# Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- Economic benefits:
- ◦
- DR can lead to dispatching fewer hours of uneconomical generation units when the power system becomes tight, i.e., when generation cannot meet demand or when the security of supply margins decreases;
- ◦
- End-users profit by either consuming in low-tariff hours, selling power back to the grid with the use of local storage, or other incentives (e.g., bill discount);
- ◦
- DR can decrease distribution network stress and therefore reduce the need for network investments.

- Power system operation:
- ◦
- System reliability increases when providing frequency response, contingency reserves, and flexibility services;
- ◦
- Renewable energy source (RES) curtailment is reduced by modifying demand to match green power generation.

- Reduction in greenhouse gas emissions:
- ◦
- Utilization of distributed resources (EVs, PVs, and local storage) is higher;
- ◦
- Energy efficiency is higher, and thus, energy consumption decreases.

**AI group:**Some review papers classify demand response applications and investigate only a single group of AI methods. Other papers analyze more than one group of AI methods and thus provide a more holistic review of the state-of-the-art from a computational intelligence (CI) perspective.**Energy sub-systems:**This set of attributes refers to the different energy sub-systems reviewed in the literature for scheduling and control in DR applications. Distributed generation, heating/cooling devices, EVs, local energy storage, and residential appliances are the different areas considered in this category.**Energy management system (EMS) scheduling and control:**Load modeling, scheduling, and control methods in response to signals for DR are reviewed. Additionally, the classification of methods based on demand-side strategies is considered.**Optimization objectives:**AI algorithms in demand response can be used for various optimization objectives, such as the minimization of energy consumption with or without considering user comfort, the minimization of energy cost, and the provision of load balancing.

- Text found in title, abstract, or keyword: “demand AND response” OR “demand AND side AND management”;
- Text found in title, abstract, or keyword: “particle AND swarm”;
- Text found in title, abstract, or keyword: “residential OR household OR home”;
- Text found in title, abstract, or keyword: “controller OR scheduling OR control”.

- This is the only review paper that classifies PSO methods used specifically for scheduling and control based on the type of the residential energy sub-system (EVs, heating/cooling devices, local storage, residential appliances, and DG);
- It identifies different optimization objectives when using PSO methods, taking into consideration user convenience (in the form of appliances’ operational time delay) but also user thermal comfort (indoor ambient and hot water temperature);
- It discusses the limitations and challenges of PSO methods and models in residential demand response management systems and suggests potential future research areas for investigation.

## 2. Basic Principles of PSO

- Record the individual best location found so far with the help of a fitness function that evaluates how close each particle is to the optimal solution;
- Record their current direction and intensity of movement (velocity);
- Be informed about which location is the global best, defined as the optimal location among all particles.

- The inertia component w V
_{i}(t): this term tends to maintain the current movement direction (velocity) of each particle; - The cognitive/individual component ${\phi}_{1}\xb7{r}_{1}\xb7\left({P}_{i,best}-{X}_{i}\left(t\right)\right)$: this component describes the distance between each particle’s current position and the individual best location found;
- The social component ${\phi}_{2}\xb7{r}_{2}\xb7\left({P}_{glob,best}-{X}_{i}\left(t\right)\right)$: this component calculates the distance between the particle’s current position and the best position found by the entire swarm.

- Simple and easy to use;
- Fast convergence and robustness, even in complex and highly constrained multi-dimensional search spaces;
- High applicability since it can be used in various optimization problems;
- High adjustability since it can be easily hybridized and modified to fit the purpose of each problem and improve its performance.

- Risk of suboptimal solutions (local optima) due to either the problem formulation characteristics or a lack of diversity in particle movement that leads to premature convergence;
- No guarantee that PSO will reach the global optimum solution since there is a risk of premature convergence to local optima;
- Lack of interpretability/explainability given that the algorithm is not based on a strong mathematical theoretical basis (lack of mathematical proof of convergence).

## 3. Models for Residential Load Scheduling and Control Using PSO

- It requires fewer parameters for tuning and adjustment;
- Easier implementation and less computational effort are usually needed to reach a near-optimal solution compared to other heuristic algorithms;
- The histories of all particles contribute to the search, while in other methods (e.g., GA), the algorithm’s memory capability is lower due to the replacement of the old population with a new, more efficient one.

#### 3.1. Optimization Objectives

- 1.
**Single objective:**

- 2.
**Single objective with aggregated variables, weights, or penalties:**

- 3.
**Multiple objectives:**

#### 3.2. Constraints

#### 3.2.1. System Constraints

**Power grid thresholds (****E**_{grid}):The minimum and/or maximum contracted power of end-users with utility at the connection point. This increases the complexity of the optimization and potentially decreases the amount of energy savings that can be achieved since there is less flexibility to shift more loads to off-peak hours due to constraint violation.**Storage-related constraints (****E**_{storage}):Charging and discharging rates as well as the capacity of storage units are introduced as inequality constraints in works with energy storage, either in the form of batteries or in electric vehicles.**RES generation capacity (****E**_{RES}):The maximum generation capacity of renewable sources is constrained, usually as a share of total household demand (e.g., 30% of net demand is met by RES).**User convenience:**Another important consideration is user convenience in the sense of minimizing the operational delay (waiting time) of different household appliances or prioritizing the operation of appliances over others based on consumer preferences. In some works, such as [38], user convenience is introduced as the minimum amount of appliance switching needed during a DR event.**Thermal comfort:**In many works, not only appliance waiting time but also indoor temperature and water heater temperature is considered when using thermostatically controlled loads. To operate appliances within the preferred temperature range, smart sockets and temperature sensors can be installed, as in [23].**Voltage level:**

#### 3.2.2. Electricity Costs and User Convenience/Comfort

#### 3.3. Applications

#### 3.3.1. Energy Systems

#### 3.3.2. DR Programs—Electricity Tariffs

#### 3.4. Taxonomies

## 4. PSO Methods

- Hybrid methods:
- ◦
- ◦
- PSO–ANN [37];
- ◦
- BPSO–integer linear programming [55];
- ◦
- PSO–evolutionary algorithm [58];
- ◦
- PSO–sequential quadratic programming optimizer (SQP) [59];
- ◦
- PSO–local vortex search [62];
- ◦
- PSO–bacterial foraging (BF) [63];
- ◦
- PSO–fuzzy logic [71];
- ◦
- PSO–sinusoidal and cosine acceleration (SCAC) [75];
- ◦
- BSPO–chaos optimization [76];
- ◦
- PSO–harmony search (HS) [77];
- ◦
- BPSO–gradient-based NLP solver [79];
- ◦
- BPSO–fuzzy Mamdani and fuzzy Sugeno [80].

- Quadratic BPSO [41];
- Cooperative PSO:

## 5. Evaluation

#### 5.1. Problem Design

**System architecture—HEMS design.**As the first step, it is important to define the energy system resources and the way that they are connected with other users and the main power grid. It is also useful to describe the flow of data information and the point of control where scheduling will take place: local control, decentralized control of grid-connected or standalone microgrids, or centralized control on a utility/aggregator level.**Appliance classification and user categorization.**Residential household appliances can be characterized as fixed, flexible, interruptible, and power-adjustable. In a microgrid, users can be passive (unidirectional power flow without self-consumption), semi-active (unidirectional power flow with self-consumption and storage), or active (bidirectional power flow with self-consumption and storage).**Energy consumption.**Equality constraints that ensure load balance at each time interval are introduced. In some works, a maximum and/or minimum grid consumption constraint is defined so that load shift to off-peak hours will not lead to demand spikes. Additionally, grid exchange capabilities (selling back to the grid) might increase the problem complexity even further but provide a more realistic modeling approach.**Local RES generation and energy storage.**In some works, local RES generation with coupled energy storage is considered. A set of constraints (charge/discharge rates and maximum energy stored) is essential to properly model local energy storage. Depending on the problem design, either RES production follows a predefined profile or a RES forecasting model is utilized.**DR program and electricity tariffs.**The DR program that each user follows is a crucial feature of the problem design, since in the majority of problems, cost minimization is the main objective. In research, inclined block rates (IBRs) can ensure a smoother load shift from peak to off-peak hours.**Other considerations.**PAR can be introduced as a minimization objective or can be investigated when modeling results are obtained. In other works, a set of constraints is introduced to ensure that model results respect users’ thermal comfort and convenience preferences (appliance operational delay).**Objectives.**The most common objective, as shown in Section 3, is electricity cost minimization. User convenience, thermal comfort, PAR, and emission reduction can also be considered as optimization objectives.

#### 5.2. Complexity

**High complexity**. The problems in [22,28,29,32,46,49,51,57,61,62,66,68,74,78,79,80,81] can be characterized as highly complex, since they have a complex energy system architecture (high number of users); they consider many energy resources, including interruptible and power-adjustable household appliances; they are heavily constrained; and in most cases, the optimization functions involve multiple objectives.**Moderate complexity**. The problems in [21,24,26,27,30,31,33,34,37,38,39,40,41,42,44,45,48,53,54,55,56,59,60,63,64,69,72,75,76,77,82,83,84] show moderate complexity either due to a single objective combined with a large number of constraints and energy resources or due to a combination of more objectives with fewer resources and constraints applied.**Lower complexity**. The problems in [23,25,35,36,43,47,50,52,58,65,67,70,71,73] present lower complexity compared to the research works mentioned above. The smaller number of system constraints, the simpler energy system architecture (small number of users), the lack of power-adjustable and/or interruptible appliances, and the single-objective optimization are the main reasons for this categorization.

#### 5.3. Accuracy

## 6. Future Research

#### 6.1. Advanced and Explainable Methods

#### 6.2. Consideration of Uncertainties

#### 6.3. End-Users as Price Makers

#### 6.4. Fully Utilize EV Potential

#### 6.5. Energy Model Scalability

#### 6.6. Comprehensive Metrics for DSM Evaluation

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AC | Air conditioning |

ACO | Ant colony optimization |

AI | Artificial intelligence |

ANN | Artificial neural network |

BBSA | Binary backtracking search algorithm |

BFOA | Bacterial foraging optimization algorithm |

BPSO | Binary particle swarm optimization |

CI | Computational intelligence |

CBPSO | Chaotic binary particle swarm optimization |

CLPSO | Comprehensive learning particle swarm optimization |

CPSO-R | Cooperative particle swarm optimization with stochastic repulsion |

CPSO-SARD | Cooperative particle swarm optimization with stochastic attraction–repulsion of diversity |

DG | Distributed generation |

DLC | Direct load control |

DNO | Distribution network operator |

DR | Demand response |

DSM | Demand-side management |

EV | Electric vehicle |

ES | Energy storage |

GA | Genetic algorithm |

HVAC | Heating, ventilation, and air conditioning |

IBR | Inclined block rates |

ILP | Integer linear programming |

LP | Linear programming |

LS | Local search |

MG | Microgrid |

ML | Machine learning |

MILP | Mixed-integer linear programming |

PAR | Peak-to-average ratio |

PSO | Particle swarm optimization |

PV | Photovoltaic |

RES | Renewable energy resources |

RL | Reinforcement learning |

RTP | Real-time pricing |

SRPSO | Self-regulated particle swarm optimization |

ToU | Time of use |

UPSO | Unified particle swarm optimization |

V2G | Vehicle-to-grid |

WDO | Wind-driven optimization |

## References

- Shewale, A.; Mokhade, A.; Funde, N.; Bokde, N.D. An overview of demand response in smart grid and optimization techniques for efficient residential appliance scheduling problem. Energies
**2020**, 13, 4266. [Google Scholar] [CrossRef] - Siano, P. Demand response and smart grids—A survey. Renew. Sustain. Energy Rev.
**2014**, 30, 461–478. [Google Scholar] [CrossRef] - Pallonetto, F.; De Rosa, M.; D’Ettorre, F.; Finn, D.P. On the assessment and control optimisation of demand response programs in residential buildings. Renew. Sustain. Energy Rev.
**2020**, 127, 109861. [Google Scholar] [CrossRef] - Khan, A.A.; Razzaq, S.; Khan, A.; Khursheed, F.; Owais. HEMSs and enabled demand response in electricity market: An overview. Renew. Sustain. Energy Rev.
**2015**, 42, 773–785. [Google Scholar] [CrossRef] - Aghaei, J.; Alizadeh, M.I. Demand response in smart electricity grids equipped with renewable energy sources: A review. Renew. Sustain. Energy Rev.
**2013**, 18, 64–72. [Google Scholar] [CrossRef] - Georgilakis, S.P. Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research. Energies
**2020**, 13, 186. [Google Scholar] [CrossRef][Green Version] - Rajasekhar, B.; Tushar, W.; Lork, C.; Zhou, Y.; Yuen, C.; Pindoriya, N.M.; Wood, K.L. A survey of computational intelligence techniques for air-conditioners energy management. IEEE Trans. Emerg. Top. Comput. Intell.
**2020**, 4, 555–570. [Google Scholar] [CrossRef] - Mabina, P.; Mukoma, P.; Booysen, M.J. Sustainability matchmaking: Linking renewable sources to electric water heating through machine learning. Energy Build.
**2021**, 246, 111085. [Google Scholar] [CrossRef] - Afram, A.; Sharifi, F.J. Gray-box modeling and validation of residential HVAC system for control system design. Appl. Energy
**2015**, 137, 134–150. [Google Scholar] [CrossRef] - Antonopoulos, I.; Robu, V.; Couraud, B.; Kirli, D.; Norbu, S.; Kiprakis, A.; Flynn, D.; Elizondo-Gonzalez, S.; Wattam, S. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renew. Sust. Energ. Rev.
**2020**, 130, 109899. [Google Scholar] [CrossRef] - Vazquez-Canteli, J.R.; Nagy, Z. Reinforcement learning for demand response: A review of algorithms and modeling techniques. Appl. Energy
**2019**, 235, 1072–1089. [Google Scholar] [CrossRef] - Zhang, Z.; Zhang, D. Deep reinforcement learning for power system application: An overview. CSEE J. Power Energy Syst.
**2020**, 6, 213–225. [Google Scholar] - Ahmad, M.W.; Mourshed, M.; Yuce, B.; Rezgui, Y. Computational intelligence techniques for HVAC systems: A review. Build. Simul.
**2016**, 9, 359–398. [Google Scholar] [CrossRef][Green Version] - Merabet, G.H.; Essaaidi, M.; Ben Haddou, M.; Qolomany, B.; Qadir, J.; Anan, M.; Al-Fuqaha, A.; Riduan Abid, M.; Benhaddou, D. Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renew. Sust. Energ. Rev.
**2021**, 144, 110969. [Google Scholar] [CrossRef] - Farzaneh, H.; Malehmirchegini, L.; Bejan, A.; Afolabi, T.; Mulumba, A.; Daka, P.P. Artificial intelligence evolution in smart buildings for energy efficiency. Appl. Sci.
**2021**, 11, 763. [Google Scholar] [CrossRef] - Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the ICNN’95-International Conference on Neural Networks, Perth, Australia, 27 November–1 December 1995; Volume 4, pp. 1942–1948. [Google Scholar]
- What Is Computational Intelligence? Available online: https://cis.ieee.org/about/what-is-ci (accessed on 25 February 2022).
- Engelbrecht, A.P. Computational Intelligence: An Introduction, 2nd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2007; pp. 285–358. [Google Scholar]
- del Valle, Y.; Venayagamoorthy, G.K.; Mohagheghi, S.; Hernandez, J.C.; Harley, R.G. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Trans. Evol. Comput.
**2008**, 12, 171–195. [Google Scholar] [CrossRef] - Wang, D.; Tan, D.; Liu, L. Particle swarm optimization algorithm: An overview. Soft Comput.
**2018**, 22, 387–408. [Google Scholar] [CrossRef] - Ahmad, A.; Khan, A.; Javaid, N.; Hussain, H.M.; Abdul, W.; Almogren, A.; Alamri, A.; Azim Niaz, I. An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources. Energies
**2017**, 10, 549. [Google Scholar] [CrossRef][Green Version] - Rahim, S.; Javaid, N.; Ahmad, A.; Khan, S.A.; Khan, Z.A.; Alrajeh, N.; Qasim, U. Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build.
**2016**, 129, 452–470. [Google Scholar] [CrossRef] - Ahmed, M.S.; Mohamed, A.; Khatib, T.; Shareef, H.; Homod, R.Z.; Abd Ali, J. Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Build.
**2017**, 138, 215–227. [Google Scholar] [CrossRef] - Liu, D.; Xu, Y.; Wei, Q.; Liu, X. Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming. IEEE/CAA J.
**2018**, 5, 36–46. [Google Scholar] [CrossRef] - Gudi, N.; Wang, L.; Devabhaktuni, V. A demand side management based simulation platform incorporating heuristic optimization for management of household appliances. Int. J. Electr. Power Energy Syst.
**2012**, 43, 185–193. [Google Scholar] [CrossRef] - Javaid, N.; Javaid, S.; Abdul, W.; Ahmed, I.; Almogren, A.; Alamri, A.; Niaz, I.A. A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. Energies
**2017**, 10, 319. [Google Scholar] [CrossRef][Green Version] - Lugo-Cordero, H.M.; Fuentes-Rivera, A.; Guha, R.K.; Ortiz-Rivera, E.I. Particle Swarm Optimization for load balancing in green smart homes. IEEE Congr. Evol. Comput. CEC
**2011**, 715–720. [Google Scholar] [CrossRef] - Javaid, N.; Ullah, I.; Akbar, M.; Iqbal, Z.; Ali Khan, F.; Alrajeh, N.; Alabed, M.S. An Intelligent Load Management System With Renewable Energy Integration for Smart Homes. IEEE Access
**2017**, 5, 13587–13600. [Google Scholar] [CrossRef] - Aghajani, G.R.; Shayanfar, H.A.; Shayeghi, H. Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag.
**2015**, 106, 308–321. [Google Scholar] [CrossRef] - Huang, Y.; Wang, L.; Guo, W.; Kang, Q.; Wu, Q. Chance Constrained Optimization in a Home Energy Management System. IEEE Trans Smart Grid
**2018**, 9, 252–260. [Google Scholar] [CrossRef] - Ullah, I.; Javaid, N.; Khan, Z.A.; Qasim, U.; Khan, Z.A.; Mehmood, S.A. An Incentive-based Optimal Energy Consumption Scheduling Algorithm for Residential Users. Procedia Comput. Sci.
**2015**, 52, 851–857. [Google Scholar] [CrossRef][Green Version] - Javaid, N.; Hafeez, G.; Iqbal, S.; Alrajeh, N.; Alabed, M.S.; Guizani, M. Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management. IEEE Access
**2018**, 6, 77077–77096. [Google Scholar] [CrossRef] - Huang, Y.; Tian, H.; Wang, L. Demand response for home energy management system. Int. J. Electr. Power Ener.
**2015**, 73, 448–455. [Google Scholar] [CrossRef] - Mahmood, D.; Javaid, N.; Alrajeh, N.; Khan, Z.A.; Qasim, U.; Ahmed, I.; Ilahi, M. Realistic Scheduling Mechanism for Smart Homes. Energies
**2016**, 9, 202. [Google Scholar] [CrossRef][Green Version] - Faia, R.; Faria, P.; Vale, Z.; Spinola, J. Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House. Energies
**2019**, 12, 1645. [Google Scholar] [CrossRef][Green Version] - Hassan, S.; Arshad, M.; Chen, M.; Lin, H.; Mohammed, A.; Mohammed, K.; Gohar Rehman, C. Optimization Modeling for Dynamic Price Based Demand Response in Microgrids. J. Clean. Prod.
**2019**, 222, 231–241. [Google Scholar] [CrossRef] - Ahmed, M.S.; Mohamed, A.; Homod, R.Z.; Shareef, H. Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy. Energies
**2016**, 9, 716. [Google Scholar] [CrossRef][Green Version] - Rahman, M.M.; Arefi, A.; Shafiullah, G.M.; Hettiwatte, S. A new approach to voltage management in unbalanced low voltage networks using demand response and OLTC considering consumer preference. Int. J. Electr. Power Energy Syst.
**2018**, 99, 11–27. [Google Scholar] [CrossRef] - Hafeez, G.; Javaid, N.; Iqbal, S.; Khan, F.A. Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units. Energies
**2018**, 11, 611. [Google Scholar] [CrossRef][Green Version] - Ziadi, Z.; Taira, S.; Oshiro, M.; Funabashi, T. Optimal Power Scheduling for Smart Grids Considering Controllable Loads and High Penetration of Photovoltaic Generation. IEEE Trans. Smart Grid
**2014**, 5, 2350–2359. [Google Scholar] [CrossRef] - Rezaee Jordehi, A. Binary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homes. Appl. Soft Comput.
**2019**, 78, 465–480. [Google Scholar] [CrossRef] - Correa-Flórez, C.G.; Gerossier, A.; Michiorri, A.; Kariniotakis, G. Stochastic operation of home energy management systems including battery cycling. Appl. Energy
**2018**, 225, 1205–1218. [Google Scholar] [CrossRef][Green Version] - Mohseni, S.; Moghaddas-Tafreshi, S.M. A multi-agent system for optimal sizing of a cooperative self-sustainable multi-carrier microgrid. Sustain. Cities Soc.
**2018**, 38, 452–465. [Google Scholar] [CrossRef] - Lin, Y.-H.; Hu, Y.-C. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing. Sensors
**2018**, 18, 1365. [Google Scholar] [CrossRef] [PubMed][Green Version] - Javaid, N.; Ahmed, F.; Ullah, I.; Abid, S.; Abdul, W.; Alamri, A.; Almogren, A.S. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid. Energies
**2017**, 10, 1546. [Google Scholar] [CrossRef][Green Version] - Zhu, J.; Lin, Y.; Lei, W.; Liu, Y.; Tao, M. Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm. Energy
**2019**, 171, 944–955. [Google Scholar] [CrossRef] - Pedrasa, M.A.A.; Spooner, T.D.; MacGill, I.F. Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services. IEEE Trans. Smart Grid
**2010**, 1, 134–143. [Google Scholar] [CrossRef] - Du, Y.F.; Jiang, L.; Li, Y.; Wu, Q. A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances. IEEE Trans. Smart Grid
**2018**, 9, 743–755. [Google Scholar] [CrossRef][Green Version] - Carrasqueira, P.; Alves, M.J.; Antunes, C.H. Bi-level particle swarm optimization and evolutionary algorithm approaches for residential demand response with different user profiles. Inf. Sci.
**2017**, 418–419, 405–420. [Google Scholar] [CrossRef][Green Version] - Faria, P.; Vale, Z.; Soares, J.; Ferreira, J. Demand Response Management in Power Systems Using Particle Swarm Optimization. IEEE Intell. Syst.
**2013**, 28, 43–51. [Google Scholar] [CrossRef][Green Version] - Hussain, B.; Khan, A.; Javaid, N.; Hasan, Q.U.; Malik, S.A.; Ahmad, O.; Dar, A.H.; Kazmi, A. A Weighted-Sum PSO Algorithm for HEMS: A New Approach for the Design and Diversified Performance Analysis. Electronics
**2019**, 8, 180. [Google Scholar] [CrossRef][Green Version] - Khan, A.; Javaid, N.; Ahmad, A.; Akbar, M.; Khan, Z.A.; Ilahi, M. A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. J. Ambient Intell. Humaniz. Comput.
**2019**, 10, 1655–1678. [Google Scholar] [CrossRef] - Jing, Z.; Zhu, J.; Hu, R. Sizing optimization for island microgrid with pumped storage system considering demand response. J. Mod. Power Syst. Clean Energy
**2018**, 6, 791–801. [Google Scholar] [CrossRef][Green Version] - Hafeez, G.; Wadud, Z.; Khan, I.U.; Khan, I.; Shafiq, Z.; Usman, M.; Khan, M.U.A. Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid. Sensors
**2020**, 20, 3155. [Google Scholar] [CrossRef] [PubMed] - Wang, J.; Li, Y.; Zhou, Y. Interval number optimization for household load scheduling with uncertainty. Energy Build
**2016**, 130, 613–624. [Google Scholar] [CrossRef] - Imran, A.; Hafeez, G.; Khan, I.; Usman, M.; Shafiq, Z.; Qazi, A.B.; Khalid, A.; Thoben, K.D. Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid. IEEE Access
**2020**, 8, 139587–139608. [Google Scholar] [CrossRef] - Aghajani, G.R.; Shayanfar, H.A.; Shayeghi, H. Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy
**2017**, 126, 622–637. [Google Scholar] [CrossRef] - Pinto, R.; Bessa, R.J.; Matos, M.A. Multi-period flexibility forecast for low voltage prosumers. Energy
**2017**, 141, 2251–2263. [Google Scholar] [CrossRef][Green Version] - Huang, Y.; Wang, W.; Hou, B. A hybrid algorithm for mixed integer nonlinear programming in residential energy management. J. Clean. Prod.
**2019**, 226, 940–948. [Google Scholar] [CrossRef] - Khan, Z.A.; Khalid, A.; Javaid, N.; Haseeb, A.; Saba, T.; Shafiq, M. Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid. IEEE Access
**2019**, 7, 140102–140125. [Google Scholar] [CrossRef] - Zhang, Y.; Zeng, P.; Li, S.; Zang, C.; Li, H. A Novel Multiobjective Optimization Algorithm for Home Energy Management System in Smart Grid. Math. Probl. Eng.
**2015**, 2015, 807527. [Google Scholar] [CrossRef][Green Version] - Lezama, F.; Faia, R.; Faria, P.; Vale, Z. Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms. Energies
**2020**, 13, 2466. [Google Scholar] [CrossRef] - Nawaz, A.; Hafeez, G.; Khan, I.; Jan, K.U.; Li, H.; Ali Khan, S.; Wadud, Z. An Intelligent Integrated Approach for Efficient Demand Side Management with Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid. IEEE Access
**2020**, 8, 132551–132581. [Google Scholar] [CrossRef] - Sisodiya, S.; Shejul, K.; Kumbhar, G.B. Scheduling of demand-side resources for a building energy management system. Int. Trans. Electr. Energy Syst.
**2017**, 29, e2369. [Google Scholar] [CrossRef] - Pedrasa, M.A.A.; Spooner, T.D.; MacGill, I.F. Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization. IEEE Trans. Smart Grid
**2009**, 24, 1173–1181. [Google Scholar] [CrossRef] - Soares, J.; Morais, H.; Sousa, T.; Vale, Z.; Faria, P. Day-Ahead Resource Scheduling Including Demand Response for Electric Vehicles. IEEE Trans. Smart Grid
**2013**, 4, 596–605. [Google Scholar] [CrossRef][Green Version] - Rezaee Jordehi, A. Enhanced leader particle swarm optimisation (ELPSO): A new algorithm for optimal scheduling of home appliances in demand response programs. Artif. Intell. Rev.
**2020**, 53, 2043–2073. [Google Scholar] [CrossRef] - Ma, K.; Hu, S.; Yang, J.; Xu, X.; Guan., X. Appliances scheduling via cooperative multi-swarm PSO under day-ahead prices and photovoltaic generation. Appl. Soft Comput.
**2018**, 62, 504–513. [Google Scholar] [CrossRef] - Zhang, H.; Zhao, D.; Gu, C.; Li, F.J. Bilevel economic operation of distribution networks with microgrid integration. Renew. Sustain. Energy
**2015**, 7, 023120. [Google Scholar] [CrossRef][Green Version] - Chellamani, G.K.; Chandramani, P.V. An Optimized Methodical Energy Management System for Residential Consumers Considering Price-Driven Demand Response Using Satin Bowerbird Optimization. J. Electr. Eng. Technol.
**2020**, 15, 955–967. [Google Scholar] [CrossRef] - Parvin, K.; Hannan, M.A.; Al-Shetwi, A.Q.; Ker, P.J.; Roslan, M.F.; Mahlia, T.M.I. Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration. IEEE Access
**2020**, 8, 210784–210799. [Google Scholar] [CrossRef] - Wang, L.; Wang, Z.; Yang, R. Intelligent Multiagent Control System for Energy and Comfort Management in Smart and Sustainable Buildings. IEEE Trans. Smart Grid
**2012**, 3, 605–617. [Google Scholar] [CrossRef] - Gudi, N.; Wang, L.; Devabhaktuni, V.; Depuru, S.S.S.R. Demand response simulation implementing heuristic optimization for home energy management. In Proceedings of the North American Power Symposium 2010, Arlington, TX, USA, 26–28 September 2010; pp. 1–6. [Google Scholar]
- Rehman, A.U.; Wadud, Z.; Elavarasan, R.M.; Hafeez, G.; Khan, I.; Shafiq, Z.; Alhelou, H.H. An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management. IEEE Access
**2021**, 9, 84619–84638. [Google Scholar] [CrossRef] - Ebrahimi, J.; Abedini, M.; Rezaei, M.M. Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting. Sustain. Energy Grids Netw.
**2020**, 23, 100368. [Google Scholar] [CrossRef] - Li, P.; Xu, D.; Zhou, Z.; Lee, W.J.; Zhao, B. Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization. IEEE Trans. Smart Grid
**2016**, 7, 66–73. [Google Scholar] [CrossRef] - Zhang, Z.; Wang, J.; Zhong, H.; Ma, H. Optimal scheduling model for smart home energy management system based on the fusion algorithm of harmony search algorithm and particle swarm optimization algorithm. Sci. Technol. Built. Environ.
**2019**, 26, 42–51. [Google Scholar] [CrossRef] - Esmaeili, S.; Jadid, S. Economic-Environmental Optimal Management of Smart Residential Micro-Grid Considering CCHP System. Electr. Power Compon. Syst.
**2019**, 46, 1592–1606. [Google Scholar] [CrossRef] - Huang, Y.; Zhang, J.; Mo, Y.; Lu, S.; Ma, J. A Hybrid Optimization Approach for Residential Energy Management. IEEE Access
**2020**, 8, 225201–225209. [Google Scholar] [CrossRef] - Javaid, S.; Javaid, N. Comfort evaluation of seasonally and daily used residential load in smart buildings for hottest areas via predictive mean vote method. Sustain. Comput. Inform. Syst.
**2020**, 25, 100369. [Google Scholar] [CrossRef] - Kanakadhurga, D.; Prabaharan, N. Demand response-based peer-to-peer energy trading among the prosumers and consumers. Energy Rep.
**2021**, 7, 7825–7834. [Google Scholar] [CrossRef] - Zeeshan, M.; Jamil, M. Adaptive Moth Flame Optimization based Load Shifting Technique for Demand Side Management in Smart Grid. IETE J. Res.
**2021**, 1–12. [Google Scholar] [CrossRef] - Chen, H.; Gao, L.; Zhang, Z.; Li, H. Optimal Energy Management Strategy for an Islanded Microgrid with Hybrid Energy Storage. J. Electr. Eng. Technol.
**2021**, 16, 1313–1325. [Google Scholar] [CrossRef] - Abbasi, A.; Sultan, K.; Aziz, M.A.; Khan, A.U.; Khalid, H.A.; Guerrero, J.M.; Zafar, B.A. A Novel Dynamic Appliance Clustering Scheme in a Community Home Energy Management System for Improved Stability and Resiliency of Microgrids. IEEE Access
**2021**, 9, 142276–142288. [Google Scholar] [CrossRef] - Cao, Y.; Zhang, H.; Li, W.; Zhou, M.; Zhang, Y.; Chaovalitwongse, W.A. Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions. IEEE Trans. Evol. Comput.
**2019**, 23, 718–731. [Google Scholar] [CrossRef] - Lodewijks, G.; Cao, Y.; Zhao, N.; Zhang, H. Reducing CO
_{2}Emissions of an Airport Baggage Handling Transport System Using a Particle Swarm Optimization Algorithm. IEEE Access**2021**, 9, 121894–121905. [Google Scholar] [CrossRef]

Ref. | AI Group | Energy Sub-Systems | Scheduling and Control | Optimization Objectives | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

PSO | Other Single | Multiple | Heating/Cooling Devices | EVs | DG | Local Energy Storage | Residential Appliances | Energy Consumption and User Comfort | Load Balancing | Energy Cost | ||

[7] | √ | √ | √ | √ | √ | |||||||

[8] | √ | √ | √ | √ | √ | √ | √ | |||||

[10] | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||

[11] | √ | √ | √ | √ | √ | √ | √ | √ | ||||

[12] | √ | √ | √ | √ | √ | √ | √ | |||||

[13] | √ | √ | √ | √ | √ | |||||||

[14] | √ | √ | √ | √ | √ | √ | ||||||

[15] | √ | √ | √ | √ | √ | √ | √ | |||||

This paper | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |

Refs | Type of Constraints | Objective Type | Objectives |
---|---|---|---|

[21] | Egrid + Estorage + User Convenience | Single | Electricity cost minimization |

[22] | Egrid + Estorage + ERES + User Convenience | Single with weights | Electricity cost minimization + user convenience maximization |

[23] | Thermal Comfort | Single | Energy consumption minimization |

[24] | Estorage | Single | Electricity cost minimization |

[25,56] | Estorage + User Convenience | Single | Electricity cost minimization |

[26,44,67] | User Convenience | Single with weights | Electricity cost minimization + user convenience maximization |

[27] | ERES | Multiple (Pareto) | Electricity cost minimization, distributing load across two energy sources (wind + solar) with different fitness functions |

[28] | ERES + User Convenience | Single | Electricity cost minimization |

[29] | Egrid + Estorage + ERES | Multiple (Pareto) | Electricity cost minimization + Environmental cost (emissions) minimization |

[30,33] | Egrid + Estorage + Thermal Comfort + User Convenience | Single | Electricity cost minimization |

[31] | Egrid + ERES | Single | Electricity cost minimization |

[32] | Egrid + Estorage + User Convenience | Single (aggregated objectives) | Electricity cost minimization + PAR minimization |

[34] | Egrid + Estorage + ERES + Thermal Comfort + User Convenience | Single | Electricity cost minimization |

[35] | Egrid + Estorage | Single | Electricity cost minimization |

[36] | Egrid + ERES | Single | Consumer profit maximization |

[37] | Thermal Comfort + User Convenience | Single | Energy consumption minimization |

[38] | Voltage levels + User Convenience | Single with penalties | Electricity cost minimization + power loss cost minimization + constraints (penalties) |

[39] | Estorage + User Convenience | Single (aggregated objectives) | Electricity cost minimization + PAR minimization |

[40] | Egrid + Estorage + ERES + Voltage levels | Single | Distribution power loss minimization |

[41] | User Convenience | Single with weights | Electricity cost minimization + discomfort index minimization |

[42] | Egrid + Estorage | Single | Utility electricity cost minimization (DA, imbalance costs, and battery cycling cost) |

[43] | Estorage | Single | Total system cost minimization (incl. investments) to optimize minigrid components |

[45] | Egrid + User Convenience | Single with weights | Electricity cost minimization + user convenience maximization |

[46] | Egrid + Estorage + Thermal Comfort + User Convenience | Single with weights | Electricity cost minimization + user convenience maximization + grid load variance minimization (peak caused by DR actions) |

[47] | Estorage + Thermal Comfort | Single | Consumer profit maximization |

[48] | User Convenience | Multiple (bi-level) | Consumer profit maximization, after scheduling manually operated appliances with the worst impact on electricity payments |

[49] | Egrid + User Convenience | Multiple (bi-level) | Retailer profit maximization, after consumer electricity cost minimization |

[50,82] | - | Single | Electricity cost minimization |

[51] | Estorage + User Convenience | Single with weights | Electricity cost minimization + user convenience maximization |

[52] | Egrid + User Convenience | Single | Electricity cost minimization |

[53] | Estorage | Multiple (bi-level) | System cost minimization (NPC) + power shortage minimization |

[54] | Egrid + User Convenience | Single (aggregated objectives) | Electricity cost minimization + PAR minimization + user convenience maximization |

[55] | Egrid + Thermal Comfort + User Convenience | Single | Electricity cost minimization |

[57] | Estorage + ERES | Multiple (Pareto) | Electricity cost minimization + environmental cost/emission minimization |

[58] | Estorage + Thermal Comfort | Single | Flexibility potential estimation |

[59,81] | Estorage + Thermal Comfort + User Convenience | Single | Electricity cost minimization |

[60] | User Convenience | Multiple (Pareto) | Electricity cost minimization + load deviation minimization + user convenience maximization |

[61] | Estorage + Thermal Comfort + User Convenience | Single with weights | Electricity cost minimization (incl. battery degradation costs) + user comfort (incl. thermal and convenience) |

[62] | Egrid + Estorage | Single with penalties | Electricity cost minimization + DR curtailment minimization + Pmax violation (penalty) |

[63] | Egrid + User Convenience | Multiple (Pareto) | Electricity cost minimization + PAR minimization + CO_{2} minimization |

[64] | Estorage + Thermal Comfort | Single with penalties | Electricity cost minimization + User comfort (penalties) |

[65] | User Convenience | Single with penalties | Utility electricity cost minimization for DR + consumer load interruptions (penalties) |

[66] | Egrid + Voltage levels + Estorage + User Convenience | Single | Total system cost minimization |

[68] | Estorage + User Convenience | Single with weights | Electricity cost minimization + user convenience maximization + CO_{2} minimization |

[69] | Egrid | Multiple (bi-level) | DNO operational cost minimization after MG operational cost minimization |

[70,73] | User Convenience | Single | Electricity cost minimization |

[71] | Thermal Comfort | Single | Electricity cost minimization |

[72] | Estorage + Thermal Comfort + User Convenience | Single | User comfort maximization |

[74] | Egrid + Estorage + ERES + Thermal Comfort + User Convenience | Single (aggregated objectives) | Electricity cost minimization + PAR minimization + user convenience maximization + CO_{2} minimization |

[75] | Egrid + User Convenience | Single with weights | Load deviation minimization + MG profit maximization |

[76] | Egrid + Voltage levels + Estorage | Single with penalties | Total system cost minimization + network loss minimization + constraints (penalty) |

[77] | Egrid + Estorage + Thermal Comfort | Single | Electricity cost minimization |

[78] | Egrid + Estorage + Thermal Comfort | Multiple (Pareto) | System cost minimization + Environmental cost minimization |

[79] | Egrid + Estorage + Thermal Comfort + User Convenience | Single | Energy bill (electricity and gas) minimization |

[80] | Thermal Comfort + User Convenience | Single | Electricity cost minimization |

[83] | Estorage + ERES | Single with weights | Total system cost minimization + CO_{2} minimization + curtailed RES minimization |

[84] | User Convenience | Single | Electricity cost minimization (per appliance cluster) |

Ref. | No. Users | Control Level | Electricity Tariffs | Energy Resources |
---|---|---|---|---|

[21,25,35] | Single | Local—Household | ToU | DG + energy storage + household appliances (excl. heating/cooling) |

[22] | Multiple | Local—Household | ToU + IBR | Heating/cooling + DG + energy storage + Household appliances (excl. heating/cooling) |

[23] | Single | Local—Household | DLC | Heating/cooling + household appliances (excl. heating/cooling) |

[24] | Multiple | Decentralized—Microgrid | RTP | DG + energy storage |

[26] | Multiple | Local—Household | RTP | Heating/cooling + household appliances (excl. heating/cooling) |

[27] | Single | Local—Household | - | Heating/cooling + DG + household appliances (excl. heating/cooling) |

[28] | Single | Local—Household | ToU | Heating/cooling + EV + DG + energy storage + Household appliances (excl. heating/cooling) |

[29,57] | Multiple | Decentralized—Microgrid | Price-offer packages (incentive-based) | DG + energy storage |

[30,72] | Single | Local—Household | RTP | Heating/cooling + DG + energy storage + household appliances (excl. heating/cooling) |

[31] | Single | Local—Household | ToU | EV + DG + energy storage + household appliances (excl. heating/cooling) |

[32] | Multiple | Local—Household + Decentralized—Microgrid | RTP + IBR | Heating/cooling + DG + energy storage + household appliances (excl. heating/cooling) |

[33] | Single | Local—Household | ToU | Heating/cooling + EV + DG + Household appliances (excl. Heating/cooling) |

[34,59,79] | Single | Local—Household | RTP | Heating/cooling + energy storage + household appliances (excl. heating/cooling) |

[36] | Multiple | Decentralized—Microgrid | Dynamic pricing based on RES generation | DG |

[37] | Single | Local—Household | - | Heating/cooling + household appliances (excl. heating/cooling) |

[38] | Multiple | Centralized— Utility or Aggregator | Consumer bidding prices | Power transformers + EV + household appliances (excl. heating/cooling) |

[39,51] | Single | Local—Household | RTP + IBR | Heating/cooling + DG + energy storage + Household appliances (excl. heating/cooling) |

[40] | Multiple | Centralized— Utility or Aggregator | - | Power transformers + DG + energy storage |

[41,54,60,71] | Single | Local—Household | ToU | Heating/cooling + household appliances (excl. heating/cooling) |

[41,54,55,60,73] | Single | Local—Household | RTP | Heating/cooling + household appliances (excl. heating/cooling) |

[42] | Multiple | Decentralized—Microgrid | - | Heating/cooling + DG + energy storage |

[43] | Multiple | Decentralized— Standalone Microgrid | - | EV + DG + energy storage + Household appliances (excl. heating/cooling) |

[44] | Single | Local—Household | RTP + IBR | Heating/cooling + DG + household appliances (excl. heating/cooling) |

[45] | Multiple | Local—Household | CPP, RTP | Household appliances (excl. heating/cooling) |

[46] | Multiple | Decentralized—Microgrid | RTP | Heating/cooling + household appliances (excl. heating/cooling) |

[47] | Single | Local—Household | ToU, CPP | Heating/cooling + EV + DG |

[48] | Single | Local—Household | RTP + IBR | heating/cooling + household appliances (excl. heating/cooling) |

[49] | Multiple | Decentralized—Microgrid | RTP | Heating/cooling + EV + household appliances (excl. heating/cooling) |

[50,63,82] | Multiple | Centralized— Utility or Aggregator | RTP | Household appliances (excl. heating/cooling) |

[51] | Single | Local—Household | ToU, CPP, RTP + IBR | Heating/cooling + DG + energy storage + household appliances (excl. heating/cooling) |

[52] | Single | Local—Household | RTP | Household appliances (excl. heating/cooling) |

[53] | Multiple | Decentralized— Standalone Microgrid | - | heating/cooling + DG + energy storage + household appliances (excl. heating/cooling) |

[56,61,74] | Single | Local—Household | RTP | Heating/cooling + EV + DG + energy storage + household appliances (excl. heating/cooling) |

[58] | Single | Local—Household | - | Heating/cooling + DG + energy storage |

[62] | Multiple | Centralized— Utility or Aggregator | ToU | Heating/cooling + DG + energy storage + household appliances (excl. heating/cooling) |

[63] | Multiple | Centralized— Utility or Aggregator | ToU, CPP, RTP | Household appliances (excl. heating/cooling) |

[64] | Multiple | Local—Household | RTP | Heating/cooling + EV + energy storage |

[65] | Multiple | Centralized— Utility or Aggregator | Load curtailment (incentive-based) | Household appliances (excl. heating/cooling) |

[66] | Multiple | Centralized— Utility or Aggregator | Trip-reducing and trip-shifting schemes (incentive-based) | Power transformers + EV + DG |

[67] | Single | Local—Household | RTP, ToU, load curtailment (incentive-based) | Heating/cooling + EV + household appliances (excl. heating/cooling) |

[68] | Single | Local—Household | RTP | Heating/cooling + EV + DG + household appliances (excl. heating/cooling) |

[69] | Multiple | Decentralized-—Microgrid | RTP | DG |

[70] | Single | Local—Household | ToU | Household appliances (excl. heating/cooling) |

[75] | Multiple | Decentralized—Microgrid | - | DG + Household appliances (excl. heating/cooling) |

[76] | Multiple | Decentralized—Microgrid | - | DG + energy storage + household appliances (excl. heating/cooling) |

[67,77] | Single | Local—Household | ToU | Heating/cooling + EV + household appliances (excl. heating/cooling) |

[78] | Single | Decentralized—Microgrid | RTP | Heating/cooling + DG + energy storage + household appliances (excl. heating/cooling) |

[80] | Multiple | Local—Household | RTP | Heating/cooling + DG + household appliances (excl. heating/cooling) |

[81] | Multiple | Decentralized—Microgrid | RTP | Heating/cooling + EV + DG + energy storage + household appliances (excl. heating/cooling) |

[82] | Multiple | Centralized— Utility or Aggregator | RTP | Household appliances (excl. heating/cooling) |

[83] | Multiple | Decentralized— Standalone Microgrid | - | DG + energy storage + household appliances (excl. heating/cooling) |

[84] | Multiple | Decentralized—Microgrid | RTP + IBR | Household appliances (excl. heating/cooling) |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Menos-Aikateriniadis, C.; Lamprinos, I.; Georgilakis, P.S.
Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision. *Energies* **2022**, *15*, 2211.
https://doi.org/10.3390/en15062211

**AMA Style**

Menos-Aikateriniadis C, Lamprinos I, Georgilakis PS.
Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision. *Energies*. 2022; 15(6):2211.
https://doi.org/10.3390/en15062211

**Chicago/Turabian Style**

Menos-Aikateriniadis, Christoforos, Ilias Lamprinos, and Pavlos S. Georgilakis.
2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision" *Energies* 15, no. 6: 2211.
https://doi.org/10.3390/en15062211