- •Energy Saving Technologies Riga Technical University
- •Content
- •Introduction 10
- •1. Energy Saving Technologies in generation, conversion of electrical energy 11
- •Executive summary
- •Introduction
- •1.Energy Saving Technologies in generation, conversion of electrical energy
- •1.1.Cogeneration
- •1.1.1.Introduction
- •1.1.2.Performance indices of cogeneration systems
- •1.1.3.Types of cogeneration systems
- •Comparison of Fuel Cell Systems [12].
- •1.1.4.Distributed energy resources
- •Characteristics of cchp Systems [15].
- •References
- •1.2.Smart metering concept
- •1.2.1.Introduction
- •1.2.2.Communication concept of smart metering
- •1.2.2.1.Customer domain
- •1.2.2.2.Critical infrastructure energy domain
- •1.2.2.3.The utility business market communication domain
- •1.2.2.4.Third parties services - data analysis
- •Ip service provider’s domain
- •1.2.3.Wireless sensor networks in smart metering
- •1.2.3.1.Main characteristics of wireless sensor networks
- •1.2.3.2.Examples of application of wireless sensor networks
- •1.2.4.Security issues
- •1.2.5.The future of smart metering
- •1.3. Energy from biomass
- •1.3.1. Biomass resources
- •Yeld of Som Biomass Types [2].
- •Yield of Agricultural Residues [2].
- •1.3.1.Biomass conversion technologies
- •Characteristics of Solid Biofuels and their Effects.
- •Ultimate Analysis of Different Solid Biofuels (Dry Basis) [5, 6, 7].
- •Proximate Analysis of Solid Biofuels (Dry Basis) [5, 6, 7].
- •Characteristics of Compacted Biomass [2].
- •Higher Heating Value of Solid Biofuels [8, 9, 10].
- •Composition of Biomass Ash [5, 13].
- •Types of Biomass Furnaces [14].
- •Heat Capacity of Combustible Gas [17].
- •Contaminants in Combustible Gas: Problems and Cleanup Methods [17].
- •Syngas Quality Parameters.
- •Operating Parameters of Pyrolysis Processes.
- •1.4.Energy Storage
- •1.4.1.Introduction
- •1.4.2.Classification of energy storage technologies
- •Types of Energy Storage Technologies and Their Applications [2].
- •1.4.3.Characteristics of energy storage techniques
- •1.4.4.Direct electric storage
- •1.4.5.Electrochemical energy storage
- •1.4.6.Mechanical energy storage
- •The response time of sudden changes in electrical demand for power plants [5].
- •1.4.7.Thermal energy storage
- •Physical Properties of Sensible Energy Storage Media [7, 8]
- •Commercial Phase Change Materials which can be Used for Heat Storage in the Buildings [10].
- •Properties of Some Phase Change Materials Produced by eps Ltd, uk [11].
- •Properties of Some Phase Change Materials Produced by teap Energy, Australia [11].
- •Properties of some phase change materials (paraffins) produced by the Rubitherm GmbH Germany [11].
- •Chemical Storage Materials and Reactions [8].
- •Main Characteristics of Energy Storage Materials [8].
- •References
- •1.5.Waste heat recovery
- •1.5.1.Characteristics of waste heat
- •Sources of waste heat at high-temperature range [2].
- •Sources of Waste Heat at Medium-Temperature Range [2].
- •Sources of Waste Heat at Low-Temperature Range [2].
- •1.5.2.Waste heat recovery systems
- •Waste Heat Recovery Systems [3].
- •Heat Exchangers Characteristics.
- •References
- •1.6.Energy Saving Technologies of the Thermochemical Conversion of Biomass and lignocarbonaceous Waste
- •1.6.1.Introduction
- •1.6.2.Pyrolysis
- •1.6.3.1.2 Torrefaction
- •1.6.4.1.3 Fast pyrolysis
- •1.6.5.1.4. Flash and ultra-rapid pyrolysis
- •1.6.6.1.5. Solar driven pyrolysis
- •1.6 Pyrolizer types
- •1.7.Gasification
- •1.8. Poly-generation of heat, power and biofuel
- •1.9.Design of renewable energy systems for small (local) consumers - description of a software for design and examples of design exercises.
- •1.9.1.Introduction.
- •1.9.2.A software for design renewable energy systems.
- •1.9.3.Description of the polysun platform
- •1.9.3.1.Polysun modules
- •1.9.3.2.User Interface
- •1.9.3.2.1.Menu bar
- •1.9.3.2.2.Icon bar
- •1.9.3.2.3.Managing the project.
- •1.9.3.2.4.Project tools
- •1.9.4.Creating a project
- •1.9.4.1.Design steps of the simple solar system.
- •1.9.4.2.Design steps of the pv system.
- •1.9.5.Result analysis and reports
- •1.9.5.1.The results of simulation
- •1.9.5.2.Reports
- •1.9.6.Literature
- •Conclusion
- •2.Energy Saving Technologies in transmission, distribution of electrical energy Energy Cost and Power Loss Minimization in Distribution Networks with Distributed Generation
- •Introduction
- •2.1.Opf problem formulation for distribution networks
- •2.1.1.Objective function
- •2.1.2.Constraints
- •Dg units modeling for optimal power flow
- •Opf Solution Using Multi-objective Genetic Algorithm
- •Opf Solution Using Gravitational Search Algorithm
- •2.2.Dc transmission systems
- •3. Energy Saving Technologies: in industry
- •3.1. Electric Motors
- •3.2. Electrical Drives
- •3.1.Waste heat utilization technologies
- •Introduction
- •1 Sources of waste heat
- •2 Main definitions used for heat waste assessment
- •3 Using of waste heat for heating and hot water supply. Equipment for using of industrial waste heat
- •3.1 Closed-circuit schemes of waste heat utilization
- •3.2 Opened-circuit schemes of waste heat utilization
- •Indirect Contact Condensation Recover
- •4. Utilization of low-temperature heat waste
- •4.1 Heat pumps
- •Common types of industrial heat pumps
- •4.2 Applications of heat pumps in drying process
- •4.2.1 Closed-cycle mechanical heat pumps for lumber drying
- •4.2.2 Evaporation - open-cycle mechanical vapour compression (mvc) for sugar solution concentration
- •4.2.3 Thermo-compression for paper-dryer flash steam recovery
- •4.3 Heat pumps working fluids
- •5 Using of waste heat for power generation
- •5.1 The opportunity for waste heat to power generation
- •5.2 Applicable Technologies
- •5.3 Applications
- •Using of combustible waste
- •7 Economic efficiency analysis of heat waste utilization
- •4.Energy Saving Technologies: in public and private sector
- •4.1.Building: fundamental physical processes in buildings and building envelopes. Reduction of heat losses. Heating and conditioning. Heat pumps.
- •5.Supercapacitors
- •Viesturs Brazis
- •5.1.Supercapacitor energy storage
- •5.1.1.Introduction
- •5.1.2.Supercapacitor design
- •5.1.3.Supercapacitor energy storage systems
- •5.1.4.Simulation of supercapacitor energy storage system
- •5.1.5.Ess scaling
- •5.1.6.Conclusions
- •5.1.7.Tasks
- •References
- •5. Standartisation and legal bases on existing Energy Saving Technologies
- •5.2.Introduction
- •5.3.Legistlative base mandatory for eu Member states
- •5.4.Legistlative base non - mandatory for eu Member states
- •5.5.Eu supported actions for development of Energy Saving Technologies
- •5.6.Iso 50001 - Energy management
- •5.7.Conclusions
- •References
2.Energy Saving Technologies in transmission, distribution of electrical energy Energy Cost and Power Loss Minimization in Distribution Networks with Distributed Generation
Jordan Radosavljević, Nebojša Arsić
Faculty of Technical Sciences, University of Priština in Kosovska Mitrovica, Kneza Miloša 7, 38220 Kosovska Mitrovica, Serbia.
Introduction
Electricity distribution companies tend to integrate different types of distributed generation (DG) units in the distribution network [1]. Integration of DG units with renewable energy such as photovoltaic (pv) systems, small wind turbines (wts) and small hydro power plants is influenced by geographical and meteorological conditions. The DG units with non renewable energy, including fuel cells (fcs), micro turbines (mts) and diesel generators (dgs), can be connected to any point in the distribution network but fuel cost must be taken into account.
Depending on the DG units technologies, the primary energy of DG units may be injected to the distribution network through either a synchronous or asynchronous electric machine which is directly connected to the grid, a combination of an electric machine and a power electronic interface, or only through power electronic interface. If electric machine is directly connected to the grid, its operation determines the model of DG unit (PQ or PV node) for power flow studies. In other cases, characteristics of the interface control circuit determine the DG unit model. As a general rule, in case the control circuit of the of the converter is designed to control active power and voltage independently, the DG unit model shall be as a PV node and being designed to control active and reactive power independently, the DG unit model shall be as a PQ node [2].
Generally, DG units, if they are properly planned and controlled, may offer the improved voltage profile and power losses reduction of the distribution network, better economics and a reduced dependence on the local utility [3]. Benefits can be achieved only by efficiently coordination of the DG units operation, voltage regulation (voltage regulators, ULTC) and reactive power compensation (VAR compensators) in the distribution network. In fact, it is an optimal power flow (OPF) problem. Main objective of the OPF problem for distribution network is to minimize the fuel cost of DG units, voltage profile improvement and minimization of power losses, though optimal settings of the control variables, while at the same time satisfying various distribution system operating constraints.
In [4], an optimal distribution power flow strategy based on decomposition of the overall system problem into two components: economic dispatch for energy at the system level and loss minimization at the distribution level has been proposed. A combined quadratic programming and sectioning algorithm is used to find solution.
Generalized formulation to determine the optimal operating strategy and cost optimization scheme for a micro-grid consisting of a wt, a mt, a dg, a pv array, a fc, and a battery storage has been presented in [5]. In this strategy, the mesh adaptive direct search algorithm is used to minimize cost function of the system while constraining it to meet the customer demand and safety of the system. Other objectives, such as power losses minimization and voltage profile improvement, as well as power flow constraints, bus voltage constraints are not considered.
The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. The following objective functions are considered: (i) fuel cost minimization for DG units (ii) simultaneous minimization of the fuel cost and power losses (iii) simultaneous minimization of the fuel cost and voltage deviation and (iv) simultaneous minimization of the fuel cost, power losses and voltage deviation.
The chapter is organized as follows. After introduction, the mathematical formulation of the OPF problem for distribution networks is exposed in Section 2. Optimization models of DG units are formulated in Section 3. OPF solution using multi-objective genetic algorithm is presented in Section 4. In Section 5, gravitational search algorithm for solution of the OPF problem is proposed.