Effect Of Demand Response Programs Research Articles

Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations

As renewable energy expands, its fluctuations challenge grid security. Demand response (DR) is a crucial solution for ensuring grid stability. This paper designs a two-stage optimization model for the retail electricity provider (REP), aiming to develop differentiated prices to incentivize multiple types of users in DR. In the first stage, the REP utilizes energy storage to adjust electricity purchases. In the second stage, a bi-layer optimization model is constructed. The REP participates in incentive-based DR and sets differentiated prices for lower-level users. Based on these prices, multiple types of users modify their electricity consumption behavior, aiming to maximize the combined satisfaction with the comfort and economy of electricity consumption. Finally, the Karush-Kuhn-Tucker (KKT) method is used to derive the equilibrium solution between user response quantity and price. The results indicate that (1) Combining energy storage and user-side DR boosts responsiveness by 28.5% compared to storage alone and 23.2% compared to user-side DR alone. (2) Energy storage allows the REP to create more compelling prices for residential users. (3) Differentiated pricing boosts industrial users participating in DR, resulting in over 50% higher response rate than uniform pricing. This study provides policy implications for grid managers to develop effective DR programs.

Evaluation of a demand response online algorithm on the costs sustained by the user

The Demand Response (DR) algorithm is a technique that allows users to reduce their electricity consumption during peak hours, thereby reducing their overall energy costs. This study aims to evaluate the impact of the DR algorithm on the electrical costs sustained by the user. The research was conducted by analyzing the energy consumption patterns of a group of residential users with and without the implementation of the DR algorithm in their homes. The results of the study indicate that the implementation of the DR algorithm resulted in a significant reduction in the electrical costs sustained by the users. The study also found that the effectiveness of the DR algorithm was dependent on various factors such as the type of appliance, the time of day, and the user's behaviour. Overall, the study demonstrates that the DR algorithm is an effective tool for reducing energy costs and can be optimized through user participation and engagement. The findings of this research can help policymakers and energy providers to develop more effective demand response programs that benefit both users and the grid.

Design and implementation of a low-cost power logger device for specific demand profile analysis in demand-side management studies for smart grids

Demand-side management is vital for achieving a balance between electricity demand and generation, ensuring a reliable grid. Achieving this balance necessitates a deep understanding of consumption patterns and behaviors, making the analysis of demand profiles a critical component in the development of effective demand response programs. This study focuses on designing a cost-effective power logger device for collecting household power consumption profiles. The device features adjustable recording resolution, a user-computer interface for easy operation, long-term offline recording, compact size, and affordability. Design details of the proposed device are presented, and the prototype is tested to demonstrate its feasibility and reliability. The results, obtained over an extended period, are presented in this paper, enhancing the resources available for advancing machine learning studies, forecasting studies, and demand-side management research.

Effective demand response program addresing carbon constrained economic dispatch problem of a microgrid system

Electricity is produced, transferred, and delivered within a small geographic area using a constrained power distribution system called a microgrid. Such microgrids employed to guarantee that renewable energy sources (RES) are utilised to their fullest extent. Additional advantages of microgrids include reduced losses during transmission and related costs. The objectives of economic dispatch, emission dispatch, combined economic emission dispatch (CEED) founded on fractional programming (FP), and environmental constrained economic dispatch (ECED) are compared and contrasted in this study. The feasibility of a low-voltage microgrid system is examined for three situations. A novel, robust, and hybrid swarm-intelligence optimisation algorithm, based on merging the characteristics of the conventional grey-wolf optimizer (GWO), sine-cosine algorithm (SCA), and crow search algorithm (CSA), is used as a research optimisation tool. Results show that switching from a fixed pricing plan to a time-of-use (TOU) model resulted in a 15% less cost in production during the period of the research. When a portion of the microgrid customers engaged in the incentive-based demand response (IBDR) programme, further reductions in total generation costs and emissions were achieved. The peak demand was reduced by 3.5% as a result of participation in the IBDR programme, from 90 kW to 86.863 kW. and provided participants with incentives, according to numerical results.

Would employees accept curtailments in heating and air conditioning, and why? An empirical investigation of demand response potential in office buildings

Support for demand response has mostly been investigated in the context of private households. We extend the examination of support to organizational contexts. Specifically, we analyze employee support for a scenario in which employees are faced with temporary heating or air conditioning curtailments in their workplace. We also examine the factors underpinning support, including socio-demographic and workplace characteristics and psychological motivations and beliefs. Our empirical results are based on a sample of 551 employees from different organizations who work in air-conditioned offices. Results indicate that employees generally support corporate demand response programs and are willing to accept small deviations from their ideal office temperature, but the level of support is heterogeneous across employees. Subsequent modeling of potential underlying factors shows that socio-demographic or workplace-related characteristics only play a minor role in explaining these variations. In contrast, psychological factors, including environmental motivations and perceived corporate environmental responsibility underpin support, and specific beliefs about the consequences of participation can directly explain support as well. Moreover, personal norms emerged as an important antecedent of program support. Policy makers and organizations can use these insights to design effective demand response programs that address the relevant drivers and barriers of employee support.

Stochastic bottom-up load profile generator for Canadian households’ electricity demand

The residential energy demand timing and magnitude are highly impacted by occupants' behaviors and activities. However, acquiring a reliable data source for these activities is a vital challenge, especially on an urban scale. In this context, this paper presents a stochastic bottom-up model for generating electrical loads for residential buildings in Canada. The proposed model is developed to investigate the impact of different household characteristics, appliance stock, and energy behaviors on the timing and magnitude of non-HVAC energy loads at individual or multiple houses. The proposed tool includes four main modules for generating stochastic profiles of occupancy, lighting demand, appliance load, and domestic hot water demand (DWH). The model is calibrated using the Canadian time use survey (TUS), energy use statistics, and appliance ownership surveys. The model is scalable and can be extended to serve various applications by adding new modules and data sources in the future. This paper presents the model development methodology, generated high-resolution load profiles, and validation in comparison with actual measurements. Finally, the model is used for studying the impact of household characteristics on total energy use. Future work will include incorporating this model into a comprehensive agent-based model for designing and testing effective demand response programs.

Analysis of social–Psychological factors and financial incentives in demand response and residential energy behavior

Introduction: Demand response programs are promising approaches to balance supply and demand and reduce or shift peak hour loads. However, the impacts of residents’ behavioral patterns and social–psychological factors on these programs are poorly understood.Method: We used an extended model of the theory of planned behavior to understand how social–psychological factors, demographics, and household characteristics influence the demand response behaviors (i.e., adjusting A/C and shifting the time of use) of 826 US residents who participated in our study.Results: We found that low-income households (43.7%) reported as more likely to respond to the behavior of raising A/C thermostat settings when no one is home compared to higher-income residents. Thermal comfort needs (β = 0.34) and participants’ age (β = 0.02) increased the financial incentives requested to accept higher A/C temperatures while someone is home. Thermal comfort needs (β = 0.36) and energy-saving habits (β = −0.11) are the significant predictors of increased incentives requested for higher A/C temperatures when no one is home. Older adults request higher financial incentives for shifting washer and dryer usage than younger residents. Finally, attitudes toward energy saving are the strongest predictor of electricity curtailment behavioral intention, followed by perceived behavioral control, subjective norms, cost and environmental concerns, and energy-saving habits.Discussion: This finding suggests the potential of social–psychological variables in shaping energy consumption behaviors. This study provides insights for designing effective demand response programs and broadly analyzing energy behavioral patterns.

Evaluating the effects of demand response programs on distribution cables life expectancy

• Evaluating the effects of DRPs on the LOL rate reduction of distribution cables. • Evaluating the effects of DRPs’ tariffs on the LOL rate of distribution cables. • Proposing a new economic benefit index. • Prioritizing various models of DRPs based on AHP method. • Maximizing utility's and customer's total benefits by determining the optimal model. Recently due to economic issues, asset management has taken more attention all over the world. Distribution cables are one of the most expensive assets of the networks. The life of a cable extremely depends on the power flowing through the cable. On the other hand, demand response programs are known as the one of the effective solutions for managing networks’ power consumption. They can affect the power flowing through the distribution cables. However, the impacts that the demand response programs might have on the life expectancy of the distribution cables are generally neglected in previous research studies. So, this paper proposes a new approach to evaluating the effects of demand response programs on the life expectancy of distribution cables. Simulations have been carried out on a realistic distribution network, and the results are investigated considering various models of the demand response program and different levels of their tariffs. The results show a significant economic benefit due to distribution cables’ life extension resulted from demand response programs implementation, despite differences among various models. However, the benefit's scale dramatically depends on the model of the demand response program and the level of tariffs. Also, in order to select the optimal demand response model, economic analysis is conducted from both utility's and customer's perspectives. For this purpose, a new economic benefit index is proposed, and the best solution is determined by the distribution system's regulator based on an Analytical Hierarchy Process so as to it can satisfy all the stakeholders of demand response programs.

Impact of diverse penetration levels of thermal units on a hybrid microgrid energy management considering the time of use and function priority

This paper investigates the eco-emission analysis of a hybrid microgrid by assuming different penetration levels of thermal resources and considering storage devices in the presence of demand response programs. The dual-objective scheduling is done for 24 h (i.e., 4*6-time intervals). The total cost and contamination of the hybrid microgrid are considered as objectives. The time of use program (TOU) is considered on both electrical and thermal loads to determine the effect of demand response programs (DRP) on the planning. The impact of objective function priority is also applied to overcome decision obstacles. The hybrid microgrid consists of electrical and thermal loads; on the generation side, it has a diverse set of thermal resources like combined heat and power (CHP) unit, boiler, furnace, solar water heater, air to water heat pump, wind turbine, photovoltaic systems, micro-turbine. The problem is modeled as a mixed-integer linear programming (MILP) model in General algebraic modeling system (GAMS). Epsilon-constraints method and the fuzzy method are used for dealing with multiple objectives. The results show that thermal resources availability and management significantly impact the objectives; therefore, the cost and pollution are reduced from $295.6 and 2173 kg to $ 253.5 and 1929.7 kg in a nominal scenario. Also, remarkable results are obtained by using demand response programs and function priority in electrical and thermal loads.

Effect of Demand Response Programs on Industrial Specific Energy Consumption: Study at Three Cement Plants

Demand-side management (DSM) is the modification of consumer utilization manner for energy through various methods to smooth their power consumption curve and increase their energy efficiency. Although the DSM improves the electricity grid stability and protects the environment, it allows customers to reduce their costs. The available literature classifies DSM into two different areas: (i) energy efficiency (EE) and (ii) demand response (DR). In other words, cement plants are the largest customers of electricity, so the implementation of DSM programs in these heavy industries is very important for electricity companies. Past studies have often examined the impact of DR programs in cement factories on solving peak problems and changing load profiles but have not studied the effects of DR implementation on the energy efficiency index in the short and long term in these industries. In this paper, while examining the effect of implementing three types of common DR programs in smart grids including demand bidding (DB), ancillary services (A/S), and time of use (TOU) on the load profile of cement plants, based on the mathematical model of the load and the real data of the case study, it is shown that the implementation of the TOU program has created the highest energy efficiency by reducing specific energy consumption (SEC) in cement plants.

Knowledge-Based Systematic Feature Extraction for Identifying Households With Plug-in Electric Vehicles

Hassle-free in-house charging of plug-in electric vehicles (PEVs) is getting popular. Due to high power consumption, in-house PEV charging has a significant impact on the distribution network, and the utility companies are facing challenges in balancing the power demand. The problem could be solved by increasing distribution network capacity, designing an effective demand response program aimed at PEV charging, implementing vehicle to grid (V2G) or vehicle to house (V2H), and so on. One of the fundamental parameters for the mentioned solutions is to identify households with PEVs. Multi-level charging, power consumption similar to other home appliances, and absence of submeter for charging outlets make identification difficult. In this paper, a new feature extraction technique called knowledge-based systematic feature extraction is proposed to identify households with PEVs. The extracted features are easily interpretable and validated using two datasets from different regions. Keeping real scenarios in mind, the study examines scenario-based results and finds that the accuracy using extracted features ranges from 80.20% to 100% depending on classifier, number of vehicles, and level of charging. Moreover, results show improved performance compared to existing methods for identifying households with PEVs and other state-of-the-art feature extraction techniques.

A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes

The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.

Evaluating the Effects of Demand Response Programs on Life Expectancy of Distribution Transformers

This paper presents a new method to investigate the effects of demand response programs on the life expectancy of distribution transformers. The proposed method has been applied on a realistic distribution network, and the results are evaluated under various models of the demand response program and different levels of tariffs. According to the results, distribution transformers’ life extension under various scenarios of applying demand response programs, in spite of differences among them, brings a great economic benefit. The results show a significant life extension in the interval of about 9 to 33 years. Also, this life extension brings a considerable benefit between 624.91$ to 821.669$, per year. However, the amount of obtained benefit considerably depends on the model of the demand response program and the level of tariffs. Besides, an economic analysis from both utility’s and customer’s perspectives is carried out in order to determine the optimal demand response model. To do this, an economic index is presented, and the best solution is determined by an Analytical Hierarchy Process so that it can satisfy both utilities and customers. As revealed by the results, the total annual benefits of the utility and customers are increased by 762.64$ and 73.85$.

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

Evaluating the Effect of Demand Response Programs (DRPs) on Robust Optimal Sizing of Islanded Microgrids

A microgrid is a small-scale energy system with its own generation and storage facilities and energy management system, which includes shiftable and traditional loads. The purpose of this research is to determine the size of the microgrid through (i) investigating the effect of a shiftable demand response program (DRP) on sizing of an islanded microgrid and (ii) studying the uncertainty of power output of renewable energy sources by applying the robust optimization (RO) method. Since the RO method solves the problem for lower power outputs of renewable energy sources (RES) than the predicted values, the results obtained are pessimistic and will increase the project cost. To deal with the increment of project cost, the application of a load shifting DRP is proposed to reduce the cost. In addition, DRPs are suitable means to reduce the effects of uncertain power sources. Therefore, it is shown that a shiftable DRP is effective in reducing the overall project cost and the dependency on energy storage systems by defining different scenarios and simulating them with General Algebraic Modeling System (GAMS) software. Moreover, it is indicated that the shiftable DRP and battery state of charge have correlations with solar irradiance and wind speed, respectively.

Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries

Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system’s operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

Two-stage stochastic framework for energy hubs planning considering demand response programs

Abstract The integrated use of electricity and natural gas has captured great attention over recent years, mainly due to the high efficiency and economic considerations. According to the energy hub design and operation, which allows using different energy carriers, it has turned into a critical topic. This paper develops a two-stage stochastic model for energy hub planning and operation. The uncertainties of the problem have arisen from the electric, heating, and cooling load demand forecasts, besides the intermittent output of the solar photovoltaic (PV) system. The scenarios of the uncertain parameters are generated using the Monte-Carlo simulation (MCS), and the backward scenario reduction technique is used to alleviate the number of generated scenarios. Furthermore, this paper investigates the effectiveness of demand response programs (DRPs). The presented model includes two stages, where at the first stage, the optimal energy hub design is carried out utilizing the particle swarm optimization (PSO) algorithm. In this respect, the capacity of the candidate assets has been considered continuous, enabling the planning entity to precisely design the energy hub. The problem of the optimal energy hub operation is introduced at the second stage of the model formulated as mixed-integer non-linear programming (MINLP). The proposed framework is simulated using a typical energy hub to verify its effectiveness and efficiency.

Stochastic planning and operation of energy hubs considering demand response programs using Benders decomposition approach

In this paper, an integrated approach for optimal planning and operation of energy hubs is provided considering the effects of wind energy resources. Inevitable uncertainties of electrical, heating, cooling demands as well as the wind power generation are considered in this study. The proposed model is based on two-stage optimization problems and represented as a stochastic programming problem to address the effects of uncertain parameters. In order to address the uncertain parameters in the model, different scenarios have been generated by Monte-Carlo Simulation approach and then the scenarios are reduced by applying K-means method. In addition, the effects of demand response programs on the operational sub-problem are taken into account. Benders decomposing approach is adopted in this research to solve the complex model of coordinated planning and operation problem. The master problem is supposed to determine the type and capacity of hub equipment, while the operating points of these assets are the decision variables of the operational slave problem. As a result, the proposed mathematical model is expressed as a linear model solved in GAMS. The simulation results confirm that the Benders decomposition method offers extremely high levels of accuracy and power in solving this problem in the presence of uncertainties and numerous decision variables. Moreover, the convergence time is drastically decreased using Benders decomposition method.

Flexible scheduling of reconfigurable microgrid-based distribution networks considering demand response program

In future smart distribution grids, the number of microgrids can be increased within the network. In this paper, the optimal reconfiguration of microgrid-based distribution networks is modeled by considering demand response program to enhance the network scheduling flexibility. According to the proposed method, the optimal configuration of distribution network is determined to meet the best scheduling goals. Furthermore, the optimal state of interconnecting switches between microgrids and the main grid is determined as well. The optimal topology of microgrid-based distribution networks is given for each hour of operation, using optimal power flow. The results are obtained with and without demand response program that are applied to each microgrid. Different load profiles such as residential, industrial, commercial and official are considered and manipulated with suitable demand response programs. For better observation, various cases are investigated separately to meet the different goals considered by the distribution network operator. In the presented paper, the optimization aims to minimize total system operation cost and losses with optimal networked-microgrid reconfiguration. The effects of demand response programs are shown in the results of optimal topology of microgrids. To show the effectiveness of the presented approach, IEEE 85-bus modified test system is employed.

Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand

Using renewable distributed generators as photovoltaic cell and wind turbine in microgrid includes green and free energy exploitation. However, uncertainty of generated energy from these resources may underestimate energy planning for load demands. The uncertainty may be from market price and load demand in addition to the renewable distributed generators. To overcome the uncertainty challenge, energy storage system and demand response program with cooperation of users on demand side are applied as solutions to plan the energy flow in microgrid to guarantee the voltage stability and essential load supporting. In this paper, price-based demand response for industrial, commercial and house loads are considered in which the effect of demand response on the cost reduction is analyzed and discussed. Electrical and heat demand are considered in the microgrid while uncertainty of renewable distributed generators, market price and load demand are modeled and estimated by point estimation method. Simulation results with three scenarios are performed with and without price-based demand response program and results are compared. As shown in simulation results, demand response has highly reduced total cost (22–28% related to the case without that) where voltage dip (maximum 1.5%) and power deviation (maximum 1.33%) are also improved in the microgird.