METHODOLOGY, DESIGN AND IMPLEMENTATION
Methodology
3.1 To determine available sources of renewable energy in Amoli community
Since wind and solar are in abundance in Amoli. I made reference to meterological report of Amoli and Awgu, to verify whether it can sustain the project at hand.
From my findings the annual average wind speed in Amoli is 6m/s. A hill called “Ugwuekude” in Amoli is a good site for this project. Average sunshine of 8 hours daily is observed there too.
3.2 To analysize the energy demand for a cluster of building/facilities in Amoli community
The questionnaire for investigating house hold load as regards critical and non critical loads are as enumerated in the appendix pages. The critical loads are television, lightening, radio, fan etc. The non critical loads are electric heater, freezer, air conditioner, to mention a few. The primary aim of this project is to make sure the critical loads are protected by having constant power supply when renewable energy is incorporated to the grid.
AMOLI AVAILABILITY (HOURS) MONTH February 2017 (EEDC)
S/N T – OFF AVAILABILITY (HRS)
1 OHUMAGU T – off 100% 177Hrs
2 IMEAMA T – off 80% 141 Hrs
3 ODUME T – off 70% 123 Hrs
4 IFITE T – Off 61% 107 Hrs
5 AKPOKA T – Off 73% 129 Hrs
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Load Flow on 33KV Feeders in Amechi District
AGBANI ITUKU AWGU UDI
Amps Mw Amps Mw Amps Mw Amps Mw
1 160 9.2 54 5.4 4 0.3 Eff Eff
2 158 9.0 92 5.3 4 0.3 ? ?
3 158 9.0 90 5.3 4 0.3 ? ?
4 168 9.5 89 5.0 4 0.3 ? ?
5 180 101 90 5.2 8 0.4 ? ?
6 202 11.1 98 5.7 8 0.4 ? ?
7 E/F E/F 118 6.3 8 0.4 ? ?
8 ? ? 102 5.8 8 O.4 ? ?
9 ? ? 100 5.8 8 0.4 8 4.7
10 ? ? 96 5.4 4 0.3 108 5.9
11 41 2.3 MTCE MTCE 4 0.3 108 6.0
12 39 2.2 ? ? 4 0.3 108 6.0
13 124 7.3 E/f E/f 4 0.3 112 6.2
14 212 119 ? ? 4 0.3 O/C O/C
15 230 12.4 ? ? 4 0.3 ? ?
16 MTC MTC ? ? 4 0.3 ? ?
17 ? ? ? ? 4 0.3 ? ?
18 249 13.8 ? ? 8 0.4 115 6.4
19 O/cFmt o/C ? ? 8 O.5 144 8.5
20 ? ? ? ? 8 O.5 144 8.5
21 ? ? ? ? 8 O.5 184 10.3
22 ? ? ? ? 8 O.5 e/F e/F
23 ? ? ? ? 4 O.3 ? ?
24 ? ? ? ? 4 0.3 ? ?
Source: Transmission Control Unit, AMECHI, January 10, 2017.
PEAK LOAD ON 33KV AND 11KV FEEDERS IN AMECHI DISTRICT
APRIL 2017 TR3 MVA TR4 MVA AGBANI AWGU ITUKU UDI
12.50mw 9.7mw 4.5mw 5.4mw 5.39mw 3.75mw
33kv AGBANI AWGU ITUKU UDI NKWO
0.4mw 8.7mw 15.8mw 14.9mw 9.6mw 68.44mw
MAY 2017 TR3 MVA TR4MVA AGBANI AWGU ITUKU UDI
10MW 10.63MW 4.85MW 4.85 3.93 3.75
33kv AGBANI AWGU ITUKU UDI NKWO
0.5MW 15MW 11.3MW 15MW 7.7MW 66.88MW
FEB 2017 TR3 MVA TR4 MVA AGBANI AWGU ITUKU UDI
9.20MW 9.85MW 5.2MW 4.92MW 4.27MW 4.64MW
33KV AGBANI AWGU ITUKU UDI NKWO
0.5MW 6.6MW 14.9MW 13.60 10.60MW 46.21MW
0.5MW 6.6MW 14.9MW 13.60MW 10.60MW 65.32MW
S/N FEEDER NAME ENERGY IMPORTED (KWH) ENERGY BILLED (KWH) ENERGY LOSS (KWH) REVENUE BILLED (N) CASH COLLECTED (N)
A B D E F=D-E G H
AGBANI 3.9MILLION 3.2MILLION 0.7MILLION 76.71MILLION 73.11MILLION
AGWU 2.8MILLION 2.4MILLION 0.4MILLION 66.70MILLION 62.21MILLION
3.3 To optimize the energy demand for a cluster of building facilities in Amoli
Two renewable sources wind , solar and grid of EEDC district in Enugu have two peak loads of A and B that require power P1 and P2. Each unit of type A require 0.5MW of P1 and 6.6MW of P2.Type B requires 6.6MW of P1 and 0.5MW of P2 (Each unit).The company has only 46.21MW of P1 and 65.32MW of P2. Each unit of type A brings a profit of #73.11M and each unit of type B brings a profit of #62.21M. Formulate the optimization problem to maximize profit for impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent.
SOLUTION
4 To give the information in a tabular form as shown below
Power P1 (MW) P2 (MW) Profit (#)
A 0.5 6.6 73.11
B 6.6 0.5 62.21
46.21 65.32
5 Decision variable: The decision variable are power A and B. Thus let the number of power A be x while that of B is y.
Objective function : The given problem is aimed at maximizing profit .
Let Z be the objective function profit of each unit of type A = #73.11, That is profit of x unit of type A = #73.11x.
Profit of each unit of type B =# 62.21, That is profit of y unit of type B = #62.21y.
Total profit = 73.11x + 62.21y.————————————————3.2.1
Constraints (i): Company has only 46.21MW of P1
Unit of A requires 0.5MW of P1, That is x-unit requires x-Hz of P1.
1 unit of B requires 0.5MW of P1.
Thus y-unit requires 0.5y-MW of P1
Thus total available quantity of P1 for A and B = 46.21MW
Therefore 0.5 x + 6.6y ? 46.21———————————————-3.2.2
Constraint (ii): Company has only 65.32MW of P2.
1 Unit of A requires 6.6MW of P2.
Thus x- unit requires 6.6x – MW
1 Unit of B requires 0.5MW of P2.
Thus y – units requires y – MW.
Total available quantity of P2 for A and B = 65.32MW.
That is 6.6x + 0.5y ? 65.32.——————————————————-3.2.3
Constraint (iii): Supply of A and B cannot be negative, That is x > = 0 and y > = 0
The mathematical model formulation for distribution loss reduction using network optimization svc. A case study of Abakilke district becomes
Maximize Z = 73.11x + 62.22y—————————————————–3.2.4
Subject to 0.5x + 6.6y ? 46.21—————————————————-3.2.5
2 6.6x +0.5y ? 65.32—————————————————3.2.6
3 x ? 0 and y ? 0
4 Then use simplex method to solve the mathematical model of equations 3.2.4, 3.2.5 and 3.2.6
5 z = 73.11x + 62.22y———————————————-3.2.7
6 0.5x + 6.6y ? 46.21————————————————-3.2.8
7 6.6x + 0.5y ? 65.32————————————————–3.2.9
8 Equate equation 3.2.7 to zero and remove all the constraints in equations 3.2.8 and 3.2.9 respectively by introducing slacks
Z – 73.11x – 62.22y = 0——————————————–3.2.10
0.5x + 6.6y + S1 = 46.21——————————————-3.2.11
6.6x + 0.5y + S2 = 65.32——————————————-3.2.12
No of iterations Basic Z X y S1 S2 Sol
iter 1 Z 1 -73.11 -62.22 0 0 0
S1 0 0.5 6.6 1 0 46.21
S2 leaves x enters S2 0 6.6 0.5 0 1 65.32
Iter 2 Z 1 0 -0.66 0 11 724
S1 Leaves while Y enters S1 0 0 6.56 0 0.075 41.26
X 0 1 0.76 0 0.15 9.9
Iter 3 Z 1 0 0 0 10.99 728.15
Pivot Y 0 0 1 0 0.0114 6.29
X
9
Fig 3.1 Shows the matlab code for impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent The result obtained in the ptimization are Solar and wind Denoted by X is 9. and the grid y is 6.2878
3.4 To design an optimized intelligent agent for an effective power stability
Designing a fuzzy intelligent agent membership analysis for grid and renewable energy
a.
Fig 3.1 Designed fuzzy inference system editor
Fig 3.2: Shows designed fuzzy inference system editor that has three inputs of grid, renewable energy , stability and three outputs of critical loads, noncritical loads and results.
Fig 3.3 Membership Function Of Stability Anaysis Of Impact Of Integrating Renewable Energy Sources For Rural Electrifcation Optimization Using An Intelligent Agent
Fig 3.3 Shows the membership function of stability analysis of impact of integrating renewable energy sources for rural electrificationoptimization using an intelligent agent. This fig shows when there is stable power supply as a result of incorporation of renewable energy to the grid. This therefore enhances the constant power supply to the critical loads examples television and lightening.
Fig 3.4 Membership Function For Critical Loads In Impact Of Integrating Renewable Energy Sources For Rural Electrification Optimization Using An Intelligent Agent
Fig 3.4 Shows membership function for critical loads in impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent. Some of the critical loads are DVD, TV, Incandescent lamp, fluorescent tube, laptop etc.
Fig 3.5: Membership Function For Noncritical Loads In Impact Of Integrating Renewable Energy Sources For Rural Electrification Optimization Using An Intelligent Agent
Fig 3.5 Shows membership function for noncritical loads in impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent. Some of the noncritical loads are Freezer, electric heater, Fridge, Microwave, washing machine etc.
Fig 3.6: Membership Function For Results In Impact Of Integrating Renewable Energy Sources For Rural Electrification Optimization Using An Intelligent Agent
Fig 3.6 Shows membership function for results in impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent. Some of the results are unstable power, stable power, satisfactory, unsatisfactory, trip and no trip. .
Fig 3.7 An Intelligent Agent Rules In Impact Of Integrating Renewable Energy Sources For Rural Electrification Optimization
Fig 3.7 Shows an intelligent agent rules in impact of integrating renewable energy sources for rural optimization
Fig 3.8 An Intelligent Agent Rules In Impact Of Integrating Renewable Energy Sources For Rural Electrification Optimization
Fig 3.8 Shows an intelligent agent rules in impact of integrating renewable energy sources for rural electrification. This rules are incorporated in the fuzzy controller to enhance the stability of power supply thereby securing the critical loads.
b. training these rules in artificial neural network to protect the critical loads thereby enhancing the impact of renewable energy in the community
Fig 3.9 Trained rules in artificial neural network to protect the critical loads thereby enhancing the impact of renewable energy in the community.
Fig 3.9 Shows trained rules in artificial neural network to protect the critical loads thereby enhancing the impact of renewable energy in the community.
Fig 3.10 Codes for trained rules in artificial neural network to protect the critical loads thereby enhancing the impact of renewable energy in the community
Fig 3.10 Shows Codes for trained rules in artificial neural network to protect the critical loads thereby enhancing the impact of renewable energy in the community
c. Designing an intelligent agent that validates the rules
Fig 3.11 Designed intelligent agent that validates the rules
Fig 3.11 Shows the designed intelligent agent that validates the rules. From the result gotten it is seen that training is 60%, validation 20% and testing 20%
Fig 3.12 Designed intelligent agent sensor
Fig 3.12 Shows designed intelligent agent sensor. Fig 3.12 Shows that the indicator light is green which shows that either grid is on or renewable energy is on.
Fig 3.13 Designed intelligent agent sensor
Fig 3.13 Shows designed intelligent agent sensor. Fig 3.13 Shows that the indicator light is red which shows that either grid is off or renewable energy is off.
Designing a visual basic for the impact of integrating renewable energy sources in rural communities using an intelligent agent
Fig 3.14 Designed visual basic for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent
Fig 3.14 Shows designed visual basic for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent. The critical loads used here are television and lightening while the noncritical load used here are freezer and electric heater.
3.4 To design a Simulink model for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent
Fig 3.15 Designed Simulink model for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent
Fig 3.15 shows the designed Simulink model for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent. The result of which is shown in fig 3.19.
3.5 Implementation
Fig 3.16 Designed simulated visual basic for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent
Fig 3.16 Shows designed simulated visual basic for the impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent. When the grid is on both the critical loads like television and lightening will be on and the noncritical loads like electric heater and freezer will equally be on.
Fig 3.17 Designed simulated visual basic for the impact of integrating renewable energy sources in rural communities using an intelligent agent
Fig 3.17 Shows designed simulated visual basic for the impact of integrating renewable energy sources for rural electrification using an intelligent agent. When the grid is off and the renewable energy is on, the critical loads like television and lightening will be on while the noncritical loads like electric heater and freezer will equally be off thereby securing the critical loads.The mode of operation herein is that when the grid is off the intelligent agent for the renewable energy sensor will show green light indicator making the renewable energy to be in operation.On the other hand the grid intelligent sensor will be red. The mode of operation is vice versa when the grid is on and the renwable energy off.
Figure 3.18 Simulink model for impact of integrating renewable energy sources for rural electrification optimization using an intelligent agent before simulation
The Simulinks blocks used herein are
i. Circuit breaker
ii. The circuit breaker herein is used to protect the transformer if there is any fault like overcurrent, harmonic distortion to mention a few.
iii. power transformer
transformer is used to energise the area
iv. Subsystem for intelligent agent
intelligent subsystem is the sensor that snses if the grid is off and signals the
renewable energy to start its operation.
v. Scope
The scope gives the graphical analysis of the grid and reneable energy .
vi. subsystem where some of the data gotten in EEDC were stored for simulation
This is the place where some of the data gotten in EEDC is stored.
Fig 3.19 Shows Simulink model before simulation
Figure 3.20 Simulink model after simulation
Fig 3.19 Shows the simulated result when the renewable energy is integrated to the grid.The result of which is shown in table 4.3
3.6 Flow chart
CHAPTER FOUR
RESULTS AND DISCUSSION
Table 4.1 Grid power Vs Time
Grid power (kw) Time (s)
0.3 1
0.3 2
0.3 3
0.3 4
0.4 5
0.4 6
0.4 7
0.4 8
0.3 9
0.3 10
0.3 11
0.3 12
