Participation In Demand Response Programs Research Articles

Quantifying the operational flexibility and invocation costs of urban regional integrated energy systems for participation in demand response programs

As a typical aggregation platform, the regional integrated energy system (RIES) is required to pre-submit its flexibility for each time period when participating in demand response. However, existing studies fail to quantify the flexibility parameters necessary for RIES pre-reporting. Therefore, this paper introduces a flexibility quantification framework tailored for RIES engagement in demand response. The core principle of the framework is grounded in the RIES day-ahead dispatch model considering virtual energy storage, which transforms the flexibility quantification problem into dispatch optimization problems under baseline scenarios and flexibility invocation scenarios. In the flexibility invocation scenario, a control term is incorporated into the objective function to steer the system towards fulfilling flexibility requirements in the most economical strategy. The deviation of power and operating costs in both scenarios defines the system's flexibility and flexibility invocation cost, respectively. This framework holds broad applicability across various aggregation platforms and can measure flexibility across different time scales by adjusting the control interval of the day-ahead dispatch optimization model. Finally, the framework is validated through case studies, revealing the impact characteristics of system type and capacity on flexibility, and analyzing the sensitivity of flexibility to various uncertainty parameters.

Behavior-aware assessment of demand response participation in power systems using causal artificial intelligence

Demand response (DR) is pivotal in enhancing the operational efficiency and reliability of power systems. Understanding prosumers’ behavior and their participation in DR programs is essential for utilities to design effective DR programs. Typically, prosumers are not concurrently exposed to multiple electricity price plans or engaged in distinct DR programs, which make the available data limited and introduces complexity in assessing their DR participation. Causal artificial intelligence offers a solution by enabling causal intervention and counterfactual analysis, facilitating the estimation of prosumers’ responses to demand-side programs without the necessity of experimental and control groups. This paper introduces a framework that integrates causal learning approaches for causal intervention and counterfactual analyses of prosumers’ DR participation and consumption behavior. The proposed framework incorporates domain knowledge of the power system to enhance model accuracy and performance. Simulation results demonstrate the effectiveness of the proposed framework with causal learning models in estimating prosumers’ DR participation and consumption behavior under different prices or DR programs. This capability allows utilities to gain a profound understanding of their prosumers, offering valuable insights for designing DR programs. Ultimately, this knowledge contributes to the reliable and efficient operation of power systems.

Revolutionizing Demand Response Management: Empowering Consumers through Power Aggregator and Right of Flexibility

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of “acquiring flexibility rights”, wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method’s effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices.

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demands and the timing of their usage. In our view, grid management should leverage on EV scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising, showing the effectiveness of the proposed solution in scheduling the charging and discharging actions for a fleet of 30 EVs to align with the target energy profile in demand response programs, achieving a Pearson coefficient of 0.99. This solution also demonstrates a high degree of adaptability in effectively managing scheduling situations for EVs that involve dynamicity, influenced by various state-of-charge distributions and e-mobility features. Adaptability is achieved solely through learning from data without requiring prior knowledge, configurations, or fine-tuning.

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.

Dynamic reconfiguration of active distribution network based on improved backtracking search algorithm

This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method is validated by an IEEE 30-node simulation distributed system.

Demand Response in Building Microgrids with Variational Quantum Circuit Enabled Hybrid Control Strategy

Participation in demand response programs encourages grid-interactive buildings to reduce energy consumption and promote sustainability by curbing load demand. Quantum computing has the potential to improve demand response, addressing the limitations of traditional techniques. We propose a hybrid control strategy, utilizing variational quantum circuits (VQC) and classical computing, to enhance demand response. The VQC-based technique combines quantum circuits trained through reinforcement learning with classical optimization solvers to optimize energy management in building microgrids. Computational experiments demonstrate that the hybrid strategy outperforms traditional techniques, reducing energy consumption and carbon emissions while maintaining scalability for larger building microgrids. By leveraging the strengths of both quantum and classical computing, the hybrid VQC-based strategy offers a promising approach to improving demand response and promoting energy efficiency in buildings, ultimately contributing to global sustainability efforts.

Data association load uncertainty and risk aversion in electricity markets with data center participation in the demand response

Recently, demand response (DR) programs are being promoted in the power market to strengthen the role played by the load side in the power market and to increase the flexibility of loaders to participate in the power market. Data centers are uniquely positioned to participate as large-scale power users in the power market operation process. However, the issue of load uncertainty risk is not adequately considered in recent DR programs for data center participation in the power market. Regarding this issue, we propose a framework for facilitating bids for data center participation in DR programs in the power market. In the proposed framework, the data association Dirichlet process mixture model (DA-DPMM) is introduced to characterize the load uncertainty, and the association of load data is investigated to obtain higher prediction accuracy. To further reduce the uncertainty risk, we propose a two-stage market cleaning model based on the conditional value at risk (CVaR). We also analyze the CVaR, the social welfare derived from the Gaussian process model (GM), the Dirichlet process mixture model (DPMM), and the DA-DPMM. The results demonstrate the superiority of the DA-DPMM approach in a two-stage market cleaning model based on CVaR.

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.

Elasticity modelling of price-based demand response programs considering customer’s different behavioural patterns

Mathematical modelling of demand response programs (DRPs) aids regulators and policymakers in analysing the advantages of price-elastic loads on distribution system and electricity market. This mathematical model is used to find updated demand after DR for calculation of different technical and economical indices. In accordance with the concept of price elasticity of demand, an economic model of price sensitive load is developed. Price elasticity model (PEM) is a very useful tool to assess customer participation in DRPs. In this work PEM has been attributed on the ground of price elasticity of demand where PEM has been modelled by means of analytical and stochastic elasticity approach. Analytical elasticity has been modelled on the basis of ideal (lossless) approach whereas Stochastic elasticity has been modelled using Ornstein–Uhlenbeck process based on load flexibility and results obtained from these approaches were compared to check the acceptability of DR programs. It is observed that the proposed APEM and SPEM approaches provides maximum peak curtailment of 15% and 10.2% respectively during peak hours. Similarly, it is also noticed that both approaches improve load factor by reducing its peak-to-valley span by 47.33% and 35.7% respectively. The proposed models are investigated on standard IEEE 33-bus distribution system and modified IEEE 33-bus distribution system and are compared with already existing DR models. Technical and economical comparison of proposed approaches on both distribution systems shows that proposed models are competent enough to model customer behaviour properly.

Barriers to demand response in the commercial and industrial sectors – An empirical investigation

Demand response can be an effective mechanism to balance electricity demand and supply. While there is an increasing research interest in demand response for private households, its realistic potential in industrial and commercial sectors remains largely untapped, with limited research on the barriers hindering adoption. This study identifies and examines the barriers in industrial and commercial sectors, shedding light on their significance. We conducted 20 semi-structured interviews with experts from German industrial and commercial companies and national industry associations. The most frequently cited barriers encompass concerns about diminished product quality, disruptions to production processes, human resource management, and revenue uncertainty. Despite some recent industrial participation in demand response programs in Germany, our findings suggest that numerous barriers impede widespread participation, which can partly be explained by the heterogeneity of production processes and facilities. Overcoming these barriers entails bridging knowledge gaps and allocating sufficient resources within an organization. Moreover, adapting external incentives and policies may be necessary to encourage widespread demand response adoption. Recognizing these challenges, alongside the underlying motivations and apprehensions, can guide policy makers in devising strategies to support the adoption of demand response among industrial and commercial consumers.

Economic, Technical, and Environmental Benefits of Occupants’ Thermal Comfort-Based Heat Loads Participation in Demand Response Program

The electrification of heating and transportation systems is one of the objectives of developed countries to minimize CO2 emissions. This objective pushes distribution systems’ (DBs) operators to incorporate numerous high-power loads into low-voltage networks that were not designed for such loads. DBs’ reconfiguration and the loads’ flexible characteristics are two remedies exploited to overcome this issue. Heat pumps (HPs), as the most prevalent loads, can be managed by demand response programs (DRPs) to postpone the costly reconfiguration of distribution systems. HPs’ DRP participation affects indoor air temperature. If this is not accomplished reasonably, the occupants’ thermal comfort (OTC) will be compromised, and it will be impossible to convince them to continue contributing to DRPs. Based on the ASHRAE55 standard and an experimental building electrothermal model, this article presents a novel framework for determining the HPs’ DRPs participation. This framework ensures the OTC and optimizes the HPs’ DRP participation. The modified IEEE 33-bus network is employed as the test system to evaluate the proposed method. The simulation results confirm the usefulness of the proposed strategy to improve the technical and economic aspects of the network.

Eco‐environmental operation of energy hub considering uncertainties of supply/demand‐side resources with introducing a novel optimization algorithm

AbstractIn this paper, the optimal operation of Energy hub (EH) based upon both demand and renewable energy resources (RESs) uncertainties is investigated. Here, an innovative scenario‐based energy hub energy management system (EH‐EMS) is proposed for simultaneous cost and emission reduction considering the abovementioned uncertainties. Moreover, a multi objective hybrid heuristic algorithm, the so‐called hBES‐MOGWO, is introduced for solving the proposed EH‐EMS, which is a combination of bald eagle search (BES) and multi objective gray wolf optimization (MOGWO) algorithms. The proposed EH consists of photovoltaic (PV), wind turbine (WT), boiler, heat storage (HS),combined heat and power (CHP) and multi‐carrier energy storage technology such as power to gas (PtG) technology, electric vehicles and heat storages. Flexible loads are also taken into account for reducing cost and emission by participation in demand response programs (DRPs). The optimization results demonstrate the effectiveness of the proposed hBES‐MOGWO algorithm. The simulation results illustrated that the total cost of EH‐EMS can be effectively reduced by installing energy storage systems and implementing DRPs. The results emphasize that considering uncertainties can lead to increase the operating costs and emission, while flexible loads participation in DRPs and PtG can reduce the aforementioned ones.

Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted.

WITHDRAWN: Short term optimal scheduling of a virtual power plants integrated with electrothermal hybrid energy storage system using an effective adaptive multi-objective differential evolution algorithm

virtual power plants (VPPs) in the power distribution grid play an important role in balancing the power and heat generation and consumption in residential, commercial, and industrial sectors. This study proposes VPPs for risk-averse energy exchange of resources and customers in the presence of the hybrid storage system. An appropriate energy management program is presented to make a balance between generators and consumers in VPPs considering generation uncertainties including electricity demand and price forecasting in the electricity market and renewable energy uncertainties. Hence, monetary exchange methods such as day-ahead agreements are proposed for energy exchange among VPPs. The expanded exchange structure of the suggested framework simplifies the renewable energy's sources to the electricity market despite its small-scale and non-dispatchable features. Also, a novel model is presented for short-term programing of virtual power plant considering technical and economic uncertainties. Given that VPPs can enter the electricity market based on time-of-use (TOU) pricing strategy and participation in demand response (DR) program, minimizing the expected cost of energy generation and environmental pollution is considered as the objective function. A multi-objective efficient model of energy management employing the machine speed scaling mechanism is suggested. An effective adaptive multi-objective differential evolution (AMODE) algorithm is presented to solve the multi-objective optimization problem. AMODE usages a novel Fitness Evaluation Mechanism (FEM) using dynamic reference point and fuzzy association randomness study to evaluate solutions in the evolutionary population by implementing the conditional value at risk (CVaR) method for risk management. The simulation studies and sensitivity analysis results are evaluated to confirm the effectiveness of the proposed model on a VPP in the island and grid-connected modes with and without participation in DR program. Finally, the conceptual results are discussed.

Energy storage system impact on the operation of a demand response aggregator

In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

A genetic algorithm optimization approach for smart energy management of microgrids

Optimal management and planning of microgrids (MG) are the most important goals for operators. In this study, a Multiobjective Genetic Algorithm (MOGA) is applied to the technical and economic problems of the MG. This stochastic programming considers demand response (DR) programs, reactive loads, and uncertainties due to renewable energies. Demand-side management (DSM) is how to manage and schedule the generation and consumption with the objective of cost and greenhouse gases (GHG) emissions minimization. In this work, with the contribution of various customers to demand response programs and reserve schedules, a reduction in the operation cost of the microgrid has resulted. This method facilitates obtaining a complete and comprehensive microgrid model for energy management in the power system, and the results demonstrate that participation in demand response programs and reactive loads can reduce generation, reservation, startup costs, and the amount of pollution. Regarding reservation costs, a 16% reduction was obtained in the presence of the load response, and wind power is a good compromise between cost and pollution among various resources.

Near real-time management of appliances, distributed generation and electric vehicles for demand response participation

Consumer-centric energy management approaches are emerging as a major solution for future power systems. In this context, intelligent home management systems should control different kinds of devices existing in the houses assuring convenient comfort levels and understanding the users’ behaviour. At the same time, the home management systems should be able to interact with other actors such as energy communities, aggregators, and system operators. The main contribution of this work is a new methodology allowing intelligent management, in near real-time (1 minute), of different types of energy resources existing in a smart home. The energy resources include appliances and other loads, micro-generation, and electric vehicles. The proposed system includes a permanent evaluation of the operation state of each energy resource considering their functional model and the behaviour and comfort level defined by the users. Participation in demand response programs reducing the power consumption limits is also considered showing the advantage of the proposed approach. The case study contains two scenarios considering a demand response program of power limitation with 120 minutes duration. To guarantee the participation in these demand response events, the system should evaluate the priority of each device according to its model. A domestic consumer with 45 energy resources (appliances, generation, and electric vehicles) is used for demonstration purposes.

Hybrid Scenario-IGDT-Based Congestion Management Considering Uncertain Demand Response Firms and Wind Farms

Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units.

A Trustworthy Building Energy Management System to Enable Direct IoT Devices’ Participation in Demand Response Programs

Nowadays, internet of things devices are becoming more prominent since they can integrate energy management systems. However, data sharing between end-users and other external entities is a concern that must be addressed to ensure data privacy. Given the above, this paper proposes a trustworthy energy management system for energy communities to enable direct internet of things devices’ participation in demand response programs at the community level. This solution is based on a building energy management system framework that considers signed tokens to maintain end-user data privacy and manage data access. Likewise, this solution can plan future demand response events to balance the consumption and generation, using the internet of things device’s flexibility. The results demonstrated that the planned demand response event for one hour efficiently balanced the energy community energy resources, enabling a reduction of approximately 2.8 kWh. Thus, the proposed solution allows safeguarding data ownership rights while efficiently managing energy resources.