Microgrids for Rural Areas: Research and Case Studies

Cover
Contents
About the editors
Preface
Acknowledgements
Part I. Fundamentals
	1 Overview of microgrids for rural areas and low-voltage applications
		1.1 Overview
		1.2 Importance of microgrids
		1.3 Classification of microgrids
			1.3.1 AC microgrid system
			1.3.2 Hybrid AC–DC microgrid system
			1.3.3 DC microgrid system
		1.4 Electric vehicles
			1.4.1 What are the electric vehicles?
			1.4.2 Hybrid electric vehicle control system
			1.4.3 Advantages and disadvantages of electric vehicles over conventional vehicles
		1.5 Standards
			1.5.1 Standards for microgrid with PV and battery interconnection
			1.5.2 Standards for electric vehicles when they are used as a source
			1.5.3 Agency-related standards
		1.6 Conclusion
		References
	2 Microgrid architectures
		2.1 Introduction to microgrids
		2.2 AC microgrids architectures
			2.2.1 Structures
			2.2.2 MG point of common coupling
			2.2.3 MG distribution network configuration
			2.2.4 MG control system
			2.2.5 Control philosophy of AC microgrids
		2.3 DC and hybrid AC–DC microgrid architectures
			2.3.1 DC microgrids
			2.3.2 Hybrid AC–DC microgrids
		2.4 Multi-MGs in smart distribution networks
		2.5 Review of MGs applications and research purposes
		2.6 Conclusion
		References
	3 The microgrid investment and planning in rural locations
		3.1 Introduction
		3.2 Microgrid planning problem
			3.2.1 Microgrid investment planning tools and methods
		3.3 Basic formulation
		3.4 Addressing the specific characteristics of rural microgrids
			3.4.1 Multi-energy characteristic
			3.4.2 Multi-node characteristic
				3.4.2.1 Heat network
				3.4.2.2 Electricity network
				3.4.2.3 Illustrative case study
			3.4.3 Remote microgrids
				3.4.3.1 N – 1 contingencies
			3.4.4 Multi-objective
		3.5 Remote multi-energy microgrid case study
		3.6 Conclusions
		References
Part II. Optimization, control and storage
	4 Assessment of energy saving for integrated CVR control with large-scale electric vehicle connections in microgrids
		4.1 Introduction
		4.2 Coordinated voltage reduction control model
			4.2.1 Coordinated voltage reduction control algorithm
			4.2.2 OLTC control
			4.2.3 Capacitor control
			4.2.4 Coordination
			4.2.5 Impact metrics
				4.2.5.1 Energy reduction, dV (%)
				4.2.5.2 Energy reduction, dE (%)
				4.2.5.3 CVRC factor, CVRCf
				4.2.5.4 Transformer utilization factor, UF (%)
				4.2.5.5 Voltage problems, VP (%)
		4.3 Modeling
			4.3.1 Microgrid modeling
			4.3.2 Load modeling
			4.3.3 Electric vehicle modeling
		4.4 Result analysis
			4.4.1 Analysis of energy reduction
		4.5 Analysis of EV penetration capability
		4.6 Conclusion
		References
	5 Application of energy storage technologies on energy management of microgrids
		5.1 Introduction
		5.2 Energy storage technologies
			5.2.1 Compressed air energy storage
			5.2.2 Pumped hydroelectric energy storage
			5.2.3 Cryogenic energy storage
			5.2.4 Battery energy storage systems
				5.2.4.1 The nickel–cadmium (Ni–Cd) battery
				5.2.4.2 Lithium-ion (Li-ion) battery
				5.2.4.3 Lead–acid (PbA) battery
				5.2.4.4 Sodium–sulfur (NaS) battery
			5.2.5 Thermal energy storage
			5.2.6 Flywheel energy storage
			5.2.7 Hydrogen energy storage
			5.2.8 Superconducting magnetic energy storage
		5.3 Comparison of energy storage technologies
		5.4 Conclusion
		References
	6 Control technique for integration of smart dc microgrid to the utility grid
		6.1 Introduction
		6.2 Proposed architecture of smart dc microgrid
		6.3 Modeling of smart dc microgrid
		6.4 Modeling of three-phase VSI with LCL filter
		6.5 Determination of transfer functions of three-phase VSI
		6.6 Proposed control technique of three-phase VSI for integrating dc microgrid to utility grid
		6.7 Operational analysis with simulation results
			6.7.1 Case 1: variable generations and variable loads
			6.7.2 Case 2: fault on the utility grid
			6.7.3 Case 3: dc (L-G) fault on the dc microgrid
		6.8 Conclusions
		References
	7 Energy management system of a microgrid using particle swarm optimization (PSO) and communication system
		Nomenclature
		7.1 Introduction
		7.2 Microgrid management system
		7.3 Microgrid energy management systems
			7.3.1 Centralized energy management
			7.3.2 Decentralized energy management
			7.3.3 Comparison between centralized and decentralized EMS
		7.4 MG EMS solution
			7.4.1 EMS based on linear and nonlinear programming methods
			7.4.2 EMS based on dynamic programming and rule-based methods
			7.4.3 EMS based on meta-heuristic approaches
			7.4.4 EMS based on artificial intelligent methods
			7.4.5 EMS based on multi-agent systems (MAS)
		7.5 MG EMS-wireless communication
		7.6 MG EMS-test case
			7.6.1 Modeling of the microgrid
			7.6.2 Mathematical model of the system
				7.6.2.1 Generators cost functions
				7.6.2.2 Solar generation cost function
				7.6.2.3 Wind generation cost function
				7.6.2.4 Energy storage cost function
				7.6.2.5 Constraint functions
			7.6.3 Results of PSO
				7.6.3.1 First case study: grid-connected mode
				7.6.3.2 Second case study: islanded mode
		7.7 Conclusion
		References
	8 Reconfiguration of distribution network using different optimization techniques
		Nomenclature
		8.1 Introduction
		8.2 Optimal network reconfiguration
			8.2.1 Problem formulation
		8.3 Augmented grey wolf optimization algorithm
		8.4 Test results of IEEE 33-bus distribution system
		8.5 Conclusion
		References
	9 Intelligent optimization scheduling of isolated microgrids considering energy storage integration, traditional generation, and renewable energy uncertainty costs
		9.1 Introduction
		9.2 Background of the study
			9.2.1 Generation resources modelling
				9.2.1.1 Wind generation
				9.2.1.2 Photovoltaic generation
				9.2.1.3 Batteries model
				9.2.1.4 Diesel generation model
			9.2.2 Economic analysis
				9.2.2.1 Modified generation cost
				9.2.2.2 Batteries life loss cost
				9.2.2.3 Uncertainty cost of renewable resources
		9.3 Intelligent scheduling without consideration of uncertainty cost
			9.3.1 Plentiful renewable primary energy sources
			9.3.2 Sparse of renewable primary energy sources
		9.4 Intelligent scheduling considering the uncertainty cost functions
			9.4.1 Optimization results with uncertainty cost analysis of wind energy
			9.4.2 Uncertainty costs for wind and solar generation case
		9.5 Conclusion
		References
	10 ADMM-based consensus droop control and distributed pinning droop control of isolated AC microgrids
		10.1 Introduction
			10.1.1 Average consensus-based droop control
			10.1.2 Distributed pining-based droop control
		10.2 Droop control in isolated AC MGs
			10.2.1 Problem formulation
			10.2.2 Model descriptions
		10.3 Average consensus through ADMM
			10.3.1 Basic concepts
			10.3.2 Distributed implementations
				10.3.2.1 Method A
				10.3.2.2 Method B
		10.4 Consensus-based droop control
			10.4.1 Optimal stable equilibrium point
			10.4.2 Consensus optimization
			10.4.3 ADMM implementations
				10.4.3.1 Method A
				10.4.3.2 Method B
			10.4.4 ADMM implementations with flexible droop gain ratios
		10.5 Stability analysis of closed-loop MG systems with consensus-based droop-controlled VSGs
			10.5.2 ADMM implementations
			10.5.3 ADMM implementations with flexible droop gain ratios
		10.6 Real-time simulations of ADMM-based consensus droop control
		10.7 Distributed pinning control
			10.7.1 Basic concepts
			10.7.2 Distributed pinning control
		10.8 Distributed pinning-based droop control
			10.8.1 MAS development
			10.8.2 Pinning localization and grid partition
			10.8.3 Pinning-based droop control
			10.8.4 Stability criteria
		10.9 Simulation verifications of the pinning-based droop control
			10.9.1 Test-bed descriptions and partition
				10.9.1.1 System development and testing
				10.9.1.2 Simulation results
			10.9.2 Possible limitations
		10.10 Conclusions
		Appendix A Formulations of M, D, Â, and Âf
		Appendix B Validity of EF W(y,z)
		References
Part III. Converters
	11 Extendable multiple outputs hybrid converter for AC/DC microgrid
		11.1 Introduction
		11.2 State of the art of hybrid multiple-output converters
		11.3 Proposed generalized hybrid multi-output converters
			11.3.1 Nonshoot-through interval (DsTs ≤ t ≤ (1 – Ds)Ts)
			11.3.2 Shoot-through interval (0 ≤ t ≤ DsTs)
		11.4 Steady-state behaviour of hybrid multi-output converters
			11.4.1 Series-connected inverter bridges expression
			11.4.2 Parallel-connected inverter bridges expression
			11.4.3 Voltage stress and current expression
		11.5 Hybrid PWM control techniques
		11.6 Design consideration
		11.7 Simulation and experimental verifications
			11.7.1 Steady-state results of series topology-based converter
			11.7.2 Steady-state results of a parallel topology-based converter
			11.7.3 Analysis and comparison
		11.8 Conclusion
		References
	12 Multi-input converters for distributed energy resources in microgrids
		12.1 Overview
		12.2 Magnetically coupled multi-input converters
			12.2.1 Buck-boost multi-input converter
			12.2.2 Double-input full-bridge converter
			12.2.3 Multi-port bidirectional converter
			12.2.4 Full-bridge boost DC–DC converter based on the distributed transformers
			12.2.5 Dual-transformer-based asymmetrical triple-port active bridge isolated DC–DC converter
			12.2.6 Summary
		12.3 Electrically coupled multi-input converters
			12.3.1 Series or parallel of two DC/DC converters
			12.3.2 Double input fundamental converter (buck/buck-boost converter)
			12.3.3 Multi-input DC boost converter supplemented by hybrid PV/FC/battery power system
			12.3.4 Three-input DC–DC boost converter
			12.3.5 ZVS multi-input converter
			12.3.6 Non-isolated multi-input multi-output DC/DC boost converter
			12.3.7 Non-isolated multi-input single-output DC/DC buck/boost-boost converter
			12.3.8 Switched-capacitor-based multi-input voltage-summation converter
			12.3.9 SEPIC-based multi-input DC/DC converter
			12.3.10 Bridge-type dual-input DC–DC converters
			12.3.11 Three-input DC/DC converter for hybrid PV/FC/battery applications
			12.3.12 Modular non-isolated multi-input high step-up DC–DC converter with reduced normalized voltage stress and component count
			12.3.13 Multi-input converter with high-voltage gain and two-input boost stage
			12.3.14 Soft-switched non-isolated, high step-up three-port DC–DC converter for hybrid energy systems
			12.3.15 Summary
		12.4 Electrically-magnetically coupled multi-input converters
			12.4.1 Combined DC link and magnetic-coupled converter
			12.4.2 Direct charge converter
			12.4.3 Boost-integrated phase-shift full-bridge converter
			12.4.4 Tri-modal half-bridge converter
			12.4.5 Full-bridge three-port converters with wide input voltage range
			12.4.6 Isolated multi-port DC–DC converter for simultaneous power management
			12.4.7 Multi-input three-level DC/DC converter
			12.4.8 Summary
		12.5 Comparative study and assessment
		12.6 Conclusion
		References
	13 Modeling and control of DC–DC converters for DC microgrid application
		13.1 Introduction
		13.2 Interfacing of PV source to DC bus
		13.3 Analysis of non-ideal DC–DC buck converter
			13.3.1 Semiconductor switch is on (time interval 0 < t ≤ DT)
			13.3.2 Semiconductor switch is off (time interval DT< t ≤ T)
			13.3.3 Steady-state analysis
		13.4 Mathematical modeling of PV-source fed non-ideal DC–DC buck converter system
		13.5 Closed-loop control of the PV system
			13.5.1 Tuning of PI controller
		13.6 Design example
		13.7 Case studies with simulation and experimental results
			13.7.1 Change in PV voltage
			13.7.2 Change in DC load
			13.7.3 Change in reference DC bus voltage
		13.8 Conclusion
		References
Part IV. Case studies
	14 Case studies of microgrids systems
		14.1 Introduction to energy optimization with microgrids
		14.2 Net present cost
		14.3 Internal rate of return
		14.4 Discounted payback time
		14.5 Levelized cost of electricity
		14.6 Battery wear cost
		14.7 Load and generation modelling in MGs
			14.7.1 MG's typical radial architecture
			14.7.2 Modelling of loads in MGs
			14.7.3 Modelling of renewable output power in MGs
				14.7.3.1 PV generators modelling
				14.7.3.2 Wind generators modelling
		14.8 Case study 1: MG for energy efficiency
			14.8.1 Case study description
				14.8.1.1 PV plant
				14.8.1.2 Mini WT
				14.8.1.3 Diesel generator
				14.8.1.4 Storage
				14.8.1.5 MG components sizing options
				14.8.1.6 Cost of energy from the public network
		14.9 Results
		14.10 Case study 2: MGs for rural electrification
			14.10.1 Case study description
			14.10.2 Loads
			14.10.3 PV and wind generation
			14.10.4 ESS
			14.10.5 Diesel generator
		14.11 Case study 3: MG as an alternative to grid extension
			14.11.1 Case study description
			14.11.2 Break-even grid distance
		14.12 Case study 4: industrial district with MG arrangement
			14.12.1 Case study description
		14.13 Economic analysis
			14.13.1 Case 1: no DG is installed in the area
			14.13.2 Cases 2–4: different DG configuration
			14.13.3 Case 5: Case 2 without wind generation
			14.13.4 Discussion
		14.14 Conclusion
		References
	15 Smart grid road map and challenges for Turkey
		Nomenclature
		15.1 Introduction
		15.2 Comparison of conventional and smart grid
		15.3 Grid modernization and current trends
		15.4 Smart grid integration and its definitions
			15.4.1 Integration of distributed energy resources
			15.4.2 Interoperability challenges
			15.4.3 Challenges in communication
		15.5 Communication technologies and protocols in smart grid
		15.6 Case study: smart grid applications, market and policy in Turkey
			15.6.1 History of the Turkish electricity market
			15.6.2 Coal market
			15.6.3 Gas market
			15.6.4 Hydropower market
			15.6.5 Wind market
		15.7 Policy state and progress relevant to smart grid in Turkey
		15.8 Smart grid opportunities in Turkey
		15.9 Energy from waste and biomass market opportunity and partner selection study in Turkey
		15.10 Strategic analysis of the wind power services market in Turkey
		15.11 Strategy of Turkish electricity transmission system operator (TEIAS)
		15.12 Key trends in the Turkish market
		References
	16 Nanogrids: good practices and challenges in the projects in Colombia
		16.1 Introduction
		16.2 Literature review
		16.3 Nanogrids projects in Colombia
			16.3.1 On-grid system
			16.3.2 Off-grid system
			16.3.3 Hybrid system
			16.3.4 Solar water pumping
			16.3.5 Aqueducts
			16.3.6 Household
			16.3.7 Pumping water for irrigation
			16.3.8 Banana sector
			16.3.9 Others
		16.4 Problematic situations
			16.4.1 Growth of the demand
			16.4.2 Rational use of energy/water
			16.4.3 Changes in habits
			16.4.4 Without current problems
			16.4.5 Number of users
		16.5 General methodology to overcome current issues
			16.5.1 Identifying the problem
			16.5.2 Propose a solution
			16.5.3 Implementing the solution
			16.5.4 Monitoring and review
		16.6 Solutions for each type of issues
			16.6.1 Monitoring and review
			16.6.2 Limit the load
			16.6.3 Load shifting
			16.6.4 Increase generation and backup
			16.6.5 Rational use of energy/water
			16.6.6 Changes in habits
		16.7 Conclusions, lessons learned, and perspectives
		References
	17 Distributed generation deployment in the Libyan MV network: adverse impacts on practised protection scheme in the DN
		17.1 Introduction
			17.1.1 Protection issues in DPGS
				17.1.1.1 Variation in fault current directions and levels
				17.1.1.2 Protection blinding
				17.1.1.3 Sympathetic tripping
				17.1.1.4 Nuisance tripping of DG
				17.1.1.5 Failed reclosing
				17.1.1.6 Loss of fuse-saving scheme: furthermore, the presence
				17.1.1.7 The effect of infeed on the distance protection
				17.1.1.8 Loss of protection coordination
				17.1.1.9 Impact of interconnection transformer
				17.1.1.10 Interconnection standards
		17.2 Recommendations and suggestions
		17.3 Conclusion
		References
	18 Conclusions and future scopes
		18.1 Conclusions
			18.1.1 Overview of microgrids for rural areas and low-voltage applications
			18.1.2 Microgrid architectures
			18.1.3 The microgrid investment and planning in rural locations
			18.1.4 Assessment of energy saving for integrated CVR control with large-scale electric vehicle connections in microgrids
			18.1.5 Application of energy storage technologies on energy management of microgrids
			18.1.6 Control technique for integration of smart DC microgrid to the utility grid
			18.1.7 Energy management system of a microgrid using particle swarm optimization (PSO) and communication system
			18.1.8 Reconfiguration of distribution network using different optimization
			18.1.9 Intelligent optimization scheduling of isolated microgrids considering energy storage integration, traditional generation, and renewable energy uncertainty costs
			18.1.10 ADMM-based consensus droop control and distributed pinning droop control of isolated AC microgrids
			18.1.11 Extendable multiple outputs hybrid converter for AC/DC microgrid
			18.1.12 Multi-input converters for distributed energy resources in microgrids
			18.1.13 Modeling and control of DC–DC converters for DC microgrid application
			18.1.14 Case studies of microgrids systems
			18.1.15 Smart grid road map and challenges for Turkey
			18.1.16 Nanogrids: good practices and challenges projects in Colombia
			18.1.17 Distributed generation deployment in the Libyan MV network: adverse impacts on practised protection scheme in the DN
		18.2 Future scopes
			18.2.1 Overview of microgrids for rural areas and low-voltage applications
			18.2.2 Microgrid architectures
			18.2.3 The microgrid investment and planning in rural locations
			18.2.4 Assessment of energy saving for integrated CVR control with large scale electric vehicle connections in microgrids
			18.2.5 Application of energy storage technologies on energy management of microgrids
			18.2.6 Control technique for integration of smart DC microgrid to the utility grid
			18.2.7 Energy management system of a microgrid using particle swarm optimization (PSO) and communication system
			18.2.8 Reconfiguration of distribution network using different optimization
			18.2.9 Intelligent optimization scheduling of isolated microgrids considering energy storage integration, traditional generation, and renewable energy uncertainty costs
			18.2.10 ADMM-based consensus droop control and distributed pinning droop control of isolated AC microgrids
			18.2.11 Extendable multiple outputs hybrid converter for AC/DC microgrid
			18.2.12 Multi-input converters for distributed energy resources in microgrids
			18.2.13 Modeling and control of DC–DC converters for DC microgrid application
			18.2.14 Case studies of microgrids systems
			18.2.15 Smart grid road map and challenges for Turkey
			18.2.16 Nanogrids: good practices and challenges projects in Colombia
			18.2.17 Distributed generation deployment in the Libyan MV network: adverse impacts on practised protection scheme in the DN
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