Concerns about global warming and greenhouse gas (GHG) emissions are leading many countries to increase electricity generation from renewable sources [1]. However, electricity generation from renewable sources, such as wind, solar, and hydroelectric, can be intermittent and unpredictable ^[2][3][2,3]. Unmanaged, this intermittency can cause discrepancies between electricity supply and demand, leading to blackouts and other electricity system failures ^[4][5][4,5].

Demand Side Management (DSM) consists of acts modifying the level or pattern of electricity consumption [6]. One form of DSM is Demand Response (DR), where electricity consumption is intentionally shifted to suit supply or other constraints, either by decreasing, increasing, or shifting demand, known as peak shaving, valley filling, and load shifting, respectively ^[7][8][7,8]. By increasing electricity demand to coincide with times of excess supply, DSM can increase the utilization of electricity from renewable resources ^[9][10][9,10]. Thus, DSM can help resolve issues of intermittency and unpredictability, facilitate increased installation of renewable generation, and reduce GHG emissions from electricity generation. DSM can also reduce peak electricity demand to ensure that it remains within the limits of transmission and distribution infrastructure, extending the lifetime of components and reducing electricity distribution costs for businesses and consumers ^[11][12][11,12].

Electricity demand is typically categorized according to the end user, with three broad, main categories: industrial, commercial, and residential ^[13][14][13,14]. Each sector accounts for approximately one third of electricity consumption in developed countries ^[15][16][17][15,16,17].

In all sectors, the implementation of DSM can be subject to technical and economic constraints and is fundamentally constrained by the human needs that the energy use is serving. On the technical side, electric appliances can have different degrees of adjustability in terms of usage time and power consumption. For example, many industrial processes have high start-up costs and/or sensitive processes requiring a stable power supply. On the economic side, some electricity demand cannot be shifted without financial loss, such as for commercial loads required for successfully serving customers. Equally, some loads, such as electricity demand from equipment for critical medical applications, cannot be shifted without compromising human outcomes. Thus, understanding technical and economic constraints in each sector is required to successfully implement DSM and can inform policy and technical efforts to best address the barriers to DSM uptake.

DSM implementation can also be constrained by human behavior. For example, charging an electric vehicle is constrained by driving behavior, because the vehicle can only be charged when connected to a power source and the energy required is dependent on travel requirements. Additionally, DSM can, in turn, impact human outcomes, as actions such as restricting electricity consumption during winter can excessively reduce indoor temperatures, increasing the risk of respiratory illness. Ultimately, human outcomes are the drivers for electricity demand, as people do not desire electricity, but rather the many objects that electricity affords them, such as travel and thermal comfort.

Strategies for implementing DSM can be classified into two categories: energy efficiency and Demand Response (DR). Energy efficiency refers to enhancements in the efficiency of electrical appliances or processes, , while DR refers to intentional shifts in electricity demand to suit supply or limits on transmission and distribution infrastructure, such as distribution transformer capacity. There are three main methods of DR: (1) peak shaving, where consumption is reduced during times of high demand; (2) valley filling, where consumption is increased during times of low demand; and (3) load shifting, where consumption is shifted from times of high demand to times of low demand. Figure 1 schematically illustrates these three types of DR.

Figure 1. Illustration of Demand Response to match electricity supply by (left–right) peak shaving, valley filling, and load shifting. The black arrows indicate the effect of Demand Response on the original electricity demand.

Electricity is used in many processes across multiple industries, including metal production, oil refining, chemical and cement production, and pulp and paper production ^[18][76]. The breakdown of electricity consumption by industry for New Zealand, where industrial electricity consumption comprises 37% of the national total, is shown in Figure 2. In many cases, electricity consumption is a major contributor to industrial operating costs ^[19][20][77,78]. Additionally, industrial processes, such as process heat, are being increasingly electrified, so industrial electricity consumption and electricity costs are expected to increase ^{[21][22][23][24][25][26]}[79,80,81,82,83,84].

The breakdown in Figure 2 shows that the vast majority of industrial electricity in New Zealand is consumed by the basic metals, food processing, and wood, pulp, paper, and printing industries. Thus, implementation of DSM in these industries is likely to have more impact on overall electricity consumption than DSM in other industries in New Zealand. The high share of electricity consumption from basic metals reflects the large quantities of electricity required for the electrolytic reduction of alumina feedstock, iron smelting, and steel making ^[27][85].

Technical constraints for electricity demand can vary greatly between different industries, and between different consumers within the same industry, depending on several factors, such as processes, equipment, and schedules. Thus, industrial DR programs are typically tailored to the requirements of each consumer ^[28][29][30][27,29,30]. While flexibility is highly variable between different industrial consumers, many industrial processes are uninterruptible without advance notice, and others cannot be interrupted at all. Thus, the flexibility of industrial electricity demand is generally recognized as being considerably lower, with greater immediate negative costs for stoppages or reductions, than for other sectors ^[31][32][26,31].

The electricity demand of industrial consumers can be high enough to warrant a bespoke agreement with electricity utilities, and in some cases even their own connection to high-capacity electricity transmission infrastructure [16]. These agreements sometimes include industrial DR programs, the vast majority of which are incentive-based ^[33][86]. Many industrial processes are difficult and/or costly to interrupt, and electricity tends to be a small part of total costs for industrial consumers ^{[34][35][36][37][38]}[87,88,89,90,91]. Thus, incentives for DR participation tend to be higher for industrial consumers than for residential or commercial consumers, to encourage participation ^[31][39][26,92]. However, even with these incentives, many consumers are unable to participate due to the inflexible nature of their processes ^[31][26].

During normal operation, industrial electricity demand is typically defined purely by the technical and economic constraints of the industrial process ^[31][26]. Thus, human behavior has little effect. Behavioral constraints, such as employees’ capacity to work extra hours, can be important considerations for operational scheduling, and thus for when the plant may require electricity ^[40][35]. However, these behavioral considerations tend to have a minimal effect on overall electricity consumption and participation in DR, and the negative costs of stoppages or reductions may point towards hiring extra staff as being more cost-effective.

The majority of industrial DR is carried out by large electricity consumers, as their high consumption increases their bargaining power for bespoke agreements with utility companies ^[31][26]. Industrial electricity consumption can contribute a large portion of total costs for industrial operators ^[19][20][77,78] and a large portion of total demand for utility companies ^[15][16][15,16]. Thus, management of industrial electricity demand has the potential to mutually benefit both suppliers and consumers.

However, industrial demand flexibility is often limited due to technical constraints. Nevertheless, some industries are better suited than others to participate in DR. In general, the industrial consumers with the highest flexibility, and thus the highest DR potential, are pulp and paper mills, metalworking industries with furnaces, cold storage warehouses, cement manufacturers, and electrolysis plants ^[30][40][30,35].

Pulp and paper mills convert wood chips or other fibers into fiberboards, which are then processed into paper. The inventory of pulp tends to be higher than for paper, which enables flexibility in the scheduling of pulp production ^[40][35]. Helin et al. ^[41][93] assess the potential for, and profitability of, DR for a Finnish pulp and paper mill active on the Nordic electricity market. The large on-site storage, particularly of pulp, allows shifting of the use of grinding machines and paper machines to times of lower electricity prices, offering demand flexibility “for most hours of the year”. However, the authors indicate that participating in DR can put the mill at risk of failing to fulfill obligations to customers, so a risk premium should be added for sensitive processes, which could limit the mill’s DR capacity.

Metalworking industries have many uses for furnaces, including blast furnaces for the production of iron and heat treatment furnaces for tempering. While electric furnaces can be used for demand flexibility due to their very high thermal storage, metalworking plants are recognized as difficult environments in which to implement DR, due to their complex processes and tight temperature tolerances ^[42][94]. The potential for DR from optimal scheduling of furnaces and rolling mills in a steel plant in Germany is assessed in ^[43][95], where the proposed optimal schedule is shown to reduce electricity costs by 12–52% from the non-optimized costs, depending on capacity utilization.

Cold storage warehouses use large fridges or freezers to store temperature-sensitive products. Most products have a temperature range within which they can be stored, such as frozen meat products requiring temperatures between −24 °C and −18 °C ^[44][96]. Thus, fridges/freezers do not consume electricity power constantly, which allows flexibility of electricity demand through heat storage. In ^[33][86], the flexibility potential of price-based DR for cold storage is assessed for such a warehouse in Ireland. By regularly updating their operations schedule in response to changing electricity prices, the warehouse shifted electricity demand to off-peak times, reducing monthly electricity costs between 5% and 15% and increasing the utilization of electricity from wind generation.

Cement manufacturers make cement powder from stone and other raw materials through grinding, mixing, and milling processes. Cement manufacturers typically have large inventories, which allow flexible scheduling of mills and grinders. Additionally, many cement manufacturers have mills fitted with variable speed drives, or could be retrofitted with such drives, which allow the speed, and thus the electricity consumption, of the mills to be increased/decreased, which can be used for DR ^[45][97]. In ^[46][98], operations in a cement plant in the United Kingdom are optimally scheduled according to price-based DR, which utilizes the flexibility of these processes to reduce electricity costs by 4.2%.

Electrolysis is the process of using electrical current to drive a chemical reaction. Industrial electrolysis, such as that used for the extraction of aluminum from aluminum ore, takes place in electric smelting pots, where input voltage can be directly controlled ^[31][26]. The effects of large-scale DR of aluminum electrolysis on the German electricity system are investigated in ^[47][99]. Flexibility of the extraction process is shown to yield national DR load-shifting potential of up to 13 GWh; however, this industrial DR potential is shown to be lower than for non-industrial DR strategies, such as controlled charging of EVs or thermal storage in district heating networks.

While industrial DR is typically implemented by large electricity consumers, flexibility in industrial processes can also be utilized by smaller consumers. The potential for DR in a textile factory in Ethiopia is assessed in ^[48][100], where a model of the factory’s oil-powered boiler is used to simulate the integration of solar-assisted heating. By using DR strategies to align the factory’s operation schedule with solar production, the utilization of solar heat for the boiler is shown to increase by up to 10% per month. The authors also identify the requirement for “sector-specific solution(s)” to increase the use of solar power for low- and medium-heat industries whose participation in large-scale DR programs is limited.

Utilizing the flexibility of industrial electricity consumers has been shown to be an effective method of DR, particularly for large consumers. However, a number of technical and economic challenges remain before the full potential of industrial DR can be realized [30]. While industrial DR has been analyzed in some industries, the flexibility potential of many others remains largely unstudied and unknown. For example, the focus on industry-specific DR programs may overlook flexibility opportunities existing across multiple industries, such as industrial compressed air systems ^[49][50][101,102].

Thus, the first challenge to increasing the potential for industrial DR is to assess the flexibility of processes in a wider range of industries and increase this flexibility where possible ^[28][27]. This assessment would involve modeling to understand the technical constraints of the electricity-dependent processes in these industries, which would facilitate greater understanding of how to increase their flexibility. Second, industrial DR remains largely available only to those consumers who have knowledge both of their plant’s processes and of scheduling according to DR incentives. Hence, those without these two skillsets are unable to participate ^[30][31][26,30].

Economic constraints tend not to be challenges to participation in DR for large industrial consumers, as these consumers typically have bespoke DR programs tailored to their process requirements and economics. However, smaller consumers can be overlooked, with their lower demand and resulting lower priority for electricity utilities ^[31][26]. Thus, the third challenge for industrial DR is to extend DR programs to smaller consumers. This extension could either be implemented by a utility extending bespoke DR agreements to these consumers, or through industrial demand aggregation.

Industrial electricity consumption can contribute a large portion of operating costs for consumers and a large portion of demand for electricity suppliers and distributors. Thus, industrial DR is beneficial both for consumers and for electricity utility companies. DR is highly utilized in industries such as pulp and paper mills, metalworking with electric furnaces, cold storage warehouses, cement manufacturers, and electrolysis plants. In general, industries with large inventory buffers and/or thermal storage in their processes, and those who have longer operational shifts, are best suited for industrial DR.

However, there exist several barriers to further industrial DR uptake: (1) small consumers are often overlooked, given their lower DSM potential than larger consumers; (2) while the knowledge of DR incentives and detailed knowledge of an industry’s processes are both required to adopt DR, both skillsets are not often present in the same organization; and (3) DR programs tend to be customer-specific, so industry-wide process-specific applications with high DSM potential, such as compressed-air systems, are often overlooked.

Industrial DR is highly dependent on the types of electricity-dependent processes and the nature of the industry, and as such tends to be through individual agreements between consumers and utility companies where the consumer’s process requirements are acknowledged. Thus, overall human behavioral constraints are typically of little concern for industrial DR.

Electricity is used in the commercial sector for a wide range of applications, such as space heating and cooling, lighting, water heating, and electronic devices. Figure 3 shows the breakdown of commercial electricity consumption by end use in New Zealand, where commercial consumption comprises 24% of the national total. It should be noted that electricity costs tend to contribute a small proportion of commercial operating costs, particularly for smaller businesses such as retail and office buildings ^[51][52][53][103,104,105].

Figure 3 shows the overall end use pattern for electricity use, yet this is likely to change depending on the type of premise. A high share of electricity use for space conditioning (25–30%) is characteristic in office, service, and wholesale trade buildings. However, retail buildings are characterized by having a predominant share of electricity use for refrigeration (35–40%) ^[52][104].

While commercial electricity demand is less process-focused than industrial demand, most is still subject to technical constraints. Much of the electricity demand of commercial customers is subject to regulation, such as for lighting, refrigeration, and space heating. For example, workplaces have recommended task-specific illuminance levels to meet while businesses are operational ^[55][107], which are set by standards such as AS/NZS 1680 ^[56][108]. Thus, the flexibility of electricity demand for lighting is limited. These constraints mean much of the electricity demand during business operating hours has limited flexibility, and there is little consumption in many businesses outside of these hours. Additionally, electricity demand may not make up a dominant part of operational costs, compared to building costs, employee wages, and materials, and the financial motivation for DSM uptake is thus of a lower priority than other financial considerations.

Commercial electricity demand is also subject to economic constraints relating to business operation. Most commercial electricity loads are required for business operation, so they have low demand elasticity. For example, a restaurant using electric cooking appliances is unlikely to shift their appliance use based on changing electricity prices, as doing so would compromise their ability to serve customers and generate revenue.

Therefore, the majority of commercial DR is carried out by appliances with thermal storage, such as water heaters and space heaters ^[57][109]. The electricity loads of these appliances are more flexible than most other commercial loads, as their thermal storage allows electricity demand to be decoupled from heating/cooling demand. However, these loads are still subject to technical constraints, such as the building’s thermal insulation, and behavioral constraints, such as employee and customer thermal comfort.

As the majority of commercial electricity demand is defined by technical and economic constraints, human behavioral constraints tend to be unimportant. Most commercial electricity demand is for appliances required either by regulations (such as lighting standards) or by business operations (such as the ability to serve customers), so human behavior is typically not a significant consideration for the majority of demand.

However, human behavior is a consideration for applications catering to thermal comfort. Preferences for thermal comfort can vary between individuals, which can influence electricity demand for space heating and cooling. While not all commercial consumers have the wherewithal or desire to adjust electricity consumption based on these differing preferences, when accounted for, these preferences can influence electricity demand ^[58][59][110,111].

Commercial electricity demand has fewer opportunities for flexibility than industrial demand, so commercial DR is less well-studied. The majority of research into commercial DR investigates applications with thermal storage, such as space heating/cooling and refrigeration, or other forms of storage, such as electric vehicle batteries.

Office buildings have been shown to be effective participants in DR programs ^[60][36]. Many offices have Heating, Ventilation, and Air Conditioning (HVAC) systems with adjustable temperature setpoints, which can perform DR using the thermal storage of the rooms they service. However, DR programs with space heating/cooling appliances must balance DR participation with the thermal comfort of occupants. In ^[61][112], DR using space heating and other electrical loads is implemented in a small office in Portugal. The DR program aligns electricity demand with generation from the office’s rooftop solar PV panels, increasing the utilization of renewable electricity, and does so without compromising occupant comfort. In ^[62][113], the use of building thermal capacitance and behavior adaptation are proposed to adjust the HVAC system operation and reduce energy demand during shoulder (before and after winter) months, when electricity supply can be constrained.

In addition, pilot studies of DR programs in offices are described in ^[60][36], where in an administration building and a library in China, the flexibility of space heating and other appliance loads is used to respond to direct DR requests from the electricity utility company. These offices are shown to be capable of load shedding 15–20% of their electricity demand.

Supermarkets are also good candidates for commercial DR. Supermarkets have many electricity loads, such as lighting, cooking, and space heating/cooling, but their main contribution to DR programs is from the thermal storage in their large refrigerators ^[63][114]. In a pilot study in a Finnish supermarket ^[64][115], refrigerators are used in a DR program to reduce the supermarket’s greenhouse gas emissions. Leveraging the refrigerators’ thermal storage and aligning electricity with generation from renewables reduced greenhouse gas emissions and increased energy efficiency. However, the authors highlight that current practices in the Finnish electricity market make it infeasible for small electricity consumers to participate in DR, and this pilot study was only possible because the supermarket was operated by S-Group, which is Finland’s largest supermarket chain and the country’s largest non-industrial energy consumer.

An emerging category of commercial DR is controlled charging of electric vehicles (EVs), as many businesses are increasingly adopting EVs for their vehicle fleets. EVs are often connected to a charger for longer than it takes them to fully charge, so their batteries can offer demand flexibility to the electricity grid ^[65][116]. Optimal scheduling of commercial EV charging has been shown to be beneficial for both EV owners, by shifting charging to lower-priced times and reducing electricity costs, and for electricity utility companies, by reducing peak demand and providing ancillary services ^[66][67][68][117,118,119].

Commercial DR programs are currently limited, so opportunities for improvement exist. Most commercial demand is for business-critical applications, so improvement in commercial DR should come from better understanding of existing flexibility. Simulation of thermal storage in buildings can be made more accurate using building information modeling (BIM), which is a three-dimensional modeling technique closely approximating the thermal characteristics of real buildings ^[69][120]. Further improvements can be made in DR programs accounting for thermal comfort, by using more accurate models of building occupancy and occupant behavior ^[70][121]. More accurate behavioral models would allow better quantification of occupants’ preferences and comfort, allowing DR programs to better optimize electricity demand without compromising thermal comfort.

Additionally, as with industrial DR, commercial DR tends to be limited to large consumers who can make bespoke agreements with electricity utility companies ^[64][115]. Thus, the DR potential of smaller consumers is often overlooked. To expand DR programs to smaller commercial consumers and utilize their demand flexibility, utility companies should extend offers for bespoke DR programs to smaller consumers or encourage DR through demand aggregators.

Electricity costs tend to contribute a small proportion of commercial operating costs compared to employee wages, material costs, and facility costs. Thus, the management of electricity demand tends to be of less importance for commercial consumers than for industrial consumers. The majority of commercial electricity demand is subject to technical and/or economic constraints limiting flexibility, such as regulations for workplace lighting and requirements to serve customers. Thus, commercial DR programs focus on applications with inherent storage, such as thermal storage in refrigerators and battery storage in EVs.

However, in comparison to other sectors, research in commercial DSM is limited, which may either indicate the sector has little potential for DSM, potentially due to the lower share of electricity consumption to their overall costs, or there is a significant research gap in this sector. Opportunities for improvements in commercial DR include a better understanding of demand flexibility for consumers, better modeling of building occupant preferences and comfort levels, and the extension of DR programs to smaller consumers. As human behavior is typically not important in defining commercial electricity demand, improvements in commercial DR should focus primarily on technical and economic constraints.

Electricity is used in the residential sector for a number of applications, including cooking, lighting, and heating. Figure 4 shows a breakdown of energy consumption by end use in the residential sector in New Zealand, where residential electricity consumption comprises 33% of the national total.

As shown in Figure 4, most residential electricity consumption in New Zealand is for water heating, space heating/cooling, and refrigeration, all of which have some thermal storage ^[59][111]. Thus, residential DSM programs with thermal storage are likely to have an outsized impact on total electricity demand. Note that household energy consumption varies seasonally, with increased heating (up to 25% of total household energy use) during the winter months ^[71][122].

Unlike in the industrial and commercial sectors, residential electricity demand is not constrained by process requirements or workplace regulations. Instead, technical constraints for residential electricity demand are the physical properties of appliances and buildings, such as the power capacity of a heater and the thermal insulation of the room it is heating ^[72][73][123,124].

Electricity can contribute a large portion of household expenses ^[74][125]. Thus, managing electricity demand is a method used by many consumers to reduce their expenses ^[75][126]. However, for most consumers, this management consists solely of curtailing demand ^[76][127], such as reducing electricity use by heating homes to a lower temperature during colder months. Further, demand elasticity is higher for consumers in lower-income households, for whom economic constraints are a salient issue ^[77][24]. Thus, the adverse effects of demand curtailment due to economic constraints, such as lower indoor temperatures, residential dampness, and an increased risk of respiratory illness, are experienced most by populations characterized by low income, tenancy, and residential mobility ^[78][128].

Unlike in the commercial and industrial sectors, where electricity is consumed to fulfill process and business requirements, electricity in the residential sector is consumed primarily to fulfill human desires, such as the desire for thermal comfort, entertainment, and transportation. Thus, behavioral constraints are typically more important than technical and economic constraints in defining residential electricity demand ^[79][129]. While technical and economic constraints define the limits and elasticity of residential demand, respectively, participation in DSM depends primarily on the willingness of residential consumers to change their demand patterns, which may require them to compromise on the commodities for which they use electricity.

Additionally, behavioral constraints mean that the residential sector is the main contributor to peak electricity demand ^[80][130]. Residential electricity demand is highly dependent on time of day, and on external conditions such as weather ^[81][131]. In New Zealand, national power demand is typically highest between 7 a.m. and 9 a.m., when people ready themselves to leave home, and between 5 p.m. and 9 p.m., when people return home ^[82][83][84][132,133,134]. Demand is also highest during hot or cold weather events, such as heat waves and cold spells, as consumers use more electricity for cooling or heating, respectively. This increased demand is typically representative of a large portion of residential consumers, so this aggregate behavior can affect the entire electricity system.

Surveys of residential consumers show an overall positive attitude towards DR ^[80][85][130,135]. Residential consumers are generally sympathetic to price-based DR programs and respond to these programs by shifting electricity use away from higher-priced times ^[80][130]. However, price is not the only factor influencing the willingness to participate in DR programs, as many consumers are also concerned about increasing electricity supply security to avoid blackouts and reducing the GHG emissions of electricity generation ^[85][135]. Electricity consumption depends on both structural and motivational factors, so the receptiveness for DR schemes may also depend on internalized norms and self-expectations ^[86][136].

Residential electricity loads can be classified into two categories, based on whether they can be shifted without altering consumer behavior ^{[87][88][89][90]}[73,137,138,139]. “Flexible loads” can be shifted without requiring major behavioral changes ^[91][140], such as water heating and electric vehicle (EV) charging. Conversely, “inflexible loads” or “fixed loads” cannot be shifted without major behavioral changes, such as computers and electric stoves.

Residential Demand Response (DR) research is typically aimed at assessing the DR potential of flexible loads ^[92][141]. In particular, most of the research focuses on EVs, space heating and cooling, water heating, battery storage, and flexible white appliances, such as washing machines and dishwashers ^{[7][8][93][94]}[7,8,32,33].

If unmanaged, EV charging can increase peak electricity demand and reduce the feasibility of a transition to EVs ^[95][142]. However, managed EV charging can provide storage and ancillary services to the electricity grid ^[96][97][143,144], reduce GHG emissions from electricity generation ^[97][98][99][144,145,146], reduce peak electricity demand ^{[98][100][101][102]}[145,147,148,149], reduce electricity costs for consumers ^{[100][102][103]}[147,149,150], and increase the feasibility of a transition to EVs ^[104][151]. EV charging is one of the simplest ways to implement residential DR, because EVs’ battery storage provides inherent flexibility, so electricity demand can be shifted to times of lower electricity prices. Additionally, EV batteries can be used to feed electricity back into the grid when required for ancillary services. This bidirectional electricity flow to/from EV batteries is known as “Vehicle to Grid” (V2G), which can provide benefits both for electricity utilities and for consumers ^{[105][106][107][108]}[152,153,154,155]. However, the use of EVs for V2G can increase the rate of battery degradation, as batteries are meant to fulfill either autonomy requirements or ancillary services, and fulfilling both conditions has been shown to compromise battery lifespan ^{[105][109][110][111][112][113]}[152,156,157,158,159,160]. Thus, consumers may be less willing to participate in DR programs involving V2G if not properly compensated for this degradation.

Flexibility of space heating and cooling loads, and their implied potential for DR applications, is related to the thermal storage potential of the space in which they operate ^{[59][114][115]}[111,161,162]. Well-insulated buildings can store thermal energy for longer periods, providing greater demand flexibility. This thermal storage means electricity demand can be decoupled from heating/cooling demand, allowing space heaters/coolers to be used for DR. Air conditioning units can be used for DR to reduce consumer electricity bills and peak power demand without compromising occupants’ thermal comfort ^[116][117][163,164]. The use of combined Heating, Ventilation, and Air Conditioning (HVAC) systems for DR can reduce total electricity demand ^[118][165], match demand with supply ^[119][120][166,167], and reduce consumer electricity costs by shifting electricity consumption to lower-priced off-peak times ^[120][167]. As in the commercial sector, the success of residential DR programs involving space heating/cooling loads can depend on occupants’ temperature preferences, and their sensitivity to deviations from their preferred temperature ^{[121][122][123][124]}[168,169,170,171]. Thus, the success of DR programs involving space heating/cooling loads depends both on technical constraints such as thermal insulation and on behavioral constraints such as thermal comfort preferences, which can vary greatly.

Electric hot water cylinders (HWCs) can store energy in their tanks, allowing electricity demand to be decoupled from domestic hot water (DHW) demand and facilitating their use for DR ^[59][125][111,172]. The temperature in HWCs is typically regulated using setpoint controllers, which aim to keep water at a constant temperature. By predicting DHW time-of-use using stochastic models ^[126][127][173,174] or machine learning techniques ^{[128][129][130]}[175,176,177], “smart controllers” can increase the DR potential of HWCs ^[131][74], reduce consumer electricity costs ^{[126][129][132][133][134][135]}[173,176,178,179,180,181], overall electricity consumption ^{[128][136][137]}[175,182,183], and peak demand ^[127][174], and increase the utilization of electricity generated from renewable sources ^[138][184].

The use of smart controllers is typically simulated to assess their accuracy before installation in HWCs, which requires a model of HWC temperature. These models can be separated into three levels of complexity: (i) fully mixed temperature models, which assume a constant cylinder temperature ^{[132][133][134]}[178,179,180]; (ii) stratified temperature models, which use two variables to describe temperature of the upper (hotter) and lower (colder) halves of the cylinder ^[139][185]; and (iii) multi-nodal temperature models, which split the cylinder into a large number of nodes and calculate the temperature of each node ^[132][178]. However, increasing the DR potential of HWCs can increase the likelihood of failing to fulfill DHW demand ^[131][74], so consumers’ willingness to experience unmet demand may limit DR potential.

Residential DR potential can also be increased by using battery storage ^[140][141][186,187]. The utilization of electricity from “distributed generation”, electricity generated at homes or businesses ^[142][143][188,189], can be increased with battery storage ^{[144][145][146]}[190,191,192]. Batteries can shift electricity demand to coincide with supply, without shifting appliance use ^[147][193]. Batteries can also be used for DR without the presence of distributed generation, by charging when supply is high and discharging when supply is low, which can provide ancillary services to the grid ^[148][149][194,195]. Further, other storage methods can be used for residential DR. In ^[150][196], residential hydrogen storage is used to reduce power system operational costs and increase the utilization of wind generation. However, these added storage capabilities come with capital costs too high for many to participate in.

Many white appliances, such as dishwashers, washing machines, and clothes dryers, can schedule their loads according to user preferences and can thus be used in DR programs ^[151][152][197,198]. By scheduling runtimes to coincide with DR requests or low electricity prices, white appliances can participate in load shifting ^[153][199]. DR scheduling for white appliances can be more complicated than for loads with inherent storage, such as HWCs or batteries, as white goods are subject to technical constraints, such as the length of time required for the appliance to complete its task (“runtime”), and behavioral constraints, such as the time a consumer requires the appliance to be finished ^{[149][154][155]}[195,200,201]. For example, a dishwasher may take one hour to complete its washing cycle and may be required to provide clean dishes by 7 p.m.; if the dishwasher is turned on at 3 pm, its electricity demand can only be delayed by up to three hours, as its cycle must start no later than 6 pm. Within these constraints, white appliances can successfully participate in DR programs ^[156][202] and be used to increase the utilization of electricity from renewable sources and reduce peak electricity demand ^[157][158][203,204].

Residential Demand Response (DR) can be beneficial for both consumers and electricity utilities, as electricity can contribute a large portion of household expense and residential demand is the main contributor to peak total electricity demand. Therefore, as the main driver of peak demand, residential DSM is the key to maintaining network functionality, reducing network costs, and effectively integrating renewable energies. While technical and economic constraints can limit the feasibility of residential DR, behavioral constraints can have a far greater impact in this sector. Thus, residential DR programs typically focus on flexible loads, which can be shifted with minimal impact on occupant behavior.

Of these flexible loads, those with inherent storage are most used for DR, such as electric vehicles (EVs), hot water cylinders (HWCs), and space heating/cooling. Storage devices such as batteries and hydrogen storage are also used for residential DR but come at high capital costs for owners. Additionally, certain flexible loads, such as white appliances (e.g., dishwashers and washing machines), lack built-in storage. However, they can still contribute to shifting electricity loads by adjusting their operating schedules. By delaying their start times, these appliances can align their electricity consumption with periods of higher renewable energy generation or lower electricity demand, all while meeting consumers’ needs for clean dishes and clothes.

Unlike in the commercial and industrial sectors, human behavior is a primary consideration for the success of residential DR. In particular, residential electricity demand is primarily aligned with human needs rather than process or business requirements. Thus, an accurate understanding of human behavior, and the impact this behavior has on electricity demand and willingness to participate in DR programs, is crucial to the successful implementation of residential DR.