy from pressure forces and vibrations during activities such as walking and traveling into electrical energy. Herein, a pilot device is designed, involving the modification of a bicycle into a stationary exercise bike with a piezoelectric generator, to study energy conversion and storage generated from using the bike. Secondly, the piezoelectric energy harvesting system is used on bicycles as a micro-mobility, light electric utility vehicle with smart operation, providing a novel approach to smart city design. The results show that the energy harvested from the piezoelectric devices can be stored in a 3200 mAh, 5 V battery and power sensors on the bicycle. Moreover, 13.6 mW power can be generated at regular cycling speed, outputting 11.5 V and 1.2 mA. Therefore, the piezoelectric energy harvesting system has sufficient potential for application as a renewable energy source that can be used with low power equipment.

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“…Integrated multicarrier energy systems have been considered recently as basic units, energy hub (EH), and MG . Microgrid and EH system propound and share some of the same characteristics in the modelling of future energy systems.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the analysis of electric power systems should encompass all forms of energy to identify optimal design, operational strategies, and synergies. 6 Integrated multicarrier energy systems have been considered recently as basic units, 7 energy hub (EH), 6 and MG. 8 Microgrid and EH system propound and share some of the same characteristics in the modelling of future energy systems. So, the study of the papers related to EH can help and contribute to the development and modelling of MG systems.…”

Section: Introductionmentioning

confidence: 99%

Reliability‐constrained optimal design of multicarrier microgrid

Amir

Azimian

Razavizadeh

2019

Int Trans Electr Energ Syst

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Summary The unprecedented penetration of distributed generation in distribution energy networks provides utilities with a unique opportunity to manage portions of networks as microgrids (MGs). The implementation of an MG may offer many benefits, such as capital investments deferral, reduction of greenhouse gas emissions, improvement in reliability, and reduction in network losses. Future energy networks will contain various forms of energy which are acquired by mixing several sources and energy storages in the concept called multicarrier microgrid (MCMG). In order to draw the most effective performance from MCMG systems, appropriate design and operation are essential. This paper represents a compound co‐optimization strategy to find the best type and size of components and the associated optimum dispatch in a grid‐tied community MCMG implementing reliability criteria. Here, the required level of reliability is handled within the optimization process to fulfill multiple demands. The mixed‐integer nonlinear programming (MINLP) technique of general algebraic modeling system (GAMS) and the genetic algorithm of MATLAB software are utilized to solve the co‐optimization problem. Additionally, a contemporary time‐based demand response program is modeled to reshape the load curve, as well as prevent the undue use of energy in peak hours. Eventually, the proposed strategy is applied to a test case to select the best components while respecting reliability restrictions. Numerical simulations prove the effectiveness of the proposed expansion planning.

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Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

Cited by 276 publications

References 35 publications

Bi-level Planning Model for Integrated Energy Network in Solid Waste Treatment Park

Bi-level Planning Model for Integrated Energy Network in Solid Waste Treatment Park

Development of Micro-Mobility Based on Piezoelectric Energy Harvesting for Smart City Applications

Reliability‐constrained optimal design of multicarrier microgrid

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