Related literature spans topics from load profiling, demand-side management, and PV adoption studies. The load profiling section presents existing studies, the methods used to measure electricity consumption, and the factors affecting the electricity consumption. Electricity consumption measurement studies are ultimately related to DSM and PV-rooftop system design studies. By analyzing the consumption behavior of households (e.g., seasonal patterns, appliance usage, etc.), decision-makers can identify opportunities to maximize energy saving. PV-rooftop studies, on the other hand, deal with how PV generation and electricity consumption align and propose ways (e.g., DSM, energy storage) to improve it. Our review further discusses the unique challenges faced in Qatar and in the GCC region. Details are presented in the next three subsections.
2.1. Load Profiling
The load profile of a residential unit can be obtained through an intrusive or a non-intrusive load monitoring technique [8
]. In intrusive load monitoring, each appliance is monitored separately via measurement devices. This method is precise and allows the monitoring of each appliance individually. Intrusive load monitoring has two major issues. First, this method is very costly, as it requires dedicated monitoring equipment for each load. The other issue is that it creates greater discomfort to the occupants as it takes longer to install and dismantle, and occupies more physical space. The second method, non-intrusive power monitoring (NIPM), overcomes the discomfort issue at the cost of losing load identification precision. NIPM requires a single meter at the point of the household supply, typically at the main incoming feeder, and logs the total incoming power supply. In NIPM, individual appliance recognition is typically conducted by analyzing the electrical signatures of electrical loads via machine learning algorithms [9
]. However, in order to perform a high accuracy appliance detection, it is critical to have a high sampling rate, e.g., 1 s to 1 min, for disaggregating algorithms to extract electrical signatures [10
]. A list of publicly available datasets is presented in Table 1
. It is important to note that most studies presented in the table aim to gather information about appliance type (e.g., resistive, inductive, non-linear, etc.) and usage patterns. Datasets serve as case studies to improve the accuracy of data mining and appliance detection algorithms. For instance, dataset REDD (Reference Energy Disaggregation Dataset) [11
] includes 10 to 24 individual monitoring plugs dedicated to each major appliance at each house. However, a similar approach has minor significance in Qatar due to the fact that majority of the total load is dominated by AC loads while the remaining appliances add up to a small portion of the total load.
Residential load profiles are subject to a number of factors that influence and shape the load curve. Those factors can be classified into three different categories: socio-economic, climate, and technological. The socio-economic category includes factors such as the dwelling size and type, number of occupants [12
], working status, level of income, and age of the responsible occupants [13
]. Climate-related factors determine the required energy to heat or cool the house and keep hot or chilled water at desired temperatures. Technological factors that are related are the efficiency of the appliances and the amount of energy required to perform tasks. Globally, several studies have been conducted in an attempt to represent residential load profiles on a local scale. A study in Ottawa, Canada, with a sample size of 12 houses, investigated the impact of socio-economic and seasonal factors on load profiles [14
]. Analysis of the measured data led to the indication that both lighting and appliances, aside from heating, ventilation, and air conditioning (HVAC), significantly impact the total load profile and cannot be neglected. Some of these appliances, such as refrigerators, are controlled by a thermostat. There was no direct relationship between the annual consumption of appliances and house size. On the other hand, there was a strong correlation between appliance use and the number of occupants. Another notable outcome of the study is the strong correlation between house size and HVAC load magnitude. Because the weather in Canada is cold for a large part of the year, air conditioning (AC) loads are notably significant and exhibit specific patterns.
A second study [15
] was performed in Lochiel Park, Australia, with a sample size of 60 houses. The study concluded that HVAC loads are crucial to the occupants, even if electricity demand is reduced. It was found that the HVAC loads account for approximately 30% of the total consumption load. A study by Cetin (2014) [16
] in Austin, Texas, with a sample size of 40 houses, found that the patterns of use of appliances are similar to the patterns indicated in a study from 1989 [17
]. Appliances that are automated, such as a thermostat, are similar in different households. However, user-dependent appliance loads have significantly varying patterns among houses. Weekday and weekend use patterns of appliances are similar, but weekdays patterns, on average, have a lower standard deviation, indicating that they are more predictable due to routine lifestyle. Weekday use patterns, as well as those of households where no one stays at home during working or school hours, have more predictable, consistent electricity use patterns. In contrast, homes with occupants at home during working hours experience a large increase in overall appliance use. Washer and dryer use are influenced by the weather. Refrigerator and dishwasher energy use being affected by whether or not it is a weekday or a weekend. Most appliances use more than 25% of their daily energy use during peak use times, demonstrating the potential to reduce peak use on the electric grid. Table 2
is a summary of selective studies that focus on load profiling and factors that influence the load profile. The diversity in presented results suggests that electricity consumption patterns are region-specific, and there is a need for residential load profiling study in Qatar to analyze factors affecting energy usage patterns.
Electrical loads in the residential sector can be classified by a number of such as physical properties (e.g., resistive, inductive, etc.) and size (e.g., home, building, etc.). Another classification methodology is by job type, which can be split into two categories [18
]. The first category includes an elastic load that can be flexible for the user to operate at various times. Wet appliances and HVAC loads are typically considered as flexible loads. These loads play a role in determining the shape of the profile and the extent of the degree to which it can be changed and play a critical role in demand-side management activities. The second type is an inelastic or uncontrollable load that has priority, impacting the comfort level of the user, and, thus, cannot be shifted. Cooking and lighting loads are considered to be part of this group.
Even though high-resolution power and energy measurement studies are rare, a few studies have presented the impacts of retrofitting [19
] and occupant behavior [20
] on the energy performance in neighboring countries. In these studies, the impact assessment is made based on monthly energy consumption figures gathered from electricity bills. Another commonly used method is to design building envelopes and develop simulation-based models to examine various energy saving studies [21
]. Our study not only provides daily and monthly energy consumption profiles, but also presents five-minute resolution power demand, which is critical for PV and power system studies.
2.2. Demand-Side Management (DSM)
Load profiling studies are closely related to demand side management, which can be described as the set of activities that manage the timing and amount of energy consumed by the customer in a cost-effective manner [22
]. DSM uses different measures to control electric loads, such as pricing-based, incentive-based, and remote load control [23
]. In this sense, DSM is known to be a very successful method in cutting energy generation and enhancing electrical efficiency [24
]. The process of evaluating and selecting the DSM method to implement requires a detailed understanding of how the customer is consuming electricity. The energy consumption is generally read from the electricity meter of the residence, store, business, or factory. The value taken from the meter represents the overall consumption and, therefore, submetering may be required to identify curtailable loads. Hence, monitoring and profiling the load is essential to evaluate the applicability of a particular demand-side management approach. In a related publication that is part of our study [25
], a direct load control of air-conditioner unit experiments were conducted in a villa in Qatar. The experimental result show that nearly 10 kW of demand can be reduced from the testbed villa for 15 to 30 minutes duration without violating customer comfort.
Although demand-side management has gained a global appeal, countries in the GCC region lag behind, with only a few implementations of this type of management. The studies by Al-Iriani21
and Alasseri et. al. [26
] are good examples for taking the initiative to introduce DSM to United Arab Emirates (UAE) and Kuwait. They discuss the possible and feasible forms of DSM that can be applied in their respected countries. Moreover, these references argue that the lack of broader DSM implementation is due to the abundance of domestic fossil fuel, which discourages energy conservation policies. On the other hand, rising global awareness about sustainability, and the analysis of the GCC growing population and energy demand have motivated the region to invest in sustainability. The projected depletion of fossil fuels and natural gas in the region is another strong driver to transform the current energy systems into a renewable, clean, and sustainable one.
In Qatar, DSM implementation is limited to energy efficiency measures, a national program called “Tarsheed,” which is Arabic for “awareness,” was launched in 2012 by the local utility company Kahramaa. It is estimated that the program succeeded in reducing the per capita consumption of electricity and water in Qatar by 17% and 18%, respectively, by the end of 2017 [27
]. The program works through the enforcement of regulations that targets household appliance energy efficiency. However, the implementation of demand response programs, both price-based and incentive-based, faces the following issues: (1) electricity prices are subsidized; low electricity bills and employee benefits where bills are paid by employers lead to overconsumption of electricity; (2) the climatic conditions necessitate the need for the continuous and heavy use of AC and water; (3) high disposable annual income limits the applicability of monetary incentives capitalized by price-based DSM techniques. To that end, the analyses of consumption data presented in the current study will be a basis for future research efforts to quantify theoretical demand response potential of the country.
2.3. PV Rooftop Adoption
Residential load profiles can also be used in studies addressing PV integration in low-voltage systems and assessing the viability of PV rooftop-energy storage systems. The integration of PV energy sources can cause a number of issues to the power grid. Some of these issues are related to overloading network capacity, voltage issues, harmonics, and islanding detection, among other technical issues. Major relevant voltage issues are: (1) voltage fluctuation; (2) voltage unbalance; and (3) voltage rise [28
]. Such issues may arise because traditional power grids are designed for one-way power flow, while the production from PVs causes bidirectional power flow. Traditional DSM system may not work as per the requirement with the integration of PV systems. Therefore, fast-response storage units can be more effective from the technical side. In Qatar, PV adaptation on a domestic level faces a greater challenge on the economic aspect, primarily due to the lack of incentives and regulations that promotes PV adoption. For example, due to the absences of income tax, tax credits are not applicable. Furthermore, there is no feed-in tariff to allow residential PV systems owners to sell back to the grid. Finally, electricity is highly subsidized for expatriates and businesses and free for citizens in Qatar [29
]. In her study [30
], Mohandes shows how residential PV adoption is strongly influenced by the introduction of a carbon tax, falling cost of PV, reduction of electricity subsidies and the extension of the electricity tariff to Qatari households. Furthermore, in a different study [31
], it was found that market-based policies and government intervention is needed to influence public attention toward increasing energy efficiency. On the other hand, it is the mandate for the public utility operator to provide continuous electric service to residents. Therefore, energy storage units emerge as a solution to support PV penetration. Although this paper focuses on the design viability aspect of the rooftop PV-storage systems in Qatar, our future research will investigate the economic viability of such systems under various scenarios.