Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment
Abstract
:1. Introduction
Research Gaps
- To optimize the usage of renewable power on its incidence with the designed smart fuzzy controller.
- To forecast the energy cost based on the present value cost of energy.
- To meet the energy demand of the consumers using RES effectively without energy storage devices.
2. Design of Virtual Model for the Proposed System
2.1. Design of Virtual VAWT Model
2.2. Design of Virtual PV Module
2.3. Proposed Autonomous Fuzzy Controller (AuFuCo)
- Virtual Source Model: Wind and PV power resources are designed with their characteristic equations as a virtual model for a rated capacity of 2 kW each and a total capacity of 4 kW. This model is constructed using Simulink and can be easily tunable with input side variations like wind speed and solar irradiance parameters which reflect on the output power.
- Forecasting System (FS): It is constructed with a fuzzy inference system (FIS) for predicting the load demand variations based upon electricity tariff, time of consumption, and ambient temperature.
- Grid Power Selection (GPS) Switch: This component plays an important role in the selection of energy resources for different load conditions at different times and is constructed using a fuzzy inference system. This fuzzy system effectively decides and manages the amount of grid power needed, considering the availability of renewable energy sources on its incidence at all times.
- Demand Response System (DRS): This unit considers consumption time, comfort level, temperature variations, and energy consumption by sacrificing a little amount of comfort level to calculate the running load in the system.
- REMS: The main purpose of this system is to supply as much renewable energy to different categories of loads available on the domestic consumption side.
- Fuzzy Load Switch (FLS): This is an intelligent power switch, which enables us to classify different loads to consume power according to the availability of renewable energy sources on the generation side. The loads are classified into four important categories to illustrate the renewable energy impact on demand-side management [40].
- ○
- Baseline load: It may be activated at any time or maybe in standby mode to consume the electricity. Example: TV, fan, refrigerator, computer and lighting loads.
- ○
- Priority load: It may consume power for a longer time but it can be interrupted for modifying the consumption pattern. Example: air conditioners, heating, and ventilation systems.
- ○
- Burst or short term loading: Appliances that consume energy in a single stretch called burst loads. Example: mixers and other cooking appliances
- ○
- Schedulable load: Appliances that consume power in a flexible mode. Example: washing machines, wet grinders or pump motors.
3. Methodology
3.1. Introduction to Fuzzy System
3.2. Fuzzy Inference Systems Used in the Controller
3.2.1. Forecasting System (FS)
3.2.2. Grid Power Selection (GPS) Switch
3.2.3. Renewable Energy Management System (REMS)
3.2.4. Fuzzy Load Switch (FLS)
4. Results and Discussion
- The main goal of this analysis is to utilize the hybrid wind and solar energy on its incidence (any time in 24 h) for the maximum possible connected load with grid support through a proposed autonomous (mode) controller and aims to reduce the dependency on the grid.
- This work is carried out without battery storage support with a high-efficiency inverter configuration (98% percentage of efficiency) but its effect is not considered for output results.
- Hybrid energy resources are capable of delivering a rated power to a 4 kW connected load.
- To reduce the peak demand on the grid, a schedulable load is proposed and it consumes energy in the presence of maximum renewable energy incidences.
4.1. Analysis of Different Combinations of Hybrid Renewable Energy with Grid Supply
4.2. Analysis of Energy and Cost Savings Based on Home Environment Energy Tariff
4.3. Effect of Energy Conversion on DSM Technique
4.4. Effect of Hybridization on Demand-Side by 2030
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations and Symbols
VAWT | Vertical axis wind turbine |
HAWT | Horizontal axis wind turbine |
PV | Photovoltaic |
REMS | Renewable energy management system |
RE | Renewable energy |
HIHREM | Home renewable energy management system |
HOMER | Hybrid Optimization Model for Electric Renewable |
HVAC | Heating, Ventilating and Air conditioning |
DRS | Demand response system |
DSM | Demand side management |
AuFuCo | Autonomous Fuzzy Controller |
SFLC | Smart Fuzzy Logic Controller |
EMS | Energy management system |
EPSR | Electric power survey report |
IEA | International energy agency |
LF | Load forecasting |
MAS | Multi-Agent System |
ABC | Artificial bee colony |
STC | Standard test condition |
GH | Grid power high |
GL | Grid power low |
GM | Grid power medium |
GPS | Grid power selection |
GVH | Grid power very high |
GVL | Grid power very low |
COG | Center of gravity |
Egrid,t | Energy supplied by the grid |
EtR | Energy request by the consumer at time t |
EPV,t | Energy supplied by the PV panel at time t |
Etcost | Total energy cost |
Ewind,t | Energy supplied by the wind VAWT model at time t |
m | Number of rules applied to the controller |
Np | No of PV panels in parallel |
Ns | No of PV panels in series |
Pm | Mechanical output power of the turbine |
Rp | Parallel resistance |
Rs | Series resistance |
Top | Cell operating temperature |
Tref | Cell temperature at 25 °C |
TOEprice,t | Energy cost at time t |
SGDMS | Smart Grid Distribution Management System |
TERI | The Energy and Resources Institute |
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Method Adopted | Inferences |
---|---|
Fuzzy controller with decision boundaries. | Presented a flexible allocation strategy based on a two-stage fuzzy logic controller to address energy management and cost control [18]. |
Reconfigurable MPPT fuzzy controller. | Proposed adoption of a fuzzy logic system in the actual implementation of the MPPT controller. Was tested under various environmental limitations [19]. |
Fuzzy-DEcision-MAking Trial and Evaluation Laboratory (F-DEMATEL). | An integrated model for determining the best locations for sustainable biomass plants were established. The optimal locations for biomass plants were demonstrated to be those near forested areas to ensure low transport costs [20]. |
Home renewable energy management system (HIHREM) | Proposed a hybridized intelligent home renewable energy management system (HIHREM) that integrates solar energy and energy storage services and a smart home is designed based on the demand response. The energy consumption rate is reduced significantly, specifically during high-demand periods [21]. |
Fuzzy based Hybrid Optimization Model for Electric Renewable | Applied the fuzzy logic rule in the Hybrid Optimization Model for Electric Renewables (HOMER) software for an analytical model of solar, wind, and hydropower, which are merged with cost criteria to form an objective function [22]. |
Bio-inspired firefly optimizer | Reported a hybrid wind speed prediction model with a bio-inspired firefly optimizer algorithm integrated with a multilayer perceptron [23]. |
A multi-agent system (MAS) | Presented a smart grid distribution management system (SGDMS) for contestable and non-contestable consumers. Multi-agent system (MAS) technology reduced the electricity price of consumers but foresting prices are not presented [24]. |
Integrated energy management system | Reported an integrated energy management system (EMS) with PV, an electric vehicle, a battery, and thermal power storage devices with complex forecasting [25]. |
Artificial bee colony (ABC) | Implemented an artificial bee colony (ABC) as an optimizer to improve the HEMS but cost optimization concepts are not incorporated [26]. |
Intelligent energy management system | A suggested intelligent energy management system that finds the optimal energy efficiency to maximize energy usage. However, energy forecasting and cost optimizations are not presented [27]. |
Energy controller system | An implemented energy controller system to control household appliances such as air conditioners and lighting. The concepts of cost optimization with hybrid renewable sources are not incorporated [28]. |
Levelized cost function | Suggested Levelized cost of systems for electricity generation and reported that the cost can be reduced considerably with increased renewable energy penetration at no extra cost. However, individual energy and forecasting prices are not discussed greatly [29]. |
PV Inputs | Wind Power Inputs | ||
---|---|---|---|
Reference Temperature (Tref in °C) | 25.00 | Cut in speed (m/s) | 2.00 |
Operating Temperature range (Top in °C) | 25.00–50.00 | Cut out speed (m/s) | 24.00 |
Solar Radiation variations (puW/m2) | 0.25–1.09 | Wind speed variations (m/s) | 0.00–24.00 |
PV Power variation (kWP) | 0.00–2.00 | Wind Power variation (kW) | 0.00–2.00 |
Base Load | Priority Load | ||||
Appliance Name | Numbers & individual Ratings (kW) | Power Ratings (kW) | Appliance Name | Numbers & Individual Ratings (kW) | Power Ratings (kW) |
Refrigerator | 1 (0.50) | 0.50 | Air conditioning | 1 (1.50) | 1.50 |
Lights | 5 (0.04) | 0.20 | |||
Fan | 3 (0.08) | 0.24 | Heating load | 1 (0.50) | 0.50 |
Television | 1 (0.15) | 0.15 | |||
Computers | 1 (0.15) | 0.15 | |||
Total power (kW) | 1.24 | Total power (kW) | 2.00 | ||
Schedulable Load | Short Term or Burst Load | ||||
Appliance Name | Numbers & Individual Ratings (kW) | Power Ratings (kW) | Appliance Name | Numbers &Individual Ratings (kW) | Power Ratings (kW) |
Washing machine | 1 (0.50) | 0.50 | Mixer | 1 (0.50) | 0.50 |
Wet grinder | 1 (0.40) | 0.40 | Pump motor | 1 (0.50) | 0.50 |
Iron box | 1 (0.50) | 0.50 | Induction stove | 1 (1.00) | 1.00 |
Total power (kW) | 1.40 | Total power (kW) | 2.00 |
Energy Combinations | PV and Grid | Wind and Grid | PV, Wind, and Grid | Grid Supply | ||||
---|---|---|---|---|---|---|---|---|
Energy (kWh) | Cost (USD) | Energy (kWh) | Cost (USD) | Energy (kWh) | Cost (USD) | Energy (kWh) | Cost (USD) | |
PV (EPV,t) | 13.23 | −0.55 | 0 | 0 | 13.23 | −0.55 | 0 | 0 |
Wind (Ewind,t) | 0 | 0 | 14.10 | −0.51 | 14.10 | −0.51 | 0 | 0 |
Grid (Egrid,t) | 76.80 | 4.70 | 85.90 | 5.25 | 62.67 | 3.83 | 96.04 | 5.87 |
Total (EtR) | 90.03 | 4.15 | 99.22 | 4.74 | 90.00 | 2.77 | 96.04 | 5.87 |
S. No. | Energy Block | Energy Cost /Unit |
---|---|---|
1 | 0–100 units (bimonthly) | 0.034 USD/unit and no Fixed charges [No Energy bill scheme] |
2 | 101–200 units (bimonthly) | 0.048 USD/unit and fixed charges 0.410 USD/Service |
3 | 201–500 units (bimonthly) | 0.063 USD/unit and fixed charges 0.540 USD/Service |
4 | Above 501 units (bimonthly) till 1000 units | 0.090 USD/unit and fixed charges 0.680 USD/Service |
Energy Block (kWh) | Wind Energy (kWh) | PV Energy (kWh) | Grid Energy (kWh) | Total Energy (kWh) | ||||
---|---|---|---|---|---|---|---|---|
Optimum | Average | Optimum | Average | Optimum | Average | Optimum | Average | |
0–100 | 15.36 | 17.35 | 12.90 | 13.20 | 71.46 | 72.70 | 99.72 | 103.25 |
101–200 | 31.11 | 34.70 | 26.38 | 26.70 | 143.20 | 138.60 | 200.69 | 200.60 |
201–300 | 43.80 | 52.05 | 39.58 | 40.30 | 216.90 | 207.70 | 300.28 | 300.80 |
301–500 | 76.40 | 86.60 | 66.20 | 69.20 | 357.40 | 344.20 | 500.00 | 513.20 |
501–1000 | 161.80 | 175.10 | 142.10 | 130.20 | 709.40 | 703.90 | 1013.30 | 1000.90 |
Source Combinations | Controller | Wind Power Cost (USD) | PV Power Cost (USD) | Grid Power Cost (USD) | Total Cost (USD) |
---|---|---|---|---|---|
Wind 45% and PV 23% efficiency | SGDMS | 0.28 | 0.50 | 5.04 | 5.82 |
MAS | 0.27 | 0.49 | 5.02 | 5.78 | |
AuFuCO | 0.27 | 0.49 | 5.00 | 5.76 | |
Wind 35% and PV 18% efficiency | SGDMS | 0.22 | 0.52 | 5.09 | 5.83 |
MAS | 0.22 | 0.52 | 5.08 | 5.81 | |
AuFuCO | 0.21 | 0.51 | 5.07 | 5.78 |
Time Units (S) | Forecasted Energy (Units) | Generated Energy (Units) | Consumed Energy (Units) | Grid Power (kW) | PV Power (kW) | Wind Power (kW) | Energy Cost at Present (USD) | Energy Cost by 2030 (USD) | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Wind (USD) | PV (USD) | Total (USD) | PV (USD) | Wind (USD) | Total (USD) | |||||||
1 | 3.5000 | 3.7000 | 1.8000 | 3.2000 | 0 | 0.7000 | 0.0126 | 0.0014 | 0.2660 | 0.0328 | 0.0041 | 1.1767 |
2 | 8.8000 | 7.6000 | 3.7000 | 4.0000 | 0 | 0.3000 | 0.0224 | 0.0028 | 0.5278 | 0.0574 | 0.0041 | 2.3452 |
3 | 12.7000 | 11.9000 | 5.5000 | 4.0000 | 0 | 0 | 0.0266 | 0.0042 | 0.7840 | 0.0656 | 0.0082 | 3.5014 |
4 | 15.6000 | 15.9000 | 8.3000 | 3.2000 | 0 | 0.6000 | 0.0266 | 0.0056 | 1.0374 | 0.0656 | 0.0082 | 4.6494 |
5 | 20.9000 | 19.7000 | 11.2000 | 4.0000 | 0 | 2.4000 | 0.0336 | 0.007 | 1.2978 | 0.0861 | 0.0123 | 5.8138 |
6 | 26.9000 | 26.1000 | 15.7000 | 1.8000 | 1.800 | 0 | 0.0672 | 0.0112 | 1.5526 | 0.1722 | 0.0205 | 6.8880 |
7 | 31.0000 | 29.7000 | 20.2000 | 0.6000 | 1.900 | 0 | 0.0672 | 0.0322 | 1.7864 | 0.1722 | 0.0574 | 7.8925 |
8 | 35.1000 | 32.2000 | 24.7000 | 2.1000 | 1.900 | 0.3000 | 0.0672 | 0.0602 | 2.0104 | 0.1722 | 0.1107 | 8.8314 |
9 | 40.3000 | 36.6000 | 29.2000 | 0.5000 | 1.900 | 0 | 0.0728 | 0.0938 | 2.2722 | 0.1845 | 0.1722 | 9.9179 |
10 | 44.4000 | 39.1000 | 33.7000 | 1.5000 | 1.900 | 0 | 0.0728 | 0.1302 | 2.5032 | 0.1845 | 0.2378 | 10.8690 |
11 | 47.3000 | 42.6000 | 38.2000 | 2.2000 | 1.800 | 0.7000 | 0.0728 | 0.1624 | 2.7370 | 0.1845 | 0.2952 | 11.8490 |
12 | 50.1000 | 47.3000 | 42.7000 | 1.0000 | 2.000 | 0 | 0.0826 | 0.1918 | 3.0002 | 0.2091 | 0.3526 | 12.9390 |
13 | 52.9000 | 50.3000 | 47.2000 | 4.0000 | 2.000 | 2.5000 | 0.0826 | 0.2254 | 3.2312 | 0.2132 | 0.4100 | 13.9010 |
14 | 56.7000 | 56.9000 | 51.7000 | 1.0000 | 2.000 | 2.0000 | 0.1176 | 0.2618 | 3.4356 | 0.2993 | 0.4797 | 14.6540 |
15 | 59.6000 | 59.8000 | 56.2000 | 1.7000 | 1.900 | 0.6000 | 0.1456 | 0.2954 | 3.6624 | 0.3690 | 0.5371 | 15.5420 |
16 | 62.5000 | 64.1000 | 60.7000 | 2.0000 | 1.900 | 0.6000 | 0.1526 | 0.3206 | 3.9200 | 0.3895 | 0.5863 | 16.6250 |
17 | 67.8000 | 68.5000 | 65.2000 | 3.0000 | 1.900 | 0.9000 | 0.1610 | 0.3388 | 4.1958 | 0.4100 | 0.6191 | 17.8150 |
18 | 73.2000 | 72.4000 | 69.7000 | 2.5000 | 1.800 | 1.0000 | 0.1736 | 0.3612 | 4.4562 | 0.4428 | 0.6601 | 18.9020 |
19 | 78.5000 | 75.9000 | 74.2000 | 2.6000 | 0 | 1.3000 | 0.1876 | 0.3962 | 4.7250 | 0.4756 | 0.7216 | 20.0110 |
20 | 83.8000 | 79.8000 | 78.7000 | 4.0000 | 0 | 0.3000 | 0.2058 | 0.4298 | 4.9574 | 0.5207 | 0.7831 | 20.9370 |
21 | 88.3000 | 84.1000 | 83.2000 | 4.0000 | 0 | 0.3000 | 0.2100 | 0.4606 | 5.2136 | 0.533 | 0.8405 | 22.0080 |
22 | 90.2000 | 88.4000 | 87.7000 | 4.0000 | 0 | 0 | 0.2128 | 0.4970 | 5.4782 | 0.5412 | 0.9061 | 23.1050 |
23 | 91.9000 | 92.4000 | 92.2000 | 4.0000 | 0 | 0 | 0.2142 | 0.5236 | 5.7050 | 0.5453 | 0.9553 | 24.0690 |
24 | 94.7000 | 96.4000 | 96.1000 | 3.9000 | 0 | 0.4000 | 0.2142 | 0.5250 | 5.9570 | 0.5453 | 0.9553 | 25.2190 |
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Anthony, M.; Prasad, V.; Kannadasan, R.; Mekhilef, S.; Alsharif, M.H.; Kim, M.-K.; Jahid, A.; Aly, A.A. Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment. Electronics 2021, 10, 1618. https://doi.org/10.3390/electronics10141618
Anthony M, Prasad V, Kannadasan R, Mekhilef S, Alsharif MH, Kim M-K, Jahid A, Aly AA. Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment. Electronics. 2021; 10(14):1618. https://doi.org/10.3390/electronics10141618
Chicago/Turabian StyleAnthony, Mohanasundaram, Valsalal Prasad, Raju Kannadasan, Saad Mekhilef, Mohammed H. Alsharif, Mun-Kyeom Kim, Abu Jahid, and Ayman A. Aly. 2021. "Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment" Electronics 10, no. 14: 1618. https://doi.org/10.3390/electronics10141618
APA StyleAnthony, M., Prasad, V., Kannadasan, R., Mekhilef, S., Alsharif, M. H., Kim, M.-K., Jahid, A., & Aly, A. A. (2021). Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment. Electronics, 10(14), 1618. https://doi.org/10.3390/electronics10141618