Demand Response Implementation Research Articles

What makes electricity consumers change their behavior? Influence of attitude and perceived impact of DR programs on awareness

PurposeIn this study, the authors propose a confirmatory factor analysis (CFA) to create a tenable measurement model and identify the factors that have the potential to enhance awareness of pro-environmental measures. The successful implementation of demand response (DR) programs and their required infrastructure is significant for moving towards green energy communities and a better environment for living. Not only can renewable energy capacities contribute to this desideratum, but also electricity consumers who, until the last decade, have played a passive role.Design/methodology/approachTo answer these questions, a complex data set of 243 post-trial questions created by the Irish CER are analyzed using first-order and hierarchical CFA models with several SAS procedures (PROC CALIS, MIANALYZE). The questionnaire was launched to over 3,000 electricity consumers from Ireland that were participants to a trial program after the installation of smart metering systems and implementation of DR programs.FindingsThe effect of five latent factors – positive attitude, negative attitude, perceived impact of own actions, price- and incentive-DR programs – is measured. With a bi-factor CFA measurement model, the authors assess that they significantly influence the electricity consumers' awareness.Research limitations/implicationsHowever, these findings have to be backed up by relevant information and simulations showing consumers benefits in exchange to their efforts. They have research implications on the design of the business models and DR programs pointing out the importance of benefits and fairness of value sharing mechanisms within energy communities.Practical implicationsThus, the electricity consumers may change their consumption behavior as they positively perceive the implementation of DR programs.Originality/valueThis paper fulfills an identified need to study post-trial questionnaire and reveal latent factors that make electricity consumer change their behavior.

Demand Response Implementation: Overview of Europe and United States Status

The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with more environment-friendly solutions. Enabling Distributed Generation (DG), namely using renewable resources such as wind and solar, can be part of the solution to reduce the greenhouse effects. However, their unpredictable behavior might result in several problems for network management. Therefore, the consumer should become more flexible towards this new paradigm where the generation no longer follows the demand requests. With this, Demand Response (DR) concept is created as part of this solution. This paper studies the European Union and United States’ current status, with over 50 references.

Design and Implementation of Smart Energy Management System

It is crucial to create systems that lessen our reliance on non-renewable resources in a world with declining energy reserves and escalating economic crises. Smart Home Energy Management Systems, which employ smart switches to actively monitor energy use by each load, available power, and other parameters to transition between renewable resources and the primary power grid, are becoming more and more popular. The results of several methodologies were combined and analysed after a thorough literature review. The approach described is a synthesis of all the most well-liked approaches. The suggested methodology aims to implement demand response management simultaneously on grid electricity and solar power. The issue was divided into two scheduling scenarios: one assuming only solar power is available, the other assuming just grid power. In order to use the resources available as effectively as possible, the two halves are then combined.

Automation architecture for harnessing the demand response potential of aqueous parts cleaning machines

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory.

Multi-objective optimization of integrated energy systems based on demand response

ABSTRACT The implementation of demand response (DR) in the operation optimization of the integrated energy system has become one of the research hotspots worldwide with the rapid development of the energy network. Integrated gas-electricity system (IGES) operators can allocate different energy carriers more rationally and decrease energy cost and exergy input considering the DR scheme. However, DR was mainly applied to electric power systems in the past, while natural gas systems were less considered. In this paper, based on the theory of price-elasticity, and simultaneously with the consideration of the DR for end-user (EDU) of electricity and natural gas, the optimization model of the IGES is established, which includes two objective functions, the minimum operating cost of the integrated system and the maximum EDU satisfaction. The constraints include compressors and gas storages in the natural gas system, coal and gas generator units in the electric power system, Pow to gas units, and EDU gas/electric load changes. Subsequently, the proposed model is employed for an integrated 7-node natural gas system and 6-node power system with the sensitivity analysis for operating cost. The results demonstrate that the implementation of DR brings specific economic benefits to the operation of the entire system, the total operating cost is reduced by 16.6%, and the natural gas load is reduced by 89.9%. The implementation of DR balances the 24-hour load, reduces the fluctuation of the EDU load, increases the efficiency of the load shifting, and alleviates the instability of upstream production and transportation.

A Hybrid Heuristic Algorithm for Energy Management in Electricity Market with Demand Response and Distributed Generators

Optimal energy management trends are indispensable in improving the power grid’s reliability. However, power usage scheduling for energy management (EM) poses several challenges on a practical and technical level. This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal EM and overcome the major challenge in demand response (DR) implementation. This work aims to solve the power usage scheduling problem for EM to optimize utility bill, peak energy demand, and pollution emission while considering the varying pricing signal, distributed generators (DGs), household load, energy storage batteries, users, and EUC constraints. The ECS is based on a stochastic algorithm (genetic wind-driven optimization (GWDO) algorithm) because generation, DGs, demand, and energy price are stochastic and uncertain. The ECS based on the GWDO algorithm determines the optimal operation schedule of household appliances and batteries charge/discharge for a day time horizon. The developed model is analyzed by conducting simulations for two cases: home is not equipped with DGs, and home is equipped DGs in terms of utility bill, peak energy demand, and pollution emission. The simulation results validated the proposed model’s applicability to EM problems.

Per-Phase Power Controller for Smooth Islanded Transitions in Three-Phase Three-Wire Systems

This manuscript describes the operation of a droop-based controller for three-phase converters in the case of the absence of a neutral connection to the grid. The controller is capable of output power tracking and smooth transitions into the islanded operation. While independent per-phase control of the converter output power is possible if a neutral connection is present, its absence implies additional constraints to be considered. Focusing on this latter case, the controller described herein allows the independent control of the active power at the output of each phase of the converter and a smooth transition to the islanded operation. These features are paramount in future smart power systems, such as smart microgrids, for implementing demand–response, power-flow management, and uninterrupted power operation.

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

The ever-increasing penetration of intermittent renewable resources in low-voltage power grids necessitates efficient operational strategies for voltage regulation as well as power scheduling of the available resources. In this paper, a risk-aware Volt/VAR support framework followed by a real-time reinforcement learning controller is presented for three-phase distribution systems. In the risk-aware stochastic scheduling stage, the legacy voltage regulating assets along with inverter-based photovoltaics (PVs) and energy storage system (ESS) are optimized considering day-ahead and intra-day markets. Moreover, demand response (DR) and voltage reduction plans are included in the proposed scheduling framework. By incorporating voltage-dependent load modeling in this study, the implementation of the voltage reduction plan reduces energy consumption in feeders by running the network at lower permissible voltage limits. The shiftable loads under the DR program are employed for peak shaving and to reduce operational costs. The result shows that DR implementation also reduces dependencies on the operation of traditional devices. The stochasticity of abrupt changes in PV generations is represented as the Gaussian Mixture Model (GMM), indicating a non-unimodal probability distribution in day-ahead PV forecasting errors. The scenario sets for uncertain variables are then reduced using a fuzzy clustering technique. Decisions made in the scheduling, associated with PV inverters and ESS operation, are revised with a real-time controller, i.e., Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The DDPG is adopted in the control stage of the framework considering the detailed modeling of unbalanced three-phase distribution grids to minimize the voltage deviation and power ramping of ESS. The performance of the proposed multi-stage scheme is verified using a three-phase active distribution grid under different scenarios.

Distributed Baseline Load Estimation for Load Aggregators Based on Joint FCM Clustering

Residential customer baseline load (CBL) estimation is critical to Demand Response (DR) implementation. The accurate estimation of baseline load is challenging because of the complexity and uncertainty of customer behavior. In addition, conventional methods are based on centralized datasets, which may be unrealistic considering the customer privacy issues and the geographical distribution of data storage under multiple load aggregators (LA). This paper proposes a fully distributed framework for CBL estimation based on joint Fuzzy C-Means (FCM). The fuzzy membership matrix is utilized to capture the load features. LAs can acquire the joint clustering result through limited information exchange in a privacy-preserving way. At last, the CBL is calculated through an inverse process of clustering. Case studies have been conducted utilizing the dynamic Time of Use trail data from Low Carbon London project. Compared with widely used averaging methods, the proposed method significantly improves the estimation accuracy.

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme’s optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently.

A Framework for Several Electricity Retailers Cooperatively Implement Demand Response to Distributed Data Center

A distributed data center cluster (DDCC) consists of data centers that are geographically dispersed while managed by the same company. A DDCC is composed of several data centers in different regions, so it purchases electricity from several electricity retailers in different regions. Commonly, there is a lack of cooperation between the several electricity retailers. In this paper, a cooperation framework where several electricity retailers cooperatively implement incentive-based demand response (DR) to the same DDCC is proposed. Firstly, several electricity retailers which power for the same DDCC together publish their DR instructions to the DDCC. The DR instructions are analyzed in this paper. Then a novel DDCC energy management model is proposed for the DDCC to participate in the collaborative DR. Finally, a reasonable profit distribution mechanism is adopted for the several electricity retailers and the DDCC to allocate the entire profit of the collaborative DR. The case study verifies that the proposed cooperation framework can maximize the whole benefit of the DDCC and the several electricity retailers meanwhile ensures that the profits obtained by each member (the DDCC and the several electricity retailers) from the collaborative DR are not fewer than that of the independent operation.

A Distributed Energy Management Approach for Smart Campus Demand Response

In a smart campus, the potential to reduce energy consumption in buildings is high. In this sense, campus utility operators increasingly need an energy management system capable of interacting with buildings' demand response (DR), which can be automated and cloud-based with the help of information and communication technologies (ICTs). Even so, cloud-based DR implementation still faces some challenges, including computation load, communication delay, and privacy concerns. To address these issues, we developed a cloud-based DR model, which is implemented as a cloud-edge combined computing platform. A competitive equilibrium state of load shedding among the campus buildings is designed to balance the trade-off between occupant discomfort and energy usage. In this article, we adopt the clustering-based distributed alternating direction method of multipliers (ADMM) to decompose the campus load reduction assignment problem into a series of DR subproblems, each of which could be solved by each building with the edge layer computing devices. Two different campus study cases demonstrate the feasibility and performance of the proposed distributed DR algorithm.

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

The use of energy storage power plants (ESPP) seems necessary to create flexibility in the operation of smart grids and increase economic benefits. The power storage power plants connect directly to the smart distribution substation (DS), saving energy costs and increasing energy efficiency. Also, demand response (DR) program management has improved the performance of smart grids by shifting loads. In this regard, the main challenge is the optimal coordination between these two problems by considering control challenges such as limiting the number of charge/discharge times and the number of hours of DR implementation using a linear model that can be solved with powerful commercial solvers. In this paper, we proposed an economic mixed-integer linear programming (MILP) model for optimal coordination of DR and large-scale ESPP considering the practical limitations of charge/discharge times and DR action times with optimal management of distributed generation (DG) resources, which provides more realistic results due to the constraints in operating times in both DR and ESPP. To validate and analyze the proposed model, a standard 33-bus distribution network with a standard Vanadium redox battery power plant and a large 874-bus system with three Vanadium redox battery power plants are considered. Various scenarios have been considered to demonstrate the constraints imposed on the demand side management program and battery charge and discharge, the results of which show that these constraints have a significant impact on the objective function of the problem. Also, by comparing the proposed method with other methods, it was found that the proposed method is more efficient in improving the objective function and limiting options.

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.

A robust energy management system for Korean green islands project

Penetration enhancement of renewable energy sources is a core component of Korean green-island microgrid projects. This approach calls for a robust energy management system to control the stochastic behavior of renewable energy sources. Therefore, in this paper, we put forward a novel reinforcement learning-driven optimization solution for the convex problem arrangement of the Gasa island microgrid energy management as one of the prominent pilots of the Korean green islands project. We manage the convergence speed of the alternating direction method of multipliers solution for this convex problem by accurately estimating the penalty parameter with the soft actor-critic technique. However, in this arrangement, the soft actor-critic faces sparse reward hindrance, which we address here with the normalizing flow policy. Furthermore, we study the effect of demand response implementation in the Gasa island microgrid to reduce the diesel generator dependency of the microgrid and provide benefits, such as peak-shaving and gas emission reduction.

Demand response with heat pumps: Practical implementation of three different control options.

Three case studies of different heat pump demand response control strategies in real homes are presented. All three households reduced their electricity consumption during a peak period but delivered unintended consequences where the heat pump's logic did not correspond to the demand response requirements. This study highlights that the implementation of heat pump demand response to support electricity system operation requires a clear definition of electricity system need as well as practical demand response mechanisms to be integrated into heating system design.

Bi-level planning model of distributed PV-energy storage system connected to distribution network under the coordinated operation of electricity-carbon market

The disordered connection of Distributed PV-Energy Storage Systems (DPVES) in the Distribution Network (DN) will have negative impacts, such as voltage deviation and increased standby costs, which will affect the demand of urban consumers for reliable and sustainable power consumption. Therefore, reasonable planning of the location and capacity of the DPVES in the DN is one of the key measures to reduce these negative impacts, while promoting the consumption of renewable energy and protecting the income of the DPVES. In this paper, under the background of coordinated operation of China's electricity market and carbon market, considering the uncertainty of distributed photovoltaic (DPV) output power and the demand response (DR) behavior of users, a bi-level planning model of DPVES in DN is established. The upper-level model aims at the maximum revenue of the DPVES in the DN and the highest local consumption rate of DPV, and the decision variables are the connection location and capacity of the DPVES. The lower-level model aims at the minimum average voltage deviation of the DN nodes, and the decision variables are the implementation of load-side DR and the output power of the DPVES. Then, the solution method and process of the model are given. Finally, four planning scenarios are set up in the IEEE-33 node distribution system, and the planning results of different scenarios are analyzed and discussed. The research results show that the bi-level planning model proposed in this paper can reduce the average voltage deviation of DN nodes while effectively increasing the average return of the DPVES and improving the local consumption rate of DPV.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

A system dynamics investigation on the long-term impacts of demand response in generation investment planning incorporating renewables

By exchanging demand and price data between demand and supply sides, demand response (DR) exerts a substantial impact on the power system planning. This study proposes a novel dynamic model for long-term generation capacity investment planning with DR implementation. The relation between price and responsive loads is modeled regarding a two-way market-based relevance. Renewable energy resources alongside common thermal technologies are considered in the proposed investment process. Further, uncertainty of power generation in the thermal units, renewables' availability, and participation in the DR programs are modeled. Different levels of DR, penetration of renewables, and capacity payment are investigated to obtain the most advantageous supply by simulating several cases. Special attention is drawn to quantifying the results of DR implementation in long-term planning. The results identify that, from an environmental point of view, DR can contribute to enhancing renewables penetration and diminishing CO2 emissions. From the economic viewpoint, by postponement or preventing the new power plants construction and mitigating price spikes, DR causes more economic power system. Furthermore, from the reliability perspective, with the participation of responsive consumers, DR improves the power system's reliability and security. The proposed model allows the regulatory body to analyze the economical, technical, and environmental effects of DR in the long-term.

Multi-stage time sequence planning model of integrated energy system considering extreme scenarios and uncertainty

Integrated Energy System (IES) is far-reaching significance for improving energy utilization efficiency and reducing carbon dioxide emissions. However, due to the lack of consideration of construction sequencing, carbon trading development trends and uncertainty risks in the current IES planning scheme, problems such as mismatch between supply and demand, excess carbon emissions and weak renewable energy carrying capacity have emerged. To address these problems, this study proposes a multi-stage time sequence robust planning model for IES that considers carbon trading and extreme scenarios. First, the synergistic and complementary energy framework of IES is introduced, and the operation principle of each unit in the system is briefly shown by matrix. Then, considering the construction sequence of multiple stages and the load difference in different seasons, a life cycle planning model is established, which includes carbon trading cost, renewable energy curtailment penalty cost, integrated demand response cost. In order to improve the system’s ability to withstand the uncertainty of power and load, the extreme scenario is obtained according to the confidence level, and the improved robust optimization model is used to enhance the robustness of the system, which plays a dual role of risk prevention and control. Finally, a green ecological park is used as the object to implement simulation, and a safer planning scheme is obtained under extreme scenarios. The case study shows that: (1) Compared with the single-stage planning method, the method proposed in this study can reduce the depreciation cost in the whole life cycle by 5.44%, and improve the phenomenon that the curtailment rate of wind power exceeds 20%. (2) After participating in the carbon market, the cumulative income generated in the three stages is 17.92 million ¥. (3) After implementing demand response, the cumulative configuration capacity of wind turbine, ground source heat pump and energy storage decreased by 1.02%, 7.85% and 57.6%, respectively. (4) The improved robust optimization model improves the risk resistance of the system.