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The agricultural energy internet : theories, methods and future prospects.

The Agricultural Energy Internet: Theories, Methods, and Future Prospects provides a pioneering guide to the grid integration and impact of agricultural energy systems for a distributed and sustainable power grid.

Bibliographic Details
Main Author: FU, XUEQIAN
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: [S.l.] : Elsevier, 2025.
Series:Advances in Intelligent Energy Systems Series.
Subjects:
Agriculture and energy.
Agriculture et énergie.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • The Agricultural Energy Internet
  • Copyright
  • Contents
  • Contributors
  • About the series
  • About the series editors
  • About the authors
  • Preface
  • Acknowledgments
  • Chapter 1: The concept of the agricultural energy internet
  • 1.1. Introduction
  • 1.2. Problem analysis
  • 1.2.1. Overview
  • 1.2.2. Challenges
  • 1.3. Key technologies
  • 1.3.1. Model-driven energy flow simulation technology
  • 1.3.2. Comprehensive safety analysis technology
  • 1.3.3. Rapid static safety computation technology
  • 1.3.4. Modeling methods for deep coupling behavior of multienergy heterogeneity and agricultural production
  • 1.3.5. Study on the composition and measurement methods of safety risks in park energy systems and facility agriculture
  • 1.3.6. Online comprehensive safety analysis method based on agricultural energy data fusion
  • 1.4. Outlook
  • 1.4.1. Facility environment energy consumption modeling
  • 1.4.2. Agricultural energy system safety analysis
  • 1.4.3. Deepened integration of multienergy systems and agriculture
  • 1.4.4. Enhanced safety and risk management
  • 1.4.5. Focus on sustainability and environmental impact
  • Funding
  • References
  • Chapter 2: Comprehensive safety analysis of the agricultural park energy internet considering crop physiological characte ...
  • 2.1. Introduction
  • 2.2. Methodology
  • 2.2.1. Analysis on static security mechanism of energy internet in agricultural parks
  • 2.2.2. Environmental control load model for facility agriculture
  • 2.2.2.1. Light environment control load model for facility agriculture
  • 2.2.2.2. Thermal environment control load model for facility agriculture
  • 2.2.3. Facility agriculture and energy system safety indicators
  • 2.2.3.1. Static safety indicators of energy systems
  • 2.2.3.2. Photothermal physiological safety indicators of crops
  • 2.3. Case analysis.
  • 2.3.1. Objective
  • 2.3.2. Scope
  • 2.3.3. Audience
  • 2.3.4. Rationale
  • 2.3.4.1. Reasons for the study
  • 2.3.4.2. Main issue to be resolved
  • 2.3.4.3. Purpose of the experiment
  • 2.3.5. Expected results and deliverables
  • 2.3.5.1. Expected results
  • 2.3.5.2. Deliverables
  • 2.3.6. Safety considerations
  • 2.3.6.1. Technical safety considerations
  • 2.3.6.2. Environmental safety considerations
  • 2.3.6.3. Operational safety considerations
  • 2.3.7. Workflow
  • 2.3.8. Challenges and solutions
  • 2.3.9. Results
  • 2.3.9.1. Static safety analysis considering crop physiological characteristics
  • 2.3.9.2. Definition of the safety boundary of artificial lighting intensity
  • 2.3.10. Learning and knowledge outcomes
  • 2.4. Conclusion
  • 2.5. Study questions
  • Funding
  • References
  • Chapter 3: Enhancing the synergy of agriculture, energy, and environment in agricultural microgrids via carbon accounting
  • 3.1. Introduction
  • 3.2. Methodology
  • 3.2.1. Carbon footprint flow model of the rural power system
  • 3.2.1.1. Basics of carbon footprint flow calculation in rural energy networks
  • 3.2.1.2. Method for calculating carbon footprint flow in rural energy systems
  • 3.2.1.3. Carbon footprint flow calculation process for rural energy networks
  • 3.2.2. Optimization model for rural energy networks considering carbon footprint flow
  • 3.2.2.1. Optimization objective
  • 3.2.2.2. Limit conditions
  • 3.2.3. Model solution methods
  • 3.3. Case analysis
  • 3.3.1. Objective
  • 3.3.2. Scope
  • 3.3.3. Audience
  • 3.3.4. Rationale
  • 3.3.5. Expected results and deliverables
  • 3.3.6. Safety considerations
  • 3.3.7. Results
  • 3.3.7.1. Basic data for case study
  • 3.3.7.2. Optimization results
  • 3.4. Conclusion
  • 3.5. Study questions
  • Funding
  • References
  • Chapter 4: Optimal management strategy for rural microgrids incorporating greenhouse load control.
  • 4.1. Introduction
  • 4.1.1. Research background
  • 4.1.2. Research status
  • 4.1.2.1. New energy and modern agricultural technology
  • 4.1.2.2. Methods for analyzing the safety of agriculture and energy systems
  • Synergistic optimization method of agriculture and energy system
  • 4.1.3. Main research contents and chapter arrangement of this chapter
  • 4.2. Methodology
  • 4.2.1. Analysis and modeling of agricultural energy coupling mechanism
  • 4.2.1.1. Spatial coupling analysis and modeling
  • 4.2.1.2. Energy coupling analysis and modeling
  • 4.2.1.3. Carbon coupling analysis and modeling
  • Consumption of CO2 in greenhouse
  • Supplementation of CO2 inside the greenhouse
  • 4.2.2. Static safety analysis considering photovoltaic coverage
  • 4.2.2.1. Electricity-heat coupling system model construction and solution
  • Electric-thermal coupled system model construction
  • Electric-thermal coupled system model solution
  • 4.2.2.2. Energy and agricultural security indicator construction
  • 4.2.2.3. Safety analysis process
  • 4.2.3. Collaborative optimization of greenhouse load and energy system
  • 4.2.3.1. Greenhouse load control
  • 4.2.3.2. Synergistic optimization model for agriculture and energy
  • 4.2.3.3. Optimization model solving
  • 4.3. Case analysis
  • 4.3.1. Simulation conditions
  • 4.3.2. Safety analysis considering PV coverage
  • 4.3.3. Simulation conditions
  • 4.3.4. Optimization analysis of different PV coverage rates
  • 4.3.5. Summary of this section
  • 4.4. Conclusion
  • 4.4.1. Summary of findings
  • 4.4.2. Outlook
  • 4.5. Study questions
  • Funding
  • References
  • Chapter 5: Distributed energy planning in rural areas based on agri-energy-environment synergy
  • 5.1. Introduction
  • 5.2. Methodology
  • 5.2.1. Facility agriculture load model
  • 5.2.1.1. Supplementary lighting load model
  • 5.2.1.2. Irrigation electricity load model.
  • 5.2.1.3. Hothouse thermal demand framework
  • 5.2.1.4. Carbon model for load side
  • 5.2.2. Indicators for load adjustment
  • 5.2.3. Load regulation indicators
  • 5.2.3.1. Method for selecting representative days
  • 5.2.3.2. Objective function
  • 5.2.3.3. Constraint functions
  • 5.2.3.4. Optimization solution algorithm
  • 5.2.3.5. Flowchart
  • 5.3. Case analysis
  • 5.3.1. Objective
  • 5.3.2. Scope
  • 5.3.3. Audience
  • 5.3.4. Rationale
  • 5.3.5. Expected results and deliverables
  • 5.3.6. Safety considerations
  • 5.3.7. Actions taken
  • 5.3.8. Challenges and solutions
  • 5.3.9. Results
  • 5.3.9.1. Research subject and simulation parameters
  • 5.3.9.2. Simulation and validation of agricultural loads
  • 5.3.9.3. Validation of the proposed planning model
  • 5.3.9.4. Validation of the agri-energy-environment synergistic effect
  • 5.3.9.5. Case study analysis of the impact of uncertainty
  • 5.3.10. Learning and knowledge outcomes
  • 5.4. Conclusion
  • 5.5. Study questions
  • Funding
  • References
  • Chapter 6: Modeling comprehensive power loads in fisheries energy internet with consideration of fishery meteorology
  • 6.1. Introduction
  • 6.2. Methodology
  • 6.2.1. Model of oxygenation load
  • 6.2.1.1. Variation features of dissolved oxygen under ambient circumstances
  • 6.2.1.2. Fish's respiratory oxygen consumption requirement
  • 6.2.1.3. Aerator configuration
  • 6.2.1.4. Power of aeration
  • 6.2.2. Model of baiting load
  • 6.2.3. Model of fill and drain loads
  • 6.2.3.1. Water surface evaporation calculation
  • 6.2.3.2. Calculation of water level change
  • 6.2.3.3. Calculation of supplementary drainage power
  • 6.3. Case analysis
  • 6.3.1. Objective
  • 6.3.2. Scope
  • 6.3.3. Audience
  • 6.3.4. Rationale
  • 6.3.5. Expected results and deliverables
  • 6.3.6. Safety considerations
  • 6.3.7. Actions taken
  • 6.3.8. Challenges and solutions
  • 6.3.9. Results.
  • 6.3.9.1. Simulation conditions
  • 6.3.9.2. Simulation results
  • 6.3.10. Learning and knowledge outcomes
  • 6.4. Conclusion
  • 6.5. Study questions
  • Funding
  • References
  • Chapter 7: Carbon tracking system platform for agricultural energy internet
  • 7.1. Introduction
  • 7.2. Practical agricultural energy internet
  • 7.2.1. Foundations of carbon accounting in AEI
  • 7.2.1.1. Carbon accounting for AEI
  • 7.2.1.2. Variety of carbon emission sources
  • 7.2.1.3. Effect of new energy and plant photosynthesis on greenhouse carbon emissions
  • 7.2.1.4. Research framework for carbon cycling
  • 7.2.2. Carbon accounting models
  • 7.2.2.1. Carbon emissions accounting model
  • 7.2.2.2. Carbon sequestration accounting
  • 7.2.2.3. Carbon reductions accounting model
  • 7.3. System platform design
  • 7.3.1. Demand analysis
  • 7.3.1.1. Functional requirements
  • 7.3.1.2. Nonfunctional requirements
  • 7.3.2. System architecture
  • 7.3.2.1. User layer
  • 7.3.2.2. Application layer
  • 7.3.2.3. Transmission layer
  • 7.3.2.4. Data layer
  • 7.3.2.5. Infrastructure layer
  • 7.3.3. System function
  • 7.3.3.1. Data acquisition
  • 7.3.3.2. Variable control
  • 7.3.3.3. Emissions accounting
  • 7.3.3.4. Reductions accounting
  • 7.3.3.5. Carbon analysis
  • 7.3.3.6. Analysis reporting
  • 7.3.4. System security
  • 7.3.4.1. Data encryption and integrity check
  • 7.3.4.2. Access control and authentication
  • 7.3.4.3. Logging and monitoring tools
  • 7.3.5. Realization of carbon tracking system for AEI
  • 7.3.5.1. System operating environment
  • 7.3.5.2. Implementation of the carbon tracking system
  • 7.3.5.3. User interface of the carbon tracking system
  • 7.4. Conclusion
  • 7.5. Study questions
  • References
  • Chapter 8: The future of the artificial intelligence-based agricultural energy internet
  • 8.1. Introduction
  • 8.2. Methodology
  • 8.2.1. Cutting-edge methods.