Demand Response Actions Research Articles

A matheuristic for active flexibility management of battery energy storage systems in the context of local flexibility markets

Flexibility has gained notoriety with the advancement of topics such as microgrids, renewable generation, distributed energy resources, and energy storage. Proposals like local flexibility markets (LFMs) demonstrate the growing interest in existing demand-side flexibility and the development of methods to access this resource. This work presents a matheuristic to managing battery energy storage systems (BESS) by controlling flexibility reserves of power and energy independently and parameterized through virtual control parameters. The system executes the processes of reserving flexibility, unlocking it, and utilizing it during day-ahead planning and in real-time operation to meet flexibility actions for increasing and reducing demand within LFMs. The management system's behavior is presented in an application case that demonstrates the system’'s ability to manage different flexibility levels. We present the impact of flexibility management on operating profitability, observing a lower loss of profitability in the allocation of power (kW) capacity and a higher loss of profitability in the energy (kWh) flexibility reserve. The method's effectiveness is presented through a set of proposed flexibility metrics, which allow for representing the available flexibility based on power, energy, and time duration of the parameterized demand response action.

A two-layer and three-stage dynamic demand response game model considering the out of sync response for gases generators

Traditional integrated demand response model lacks evaluation mechanism during the response process, posing a risk of over-response and excessive changes in the original power consumption order. To address this issue, a two-layer and three-stage dynamic demand response game model is proposed on the load side, which considers the internal game of response loads, and the absolute loads time nodes of “peak-flat-valley”are set as the response signals. And the demand response strategy of this phase will be formulated based on the response evaluation results of the previous phase. On the power generation side,an analysis is conducted focusing on the out-of-sync response of gases generators caused by time delay in gas networks. Ensuring a stable energy supply since it serves as a prerequisite for effective demand response actions. Finally, four possible game schemes are proposed considering the priority of demand response loads. The case study demonstrates this proposed demand response strategy can increase original power consumption maintenance order rate to 95.82% and power supply reliability to 98.33%. Besides, the renewable energy curtailment rate and demand response cost are reduced by 4.64% and 51.43%, respectively.

Flexible Load Optimization Operation Method for Integrated Energy Systems in Parks Considering Demand Response

The ongoing advancements in energy technology are progressively unveiling the high energy utilization benefits of comprehensive energy systems (CIES) in parks. Based on the concept of an energy hub, a CIES model including distributed power supply, gas turbine, and flexible loads is constructed by combining the characteristics of flexible loads in the park that can be leveled, shifted, and curtailed. In light of the demand response mechanism, a method for the low-carbon economic optimization operation of CIES has been developed, focusing on minimizing the total cost. This method utilizes the CPLEX solver to process the algorithm. The approach involves a comparative analysis of load distribution before and after engaging in demand response activities. Such analysis is crucial in understanding the influence of the demand fluctuations mechanism and in determining the most efficient method for adjusting flexible loads. This adjustment aims to further reduce the system costs of CIES. The findings indicate that, under the demand response mechanism, the involvement of flexible loads in CIES operations can significantly enhance the system’s economic efficiency.

The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach

Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households’ electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Low-carbon optimal scheduling for multi-source power systems based on source-load matching under active demand response

To reduce the source-load uncertainty and carbon emission levels of the power system, this study proposes a novel low-carbon economic stochastic optimization scheduling model. Firstly, to partially compensate for the instability of wind power (WP) and photovoltaic power (PV) generation, enhance energy efficiency, and reduce system carbon emissions, the introduction of concentrating solar power (CSP) as one of the power sources on the generation side. Secondly, to optimize load characteristics, a peak–valley time period division method based on fuzzy theory and an active demand response model for electric load using the Logistic fuzzy function are introduced on the load side. Thirdly, taking into account the role of the carbon trading market, a ladder-type carbon trading mechanism (LCTM) is introduced to control carbon emissions and reduce overall economic costs. Finally, considering WP-PV output fluctuations, the degree of matching between power output and load, WP-PV utilization, system economic efficiency, and carbon trading factors, with the objective of maximizing the tracking of the load curve and minimizing comprehensive system economic costs, a multi-objective stochastic optimized scheduling model for power systems is established. A multi-objective Runge–Kutta algorithm based on a population-based parallel search mechanism (PSMORUN) is proposed to solve the active demand response model and the low-carbon stochastic optimization scheduling model. Additionally, constraint repair techniques are employed to handle the complex constraints of the models. To verify the effectiveness of the proposed model, a 10-generator power system, including a WP farm, a PV plant, and a CSP plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety, better economy, and lower carbon emissions.

BQL-DRS: A Novel Balanced Q-Learning Based Demand Response System for IoT based Smart Grids

The modernization of electricity networks and the integration of renewable energy resources in Internet of Things (IoT) based smart grids have led to increased variability in market prices, necessitating effective demand response (DR) strategies. To address this challenge, this paper proposes a novel Balanced Q-Learning based Demand Response System (BQL-DRS) that combines both optimistic and pessimistic targets in the Q-learning algorithm to achieve a balanced decision-making process in IoT based smart grids. It optimizes DR actions by efficiently managing consumer demand in real-time, considering IoT data from grid conditions, energy prices, and consumer preferences. The significance of the BQL-DRS lies in its ability to handle dynamic and uncertain IoT based grid environments, enabling it to make informed and cautious decisions while pursuing energy efficiency and cost-effectiveness. By effectively addressing both pessimistic and optimistic scenarios, the BQL-DRS ensures grid stability, load balancing, and substantial cost savings compared to representative models.

Reinforcement learning-based demand response strategy for thermal energy storage air-conditioning system considering room temperature and humidity setpoints

Heating, ventilation, and air-conditioning systems significantly influence demand response (DR) actions. In the literature, many studies have investigated the reduction of sensible loads through DR events by increasing temperature setpoints while satisfying indoor comfort levels. However, less consideration has been given to latent loads. Therefore, proximal policy optimization reinforcement learning (RL) algorithms are adopted to simultaneously control indoor temperature and humidity (T&H) setpoints to reduce sensible and latent loads. An EnergyPlus-Python joint simulation platform was created for the temperature-humidity independent control system. DR strategies based on RL, active thermal energy storage, and time-of-use electricity prices are formulated to find the optimal indoor T&H setpoints, considering environmental constraints, comfort levels, and energy consumption. The results showed that in the optimal experimental strategies implemented based on negative feedback regulation, savings of 29.15 % on electricity consumption were achieved during DR periods and 46.11 % savings on the daily operating costs. Moreover, a simulation strategy based on feed-forward regulation was developed and found to reduce electricity consumption by 6.92 % during DR periods. Compared with a fixed T&H setpoint, the developed strategy meets the preferences of different groups of people for thermal comfort and increases the system's flexibility.

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

AbstractA novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning

Eco-friendly technologies for sustainable energy development require the efficient utilization of energy resources. Real-time pricing (RTP), also known as dynamic pricing, offers advantages over other pricing systems by enabling demand response (DR) actions. However, existing methods for determining and controlling DR have limitations in managing an increasing demand and predicting future pricing. This paper presents a novel approach to address the limitations of existing methods for determining and controlling demand response (DR) in the context of dynamic pricing systems for sustainable energy development. By leveraging actor–critic agent reinforcement learning (RL) techniques, a dynamic pricing DR model is proposed for efficient energy management. The model’s learning framework was trained using DR and real-time pricing data extracted from the Australian Energy Market Operator (AEMO) spanning a period of 17 years. The efficacy of the RL-based dynamic pricing approach was evaluated through two predicting cases: actual-predicted demand and actual-predicted price. Initially, long short-term memory (LSTM) models were employed to predict price and demand, and the results were subsequently enhanced using the deep RL model. Remarkably, the proposed approach achieved an impressive accuracy of 99% for every 30 min future price prediction. The results demonstrated the efficiency of the proposed RL-based model in accurately predicting both demand and price for effective energy management.

Predictive control and coordination for energy community flexibility with electric vehicles, heat pumps and thermal energy storage

Electrification of private transportation and residential heating is a potential action to decrease significantly carbon emissions. However, the lack of coordination of such emerging loads can increase stress on the power distribution grid. To this end, this paper proposes a two-stage energy management strategy, to enhance the flexibility of energy communities. First, an optimization algorithm-based model predictive control (MPC) is developed for home energy management systems (HEMS) to schedule electric vehicles (EVs) for charging and discharging in cooperation with heat pump and thermal energy storages. The objective is to reduce electricity bills and EV battery degradation costs while maintaining thermal demand. Second, the predictive coordination based on the nonlinear AC optimal power flow approach is applied to enhance the grid flexibility under a community energy management system scheme. To validate the performance of the proposed method, we develop a co-simulation framework by implementing the optimization algorithm and simulation scenarios in a Python Environment and modelling the electricity grid in the PowerFactory-DIgSILENT, which are linked together. Extensive numerical simulations are carried out under the active demand response program-based hourly electricity pricing and different weather data in Sweden. The results show that the proposed method can reduce operating costs and enhance the energy flexibility of the energy community. Moreover, it can decrease the stress on the community power grid and prevent load-shedding events compared to the non-predictive coordinated method.

Real-Time Pricing-Enabled Demand Response Using Long Short-Time Memory Deep Learning

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply–demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual–predicted demand and actual–predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.

A deterministic bounding algorithm vs. a hybrid meta-heuristic to deal with a bilevel mixed-integer nonlinear optimization model for electricity dynamic pricing

In the electricity retail market, the retailer company aims to determine the optimal time-of-use (ToU) prices to maximize profits resulting from buying energy in organized (long-term, day-ahead, balancing) markets and selling it to consumers. Therefore, the retailer should take into account the consumer’s demand response actions to minimize the electricity bill in face of time varying prices. In this paper, this problem is formulated as a bilevel mixed-integer nonlinear programming model in which the retailer is the leader and the consumer is the follower. The consumer’s problem encompasses the integrated optimization of all home energy resources, considering re-scheduling appliance operation, charging/discharging of electric vehicle and stationary batteries, local microgeneration and (buying and selling) exchanges with the grid. The accurate physical modelling of appliance operation to generate effective load scheduling solutions imposes a high computational burden. Two algorithms are proposed to address this problem: a deterministic bounding algorithm (DBA) using an optimal-value-function approach for bilevel optimization, and a hybrid meta-heuristic using a particle swarm optimization algorithm to tackle the upper-level problem that calls an exact mixed-integer linear programming solver to deal with the lower-level problem. In the framework of the DBA, three different techniques were implemented to deal with the nonlinearities arising from the products of integer and continuous variables (bilinear terms): 1) solving the (non-convex) subproblems of DBA using a mixed-integer nonlinear solver, 2) using the McCormick envelopes to approximate the bilinear terms by linear ones, and 3) expressing the integer variables by binary ones and linearizing the bilinear terms using an exact form. Computational experiments are presented and discussed for real data settings of the problem under study considering a computational budget to compare the different algorithms and techniques employed. The results showed that the DBA with the approximate linearization technique (2) leads to the best solutions.

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

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

Residential Demand Response Potential Detection: Model Mechanism and Application Analysis

Demand response potential detection is essential to promote the effect of demand response activities and further enhance the operational stability of the power grid. In this article, we establish a demand response potential detection model including estimating the loads of main network and residential customers based on similar date matching, where a mathematical program is formulated to determine the parameters in the formula of date distance, and measuring the similarity between the main network and customers during the peak and valley period, where a measure of matching degree is put forward innovatively. Customer profiles are then established based on load profile pattern, electricity consumption quantity and demand response willingness. In addition, case studies based on smart meter data of residential customers in Yuhang District, Hangzhou, Zhejiang, China, validate the effectiveness of our model. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Occupant preferences on the interaction with human-centered control systems in school buildings

Human-centered control systems in buildings have gained increasing attention in recent years as they foster the integration of user preferences in the decision-making control algorithm. However, such advanced systems are built upon the assumption that occupants are willing to interact as data providers. Introducing technologies reliant on feedback from the users, without initially analysing how users accept them, can lead to impeding the anticipated operation of such systems. Hence, this rare interdisciplinary research presents a nexus of technical and social sciences, that addresses the occupant-related misconceptions regarding the interaction with advanced control systems. This study identifies the occupant preferences and provides a method to predict the willingness of occupants to interact with advanced control for indoor environment control and demand-response actions. Additionally, the proposed approach enables to identify the main drivers and motivators of that interaction. This study is built upon the behavioural science theory called the Theory of Planned Behaviour and 435 school building occupants participated in the study. The findings from this pertinent research show that many common concepts in the building control domain do not comply with the occupants’ viewpoint and need to be reconsidered. For instance, in the post-pandemic era, the most important comfort aspect according to the occupants is the indoor air quality compared to thermal and visual comfort. Furthermore, although higher automation levels are desired, control access through advanced technologies, such as human-centered control, is not easily accepted by occupants. Hence, their willingness for interaction should be predicted instead of assuming that occupants will share data with advanced control systems. The proposed model in this research has predictive potential and yielded an explained variance of 58%, showing that the target occupants of the building are willing to interact with the controller. These findings are important to researchers and practitioners who design and develop human-centered control systems to foresee the engagement level of occupants in indoor environment control or demand-response actions.

Combined effort opportunities of aggregated demand response flexibility and energy savings in households

This paper analyses possible synergies between demand response flexibility programmes and energy savings delivered by households. In the framework of the energy transition, European Union (EU) directives are endorsing energy consumers to become full-fledged participants of the energy market, mostly via independent aggregator intermediaries. The flexibility aggregators have a very arduous role in collecting, optimising and settling aggregated flexibility delivered from heterogenous sources on the energy market. Novel business models incorporating both flexibility and energy savings opportunities from households’ consumers could deliver revenue diversification for flexibility aggregators and support them in overcoming technical and motivational challenges for activating consumers in the energy market. This paper discusses the main pillars for a sustainable flexibility aggregator business model which sums up the potential for flexibility placement on energy, ancillary services and energy savings markets. The main challenge identified in this work are the requirements for programme establishment, allowing the recognition and proper interpretation of energy savings triggered by short-term events and obtained by an aggregator via explicit demand response actions. This paper proposes possible solutions for a joint venture of a flexibility and energy savings aggregator, thus alleviating possible data collection problems. Collaborative efforts have been recognised in the establishment and maintenance of information and communication technologies and infrastructure, therefore facilitating continuous monitoring and verification of flexibility programmes which are able to deliver energy savings.

Optimal Dispatch of Multiple Photovoltaic Integrated 5G Base Stations for Active Distribution Network Demand Response

Multiple 5G base stations (BSs) equipped with distributed photovoltaic (PV) generation devices and energy storage (ES) units participate in active distribution network (ADN) demand response (DR), which is expected to be the best way to reduce the energy cost of 5G BSs and provide flexibility resources for the ADN. However, the standalone PV-integrated 5G BS has the characteristics of wide distribution, small volume, and large load fluctuations, which will bring strong uncertainty to the ADN by directly participating in the DR. Therefore, a system architecture for multiple PV-integrated 5G BSs to participate in the DR is proposed, where an energy aggregator is introduced to effectively aggregate the PV energy and ES resources of 5G BSs. Then, a two-stage optimal dispatch method is proposed. Specifically, in the large-timescale DR planning stage, an incentive mechanism for multiple PV-integrated 5G BSs to participate in the DR is constructed based on the contract theory, which ensures that multiple 5G BSs respond to and satisfy the peak-shaving demand of the ADN. In the small-timescale online energy optimization stage, based on the energy sharing mode among 5G BSs, a Lyapunov-based online energy optimization algorithm is proposed to optimize the shared energy flow between the internal layer and the interactive layer of 5G BSs, which further improves PV absorption and ensures operation stability of ES in the 5G BS. Simulation results show that the proposed two-stage optimal dispatch method can effectively encourage multiple 5G BSs to participate in DR and achieve the win–win effect of assisting the ADN peak-shaving and low-carbon economic operation of 5G BSs.

Electricity Consumption Behaviors and Clustering of Distribution Grids in Terms of Demand Response

Knowing of energy consumption behaviors and similarities between consumers becomes extremely important for electric power systems operators to apply green energy trends by ensuring the stability and flexibility of the grid. Especially in the modern power systems and the energy markets, consumer-based market structures must be implemented. Consumer types, energy needs, and demand behaviors of the distribution grid can affect cumulatively the demand behavior of the full electricity system. Thus, understanding the consumption behaviors and similarities among different distribution grid operator can provide a significant advantage in determining the types and applications of demand response activities in terms of the transmission grid. In this study, the consumption of distribution grids analyzes statistically according to the consumer types (like residential, commercial, industrial), and to identify the consumption similarities making clustering analysis. For this reason, first, the total consumption reflected on the transmission grid is examined periodically concerning the distribution grids. Second, we group distribution grids by consumption similarity using past real consumption data using a MATLAB application developed. A data-driven regional demand aggregation approach is shown together with the cluster analysis. Finally, the demand response potential got from distribution loads has been evaluated.

Forecasting an electricity demand threshold to proactively trigger cost saving demand response actions

This paper presents a novel methodology that empowers virtually any electricity consumer paying for peak demand charges to proactively execute demand response actions even without receiving signals or information coming from the utility, and only when necessary to effectively reduce demand charges and user inconvenience. The proposed methodology employs different arithmetic models and tree-based machine learning models to determine an efficient electricity demand threshold value before the start of a billing period. This methodology is completely model agnostic so additional models can be integrated without changing the proposed process. The threshold value produced can be used to proactively trigger peak demand shaving and other demand response actions in order to reduce demand charges. The results obtained using real data showed that regression random decision forest based models outperformed different arithmetic models and other tree-based machine learning models at determining this threshold value for an industrial, an educational with solar photovoltaic electricity generation, and a residential consumer. The results also showed that the consumers evaluated could potentially achieve between 63% to 75% of total potential demand charge reductions during a year. These results translate to US$ 159.00, US$ 23,290.00, and US$ 107,389.00 in savings for the residential, industrial, and educational consumer respectively.

An Environment Friendly Energy-Saving Dispatch Using Mixed Integer Linear Programming Relaxation in the Smart Grid with Renewable Energy Sources

Electrical energy demand has risen over the world which results in developmentof smart grid. Smart grid has identified the areas that requiresimprovement. Because of the focus on cost-effective operation as well asenvironmental concerns in the electrical power system, the complexity ofthe optimization function has increased. In this study, a new energy-saving dispatch model is developed, which takes into account renewable energysources in the smart grid as well as dynamic generation-load interaction.Moreover, the active demand response idea is employed for interruptibleloads during peak demand. During off-peak load periods and compensatingloads, the proposed energy-saving dispatch system operates on a bi-leveldispatch system. Lower level dispatch works with four dispatch functionssuch as interruptible load cost, compensation load cost, renewable energysource cost, and emission saving cost. Whereas, upper level dispatch dealswith cost functions for operation and emission. Renewable energy sources arerepresented as a generating unit as well as a load based on usage in this work.Linear programming relaxation and mixed integer linear programming relaxationmethodologies are used to solve the constrained optimization problem.The outcomes of the experiments were compared with existing methodologiessuch as the classic NSGA-II and the improved NSGA-II. Furthermore,the algorithm’s time complexity was examined. The proposed solution hasbeen implemented using the IEEE-30 bus standard. The performance resultsdemonstrate considerable reductions in operating costs as well as reductionsin emissions.