Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
Abstract
:1. Introduction
Objectives, Contributions, and Work Organization
- To provide a review of the most recent works related to the application of MBPC in residential HEMS incorporating photovoltaics and battery systems;
- To provide a sensitivity analysis of MBPC for HEMS, considering the time step, prediction horizon (PH), and control horizon (CH) associated with the data. We show that a significant reduction in the MBPC time complexity with minimal impact on the performance can be obtained by employing a small CH, achieving substantial cost savings, or improved gains, in comparison with many of the approaches found in the literature.
2. State-of-the-Art
2.1. Background Information
2.2. Related Works
3. Methodology
3.1. Case Study
3.2. Household Power Demand and PV Production Profile
3.3. Formulation of MBPC
The Branch-and-Bound Algorithm
- an upper bound on the total cost from instant k + 1 to k + PH;
- and a lower bound on the cost from instant k + i to k + PH.
4. Results and Discussion
4.1. Model-Based Predictive Control Analysis
4.2. Analysis 1—Prediction Horizon and Time Step Sensitivity Analysis
4.3. Analysis 2—Control Horizon
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Technique | Description |
---|---|
Linear Programming (LP) | Models’ relationship between variables as linear to maximize or minimize an objective. |
MILP | Similar to LP; however, additional constraints are put on at least one decision variable so that they have to be discrete. |
Convex Programming | Minimizes a convex or maximizes a concave objective function. |
Genetic Programming | A heuristic search method inspired by biology and iteratively produces “fitter” candidates using “crossover” and “mutation” functions. |
Particle Swarm Optimization | A heuristic search that iteratively produces better candidates using “position”, “velocity”, and “fitness” values |
Model Predictive Control | Uses a model to predict plant/required output. It chooses a “control action” by repeatedly solving an online optimization problem. |
Game Theory | Models the iteration between different “players” and the environment using fixed rules. |
Artificial Neural Networks (ANN) | A modelling techniques using artificial neurons to create complex models for forecasting and classification. |
Fuzzy Logic Control (FLC) | Uses a rule-based system to produce an output for forecasting or classification. |
Reinforcement Learning | A machine learning methodology that learns how to maximize a reward function through trial and error. |
Scenario Nº | Start Day | End Day | Time Step (min) | Prediction Horizon | Control Horizon | Comp Time (s) |
---|---|---|---|---|---|---|
Cloudy Day | ||||||
S1 | 13 May 2020 | 14 May 2020 | 5 | 12 | 12 | 0.61 |
S2 | 13 May 2020 | 14 May 2020 | 10 | 12 | 12 | 0.35 |
S3 | 13 May 2020 | 14 May 2020 | 15 | 12 | 12 | 0.14 |
S4 | 13 May 2020 | 14 May 2020 | 5 | 24 | 24 | 188.86 |
S5 | 13 May 2020 | 14 May 2020 | 10 | 24 | 24 | 9.95 |
S6 | 13 May 2020 | 14 May 2020 | 15 | 24 | 24 | 18.52 |
S7 | 13 May 2020 | 14 May 2020 | 5 | 36 | 36 | 4987.00 |
S8 | 13 May 2020 | 14 May 2020 | 10 | 36 | 36 | 5.51 |
S9 | 13 May 2020 | 14 May 2020 | 15 | 36 | 36 | 312.00 |
Sunny Day | ||||||
S10 | 29 May 2020 | 30 May 2020 | 5 | 12 | 12 | 0.77 |
S11 | 29 May 2020 | 30 May 2020 | 10 | 12 | 12 | 0.27 |
S12 | 29 May 2020 | 30 May 2020 | 15 | 12 | 12 | 0.12 |
S13 | 29 May 2020 | 30 May 2020 | 5 | 24 | 24 | 0.98 |
S14 | 29 May 2020 | 30 May 2020 | 10 | 24 | 24 | 0.47 |
S15 | 29 May 2020 | 30 May 2020 | 15 | 24 | 24 | 0.35 |
S16 | 29 May 2020 | 30 May 2020 | 5 | 36 | 36 | 1.95 |
S17 | 29 May 2020 | 30 May 2020 | 10 | 36 | 36 | 0.86 |
S18 | 29 May 2020 | 30 May 2020 | 15 | 36 | 36 | 0.61 |
Scenario Nº | Start Day | End Day | Step Time (min) | Prediction Horizon | Control Horizon | Comp Time (s) |
---|---|---|---|---|---|---|
Cloudy Day | ||||||
A1 | 13 May 2020 | 14 May 2020 | 5 | 36 | 2 | 0.05 |
A2 | 13 May 2020 | 14 May 2020 | 5 | 36 | 3 | 0.18 |
A3 | 13 May 2020 | 14 May 2020 | 5 | 36 | 4 | 0.25 |
A4 | 13 May 2020 | 14 May 2020 | 15 | 36 | 2 | 0.18 |
A5 | 13 May 2020 | 14 May 2020 | 15 | 36 | 3 | 0.11 |
A6 | 13 May 2020 | 14 May 2020 | 15 | 36 | 4 | 0.05 |
Sunny Day | ||||||
A7 | 29 May 2020 | 30 May 2020 | 5 | 36 | 2 | 0.15 |
A8 | 29 May 2020 | 30 May 2020 | 5 | 36 | 3 | 0.21 |
A9 | 29 May 2020 | 30 May 2020 | 5 | 36 | 4 | 0.20 |
A10 | 29 May 2020 | 30 May 2020 | 15 | 36 | 2 | 0.33 |
A11 | 29 May 2020 | 30 May 2020 | 15 | 36 | 3 | 0.12 |
A12 | 29 May 2020 | 30 May 2020 | 15 | 36 | 4 | 0.09 |
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Bot, K.; Laouali, I.; Ruano, A.; Ruano, M.d.G. Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques. Energies 2021, 14, 5852. https://doi.org/10.3390/en14185852
Bot K, Laouali I, Ruano A, Ruano MdG. Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques. Energies. 2021; 14(18):5852. https://doi.org/10.3390/en14185852
Chicago/Turabian StyleBot, Karol, Inoussa Laouali, António Ruano, and Maria da Graça Ruano. 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques" Energies 14, no. 18: 5852. https://doi.org/10.3390/en14185852
APA StyleBot, K., Laouali, I., Ruano, A., & Ruano, M. d. G. (2021). Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques. Energies, 14(18), 5852. https://doi.org/10.3390/en14185852