Submitted:
12 October 2023
Posted:
13 October 2023
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Abstract
Keywords:
1. Introduction
2. Review method
3. Application of the Twin Concepts Review: Systematicity and Transparency
3.1. Developing a Review Plan
3.2. Searching the Literature
3.3. Selecting Studies
3.4. Assessing Quality
- Economics cover price, electricity rate structure, electricity tariff, incentive, economic optimisation, peak-off-peak-load shifting, customer satisfaction: 71 articles.
- Demand Side Management (DSM) include segmentation based on Demand Response (DR) program, smart-grid, micro-grid system: 49 articles.
- Technical aspect including control, electricity infrastructure, intelligent building, building thermal model, grid’s inverter size and grid architecture: 28 articles.
- Storage or the use of battery storage system: 16 articles.
- Environmental issues include emission, sustainability, Renewable Energy (RE) sources and RE penetration: 9 articles.
- Social practice includes flexibility to shift demand: 8 articles.
- Out of load shifting in the residential sector include manufacture, industrial, lighting road, commercial and transport: 6 articles.
- Load profile model or synthesised load profile: 4 articles.
- Policy: 4 articles.
- Real time electricity consumption: 3 articles.
- Scenario of future electricity demand: 2 articles.
- Out of load shifting scope about building material: 1 article.
- A1 [50] provided a series of analyses based on consumption data for appliance electrification efforts but it does not specifically discuss the load shifting or mention the specific appliance.
- A2 [51] discussed the Non-instrusive load monitoring (NILM) based at the appliance level with the focus on disaggregating the power consumption profiles of the appliances: Oven, microwave, kitchen outlets, dishwashers and refrigerators.
- A3 [52] proposed the methodologies that capture the variation in sequences of activities that occur on peak-on electricity demand, and introduced a set of analytical tools to examine the time use survey (TUS) data in the energy demand side. This paper is beneficial as the ground theories of our review.
- A4 [53] focused on the thermal energy storage, which offers the load shifting from the off-peak hours through sensible and/or latent methods.
- A10 [54] investigated the impact of load shedding strategies on a block of multiple buildings.
- A13 [55] has been retracted, which proposed a simple algorithm of the water pumps operational efficiency during the peak hours.
- A14 [56] discussed the load shifting at the grid level.
- A15 [57] presents the thermal flexibility of the building and a thermal energy storage (TES) for the generation of domestic hot water (DHW) with the purpose of shifting the operation of the heat pump to the times of PV-generation.
- A17 [3] discussed the load shifting at the grid level.
- A21 [58] proposed the multi-objective model predictive control strategy at the grid level.
3.4. Extracting Data
4. Analysis and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
| ID | Article's Title |
|---|---|
| a1 | Ali, S.M.H.; Lenzen, M.; Tyedmers, E. Optimizing 100%-renewable grids through shifting residential water-heater load. Int. J. Energy Res. 2019, 1479–1493. |
| a2 | Gercek, C.; Reinders, A. Smart appliances for efficient integration of solar energy: A Dutch case study of a residential smart grid pilot. Appl. Sci. 2019, 9 |
| a3 | Patteeuw, D.; Henze, G.P.; Arteconi, A.; Corbin, C.D.; Helsen, L. Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility. J. Build. Perform. Simul. 2019, 12, 56–67. |
| a4 | 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. |
| a5 | Li, K.; Zhang, P.; Li, G.; Wang, F.; Mi, Z.; Chen, H. Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management. IEEE Access 2019, 7, 133628–133640. |
| a6 | Goldsworthy, M.J.; Sethuvenkatraman, S. The off-grid PV-battery powered home revisited; the effects of high efficiency air-conditioning and load shifting. Sol. Energy 2018, 172, 69–77. |
| a7 | Muhammad, S.; Ali, H.; Lenzen, M.; Huang, J. Shifting air-conditioner load in residential buildings: benefits for low-carbon integrated power grids. IET Renew. Power Gener. 2018. |
| a8 | Hafeez, G.; Javaid, N.; Iqbal, S.; Khan, F.A. Optimal residential load scheduling under utility and rooftop photovoltaic units. Energies 2018, 11, 1–27. |
| a9 | Setlhaolo, D.; Sichilalu, S.; Zhang, J. Residential load management in an energy hub with heat pump water heater. Appl. Energy 2017, 208, 551–560. |
| a10 | Han, X.; Zhou, M.; Li, G.; Lee, K.Y. Stochastic unit commitment ofwind-integrated power system considering air-conditioning loads for demand response. Appl. Sci. 2017, 7. |
| a11 | Park, L.; Jang, Y.; Bae, H.; Lee, J.; Park, C.Y.; Cho, S. Automated energy scheduling algorithms for residential demand response systems. Energies 2017, 10, 1–17 |
| a12 | Kantor, I.; Rowlands, I.H.; Parker, P. Aggregated and disaggregated correlations of household electricity consumption with time-of-use shifting and conservation. Energy Build. 2017, 139, 326–339 |
| a13 | Liu, M.; Quilumba, F.; Lee, W.J. A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation. IEEE Trans. Ind. Appl. 2015, 51, 3561–3569 |
| a14 | Cole, W.J.; Rhodes, J.D.; Gorman, W.; Perez, K.X.; Webber, M.E.; Edgar, T.F. Community-scale residential air conditioning control for effective grid management. Appl. Energy 2014, 130, 428–436 |
| a15 | Atikol, U. A simple peak shifting DSM (demand-side management) strategy for residential water heaters. Energy 2013, 62, 435–440. |
| a16 | Lameres, B.J.; Nehrir, M.H.; Gerez, V. Controlling the average residential electric water heater power demand using fuzzy logic. Electr. Power Syst. Res. 1999, 52, 267–271. |
| a17 | Reddy, T.A.; Norford, L.K.; Kempton, W. Shaving residential air-conditioner electricity peaks by intelligent use of the building thermal mass. Energy 1991, 16, 1001–1010. |
Appendix B
| ID | Research Objective | Method | Dedicated or Simulated Appliance | Time Resolution | Result | Country |
|---|---|---|---|---|---|---|
| a1 | To analyse potential capacity reductions in a renewable-only grid that can be achieved through load-shifting | Load-shifting algorithm to simulate the capacity reduction/optimization of the 100%-renewable electricity grid | EWH | Hourly | The installed capacity of 100% renewable electricity grid in Australia can be reduced between 4 and 20% by applying 1 to 18 hours of load shifting on residential water heaters the total electricity demand in Australia). | Australia |
| a2 | To evaluate the smart homes efficiency, their ability to reduce peak electricity purchase, effects on self-sufficiency and on the local use of solar electricity. | Detailed monitoring data: Power Matching City (PMC). An energy management software has been used to operate power flows | Smart appliances: washing machines, dishwashers, and smart hybrid heat pumps (SHHP) with a condensing boiler. | Hourly | Smart appliances significantly contributed to load shifting in peak times. cleaning practices are potentially highly flexible for residential | The Netherlands |
| a3 | To apply an aggregation method to effectively characterize the electrical energy demand of air-conditioning (AC) systems in residential buildings under flexible operation | Cluster-centre aggregation (CCA): Clustering techniques to aggregate a large and diverse building stock of residential buildings to a smaller, representative ensemble of buildings | AC | 5-minute or 60-minute resolution | Reached demand flexibility of good agreement between the energy demand predicted by the aggregated model and by the full model during normal operation (normalized mean absolute error, NMAE, below 10%), even with a small number of clusters (sample size of 1%) | USA |
| a4 | To shift the electricity load from On-peak to Off-peak hours according to the load curve for electricity. | MBBSO (an extensionof existing algorithm BSO) and MBHBCO (Hybrid version of MBBSO and MOCSO) algorithms to optimize the searchspace for load shifting under DR. | Multi-appliances | Hourly | Results reveal that coordination based day-ahead scheduling is more effective in reducing the electricity cost and PAR as compared to without coordination. | Not mentioned |
| a5 | To consider the interaction between electric and natural gas appliances in households, a day-ahead optimal joint scheduling model of electric and natural gas appliances for HEMS is proposed | HEMS model based on different types of appliances | Multi-appliances | Hourly | Save the total energy costs up to 30% for customers whilst ensuring their satisfaction levels | China |
| a6 | To analyse the effect of high efficiency AC and load shifting | The sub-circuit load, ambient temperature and irradiance data were combined with mathematical models of a crystalline silicon PV array and lithium-ion battery storage system | AC | 30 minutes | Improve the economics considerably, even accounting for the fact that the appliance efficiency improvements also lower the grid connected electricity costs | Australia |
| a7 | To present a simulation of low-carbon electricity supply by demonstrating the benefit of load shifting in residential buildings for downsizing renewable electricity grids | Novel Load-shifting algorithm for AC | AC | Hourly | Reduce 14% installed capacity requirements in renewable electricity grid due to 1 hour of load shifting | Australia |
| a8 | To focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management | Shift-load algorithm: genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm. | Multi-appliances | 12 minutes | Reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption. | Not mentioned |
| a9 | To formulate a practical optimal control model for ED within a hub with modelling of appliances with a heat pump and coordination of all considered resources. | The optimal control model with sub-mathematical models | Multi-appliances | Hourly | Achieved cost saving due to appliance shifting is affected by the disparity between the peak and off-peak price, which in this case is 30%. CO2 signal could give customers a motivation to shift or reduce loads during peak hours reductions. | South Africa |
| a10 | To introduce air-conditioning loads (ACLs) as a load shedding measure in the DR project. | A two-stage stochastic unit commitment (UC) model to analyze the ACL users’ response in the wind-integrated power system | AC | Hourly | System peak load can be effectively reduced through the participation of ACL users in DR projects | Not mentioned |
| a11 | To estimate a user’s convenience without configuring the convenience for fully-automated energy scheduling | Energy scheduling optimization model and an algorithm to automatically search the preferred time for each type of appliance | Multi-appliances | Hourly | Significantly reduce the electricity bill by 10% and satisfy the user convenience | Not mentioned |
| a12 | To show which groups of appliances are responsible for observed shifts in usage times or conservation | Monitored data are checked for quality and periods of missing data are filled according to the household consumption near the gap in data and weather normalisation is considered | Multi-appliances | Hourly | Conservation behaviour is found in two of 18 households and is correlated to the consumption pattern of air conditioning units, major and discretionary loads | Canada |
| a13 | To shift the coincidental peak load to off-peak hours to reap financial benefits | Aggregated appliances operation strategy: smart control with comfort aspect | Representative appliances: AC/Heater, clothes dryer and refrigerator | Hourly | The results show that by doing load control and utilizing renewable resources, the total cost can be reduced significantly | USA |
| a14 | To achieve substantial reductions in peak electricity demand | Reduced-order modelling strategy and an economic model predictive control approach | AC | Hourly | The centralised, coordinated control of residential air conditioning systems reduces overall peak by 8.8% but increases total energy consumption by 13.3%. Decentralized control reduces overall peak by 5.7%, demonstrating that the value of information sharing for peak reduction is 3.1%. | USA |
| a15 | To avoid the peak hours | EWH peak shift DSM model | Water heater | Hourly | An effective way of shifting the load from peak hours to off-peak hours | Turkey |
| a16 | To shift the average power demand of residential electric water heaters from periods of high demand for electricity to off-peak periods. | Fuzzy logic-based variable power control strategy and Gaussian (bell-shape) membership functions for the input variables demand and temperature and the output signal (power) | Water heater | Hourly | Reduced the peaks of average residential water heater power demand profile and shift them from periods of high demand for electricity to low demand periods using the proposed customer-interactive DSM strategy. | Not mentioned |
| a17 | To predict the thermal performance of the residence when the air-conditioner is switched off and illustrate the validity of such simplified estimates with monitored data from an actual residence. | Peak-shaving strategies using building thermal mass | AC | Hourly | Reduced the peak load using the intelligent building thermal mass | USA |
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| Waterfall Statistics | Bulk | Reduced |
|---|---|---|
| Initial searched | 421 | - |
| Screening 1: Language | 421 | 0 |
| Screening 2: Publication stage | 419 | 2 |
| Screening 3: Document type | 235 | 184 |
| Selecting subject areas | 222 | 7 |
| Accessing quality 1: peer-review article | 228 | 0 |
| Accessing quality 2: title and abstract reading | 27 | 201 |
| Accessing quality 3: Paper reading | 17 | 10 |
| Final number of the selected studies | 17 | - |
| Load Shifting Method | Appliance’s Operation Time | |
|---|---|---|
| Controllable | Un-Controllable | |
| Optimisation algorithms | EWH, AC, washing machine, dishwasher, refrigerator | Lighting, oven, computers, TV, blender, hairdryer, electric stove |
| Clustering technique | AC | - |
| Smart control with comfort aspects | AC, heater, washing machine,dishwasher, clothes-dryer, refrigerator | - |
| Stochastic thermal model | AC | |
| Fuzzy logic | EWH | |
| Smart control scheduling | Washing machine, dishwasher, hybrid heat pump | Lighting, TV, electric stove, computer |
| Physical setting with mathematical model | EWH, AC, washing machine, dishwasher, refrigerator | Lighting, TV, electric stove |
| DSM-based model | AC | - |
| Article ID | Objective | Approach | Method | Result | Limitation | Model’s Input | Time Resolution | Validation | Simulated appliance | Country/Region | Score |
|---|---|---|---|---|---|---|---|---|---|---|---|
| a1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 10 |
| a5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 10 |
| a9 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a10 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 10 |
| a11 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 10 |
| a12 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 10 |
| a13 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| a15 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 10 |
| a16 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 9 |
| a17 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
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