Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid
<p>Developed system model.</p> "> Figure 2
<p>BPSO algorithm implementation flowchart.</p> "> Figure 3
<p>WDO algorithm implementation flowchart.</p> "> Figure 4
<p>GA implementation step-wise flow chart.</p> "> Figure 5
<p>DE implementation flowchart.</p> "> Figure 6
<p>EDE implementation flowchart.</p> "> Figure 7
<p>RTP scheme.</p> "> Figure 8
<p>Interruptible appliances energy consumption pattern.</p> "> Figure 9
<p>Interruptible appliances energy cost per hour evaluation.</p> "> Figure 10
<p>Interruptible appliances energy cost evaluation.</p> "> Figure 11
<p>Interruptible appliances PAR evaluation.</p> "> Figure 12
<p>User comfort in aspects of average waiting time/delay time after scheduling interruptible appliances.</p> "> Figure 13
<p>Noninterruptible appliances energy consumption pattern.</p> "> Figure 14
<p>Energy cost per hour evaluation after scheduling noninterruptible appliances.</p> "> Figure 15
<p>Net energy cost evaluation after scheduling noninterruptible appliances.</p> "> Figure 16
<p>PAR minimization evaluation after scheduling noninterruptible appliances.</p> "> Figure 17
<p>User comfort in aspects of waiting time after scheduling noninterruptible appliances.</p> "> Figure 18
<p>Hybrid appliances energy consumption pattern.</p> "> Figure 19
<p>Energy cost per hour evaluation after scheduling hybrid appliances.</p> "> Figure 20
<p>Hybrid loads net energy cost evaluation.</p> "> Figure 21
<p>Hybrid load PAR evaluation.</p> "> Figure 22
<p>User comfort in aspects of average waiting time after scheduling hybrid appliances.</p> ">
Abstract
:1. Introduction
- The desired objectives such as energy expense, PAR, and user discomfort are modeled for three scenarios: interruptible, noninterruptible, and hybrid loads.
- Then, the appliances operation scheduling problem is formulated as an optimization problem to obtain energy expenses, PAR, and user discomfort minimization by adapting the algorithms’ structure for scheduling and by tuning algorithms parameters.
- The energy consumption scheduler (ECS) is programmed based on the following algorithms: GA, WDO, BPSO, DE, and EDE to regulate the operation of appliances according to the broadcast RTP scheme that minimizes energy expense, PAR, and user discomfort for the following scenarios: interruptible, noninterruptible and hybrid loads.
2. Related Work
3. Modeling Approach
3.1. User Priorities
3.2. Activity Level
3.3. Electricity Pricing
4. Mathematical Model
Objective Functions
- Energy cost is the utility bill that consumers pay to power companies for the energy they are using. This work calculates energy cost using the RTP scheme broadcast by the power company. The primary objective of the consumer is to reduce total energy cost. This objective is achieved by optimal consumption of energy. This work use heuristic algorithms do this job to minimize overall energy cost. Energy cost using RTP scheme is calculated as follows.
- PAR is the ratio of peak and average load. The second objective of this work is to curtail PAR. This PAR is increased due to the high demand for electricity during critical hours. This huge demand increases the probability of peak formation. The ultimate objective is to reduce peak demand in peak hours to enhance grid stability and reliability. The PAR is determined as follows.
- Waiting time is the delay a user faces while operating home appliances as per the schedule generated by ECS. Waiting time is also known as user comfort in this study. For user comfort measurement unit of time i.e., second is used. Waiting time i.e., user comfort and energy cost are two interdependent in nature. Some consumers wants to curtail their electricity bill, and others don’t tolerate delay, operate devices on priority basis without confronting delay. Consumers who do not tolerate delay and wish to complete their tasks sooner have to pay more. Waiting time/delay can be determined as follows.Constraint (6) ensures that appliances net energy consumption during a time slot should not exceed specific power threshold. Those consumers who take part in DR program get savings in aspects of electricity bill reduction from utility. The constraint (6) is also helpful for power grid because it smooth load curve by avoiding peaks in demand, and curtail the need to on peak power plants.
5. Developed and Existing Heuristic Algorithms
5.1. BPSO Algorithm
5.2. WDO
5.3. GA
5.4. DE
5.5. EDE
6. Simulations and Discussion
6.1. Scenario-I
6.1.1. Energy Consumption Pattern and Energy Cost Evaluation
6.1.2. PAR
6.1.3. Average Waiting Time
6.2. Scenario-II
6.2.1. Energy Consumption Pattern and Energy Cost Reduction
6.2.2. PAR
6.2.3. Average Waiting Time/Delay
6.3. Scenario-III
6.3.1. Energy Consumption Pattern and Energy Cost Reduction
6.3.2. PAR
6.3.3. Average Waiting Time
7. Trade-Offs
8. Conclusions and Future Research Direction
- The ECS will employ Lyapunov optimization technique to solve microgrid power scheduling problem in real time, where on-site events and requests will be responded to ensure optimal power usage scheduling.
- The ECS based on Lyapunov optimization technique will be employed in fog- and cloud-based environments to solve power usage scheduling problem under dynamic and uncertain conditions considering scale-able models.
- The ECS programmed based on multi-objective optimization algorithm address dynamic scheduling for solving multi objective optimization problems.
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Symbol | Description |
Energy consumption per hour | |
Status of appliance | |
Pricing signal | |
Total energy consumption | |
PAR | |
Appliance status before scheduling | |
Appliance status after scheduling | |
Operation time total length | |
Waiting time | |
, , | Weight factors |
Specific power threshold | |
Initial weight | |
Final weight | |
Maximum velocity | |
Minimum velocity | |
Mutation probability rate | |
Crossover probability rate | |
Update velocity of particle | |
Initial position of particle | |
Final position of particle | |
Sigmoid function | |
Lower bound | |
Upper bound | |
Initial population | |
, , | Three random vectors |
Fitness values of trial vector | |
Fitness values of target vector | |
local pull | |
global pull | |
Trial vector | |
Updated population fitness | |
Initial population fitness | |
Previous fitness | |
Current fitness | |
Mutant vector | |
Target vector | |
Crossover rate |
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Exiting Scheduling Studie(s) | Techniques | DR Program(s) | Appliances | Objective(s) | Limitation(s) |
---|---|---|---|---|---|
Optimal power scheduling in micro-grid [33] | GSPO algorithm | DLC | Shiftable load | Energy usage optimization | Objectives are achieved at increased model complexity |
Optimal power scheduling in smart grid [38] | GWDO algorithm | RTP-IBR | Interruptible, noninterruptible, and critical load | Energy cost and peak load curtailment | Interdependent objectives like user comfort and carbon emission are ignored |
Deep learning GmEDE for power scheduling [39] | RBM and GmEDE model | DAP scheme | Fixed, deferrable, and regulating loads | Bill payment and PAR minimization | Objectives are achieved at increased model complexity |
HGACO algorithm for energy management [40] | HGACO | RTP scheme | Interruptible and noninterruptible loads | Flatting load profile | Rebound peaks created at cost of achieving desired objectives |
Optimal household load scheduling [49] | BPSO | ToU and CPP schemes | Fixed, deferrable, and regulating loads | Utility bill and PAR minimization | Only one aspect bill and PAR is considered |
Realistic appliances scheduling [53,62,63,64,65,66] | GA, GWDO, BPSO, K-WDO | RTP-IBR scheme | Interruptible, noninterruptible, and critical | User comfort and PAR | Only one aspect considering (User comfort or cost) is insufficient |
DSM via loading scheduling [24,67,68,69] | GWDO, ANN-GmEDE, WBFA, and GBPO | RTP, ToUP, IBR, and CPP schemes | Interruptible, noninterruptible, and critical | Cost and discomfort minimization are considered | Conflicting objectives are simultaneously not considered |
This paper developed ECS based on EDE algorithm | GA, WDO, BPSO, DE, and EDE | RTP schemes | Interruptible, noninterruptible, and hybrid loads | Energy cost reduction, peak load curtailment, and user discomfort minimization are simultaneously catered |
Class | Appliances | Duration (Hours) | Power (kW) | Start Time | End Time |
---|---|---|---|---|---|
Interruptible appliances | |||||
Vacuum cleaner | 2 | 0.7 | 10:00 a.m. | 12:00 p.m. | |
Water heater | 3 | 5 | 6:00 a.m. | 9:00 a.m. | |
Water pump | 2 | 1 | 8:00 p.m. | 10:00 p.m. | |
Dishwasher | 2 | 1.8 | 10:00 p.m. | 12:00 a.m. | |
Hairdryer | 1 | 1.8 | 7:00 p.m. | 8:00 a.m. | |
Iron | 6 | 2.5 | 6:00 p.m. | 11:00 a.m. | |
Non-interruptible appliances | |||||
Washing machine | 6 | 2.5 | 9:00 a.m. | 3:00 p.m. | |
Clothdryer | 3 | 5 | 3:00 p.m. | 6:00 p.m. | |
Oven | 1 | 2.15 | 7:00 p.m. | 8:00 p.m. |
Parameter | Values | Parameter | Values |
---|---|---|---|
Nitra | 500 | 2 | |
Popsize | 40 | 0.4 | |
n | 7 | 1 | |
2 | −1 | ||
2 |
Parameter | Values | Parameter | Values |
---|---|---|---|
Nitra | 400 | Rt | 3 |
Popsize | 30 | g | 0.2 |
n | 7 | alp | 0.4 |
1 | c | 0.4 | |
−1 |
Parameter | Values | Parameter | Values |
---|---|---|---|
Psize | 10 | 0.2 | |
n | 7 | insite | 2 |
Nitra | 300 | 0.8 |
Variables | Value | Variables | Value |
---|---|---|---|
Nitr | 100 | 50 | |
Psize | 30 | 100 | |
n | 7 |
Scheduling Algorithms | Cost (Cent) | Difference (Cent) | Decrement in Cost (%) |
---|---|---|---|
W/O employing ECS | 19 | - | - |
ECS with DE | 15 | 4 | 21 |
ECS with BPSO | 11.5 | 7.5 | 39.4 |
ECS with WDO | 12.01 | 6.99 | 36.7 |
ECS with GA | 12 | 7 | 36.8 |
ECS with EDE | 9 | 10 | 52.63 |
Scheduling Algorithms | PAR | Difference | Decrement in PAR (%) |
---|---|---|---|
W/O employing ECS | 4.5 | - | - |
ECS with DE | 3.6 | 0.9 | 20 |
ECS with BPSO | 1.98 | 2.52 | 56 |
ECS with WDO | 2.4 | 6.99 | 46 |
ECS with GA | 2.01 | 2.49 | 55.3 |
ECS with EDE | 1.4 | 3.1 | 68 |
Scheduling Algorithms | Cost (Cent) | Difference (Cent) | Decrement in Cost (%) |
---|---|---|---|
W/O employing ECS | 18 | - | - |
ECS with DE | 13.5 | 4.5 | 25 |
ECS with BPSO | 11.5 | 6.5 | 36.1 |
ECS with WDO | 12 | 6 | 33.3 |
ECS with GA | 11 | 2.49 | 38.8 |
ECS with EDE | 9 | 3.1 | 50 |
Scheduling Algorithms | Cost (Cent) | Difference (Cent) | Decrement in Cost (%) |
---|---|---|---|
W/O employing ECS | 24 | - | - |
ECS with DE | 18 | 6 | 25 |
ECS with BPSO | 15 | 9 | 37.5 |
ECS with WDO | 17 | 7 | 29.1 |
ECS with GA | 16 | 8 | 33.3 |
ECS with EDE | 13 | 11 | 45.8 |
Scheduling Algorithms | PAR | Difference | Decrement in PAR (%) |
---|---|---|---|
W/O employing ECS | 3.4 | - | - |
ECS with DE | 18 | 6 | 25 |
ECS with BPSO | 15 | 9 | 37.5 |
ECS with WDO | 17 | 7 | 29.1 |
ECS with GA | 16 | 8 | 33.3 |
ECS with EDE | 13 | 11 | 45.8 |
Scheduling Algorithms | PAR | Difference | Decrement in PAR (%) |
---|---|---|---|
W/O employing ECS | 4.8 | - | - |
ECS with DE | 4.09 | 0.71 | 14.79 |
ECS with BPSO | 3.90 | 0.9 | 18.75 |
ECS with WDO | 2.4 | 2.4 | 50 |
ECS with GA | 1.7 | 2.49 | 64.5 |
ECS with EDE | 1.3 | 3.1 | 72.9 |
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Albogamy, F.R. Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid. Energies 2022, 15, 8015. https://doi.org/10.3390/en15218015
Albogamy FR. Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid. Energies. 2022; 15(21):8015. https://doi.org/10.3390/en15218015
Chicago/Turabian StyleAlbogamy, Fahad R. 2022. "Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid" Energies 15, no. 21: 8015. https://doi.org/10.3390/en15218015