Demand Response Participation Research Articles

Design of Multi-Settlement Electricity Markets Considering Demand Response and Battery Energy Storage Systems Participation

Design of Multi-Settlement Electricity Markets Considering Demand Response and Battery Energy Storage Systems Participation

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Optimal Scheduling of Integrated Energy System Considering Hydrogen Blending Gas and Demand Response

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen-enriched compressed natural gas (HCNG). It is also injected into the natural gas pipeline network to achieve the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load’s peak and trough periods are adjusted, and the large-scale consumption of renewable energy and the reduction in carbon emissions are achieved. First of all, a fine model of hydrogen production and hydrogen use equipment is established to analyze the impact of adding hydrogen mixing on the economy and the low-carbon property of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utilization rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment operation cost, carbon emission cost, wind curtailment cost, and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natural gas was established. The model was linearized and solved by using MATLAB 2021a and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial Consumers

A Data-Driven Framework for Quantifying Demand Response Participation Benefit of Industrial Consumers

Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS.

A multi‐layer–multi‐player game model in electricity market

AbstractHere, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs.

Optimal dispatching strategy for residential demand response considering load participation

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

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.

A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy

The transition from traditional fuel-dependent energy systems to renewable energy-based systems has been extensively embraced worldwide. Demand-side flexibility is essential to support the power grid with carbon-free generation (e.g., solar, wind.) in an intermittent nature. As extensive energy consumers, commercial and industrial (C&I) consumers can play a key role by extending their flexibility and participating in demand response. Onsite renewable generation by consumers can reduce the consumption from the grid, while energy storage systems (ESSs) can support variable generation and shift demand by storing energy for later use. Both technologies can increase the flexibility and benefit by integrating with the demand response. However, a lack of knowledge about the applicability of increasing flexibility hinders the active participation of C&I consumers in demand response programs. This survey paper provides an overview of demand response and energy storage systems in this context following a methodology of a step-by-step literature review covering the period from 2013 to 2023. The literature review focuses on the application of energy storage systems and onsite renewable generation integrated with demand response for C&I consumers and is presented with an extensive analysis. This survey also examines the demand response participation and potential of wastewater treatment plants. The extended research on the wastewater treatment plant identifies the potential opportunities of coupling biogas with PV, extracting the thermal energy and onsite hydrogen production. Finally, the survey analysis is summarised, followed by critical recommendations for future research.

A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization

Data centers (DCs) are gradually becoming one of the world's largest energy consumers. Owing to their spatial-temporal transferable characteristics, DCs show great potential in demand response (DR) programs. Considering the difference in task delay sensitivity (TDS), an elaborate DR model of DCs is proposed in this work. Furthermore, to ensure the TDS requirement and relieve the data transmission pressure, TDS-based unit migration cost matrices are designed in this work. Given that most DR dispatch for DCs is conducted relying on a central dispatching center, which imposes critical requests for the reliability of the dispatching system. In this work, we establish a novel 4-level joint optimal dispatch model for the DC operator, where dispatching centers installed at each level integrate district information from bottom to top and conduct optimal task dispatch from top to bottom. Moreover, an innovative dispatch autonomy mechanism is designed to realize district autonomy in the case of dispatching center breakdowns. Finally, simulation results prove that the proposed 4-level joint optimal dispatch model has a positive effect on enhancing renewable energy accommodation and improving the economic benefits of the DC operator. More specifically, by applying the proposed 4-level joint optimal dispatch model, the DCO's total cost is decreased by 32.25% in comparison with the result of no DR participation. It also proves that our designed dispatch autonomy mechanism can achieve district autonomy under the breakdown of dispatching centers at any level.

System planning with demand assets in balancing markets

Balancing markets will become more and more relevant with the increased volatility in the electricity system due to the increase in the renewable quota. New policies are paving the way for customers flexibility participation as demand response in reserve products. This paper contributes with an assessment of the impact of demand response participation in the reserve market when planning the electricity system’s operation and investment in new technologies. The model used has been conveniently upgraded and a set of scenarios have been raised to conduct the analysis. The residential and services sectors' consumption for heating, cooling, hot water, and electric vehicles are considered as sources of flexibility. Each one has their own modeling to represent their nature. Main findings show that demand response receives and offers more benefits for the system on the wholesale market than in balancing services, although their participation in them is quite relevant. This is due to the decrease in firm capacity investment needs thanks to reducing systems’ peak technologies and the decrease of spillages from renewables. Additionally, increasing demand response percentages in the systems lead to cost reduction. However, there is a limitation associated with an increase of CO2 emissions due to the usage of existing polluting technologies to avoid investments in storages. Finally, flexibility providers are compared to determine their flexible capabilities.

Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection

Considering the flexible capacity and privacy needs of numerous flexible energy users in the context of demand response (DR), this study establishes an influence model (IM) to describe the DR participation capabilities of users considering privacy budget. Using the designed Stackelberg game mechanism that can achieve optimal selection for DR responders from users with different characteristics described by IM, a federated learning (FL)-based optimization method that uses differential privacy (DP) as the data transmission mechanism for DR economic optimal dispatch is proposed. Ideas of controlling the number of participating users and financially compensating for the privacy leakage risk by the FL-based optimization method are the guarantees for users with private data to participate in DR. The performance of the proposed optimization method is also compared with that of the Moth-flame optimization algorithm in a case study, and the guiding value of the former in selecting among user groups with different characteristics is then discussed. Results show that the proposed method exhibits good economic benefits and universal applicability.

Research on optimal dispatch model of power grid considering the uncertainty of flexible resource demand response on the residential side

AbstractResidential side flexible load participation in demand response (DR) has been widely used in recent years. However, there are uncertainties in the response willingness and response volume of residential customers, which have a non‐negligible impact on the security and economic dispatch of the power sector. This paper analyzes the uncertainty factors in the demand response process of residential users from the equipment load layer–user layer, establishes for the first time a refined model of residential users' participation in DR from the perspectives of participation uncertainty and response uncertainty, and further constructs a grid optimal dispatch model considering DR uncertainty. The experimental simulation results show that the model established in this paper is able to adjust the operation strategy according to the requirements of user comfort, enhance the enthusiasm of users to participate in DR, and at the same time reduce the dispatch security risk and economic loss caused by the uncertainty of residents' DR on the power grid.

Energy cost optimization through load shifting in a photovoltaic energy-sharing household community

Home energy management systems are essential for the optimization of resources in complex demand scheduling problems that require energy efficiency in homes. This can be achieved through the use of Renewable Electricity Sources (RES), for cleaner and more sustainable energy generation, as well as participation in Demand Response (DR) programs, for lower energy prices. The problem becomes more complex when it is considered a community of households. This paper aims to address individual and community household participation in DR programs and RES sharing while considering constraints imposed on the operation schedule of appliances, through load shifting optimization. For that, a Genetic Algorithm is proposed, implemented, and validated, which focuses on minimizing energy costs. It takes into account dynamic pricing, distributed generation, and household community energy sharing. Using real household workload data, two case studies are presented, one for the cost optimization of an individual household and another for a photovoltaic energy-sharing household community containing twenty houses. Both case studies represent five days of scheduling, where each house can have up to five appliances able to shift. The business as usual costs are 16.74 EUR and 269.99 EUR for the individual and community case studies, respectively. Results show improvements of up to 24.3% (12.67 EUR in the optimized schedule, 4.07 EUR in savings) for the individual case study and 11.8% (238.21 EUR in the optimized schedule, 31.78 EUR in savings) for the household community. In a community, households can expect cost reductions of 1.5%–26.8% when compared to individual scheduling.

From buildings to cities: How household demographics shape demand response and energy consumption

In the face of rapidly evolving energy landscapes, driven by increasing integration of renewable energy and the transition towards a more sustainable grid, the role of demand response (DR) programs in maintaining grid stability and efficiency has become increasingly critical. This study presents an in-depth methodology for assessing the nexus between household demographics and engagement in demand response (DR) programs, investigating their impact on energy consumption and financial outcomes at both building and urban scales. Six household archetypes are defined: single working individual, single retired individual, working couple, retired couple, nuclear family, and lone parent household. Additionally, three energy-use archetypes— austere, average, and wasteful—are considered. Employing a stochastic load profile generator, the study examined variables like annual occupancy hours, thermal energy needs, and appliance consumption. In a Canadian residential context, a shift from a single working adult to a nuclear family occupant archetype led to an increase in annual occupancy by 24%, causing a 21% fluctuation in Energy Use Intensity (EUI). Additionally, diverse consumption behaviors led to a 41% EUI variance. The study evaluated load curtailment capabilities across various household archetypes; notably, a ‘wasteful’ household could reduce their load by 32% through a 2 °C-thermostat adjustment during DR events, and this rose to 54% with pre-heating. At the urban scale, demographic shifts substantially influence load profiles and cause variable peak load times. Areas with more retirees have higher mid-day energy usage. The research quantified DR benefits in load reduction and fiscal gains. By raising DR participation from 10% to 50%, load curtailment grew by 69% in areas with younger families. Pre-heating further enhanced load curtailment by 12% and reduced energy rebound by 4%. Economically, ‘wasteful’ households could accumulate 11% more credits compared to average homes. This research introduced a flexible framework for incorporating household demographics and energy use behavior into DR studies, which can be adapted and re-applied to different case studies based on the availability of relevant data.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers’ electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work.

Capacity Payments in the Turkish Electricity Market: A Necessity or Policy?

Capacity mechanisms have been implemented in different forms for various purposes. Contrary to altering trends in the electricity markets, Türkiye opted for enforcing “capacity payments,” regarded as a primitive form of the mechanisms to ensure the security of supply and system reliability. This study mainly aims to build a critical review of the Turkish capacity mechanism design. The survey conducted makes valuable inferences along with the opinions of different stakeholders. The results indicate that the current capacity payment scheme, a non-market based, is not sustainable in the long run. Therefore, improving the existing payment methodology by reconsidering mechanism’s scope and different age criteria, efficiency, and deliverability for plants or implementing other types of mechanisms with a market reform, such as demand response participation, seems more efficient.

Incentive strategies for small and medium-sized customers to participate in demand response based on customer directrix load

Compared with large industrial and commercial customers, small and medium-sized customers have small electricity consumption, large quantities, lack of autonomy and low willingness to participate in demand response. In order to incentivize small and medium-sized customers to participate in demand response, we propose a comprehensive evaluation index and incentive strategy for demand response based on customer directrix load. Firstly, a large number of small and medium-sized customers are integrated with demand response resources through load aggregator to participate in demand response based on customer directrix load. Secondly, a comprehensive evaluation index weighted by response positivity and actual contribution is proposed to measure the effectiveness of small and medium-sized customers’ participation in demand response based on customer directrix load. Thirdly, a punishment factor is set on time scale and customer category to incentivize different types of small and medium-sized customers to participate in demand response based on customer directrix load. Fourthly, small and medium-sized customers subsidy optimization model and load aggregator revenue optimization model are established. Finally, we utilize the demand response pilot data in East China for simulation verification, and the results show that the proposed strategy has good rationality and effectiveness.