Residential Demand Response Programs Research Articles

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.

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.

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.

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.

Fairness of prosumers’ incentives in residential demand response: A practical decentralized optimization approach

This paper presents a new decentralized optimization-based approach for an aggregator involved in residential demand response programs. The proposed approach relies on an existing two-phase optimization framework, whereby prosumers optimize first their self-generation and household consumption, which are subsequently coordinated by the aggregator via incentives through the solution of a mixed-integer linear program. As a distinctive aspect, the notion of fairness in the distribution of incentives among prosumers is incorporated through new nonlinear constraints based on the Shapley Value while respecting standard demand response requirements. A mathematical approximation, which is proven to be exact under certain practical assumptions, is proposed to accommodate the new constraints into the linear model. Furthermore, a novel solution methodology, which combines the divide-and-conquer principle and Dantzig–Wolfe decomposition, is devised to tackle the resulting problem in a distributed fashion. For validation purposes, the behavior of the linear approximations is examined by comparing the results with those attained with their nonlinear counterparts. Moreover, scenarios for prosumers featuring different flexibility levels are generated to assess the practicality of the assumptions adopted. Simulations also show the scalability of the proposed tool by considering cases with up to 10,000 prosumers with computing times within an hour if distributed computing is implemented. Furthermore, as compared to classical Shapley Value approaches, a similar distribution of incentives is attained by the new solution methodology with relative less and acceptable computational effort. Finally, the impact of the new constraints on Peak-to-Average Ratio is quantified, obtaining an increment of up to 13.2% with respect to cases disregarding fairness.

A Behavioral Science Perspective on Consumers’ Engagement With Demand Response Programs

Residential electricity demand response programs can play a substantial role in facilitating a sustainable energy transition, but it is important to examine the consumer behaviors necessary to harness this potential. We outline three behavioral dimensions relevant for effective demand response, namely investment, participation, and actual response. We discuss the factors that underpin them as well as possible behavioral interventions to promote demand response behaviors.

Customer perspectives on demand response in Europe: a systematic review and thematic synthesis

Residential demand response is increasingly recognized as a valuable tool to increase power-system flexibility and to improve integration of renewable energy sources in the sustainable energy transition. However, low customer participation and engagement is one of the barriers hindering widespread implementation of residential demand response. To improve understanding of the factors influencing customer engagement in residential demand-response programs, this article investigates associated customer experiences and attitudes. This study is based on a systematic review and thematic synthesis of findings from past demand response-pilot projects. By synthesizing findings from multiple sources, the article provides insights into the customer perspective to inform the development of customer-oriented demand-response services and products. The results indicate that customer engagement in demand-response programs is influenced by multiple factors, including motivation, interaction and communication, and feedback. Particularly highlighted is the value of social interaction and support as well as the importance of customer education and easily interpretable information. To promote customer engagement, the electric utility industry should place more focus on building customer relationships and integrating customer perspectives into the design of demand-response programs.

Maximum demand flexibility from the demand response of a big group of residential homes

Residential demand response (DR) programs are designed to reduce peak load from residential homes. It is essential for utilities to identify how much peak load could be potentially reduced from DR to optimally plan and operate the power system. However, it is extremely challenging to identify such a maximum load reduction, i.e., demand flexibility, offered by DR for a large group of residential homes due to the difficulties in modeling the willingness of customers to participate in DR programs and diverse loads in households. The willingness is also related to the incentives; a higher incentive can obviously attract more customers to participate. Targeting these problems, this paper provides an electricity cost minimization model for residential homes. DR incentives and customer willingness to respond to incentives are modeled to identify the realistic maximum demand flexibility. A case study using actual data from 98 homes at the Pecan Street microgrid database shows 9.7% of the controllable peak load (43 kW) can be reduced when the DR incentive is half of the peak electricity tariff. This demand flexibility increases to 66 kW, accounting for 14.9% of the controllable peak loads, when the incentive increases to twice of the peak tariff.

Consumer-Centric Rate Design for Peak Time Energy Demand Coincidence Reduction at Domestic Sector Level-A Smart Energy Service for Residential Demand Response

Reducing demand coincidence of customers with distribution system peak hours is very essential in the modern world energy sector. There is a great scope for peak demand coincidence reduction at the customer level, especially in the residential sector through Residential Demand Response programs. Through smart meter installations along with IT-enabled technologies, many of the distribution company’s initiatives like residential demand response programs can be taken to domestic customers with ease, less cost, and less technology deployment. Rate design is one such effective approach. Even though time-varying rates like Time of Use have been used as an effective approach for reducing peak electricity demand in different sectors around the world, the residential sector has not gained much attention due to a few challenges like externality problems, high on-peak and low off-peak prices, improper pricing mechanisms. Hence, considering the above challenges and constraints, a rate design, named consumer-centric time of use tariff is proposed in this article for domestic customers. The tariff is consumer-centric such that each customer gets a unique on-peak unit price and off-peak unit price based on their consumption during peak hours both at the house-level and utility level rather than common and fixed on-peak and off-peak prices for all the customers, thus addressing the above-mentioned constraints. For this, customers are classified into different clusters using the Machine Learning Algorithm K-Means. The proposed rate design model has been analyzed on synthetic smart meter data of 10 houses, and it is observed that the proposed tariff shows an increase in the monthly revenue by 4.3% for the utility and a variation of -0.4% to 7% of energy charge for different customers. This study and analysis show that the proposed consumer-centric time of use rate design provides a better pricing mechanism with a win-win strategy for both customers and utility, thereby avoiding windfall gains or losses to both. Furthermore, the proposed tariff influences each residential customer of different consumption levels to reduce peak demand coincidence as well as energy consumption for the power sector.

Adaptive incentive-based demand response with distributed non-compliance assessment

Traditional residential incentive-based demand response (DR) programs use fixed incentive structures that do not incorporate closed-loop feedback to compensate for non-compliance by participants. In practice, such programs may not reliably meet their event goals. To address this challenge, real-time feedback can be used to adaptively modify the participants’ incentives, an approach which has not been proposed before. This paper proposes a flexible monitoring framework to detect potential non-compliance, whereby a second DR event is adaptively scheduled with higher incentives. In this context, constraints are presented to prevent over-compensation and gaming of the DR system by the participants. This novel dual-event design is implemented using a distributed event-stream monitoring framework to preserve scalability and ensure low monitoring costs. The merits of the proposed DR design are demonstrated at a utility-scale for 100,000 residents, while also considering the adoption of residential electric vehicles that are poised to increase the flexibility of the demand in the distribution system.

A Smart Direct Load Control Approach Using Dynamic Voltage Control for Demand Response Programs

—This study proposes a direct load control (DLC) approach for residential demand response (DR) programs, which uses a dynamic voltage control method to reduce the peak electricity consumption in homes. In the proposed approach, if a utility needs to reduce electrical load consumption, a DLC signal is sent to a smart meter (SM) either via utility’s communication infrastructure. SM communicates with a group of smart plugs attached to selected domestic appliances to individually reduce the output voltage. The electrical consumption of selected appliances can be reduced without turning them OFF. Hence, the peak demand of a residential house can be reduced without turning off selected power-intensive loads, like in a classic direct load control (DLC) approach used by several electric utilities. The proposed dynamic voltage control is operated can be done within a certain limit defined set by international standards to avoid any possible negative impacts on domestic appliances. The results indicated that the proposed DLC could reduce the peak electricity demand up to around 32% using both voltage control and renewable energy. The proposed approach is expected to give flexibility to utilities to control the electrical demand of homes while ensuring participants remain more comfortable during a DR event.

Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy

Global transactive energy has become more complex due to the extension of its efficiency. Thus, multi-residential demand responses pose a new challenge in the critical infrastructure of power transition networks in energy planning and management. By encompassing optimal scheduling with strategic energy decentralization, a reliable distribution network can suggest a resolution that contributes to optimize energy efficiency. This research proposed a holistic bidirectional distribution network with optimal scheduling-based demand-side management (DSM) for superior power sharing and beneficial performance. A DSM was developed to maintain equal power sharing between various mix-energy sources, providing a sub-level residential demand response program. Among several scenarios considered, the DSM-based scheduling distribution network resulted in optimum and equivalent sharing of power loads for residential demands. Moreover, the application of self-production-based renewable energy source (RES) is a novel strategy to improve imbalanced power sharing. The energy deficit and surplus can be solved by establishing a conservation strategy with self-installation-based photovoltaic panels by end-users. CO2 emissions were also estimated to elucidate the effect of the energy distribution network on the environment according to different category of energy sources. Considering satisfaction analysis based on appliance energy consumption, the energy distribution network was efficient in improving the end-user expenditure, sustaining a clean energy loading profile, and maximizing the benefit of cost.

Optimal Home Energy Management Considering Uncertainties in Occupancy, Consumption, and Electricity Price

Power utilities issue demand response (DR) during the hours of peak load in order to reduce the demand on the network and provide congestion relief to overloaded circuits. While traditional residential DR programs are mainly one-way in the form of remote on/off control of air conditioning (A/C) units, residential customers can adopt a more proactive role through utilizing the capabilities of smart meters and home energy management systems (HEMS). HEMS can monitor energy rates and DR incentives, and accordingly change the temperature setpoint of the A/C unit and/or shift appliance loads from peak to off-peak hours in order to maximize financial benefits. All this can be achieved in an automated human-out-of-the-loop fashion. From the HEMS’ standpoint, the task can be viewed as solving an optimization problem with the goal of reducing power consumption while maximizing financial gains. However, another equally important goal would be to ensure that the comfort level of residents, if present in the building, is not compromised. This is especially crucial during periods of extreme temperatures where maintaining an acceptable indoor temperature has a direct impact on the residents’ health, especially children and the elderly. What makes this multi-objective optimization problem more challenging is the uncertain nature of some model parameters, e.g., electricity rates, building occupancy levels, and demand. This paper presents a novel solution for energy management of a smart home using DR by considering the above factors. To ensure that the solution found is feasible against all possible uncertainties, a robust model is developed and solved for a given time horizon. As shown through simulation results, considering uncertainties are necessary, since they can change the solution in a nonnegligible way.

Expert Assessment of Prioritized Determinants For a Smarter Grid Through The Lens of Residential Demand Response: The Case Study of Ontario, Canada

In recent years, smart grids have attracted considerable attention. However, despite the promising potential of the technologies encompassed within such systems, their adoption has been slow, geographically varied, and in the context of residential demand response, often subject to public scrutiny. The heterogeneous evolution of the smart grid is not only the product of technological limitations but is additionally sensitive to socio-political considerations prevalent at the national or provincial level. Through expert interviews that were conducted in Ontario, Canada, this study provides insights into which smart grid factors are considered as most important for its development, and also what are the drivers, inhibitors, benefits, and drawbacks that a smart grid provides and / or entails, placing particular emphasis on residential demand response programs. The constructs scrutinized were adapted from previous studies, and the information collected was analyzed following the procedure of the Grounded Delphi Method. The findings indicate that a consensus was reached, in that smart grids pave the way for increased demand flexibility and loss reductions, though these are contingent on measures being implemented regarding the creation of investment opportunities, engagement of consumers, and ensuring the security of private data. Relevant policy implications and research recommendations are also explored.

A Comprehensive Scheduling Framework Using SP-ADMM for Residential Demand Response With Weather and Consumer Uncertainties

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. The results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

Shedding Light on the Factors That Influence Residential Demand Response in Japan

Residential demand response empowers the role of electricity consumers by allowing them to change their patterns of consumption, which can help balance the energy grid. Although such type of management is envisaged to play an increasingly important role in the integration of renewables into the grid, the factors that influence household engagement in these initiatives have not been fully explored in Japan. This study examines the influence of interpersonal, intrapersonal, and socio-demographic characteristics of households in Yokohama on their willingness to participate in demand response programs. Time of use, real time pricing, critical peak pricing, and direct load control were considered as potential candidates for adoption. In addition, the authors explored the willingness of households to receive non-electricity related information in their in-home displays and participate in a philanthropy-based peer-to-peer energy platform. Primary data were collected though a questionnaire survey and supplemented by key informant interviews. The findings indicate that household income, ownership of electric vehicles, socio-environmental awareness, perceived sense of comfort, control, and complexity, as well as philanthropic inclinations, all constitute drivers that influence demand flexibility. Finally, policy recommendations that could potentially help introduce residential demand response programs to a wider section of the public are also proposed.

Implementing automated residential demand response in South Korea: Consumer preferences and market potential

In recent years, electricity demand response (DR) has attracted attention as the most cost-effective way to balance the electricity grid. This study analyzes the preferences of South Korean energy consumers on residential DR and calculated the potential capacity of peak reduction in summer. The estimation demonstrates that consumers have heterogeneous preferences for automated residential DR programs and highly value reducing their monthly electricity bills. A scenario analysis shows that approximately 60.0% of metropolitan consumers in South Korea would participate in a residential DR program, and the corresponding reduction in the peak load is forecasted to be approximately 1.74 GW.

Learning-Enabled Residential Demand Response: Automation and Security of Cyberphysical Demand Response Systems

Residential Demand Response (DR) Programs have been validated as a viable technology to improve energy efficiency and the reliability of electric power distribution. However, various technical and organizational challenges hinder their full techno-economic potential. In practice, these challenges are related to the small-scale, distributed, heterogeneous, and stochastic nature of residential DR resources. This article investigates state-of-the-art online and reinforcement learning methods that are capable of overcoming these challenges in the context of DR pricing, scheduling, and cybersecurity.