Demand Response Programs Research Articles

Quantifying residential energy flexibility potential for demand response programs using observational data from grid outages: Evidence from Pakistan

Over the past few years, incentive- or price-based demand response programs targeting residential energy demand have become increasingly popular in high-income countries. Such programs aim to shift energy demand from times when there is limited supply to times of abundance, in order to ensure stable grid operation. However, despite their cost-effectiveness, these schemes have gained little traction in low-income countries, where the amount of residential flexibility cannot be accurately quantified due to a lack of existing demand response studies, and grid operators continue to rely on the blunt instrument of grid outages to manage demand. In this paper, we propose a framework to quantify the flexibility using existing grid outages. Applied to high-resolution sub-metered data from 42 households in Lahore (Pakistan), we show that the proposed method is able to quantify available flexibility at the residential or feeder-level in an automated manner. The proposed method can also be used to evaluate the efficacy of grid outages as a strategy to shift or curtail demand, which can in turn be used to design better demand response schemes. Our results provide evidence that load-shedding causes significant rebound peaks due to shifted demand, while overall energy demand is actually increased rather than curtailed due to the mitigation strategies adopted by households. The paper concludes with the most important policy implications, especially with the rapid proliferation of distributed solar PV systems.

Design and implementation of smart home energy management system for Indian residential sector

Residential power consumption has surged with the widespread adoption of smart appliances, prompts utilities to implement Demand Response(DR) programs. Home Energy Management Systems (HEMS) plays a vital role in these initiatives within the smart grid infrastructure. The rapid advancement of wireless communication technologies, especially the Internet of Things (IoT), will enable successful architectural designs for HEMS. This study proposes an incentive-based DR implementation using IoT-enabled HEMS for the Indian residential sector that represents 25% of the total power demand. The proposed HEMS automates the management of categorized loads during peak hours through load shifting and scheduling. The proposed algorithm is deployed in real-time residential setup using IoT modules. Emphasizing sustainability and consumer affordability, the work integrates a Photo-Voltaic (PV) based Battery Storage System (BSS) to support critical loads during peak hours. Consumer load preferences obtained through a survey are incorporated into the deployment of the proposed HEMS. Experimental studies demonstrated significant energy savings of 49% with the loads under consideration and substantial cost savings like approximately 49% with Load Management (LM) alone, 53% with the inclusion of Time of Day (ToD) tariff, and an impressive 78% with the proposed incentives for participating in DR. Additionally, this study highlights the synergistic savings realized through the combined implementation of the proposed LM strategy and ToD tariff in India.

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Deep reinforcement learning based dynamic pricing for demand response considering market and supply constraints

This paper presents a Reinforcement Learning (RL) approach to a price-based Demand Response (DR) program. The proposed framework manages a dynamic pricing scheme considering constraints from the supply and market side. Under these constraints, a DR Aggregator (DRA) is designed that takes advantage of a price generator function to establish a desirable power capacity through a coordination loop. Subsequently, a multi-agent system is suggested to exploit the flexibility potential of the residential sector to modify consumption patterns utilizing the relevant price policy. Specifically, electrical space heaters as flexible loads are employed to cope with the created policy by reducing energy costs while maintaining customers' comfort preferences. In addition, the developed mechanism is capable of dealing with deviations from the optimal consumption plan determined by residential agents at the beginning of the day. The DRA applies an RL method to handle such occurrences while maximizing its profits by adjusting the parameters of the price generator function at each iteration. A comparative study is also carried out for the proposed price-based DR and the RL-based DRA. The results demonstrate the efficiency of the suggested DR program to offer a power capacity that can maximize the profit of the aggregator and meet the needs of residential agents while preserving the constraints of the system.

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

Planning for a network system with renewable resources and battery energy storage, focused on enhancing resilience

The growing significance of network resilience underscores the importance of research in integrating Renewable Energy Resources (RESs) and battery energy storage Systems (BESS) with electrical networks. This paper presents a real-time simulation for systematically integrating renewable energy sources (RESs) and battery energy storage systems (BESS) in electrical networks, focusing on resilience metrics that involve a multi-objective optimization approach that considers the relative battery capacity to the total system cost. In addition, the Demand Response Program (DRP) is considered an additional layer to manage seven different loads that provide a comprehensive and accurate representation of energy consumption during 24 h. The scheduling model, which incorporates the IDR strategy, reduces the total load of data center performance by 40 %. Additionally, optimizing battery capacity can lead to an 89 % reduction in total costs, representing a substantial success in enhancing the economic resilience metric. This study has been conducted on the IEEE 24-bus RTN to assess the required investment for deploying BESS and to gauge its effectiveness in enhancing the resilience of the electrical network. The results confirm the effectiveness of the proposed model for enhancing the economic and performance metrics of resilience.

Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and IGDT-based approach

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.

Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

AbstractThe transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

A decentralized framework for economic‐resilient operation of networked microgrids, considering demand response program

AbstractThe occurrence of natural disasters can disrupt the normal functioning of distribution networks. In this situation, the activeness of distribution networks in the sense of benefiting from energy sources and related structures such as microgrids (MGs) makes them benefit from the capability of structures such as MGs, in case of High‐Impact Low‐Probability (HILP) events, so that consumers are able to disconnect from main grid and trade with other MGs. Thus, the energy management of networked MGs needs a new operation structure to encourage them providing each other with the required energy by creating the necessary incentives. This paper presents a novel cooperative framework aimed at enhancing the resilience of MGs through the establishment of peer‐to‐peer (P2P) energy trading among their constituents. In the present model, MGs engage in energy transactions with one another, aiming to minimize costs through the cooperative operation of networked MGs. The utilization of the suggested model enables MGs to enhance network resilience within the framework of peer‐to‐peer energy transactions, while also achieving cost reductions in comparison to islanding and non‐cooperative operation. The numerical results obtained demonstrate the efficacy of the proposed methodology in enhancing the resilience of MGs and mitigating their operational expenses.

Techno-economic investigation of grid integrated renewable energy resources with hydrogen storage systems

Fundamentally, a system is inherently connected to the presence of uncertainty, which consequently leads to the development of uncertain design and scheduling. In the contemporary power system, the prevailing conditions can primarily be attributed to the uncertain efficiency of several variables, such as pricing. Consequently, the utilization of uncertainty modeling turns into imperative. This study utilizes an optimization technique that employs a hybrid whale optimization algorithm and pattern search (HWOA-PS) to achieve the optimum efficiency in the smart parking of electric vehicles under uncertain conditions arising from fluctuations in the pricing of the main grid within the demand response program (DRP). The proposed method can effectively reduce daily costs by shifting the load between peak and light-load conditions. The suggested scheme has several features such as a non-dominated arrangement model, variable discovery, memory-based approach assortment and fuzzy theory to select the greatest Pareto. In addition to all the advantages mentioned above, the proposed algorithm has a high response speed in achieving the final and high possibility to reach the global point. There exist several important limitations for Hydrogen Storage Systems (HSS) that needs to be considered in modeling. The most important limitations include the restrictions of the electrolyzer and the boundaries of Fuel cell (FC) as well as a storage tank. The performance of the suggested algorithm is confirmed in a system with parking and several resources under uncertainty. The results confirm that the proposed method has a great ability to cope with uncertainty. Hence, there has been a reduction of as much as 41 % in the expense of SPL variation. Conversely, by taking into account the DPR, the mean expense of the SPL increases by 4.92 %, resulting in a corresponding decrease of 47.01 % in the variation of SPL costs. When evaluating the impact of DPR, it is seen that an increase in the mean expense of SPL leads to a decrease in its overall magnitude.

Chance-Constrained optimal scheduling of power and hydrogen systems in power distribution network with significant photovoltaic and carbon offsetting strategies

The urgent need to address climate issues increases the significance of sustainable strategies in power networks. This study focuses on the optimal operation of a hydrogen-based microgrid incorporated with photovoltaics (PVs), time-of-use demand response (TOU-DR) program, distributed generation (DG) units, hydrogen fuel stations (HFSs), and battery storage units. Employing a multi-objective approach, the study aims to minimize emissions and system operation costs, being validated by implementing it on the IEEE 33-bus test network. To address the carbon emissions issue, the Power-to-gas (P-to-G) facility is utilized to capture and recycle CO2 pollutions. The implementation of a carbon trading market mechanism is also adopted to further enhance the generation capacity of the PV units and facilitate the achievement of sustainable development goals. Besides, the presence of uncertainties in renewable generation, demand, operational characteristics of hydrogen vehicles, and market prices pose challenges for the system operator, which is addressed by employing the chance constraint programming (CCP) method. Results show that using the P-to-G system has led to a 10 % emissions reduction. However, this is accompanied by a corresponding 25 % increase in operational costs. Additionally, when operating under the CCP constraints, the system's storage level has been increased by 10 % to ensure optimal performance.

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.

A cluster-based appliance-level-of-use demand response program design

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers’ financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid’s low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers’ distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms.

A two-stage risk-based framework for dynamic configuration of a renewable-based distribution system considering demand response programs and hydrogen storage systems

Distribution feeder reconfiguration (DFR) in distribution systems can enhance operating conditions by reducing losses and improving voltage indices. This paper introduces a dual-stage risk-based framework that concurrently tackles day-ahead reconfiguration and microgrid scheduling within the distribution network. To control the negative aspects of uncertainties, the proposed framework integrates energy storage systems (ESS/HSS) and demand response programs (DRPs) at the network level, enhancing adaptability. The initial stage of the proposed model employs the AC power flow model, utilizing loss and voltage deviation functions as objective benchmarks for network reconfiguration. The scheduling is meticulously executed per interval, deriving optimized structures under diverse scenarios with the aid of a case reduction technique (CRT) to streamline solutions. The ultimate solution employs the grey wolf optimization (GWO) algorithm and CPLEX solver in the first and second stages respectively. Outcomes from applying this approach to the adjusted 118-bus network manifest improved operational conditions and voltage quality through reconfiguration. Impressively, the integration of ESS/HSS and DRPs yields a substantial 22.34% reduction in total operating costs, a conclusion substantiated by the numerical findings. Furthermore, by leveraging the CRT, a remarkable 56.17% reduction in problem-solution time is achieved.

To Enhance the Operational Planning of an Independent Microgrid Using a Novel Combination of Demand Response Programs

To Enhance the Operational Planning of an Independent Microgrid Using a Novel Combination of Demand Response Programs

On the Impact of Flexibility on Demand-Side Management: Understanding the Need for Consumer-Oriented Demand Response Programs

While demand response programs inherently depend on consumer acceptance in order to be successful, consumer behavior is often overlooked when designing such programs. This paper addresses the impact of consumer flexibility in terms of appliance use on the success of a demand response program, measured through the overall grid stability assessed by the demand’s peak-to-average ratio. We employ a bootstrapping approach to simulate energy communities from real-life consumer data and implement a state-of-the-art demand response system. Results suggest that higher consumer flexibility under real-time energy tariffs implies higher degrees of grid stability, with real-time pricing decreasing the average peak-to-average ratio by 4.6% compared to time-of-use tariff and with highly flexible consumers showing a 23% lower peak-to-average ratio than regular consumers on average. Yet, it is possible for higher flexibility to be detrimental to grid stability by increasing the peak-to-average ratio under a time-of-use tariff. This result highlights the importance of understanding the interplay between different factors that influence energy consumer behavior, a research stream that has been under investigated thus far.

A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs.

Optimal demand management in the electrical microgrid considering energy market prices uncertainty

The optimal management of electrical energy systems is a major concern for energy providers facing uncertainties. In this work, energy demand management and dispatch in the electricity microgrid are performed using an interval optimization strategy under conditions of electricity price uncertainty. Demand dispatch is implemented through the Demand Response Program (DRP) incentive modeling. As a solution, incentive modeling based on the price of the offer to reduce electricity demand during peak periods is proposed. In order to minimize the effects of electricity price uncertainty, the interval optimization approach is used. The main goal of the proposed approach is to minimize the operational cost, and the epsilon constraint-based fuzzy method was used to solve and handle the problem. Finally, the power microgrid was used in different case studies for the results of the numeric simulations and for the positive effects of the proposed modeling under conditions of uncertainty.

Cryptocurrency mining as a novel virtual energy storage system in islanded and grid-connected microgrids

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Bin modelling approach to cluster control the EVs for implementing demand response program

Bin modelling approach to cluster control the EVs for implementing demand response program