Efficient integration of demand response and plug-in electrical vehicle in microgrid: Environmental and economic assessment

Guo, Qun; Liang, Xiaodan; Xie, Dingnan; Jermsittiparsert, Kittisak

doi:10.1016/j.jclepro.2020.125581

Cited by 38 publications

(6 citation statements)

References 58 publications

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“…Wang et al 9 adopts demand response incentive mechanism to maximize the consumption of renewable energy power generation, users can be rewarded according to the response times and response capacity. Guo et al 10 analyzes the demand response characteristics of flexible load under the time‐of‐use (TOU) electricity price and incentive contract, and balances the cost and benefits of carbon reduction through source‐load collaborative optimization. Yang and Liu 11 introduced the carbon emission trading mechanism for IES, which can be proved to be a better way in promoting the capacity of renewable energy utilization and realizing the low‐carbon economic operation of IES.…”

Section: Introductionmentioning

confidence: 99%

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Liu

2024

Energy Science & Engineering

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To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network.

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Section: Introductionmentioning

confidence: 99%

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Liu

2024

Energy Science & Engineering

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“…Furthermore, a direct load control strategy proposed by Tascikaraoglu et al [30], obtains flexibility from residential heating, ventilation and air conditioning units and optimal management of storage systems with the aim of minimizing energy demand during the demand response event and minimize consumers discomfort. Guo et al [31] investigate the microgrid equipment scheduling taking into account the flexibility of demand response and plug-in electric vehicles and the results demonstrate a reduction of 3.57% and 3.4% respectively for the cost and emission of pollutants. Also, the real electric vehicles model is improved by using the ac-power flow and Wohler curve, as well as the probabilities are considered in several scenarios based on fuzzy decision making approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model presented by Zeng et al uses demand response flexibility (based on a price based approach) in MEM planning and scheduling, and with a generalized elasticity strategy, considers consumer behavior in response to price changes [32]. However, the reserve production and consumers thermal comfort are not taken into account in [31,32] (in addition, heating network and EPs constraints are lost in [31] and [32], respectively). Baldi et al presented another feedback-based method for load management using smart zoning and according to occupancy patterns [33], which in addition to utilizing renewable resources, improves consumers thermal comfort.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of flexibility in multi-energy microgrids considering voltage and congestion improvement: Robust thermal comfort against reserve calls

Kazemi‐Razi

Abyaneh

Nafisi

et al. 2021

Sustainable Cities and Society

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“…However, as vRE such as wind and solar are intermittent in nature and commonly come as nondispatchable energy, the forecasting of the generation input and controllable loads are important for the integration into MG for EV charging [9]. The high penetration of the fluctuating vRE in the MG could burden the system operation and the battery storage of the vehicles [10]. The penetration of EVs in the transport fleet can also introduce random new loads to the grid.…”

Section: Introductionmentioning

confidence: 99%

Integration of Variable Renewable Energy, Electric Vehicle, and Smart Microgrid in ASEAN: A Focus Group Discussion Approach

Bong

Hashim

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Southeast Asia is expecting rapid growth in energy and vehicle demand. Variable renewable energy such as solar and wind have high potential in the region. ASEAN aims to increase the renewable energy share in power capacity to 35% 2025. The increase in renewable energy share can drive a shift in the current energy management system. The adoption of electric vehicles can bridge the link between the energy and vehicle sectors towards a green transformation. Electric vehicles that are powered by electricity from renewable sources creates demand by consuming energy from the grid, but it can also serve as battery storage that can discharge energy back to the grid if needed. It is known that the intermittent nature and high penetration of variable renewable energy can lead to power instability. However, microgrids can be designed and incorporated with electric vehicles and variable renewable energy deployments to enhance the reliability and stability of the power system. The engagement with key stakeholders related to the deployment of the platform for the use of these three technologies is crucial to gain better insight on the market status, including the current and future price and penetration trends, and deployment opportunities and barriers. A focus group discussion involving related stakeholders on the aspects of policies and targets, technology readiness, and implementation strategies, was thus conducted. The results showed that there have been concrete policies on the renewable energy installation targets among the countries. Electric vehicles penetration is also an existing priority with different countries having varying targets for the electric vehicle fleets and charging stations. However, for microgrids, it was found that there is less information on their deployment and application. As such, more detailed technical and economic assessment following its potential connection to current grid with other features including variable renewable energy and electric vehicles, as well as the available smart features in optimising power scheduling, should be carried out.

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Efficient integration of demand response and plug-in electrical vehicle in microgrid: Environmental and economic assessment

Cited by 38 publications

References 58 publications

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Enhancement of flexibility in multi-energy microgrids considering voltage and congestion improvement: Robust thermal comfort against reserve calls

Integration of Variable Renewable Energy, Electric Vehicle, and Smart Microgrid in ASEAN: A Focus Group Discussion Approach

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