Residential Demand Response Research Articles

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

As space heating accounts for 54% of annual residential electricity consumption in Quebec, demand response programs specifically target load shifting through the automated control of thermostat setpoints during peak hours. On a district scale, varied thermostat preferences and setpoint override behaviors can have an impact on the success of the demand response program. This study examines two unique occupant types (Average, Tolerant) in terms of thermostat setpoint preferences as well as three different occupant Levels-of-Detail (LoDs) and analyzes their effects on the energy flexibility provided during demand response periods. For our baseline scenario, LoD 1, a static setpoint schedule is used and there is no control of the heat pump, while LoD 2 and LoD 3 incorporate thermostat setbacks during demand response events. LoD 2 assumes the occupant is comfortable within 2°C from the setpoint while LoD 3 allows the occupant to override the DR setbacks. We estimate the flexibility services provided by a ten-house residential community through the automated control of heat pumps during a three-month winter period, and we implement and simulate our study in CityLearn using reinforcement learning-based control for district-level energy management. When comparing LoD 3 to LoD 1, electricity cost was reduced by approximately 12% and net electricity consumption was reduced by approximately 17% during demand response periods. Likewise, we find that LoD 2 could overestimate savings in net electricity consumption, cost, and peak demand by 5% compared to LoD 3. The number of hours where the indoor temperature deviated more than ▪ from the setpoint occurred for less than 5% of the timesteps on average for the 10 buildings for LoD 3 while still achieving significant net electricity consumption reductions, thus highlighting that we can provide energy flexibility services to the grid while balancing occupant thermal comfort. Finally, the agents learned optimal decision-making that reduced the number of overrides across the training episodes for both Average and Tolerant occupants. We thus present a multi-agent framework as a means for addressing various occupant setpoint preferences and override behaviors for the control of heat pump systems at the neighborhood level.

Residential demand response online optimization based on multi-agent deep reinforcement learning

Demand side management in the context of smart grids exploits the value stream of load flexibility, promoting stable and economic operation of the power grid. To address multi-uncertainties such as device characteristics, distributed renewable generation, and residential electricity demand, this paper proposes a model-free framework based on multi-agent deep reinforcement learning to achieve efficient online optimization of demand response strategies. Firstly, the demand side management is formulated as a partially observable Markov game. Then, a multi-agent soft actor-critic algorithm is proposed, combining a temporal electricity price feature extraction technology to improve learning efficiency. Reward correction mechanism is designed to avoid new charging and discharging peaks in the demand response. Finally, case studies demonstrate the effectiveness of the proposed method in reducing the electricity cost and aggregate peak demand of residents. The proposed method reduces the daily average cost by 21.40%, 8.09%, and 5.88% compared to MAPPO, MADDPG, and SUMADRL, and exhibits the highest training efficiency and scalability against the benchmarks.

Driving factors of residential demand response for the integration of variable renewable power.

The large-scale integration of renewable power poses great challenges to grid stability. Among flexible resources, demand response (DR) stands out for its advantages in cost and efficiency. To identify key factors influencing DR, this study adopted the modified theory of planned behavior (TPB) to establish the conceptual model. Social norms were included as a front-end variable, and institutional factors and electricity consumption habits served as moderating variables. The model was subsequently tested and modified using the structural equation modelling (SEM). Results indicated that social norms can exert a substantial indirect effect on DR behavior. However, due to the deficiency of such norms, the formation of the positive attitude towards DR was hindered, resulting in a low standard coefficient of 0.16. Moreover, the influence of subjective norm on response intention was rejected due to limited perceived external pressure. Perceived behavior control exhibited the most significant direct influence on response intention (0.76). Additionally, the positive effects of situational factors and personal habits on the conversion from response intention to behavior were supported. Based on these findings, several policy suggestions including enhancing publicity and incentive policies were proposed.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

A behaviour digital twin for residential demand response: Modelling intention and motivation to improve the prediction of the likelihood of reaction to behavioural triggers

Background: Residential demand response is a resource in the evolving energy infrastructure which thus far has not achieved its full potential. Amongst the reasons for this underutilisation is a lack of understanding, and therefore predictability, in relation to the uncertainty of the behaviour of human actors and its potential impact on energy demand side management. Optimal model predictive control of energy assets requires a digital twin to operate, however, most approaches so far are focused predominantly on technical indicators only and neglect the individuality of people and their behaviour in the operation. To fully integrate human led actions into such a system, the digital twin must therefore also provide social and psychological indicators to facilitate better predictability of reactions to demand response triggers. Methods: In the following a behaviour digital twin model will be presented based on the theory of planned behaviour and the self-determination theory, which provide well-established and validated tools to capture indicators of intention and motivation. The key contribution of this work is to operationalise and combine these models into a software tool, which continuously adapts its parameters to the evolving behaviour of users and provides up-to-date predictions. Results: The resulting model predicts the likelihood of each individual to react to appropriate demand response triggers, which can be used in model predictive control involving human actors to optimally select whom to target and when. Conclusions: The presented behaviour digital twin aims at bridging the gap between research in psychology to evaluate and assess drivers of behaviour and innovations in the space of model predictive control to optimally facilitate asset operation in residential settings.

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis

This study examines the concept of demand response in household appliance use. Its primary aim is to explore the factors influencing electricity consumption behavior and employ K-means clustering to group households, estimating daily electricity consumption patterns. This understanding is essential for the development of effective demand response strategies within the Greater Accra Region, Ghana. The research leveraged metrics, such as the Silhouette Score and principal component analysis to ensure the quality of the clustering process, effectively combining qualitative and quantitative data. Insights were enhanced by incorporating consumer behavior surveys to better comprehend appliance use trends and optimize demand response strategies. The findings emphasize differences in voltage, intensity, power consumption, and smart meter data among different household clusters. Notably, clusters 1 and 3 emerge as high energy consumers, particularly in water and cold appliances. These insights offer valuable guidance for targeted energy management and optimization strategies. This study underscores the significance of using consumer behavior insights to enhance and optimize demand response programs, providing essential guidance to energy stakeholders, particularly in Ghana, for the efficient optimization of electricity consumption and the successful implementation of demand response initiatives.

Can you lower the thermostat? Perceptions of demand response programs in a sample from Quebec

The current push towards electrification in various sectors as a decarbonization strategy presents significant challenges for utility providers due to unintended consequences related to higher peak demand. To ensure grid reliability, utilities resort to importing costly electricity or using fossil fuels, which counteracts these decarbonization efforts. To address this issue, they introduced demand-side management strategies, known as demand-response (DR) programs, to engage customers in reducing peak demand. While commercial and industrial customers have greater load flexibility, the significance of residential buildings in managing peak demand is growing due to electrification of heating systems and the proliferation of electric vehicles (EV). However, the development of residential DR programs lags behind their potential, facing challenges such as limited customer knowledge, price elasticity, and infrastructure costs. To increase residential DR program adoption, understanding occupants' perceptions and behaviors is vital. This paper presents the results of a survey conducted in Quebec to identify participation rates, incentives, approaches, and contextual factors influencing enrolment in DR programs and participation in peak demand events. It was found that there is significant potential for expanding these programs, especially by identifying key demographic sectors that may be more likely to participate compared to the current participant breakdown. The research also uncovered contextual factors and demographic variables that statistically influenced occupant behavior during peak demand events and their impact on energy-saving actions. Notably, more than 80 % of respondents believed that having smart thermostats would facilitate their participation in DR programs. These results offer valuable insights for further development and enhancement of DR programs in the province.

Online transfer learning-based residential demand response potential forecasting for load aggregator

Accurate demand response (DR) potential forecasting is the basis for load aggregators (LA) to make optimal bidding strategies in DR market trading. LAs usually face practical challenges when they perform forecasts for those new customers who have no historical response data. Transfer learning provides a promising solution to this problem by leveraging knowledge acquired from other existing contracted customers. However, traditional transfer learning methods are trained offline and cannot make use of the latest response information of these new customers, which may result in large forecasting errors since the response behavior of new customers usually dynamically changes. To address the above issues, this paper proposes an online transfer learning-based DR potential forecasting framework, in which two forecasting models are established. The first one is built using the historical data of existing customers and this model is then transferred to the target domain (i.e., new customers) by parameter sharing and fine-tuning. The second model is built using the local response data of new customers, which gradually accumulates with the increasing participation of DR events. These two models are combined by an adaptive ensemble framework based on their online performances, thus enabling it to dynamically track the changes in new customers' response behavior. Case studies on a real-world dataset validate the effectiveness of the proposed framework.

Fog-Based Hierarchical Coordination of Residential Aggregators and Household Demand Response with Power Distribution Grids—Part II: Data Transmission Architecture and Case Studies

Fog-Based Hierarchical Coordination of Residential Aggregators and Household Demand Response with Power Distribution Grids—Part II: Data Transmission Architecture and Case Studies

Fog-Based Hierarchical Coordination of Residential Aggregators and Household Demand Response with Power Distribution Grids—Part I: Solution Design

Fog-Based Hierarchical Coordination of Residential Aggregators and Household Demand Response with Power Distribution Grids—Part I: Solution Design

Theoretical research on carbon emission factors of distributed smart distribution network under high proportion of renewable energy access

In the context of the “dual carbon” goal, a high proportion of renewable energy connected to the grid to replace traditional high-carbon units has become a general trend. Taking the high proportion of renewable energy power system as the research object, the medium and long term planning of the installed capacity of source, grid, load and storage of power system is carried out considering the two dimensions of investment and operation maintenance, and the optimization model of source, grid, load and storage of power system is established. A low-carbon dispatching model considering carbon emission and demand response of distribution network is also proposed. Firstly, considering the characteristics of distribution network receiving power from the main network, the carbon emission model of distribution network based on carbon emission flow theory is constructed, and the low-carbon index of distribution network is proposed. Secondly, the improved dynamic carbon emission factor of distribution network is proposed, which is used as a guide signal to provide reference for low-carbon electricity consumption of distribution network users. According to the characteristics of users in the distribution network, the industrial demand response model aiming at low dispatch cost and low carbon and the residential demand response model aiming at electricity consumption satisfaction and low carbon satisfaction are constructed respectively.

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Residential demand response program modelling to compliment grid composition and changes in energy efficiency

The path towards decarbonization in Canada is heavily reliant on electrification if the electricity grid is decarbonized. Besides the effectiveness in emission reduction, electrification offer opportunities to increase grid flexibility through demand response (DR) programs. Despite being widely seen in literature that DR programs are beneficial to the grid, there are limited, if any, residential DR programs available. As Canada has a diverse range of electricity grids, this study aims to determine how residential building DR programs impact grid operations on different archetypical electricity grids. The production cost model, SILVER, is used for this analysis. Additionally, various demand profiles are constructed to mimic changes in building stock efficiencies. It is found that grid composition plays a larger role in the effectiveness of a residential DR program than the efficiency of the building stock. Furthermore, increasing the efficiency of the building stock led to DR being more effective on grids where there are significant variable renewable energy sources. These results should motivate cities to consider residential DR programs as a tool to reducing emissions along with benefiting grid operations.

Combined peak reduction and self-consumption using proximal policy optimisation

Residential demand response programs aim to activate demand flexibility at the household level. In recent years, reinforcement learning (RL) has gained significant attention for these type of applications. A major challenge of RL algorithms is data efficiency. New RL algorithms, such as proximal policy optimisation (PPO), have tried to increase data efficiency. Additionally, combining RL with transfer learning has been proposed in an effort to mitigate this challenge. In this work, we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline. We evaluate our approach on a demand response use case where peak shaving and self-consumption is incentivised by means of a capacity tariff. We show our adapted version of PPO, combined with transfer learning, reduces cost by 14.51% compared to a regular hysteresis controller and by 6.68% compared to traditional PPO.

Residential demand response and dynamic electricity contracts with hourly prices: A study of Norwegian households during the 2021/22 energy crisis

Price-responsive demand and dynamic electricity price contracts can play a vital role in balancing renewable energy production and alleviating energy shortages such as those experienced in the European energy crisis. This study focuses on the implicit demand flexibility of residential consumers during extraordinarily high electricity prices in winter 2021/22 in Norway where most households have electric heating and spot price contracts. An econometric model is developed that compares the demand with pre-crisis levels, adjusts for factors influencing electricity consumption, such as outdoor temperature, and utilises a comprehensive dataset including hourly electricity demand data. The results reveal a quick response since the price signal was passed immediately to the customers and substantial energy savings of 11.4 % during winter. While the average household showed no significant short-term price response to daily or hourly price variations, several subgroups did. Particularly, households actively monitoring hourly prices via real-time information channels and those with automatic smart charging of electric cars showed higher load reductions in peak price hours and load shifting to low-price hours. Thus, the study concludes that households are able to respond to variable hourly electricity prices and suggests the promotion of spot price contracts to incentivise residential demand response.

Assessing the viability and environmental impact of residential demand response programs: A case study in East Legon, Greater Accra, Ghana

Residential demand response programs hold significant potential for delivering cost benefits and enhancing the overall security of the power supply network. In the context of Ghana, the current state of such programs could be improved. Regrettably, utility companies in the country currently lack an understanding of customers' behavior related to electricity use, which hinders residential demand response implementation in Ghana. This paper aims to relieve the Ghanaian power network’s demand in peak hours through a residential demand response program by assessing consumer behavior related to electricity consumption in a day. This study presents East Legon as a case and employs the diversified demand method based on consumer behavior to assess residential demand response in Ghana. The results indicate that a 3 MW of demand can be relieved on the network in a day during peak hours. The annual energy savings resulting from this initiative is 40,479,991 kWh. A 1,032,239,780 kg of CO2 emissions due to this program can be avoided. Notably, the study demonstrates a positive Net Present Value (NPV) of GHS 350.00, signifying the financial viability of the demand response program in East Legon over the specified time horizon. The research underscores the significance of leveraging consumer behavior to optimize demand response programs, offering valuable guidance to policymakers and utility companies to enhance energy efficiency and sustainability in the region.

Residential demand response in the European power system: No significant impact on capacity expansion and cost savings

Direct Load Control (DLC) is a demand response strategy in which customers receive compensation from utilities in return for permitting them to regulate the operation of specific equipment. This paper analyzes the impacts of DLC programs on the transition to renewable energy within the European electricity system towards 2060. The study quantifies the achievable hourly potential for DLC across Europe in the residential sector. By implementing and developing a DLC module within the stochastic capacity expansion model EMPIRE, we investigate how costs, long-term investments, and long-term marginal prices are affected by residential DLC participation rates. The research utilizes a comprehensive DLC dataset, including ten appliances such as electric vehicles, heat pumps, refrigeration, and others. This dataset serves as the basis for creating four storylines to investigate the integration of these programs into the European electricity system. The results indicate that residential DLC programs have some impact on grid-battery deployment, PV plant penetration, and electricity prices. In the best-case scenario, involving ambitious participation of residential loads in DLC programs without compensation, cost savings are about 1% versus not introducing DLC. The findings contribute to understanding the value of demand response programs in Europe, indicating that the savings they bring might not be sufficient to provide enough incentives or compensation for widespread participation in such programs. That is, from a long-term investment or capacity expansion perspective, it may not be worthwhile to soley include residential demand response in the planning of the electricity system.

Human-centered determinants of price and incentive-based residential demand response in Ottawa, Canada

Human-centered determinants of price and incentive-based residential demand response in Ottawa, Canada