Residential Demand Response Programs Research Articles

Performance Evaluation of Residential Demand Response Based on a Modified Fuzzy VIKOR and Scalable Computing Method

For better utilizing renewable energy resources and improving the sustainability of power systems, demand response is widely applied in China, especially in recent decades. Considering the massive potential flexible resources in the residential sector, demand response programs are able to achieve significant benefits. This paper proposes an effective performance evaluation framework for such programs aimed at residential customers. In general, the evaluation process will face multiple criteria and some uncertain factors. Therefore, we combine the multi-criteria decision making concept and fuzzy set theory to accomplish the model establishment. By introducing trapezoidal fuzzy numbers into the Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR) method, the evaluation model can effectively deal with the subjection and fuzziness of experts’ opinions. Furthermore, we ameliorate the criteria weight determination procedure of traditional models via combining the fuzzy Analytic Hierarchy Process and Shannon entropy method, which can incorporate objective information and subjective judgments. Finally, the proposed evaluation framework is verified by the empirical analysis of five demand response projects in Chinese residential areas. The results give a valid performance ranking of the five alternatives and indicate that more attention should be paid to the criteria affiliated with technology level and economy benefits. In addition, a series of sensitivity analyses are conducted to examine the validity and effectiveness of the established evaluation framework and results. The study improves traditional multi-criteria decision making method VIKOR by introducing trapezoidal fuzzy numbers and combination weighing technique, which can provide an effective mean for performance evaluation of residential demand response programs in a fuzzy environment.

Electric Vehicle Scheduling Strategy in Residential Demand Response Programs With Neighbor Connection

As smart grid enablers, demand response programs for household purposes have been in highlight recently. This paper presents a detailed structure of a household user, which is capable of energy transactions among consumer and load-serving entity. The proposed household model comprises several assets including nonshiftable appliances, shiftable appliances, and electric vehicle (EV). The EV can further contribute to the vehicle-to-home and vehicle-to-grid type of connections that can reduce the household electricity payments with their bilateral energy transactions. The day-ahead dynamic prices are superimposed with peak power limiting approach to prevent the formation of new peaks. The energy transaction among neighbor enhances the reduction in daily energy payments and flexibility among users. The centralized optimization problem is formulated as mixed integer linear programming problem. The validity of the proposed system can be proven by performance evaluation of simulation results.

A strategical game theoretic based demand response model for residential consumers in a fair environment

Demand response programs are developed to preserve the balance of demand-supply ratio and to reduce energy cost incurred by the electricity customer. Most of the existing work on residential demand response programs focused on utility-consumer interaction in a coordinating game model via two-way communication to achieve minimization of total cost and peak to average ratio. The prior work has considered the problem of energy consumption scheduling for multiple users but fails to address the optimally fair environment for scheduling. This paper proposes a consumer preference based demand response model formulated in a game-theoretic framework. The proposed model have a key feature that no consumer can acquire profit by deviating from the prescribed strategies or chronological order. The optimal game scheduling for minimizing the energy costs is achieved by using correlated equilibrium represented as Nash Equilibrium of energy consumption scheduling game. The simulation results clearly demonstrate the minimization of peak to average ratio up to 31.4% on an average for each day. The cost benefit of 261.6$ in a day is reaped by total users in the system which is highest among all days. The proof of existence of Nash Equilibrium is authenticated in the appendix.

Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm

In the domestic sector, increased energy consumption of home appliances has become a growing issue. Thus, reducing and scheduling energy usage is the key for any home energy management system (HEMS). To better match demand and supply, many utilities offer residential demand response program to change the pattern of power consumption of a residential customer by curtailing or shifting their energy use during the peak time period. In the present study, real time optimal schedule controller for HEMS is proposed using a new binary backtracking search algorithm (BBSA) to manage the energy consumption. The BBSA gives optimal schedule for home devices in order to limit the demand of total load and schedule the operation of home appliances at specific times during the day. Hardware prototype of smart sockets and graphical user interface software were designed to demonstrate the proposed HEMS and to provide the interface between loads and scheduler, respectively. A set of the most common home appliances, namely, air conditioner, water heater, refrigerator, and washing machine has been considered to be controlled. The proposed scheduling algorithm is applied under two cases in which the first case considers operation at weekday from 4 to 11 pm and the second case considers weekend at different time of the day. Experimental results of the proposed BBSA schedule controller are compared with the binary particle swarm optimization (BPSO) schedule controller to verify the accuracy of the developed controller in the HEMS. The BBSA schedule controller provides better results compared to that of the BPSO schedule controller in reducing the energy consumption and the total electricity bill and save the energy at peak hours of certain loads.

A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation

Although the locational marginal price may change dramatically within a single day in competitive wholesale electricity markets, most end users are charged monthly electricity bills over flat rates. Without financial incentives, the customers are lacking of motivation to respond to the price signals, which may result in inefficient energy consumption. In Texas, Senate Bill 1125 encourages qualified residential and commercial customer classes to participate in demand response (DR) programs. This paper proposes an idea to aggregate a number of residential customers to participate in residential DR program by employing smart appliances and a home area network to shift the coincidental peak load to off-peak hours to reap financial benefits. The operation strategies for the most representative residential load types are discussed. To further reduce electricity purchase and cut electricity bills, a solar farm with energy storage system is proposed, and the control algorithm is designed accordingly. The operation strategies are simulated for a whole year, and the annual costs are calculated and compared in this paper. The results show that, by doing load control and utilizing renewable resources, the total operation cost can be reduced significantly.

Heuristic Optimization for an Aggregator-Based Resource Allocation in the Smart Grid

We utilize a for-profit aggregator-based residential demand response (DR) approach to the smart grid resource allocation problem. The aggregator entity, using a given set of schedulable residential customer assets (e.g., smart appliances), must set a schedule to optimize for a given objective. Here, we consider optimizing for the profit of the aggregator. To encourage customer participation in the residential DR program, a new pricing structure named customer incentive pricing (CIP) is proposed. The aggregator profit is optimized using a proposed heuristic framework, implemented in the form of a genetic algorithm, that must determine a schedule of customer assets and the CIP. To validate our heuristic framework, we simulate the optimization of a large-scale system consisting of 5555 residential customer households and 56 642 schedulable assets using real-pricing data over a period of 24-h. We show that by optimizing purely for economic reasons, the aggregator can enact a beneficial change on the load profile of the overall power system.

Design of Smart Home Energy Management System.

A Home Energy Management (HEM) system plays a crucial role in realizing residential Demand Response programs in the smart grid environment. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customers preference and load priority. The HEMs communication time delay to perform load control is analyzed, along with its residual energy consumption. The main aim is to design how each load performs when being controlled by the HEM unit and measure electrical measurements for the different loads. Demand response (DR) is defined as changes in electricity use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity. HEM system comprises an HEM unit that provides monitoring and control functionalities for a homeowner, and load controllers that gather electrical consumption data from selected appliances and perform local control based on command signals from the HEM system. A gateway, such as a smart meter, can be used to provide an interface between a utility and the data base for the electrical consumption is maintained.

Outage Management in Residential Demand Response Programs

This paper proposes a novel optimization based home load control (HLC) to manage the operation periods of responsive electrical appliances, determine several recommended operation periods for nonresponsive appliances, and schedule the charge/discharge cycling of plug-in hybrid electric vehicle (PHEV) considering various customer preferences. The customer preferences are in the format of payment cost, interruption cost, and different operational constraints. The projected algorithm is online, in which household appliances are initially scheduled based on the payment cost, and when the home load is interrupted, the scheduling will be updated to minimize customer interruption cost. Due to vehicle to home capability of PHEV, the home outage can be managed through solving the proposed optimization problem. Several realistic case studies are carried out to examine the performance of the suggested method. In addition, the impacts of common electricity tariffs on the HLC results are investigated. The results reveal that employing the proposed HLC program benefits not only the customers by reducing their payment and interruption costs, but also utility companies by decreasing the peak load of the aggregate load demand.

Development and Analysis of Dynamic Time Based Pricing Scheme, RTFPP for Residential Demand Response Program

Paper proposes fair and dynamic pricing strategy, Real Time Fair Peak Pricing (RTFPP), for residential demand response program which takes into account the intensity of increased load above baseline (must run load) of each user and charge accordingly. Proposed methodology has the potential to increase user confidence, by the induction of fairness and baseline flexibility, hence increasing participation in residential demand response programs for economic operation of system. An algorithm is developed to impose RTFPP scheme on given user, in peak periods, for billing. To show the benefits, diverse realistic user load profiles are imposed with developed algorithm in MATLAB and results are evaluated and analysed.Keywords: Load Management, Residential demand response Dynamic time based pricing Demand side management

Investigating the Impacts of Different Price-Based Demand Response Programs on Home Load Management

Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.

Further exploring the potential of residential demand response programs in electricity distribution

Smart grids play a key role in realizing climate ambitions. Boosting consumption flexibility is an essential measure in bringing the potential gains of smart grids to fruition. The collective scientific understanding of demand response programs argues that time-of-use tariffs have proven its merits. The findings upon which this conclusion rests are, however, primarily derived from studies covering energy-based time-of-use rates over fairly short periods of time. Hence, this empirical study set out with the intention of estimating the extent of response to a demand-based time-of-use electricity distribution tariff among Swedish single-family homes in the long term. The results show that six years after the implementation households still respond to the price signals of the tariff by cutting demand in peak hours and shifting electricity consumption from peak to off-peak hours. Studies conducted in the Nordic countries commonly include only homeowners and so another aim of the study was to explore the potential of demand response programs among households living in apartment buildings. The demand-based tariff proved to bring about similar, but not as marked, effects in rental apartments, whereas there are virtually no corresponding evidences of demand response in condominium apartments.

Role of residential demand response in modern electricity markets

Electricity generation must match the demand at each instant, following seasonal patterns and instantaneous fluctuations. Thus, one of the biggest drivers of costs and capacity requirements is the electricity demand that occurs during peak periods. This paper reviews market-related problems of modern electric grids and possible solutions to address them. In particular, one techno-economical solution, namely residential demand response programs enabled by a smart grid, is analyzed and modeled in detail. The implications of this solution from both economic and policy perspectives are discussed. The analysis results in several insights: first a local optimum does not generally lead to a global optimum, especially for complex markets; second, in this approach, there exists a disconnection between the locus of the problem (electric utilities) and the locus of the solution (change of demand); third, any techno-economic solution must be carefully designed and global impact should be evaluated to ensure that the final objective is achieved; and fourth, two-way communication is an essential requirement for the successful deployment of smart grids.

Does Knowledge Contribute to the Acceptance of Demand Response?

More flexible demand side would benefit the electricity markets, networks and sustainable power generation in many ways. The success of demand response programs, however, relies on consumer acceptance. This paper reviews previous studies about acceptability of different kinds of residential demand response programs. Furthermore, it discusses whether consumers who are more aware of the principles and benefits of demand response have more positive attitudes towards demand response programs. The results of the literature review and two survey studies suggest that price and security of supply are currently bigger motives to change consumption behaviour than environmental issues and that the savings expected to trigger any action (and to lead to lasting change in behaviour) may be relatively high. Therefore, the framing of demand response programs goals may affect the acceptance. Additionally, consumers seem to prefer simple price structures that remain constant for a long time to more dynamic options.

Hardware Demonstration of a Home Energy Management System for Demand Response Applications

A Home Energy Management (HEM) system plays a crucial role in realizing residential Demand Response (DR) programs in the smart grid environment. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customer's preference and load priority. This paper presents the hardware demonstration of the proposed HEM system for managing end-use appliances. The HEM's communication time delay to perform load control is analyzed, along with its residual energy consumption.