Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy
Abstract
:1. Introduction
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- The use of a genetic algorithm for the re-planning of consumers to reduce the electricity cost using the energy of local producers through renewable means. The use of a genetic algorithm for consumer planning and the way in which the coding solution was realized (i.e., obtaining the chromosome) are the original components of the paper.
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- The implementation of a simulator that allows the generation of numerous consumer–producer configurations (i.e., hundreds), and the study of the impact of the consumer planning algorithm.
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- A complete platform implemented for real-time consumption monitoring, analysis, and intelligent planning, using the genetic algorithm, and consumer communication/information. Unlike other works in the field, this paper presents a complete solution that allows for the implementation of a consumer planning algorithm that consists of sensor cells for the acquisition of current consumption with self-harvesting, data collectors, a server for analysis and planning, and a client application for informing consumers. The system, developed as part of a research project, was used in two case studies that highlight the efficiency of the consumer planning algorithm.
- proposing a new strategy to reduce the electricity cost in a nano- or micro-grid through optimal consumer planning;
- implementing the proposed strategy in a simulator (using Python libraries, such as Numpy and Matplotlib);
- validation of simulation results in two experiments, using an innovative genetic algorithm proposed and analyzed in this paper;
- highlighting the advantages for all participants (micro-producers, regular consumers, or prosumers) based on case studies where this strategy was implemented.
2. Literature Review
3. Proposed Approach
3.1. The Consumer–Producer–Distributor Mode
3.2. The Genetic Algorithm
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- The individual, which has one or more chromosomes and represents a potential solution. The main challenge when working with genetic algorithms is to find the method to represent the chromosomes—this is known as the coding schema. To be solved with a genetic algorithm, the problem must be transposed into chromosomes.
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- The population represents a set of individuals, that is, a set of potential solutions. A generation represents the state of a population at an iteration of the algorithm.
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- The objective function is the criterion after which a generation is evaluated. After the evaluation, each individual will receive a rate called fitness. There may be individuals who have good fitness, so they are closer to the solutions sought, or have poor fitness, i.e., further away from the solutions. The algorithm stops when fitness has reached an acceptable level to one or more individuals; that is, when the solutions sought have been found.
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- During each generation, individuals are subjected to so-called genetic operators: crossover and mutation. The results of these operations are offspring (new individuals) or mutants (existing individuals that are changed). These will be added to the population and represent what the algorithm changes for a generation.
3.3. Simulation Platform and Data Collection
3.4. Method
4. Results
4.1. Simulation
4.2. Testing in Real Operation
5. Conclusions and Future Trends
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
ABC | Artificial Bee Colony |
AI | Artificial Intelligence |
AMI | Advanced Measurement Infrastructure |
AGV | Automatic Guided Vehicle |
BSN | Body Sensor Network |
CMOS | Complementary metal–oxide–semiconductor |
HTTP | HyperText Transfer Protocol |
HVAC | Heating, Ventilation, and Air Conditioning |
MPP | Maximum Power Point |
RF | Radio-Frequencies |
GSM | Global System for Mobile Communications |
SFL | Shuffled Frog Leaping |
SG | Smart Grids |
SMM | Social Media Marketing |
TLBO | Teaching & Learning based Optimization |
VLSI | Very-large-scale integration |
WAN | Wide Area Network |
GA | Genetic Algorithm |
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Parameter | Value |
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Population type | Fixed size |
Population size | 10 individuals |
Number of generations | 800 |
Selection | Roulette rule |
Crossover rate | 1/generation |
Crossover points | 1 |
Mutation rate | 0.5/generation |
Mutation points | 1 |
Replacement | Offspring from crossover replace individual with weakest evaluation (fitness) |
Evaluation | CT—see Relation (7) |
Objective | See Relation (8) |
Ending criteria | After 800 generations |
Consumer Unit No. | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|---|
Start time | 2:00 | 3:00 | 19:00 | 4:00 | 13:00 | 14:00 | 20:00 | 15:00 | 18:00 | 22:00 |
Stop time | 13:00 | 20:00 | 20:00 | 9:00 | 20:00 | 19:00 | 21:00 | 22:00 | 18:00 | 22:00 |
Number of operations hours per interval | 9 | 4 | 1 | 9 | 1 | 2 | 1 | 2 | 0 | 0 |
CONSUMERS NO | consMin [W] | consMax [W] | PRODUCER PEAK [W] | SUPPLIER PEAK [W] | SUPPLIER COST RON/W | PRODUCER COST RON/W |
---|---|---|---|---|---|---|
10 | 100 | 3000 | 2000 | 6000 | 0.6/1000 | 0.3/1000 |
PRODUCER_PEAK [W] | Measure No. | Range of Cost Decrease between Unplanned and Planned Consumption Schema in Favor of the Planned Consumption [%] | Average Cost Decrease between Unplanned and Planned Consumption Schema in Favor of the Planned Consumption [%] |
---|---|---|---|
2000 | 30 | 3.25–7.14 | 4.81 |
3000 | 40 | 5.48–11.59 | 8.95 |
4000 | 40 | 2.97–11.24 | 6.55 |
5000 | 40 | 7.14–16.11 | 11.48 |
6000 | 40 | 5.79–19.51 | 11.90 |
7000 | 40 | 8–21.43 | 14.23 |
8000 | 30 | 15.38–22.5 | 18.48 |
PRODUCER_PEAK [W] | Measure No. | Range of Decreasing Cost between Unplanned and Planned Consumption Schema [%] | Medium Cost Decrease between Unplanned and Planned Consumption Schema [%] |
---|---|---|---|
2000 | 40 | 3.06–36.54 | 14.15 |
3000 | 40 | 2.43–25.78 | 11.14 |
4000 | 40 | 7.52–25.39 | 16.45 |
5000 | 40 | 6.33–24.02 | 14.21 |
6000 | 40 | 19.07–35.71 | 25.33 |
7000 | 40 | 5.60–28.83 | 19.50 |
8000 | 40 | 16.41–36.49 | 25.65 |
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Ionescu, L.-M.; Bizon, N.; Mazare, A.-G.; Belu, N. Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy. Mathematics 2020, 8, 1144. https://doi.org/10.3390/math8071144
Ionescu L-M, Bizon N, Mazare A-G, Belu N. Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy. Mathematics. 2020; 8(7):1144. https://doi.org/10.3390/math8071144
Chicago/Turabian StyleIonescu, Laurentiu-Mihai, Nicu Bizon, Alin-Gheorghita Mazare, and Nadia Belu. 2020. "Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy" Mathematics 8, no. 7: 1144. https://doi.org/10.3390/math8071144