Contents & References of Controlling scattered products in the retail market with the Monte Carlo method
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List of titles
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1 The first chapter of introduction to distributed production and smart microgrid. 1
1.1 Distributed production 2
1.1.1 History of distributed production 2
2.1.1 Definition of distributed production 3
3.1.1 Benefits of distributed production 5
4.1.1 Types of distributed production technologies 6
1.2 Structure of micro network. 13
1.3 Introduction of microgrid hardware structures. 14
1.4 Getting to know the basic concepts of the electricity market. 16
1.4.1 Definitions of keywords. 16
1.4.2 Types of electricity market models. 18
2 The second chapter introduction to the topic of the thesis. 20
2.1 Introduction 21
2.2 Description of the thesis topic. 23
2.3 Review of the subject literature. 23
2.3.1 Comprehensive one-by-one enumeration method: 24
2.3.2 Priority list method. 24
3.3.2 Dynamic programming 25
4.3.2 Lagrange release. 25
5.3.2 Hierarchical method. 26
2.3.6 Method of removing from the circuit. 27
2.3.7 The method of using the genetic algorithm in the problem of controlling scattered productions 27
2.3.8 The method of anling simulation. 28
2.3.9 Taboo search method. 28
2.3.10 Economic load distribution methods 29
2.4 Review of previous works. 29
2.5 Thesis structure. 30
3 The third chapter of problem modeling and formulation. 32
3.1 Introduction 33
3.2 Planning of participation of units 33
3.2.1 Mathematical relationships of participation of units 34
3.2.2 Limitations of thermal units. 35
3.2.3 The planning horizon of the units' participation 39
3.2.4 Checking the objective functions of the problem. 40
3.3 Considering the uncertainties in the control problem of scattered productions. 42
3.3.1 Uncertainty model of wind turbine production power. 42
3.3.2 Uncertainty model of solar cell production power. 44
3.3.3 Load uncertainty model 45
3.3.4 Sampling based on the Monte Carlo method. 46
3.3.5 Reducing the scenario. 47
3.4 Particle Sourcing Algorithm (PSO) 49
3.4.1 Problem Solving Strategy with PSO Algorithm. 49
3.4.2 Primary population. 50
3.4.3 Initial speed. 50
3.4.4 Competency assessment. 51
3.4.5 Updating speed and position. 51
3.5 Summary. 52
4 The fourth chapter of simulation and checking the results. 54
4.1 Introduction 55
4.2 Results of daily deterministic planning. 61
4.2.1 Winter scenario. 62
4.2.2 Summer scenario. 66
3.4 Results of daily stochastic planning. 68
4.4 Summary. 69
5 The fifth chapter, conclusions and suggestions. 72
5.1 Conclusion. 73
5.2 Suggestions 74
Resources and references. 76
List of Page shapes
Figure 1?1: Model of a Solar-thermal power plant. 9
Figure 1-2: Circuit of a photovoltaic system. 11
Figure 1?3: Electricity generation from wind energy. 12
Figure 3?1: Probability distribution function of wind speed. 1
Figure 3-2: Probability distribution function of solar radiation. 44
Figure 3-3: Sevenfold normal distribution curve of load 45
Figure 3-4: How to reduce scenarios 47
Figure 3-5: Scenario reduction flowchart. 48
Figure 3-6: Updating the velocity and position of a particle 52
Figure 4-1: The studied microgrid. 55
Figure 4-2: Predicted PV production power curve for weekday-winter scenario. 58
Figure 4-3: Projected PJM market price curve for the day-week-winter scenario. 59
Figure 4-4: The predicted curve of the electric load consumed by the microgrid for the day-week-winter scenario. 59
Figure 4-5: Predicted curve of PV production power for the day-week-summer scenario. 60
Figure 4-6: Projected PJM market price curve for the day-week-summer scenario. 60
Figure 4-7: The predicted curve of the electric load consumed by the microgrid for the day-week-summer scenario. 61
Figure 4-8: output power of scattered productions during 24 hours. 62
Figure 4-9: Power exchanged along62
Figure 4-9: Power exchanged during 24 hours. 64
Figure 4-10: The final cost according to the capacity of PHEV and the day of use 64
Figure 4-11: The output power of scattered productions during 24 hours. 66
Figure 4-12: Power exchanged during 24 hours. 67
Figure 4-13: Final cost according to PHEV capacity and day used in summer. 67
Figure 4-14: The amount of risk index for different days. 69
List of tables
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Table 1?1. Microturbine specifications. 13
Table 4-1. Different costs of scattered productions 56
Table 4-2. Economic coefficients of production units. 57
Table 4-3. Coefficients of emission sources. 57
Table 4-4. The amount of cost and profit of participating in the load response program 65
1.1 Dispersed production
1.1.1 History of distributed production
From the middle years of the 20th century and before the 1970s, the demand for electrical energy showed a constant growth rate of about 6-7%. Environmental issues and the oil crisis caused by political events in the Middle East in the 1970s are new problems facing the world's electricity industry. These factors, along with changes in the global economy, led to a decrease in the growth rate of electric energy consumption from 6-7% to 1.6-3% in the 1980s. At the same time, energy transmission and distribution costs experienced unprecedented inflation from 25% to about 150% of the production cost.
Actually, this part of the electricity industry allocated two-thirds of the necessary budgets for investment. Following the decrease in demand, excessive increase in the aforementioned costs, public concerns for the health of the environment, access to advanced technologies and acceptance of changes in the networks, huge central power plants were left out of the attention of energy producers. In other words, the pattern of energy production changed from "seeking economic efficiency in dimensions and sizes" to "group and decentralized efficient production". [15] [
The legal perspective of the public approach to distributed generation started in 1978 with the approval of the "Power Grid Adjustment Act[1]" in the United States of America. This decree allowed small generators to be connected to the power grid, and in this way, small scattered production units even with a capacity of one kilowatt entered the competitive market of electric energy production and distribution. [16] [
Recent advances in small energy production technologies have caused electricity distribution companies to move towards making changes in the network infrastructure in order to increase the coordination of distribution networks with DG units. Also, by using DGs, it is possible to operate effectively in free markets [2], which will bring many benefits. In fact, the use of DGs in distribution systems, especially in areas where centralized production is not possible or there are inefficiencies in the transmission system, is beneficial for both consumers and power companies. 2.1.1 Definition of distributed generation In general, any type of electric energy production technology that can be integrated into the distribution system or is connected to the network from the consumer side of the measuring device can be under be called scattered production. DG systems are introduced as modular systems with a capacity of less than 100 megawatts and sometimes less than 10 megawatts. Some countries have provided their definitions based on voltage levels, and others based on other characteristics such as the use of new energy, simultaneous production of heat and electricity, no dispatching, etc. have defined.]17[
According to the CIRED survey [3], the definition of distributed generation in some countries is as follows:
England: productions that are connected to the distribution network up to a maximum of 132 kV.
Italy: productions that are connected to the distribution networks up to a voltage level of 150 kV maximum.
Germany: In general, it refers to the productions that are done with new energies. France: It is said to the productions that are connected to the distribution or load network and their voltage level is in the voltage categories of the distribution networks. India: Renewable energy sources that are connected to the network with a maximum voltage of 11 kV.