Flexible Demand Response Research Articles

Direct Load Control Strategy of Centralized Chiller Plants for Emergency Demand Response: A Field Experiment

As the penetration rate of renewable energy in the power grid increases, the imbalance between power supply and demand has become one of the key issues. Buildings and their heating, ventilation, and air conditioning (HVAC) systems are considered excellent flexible demand response (DR) resources that can reduce peak loads to alleviate operational pressures on the power grid. Centralized chiller plants are regarded as flexible resources with large capacity and rapid adjustability. The direct load control of chiller plants can respond to the power grid within minutes, making them highly suitable for participation in emergency DR. However, existing studies are generally based on simulations and lack experimental research in actual large-scale buildings to demonstrate the effectiveness of this method and provide related lessons learned. This study conducted field experiments on a centralized chiller plant within an industrial building in Guangdong, China. The results indicate that the strategy of shutting down chiller plants is an effective DR measure. It can complete the load reduction process within 15 min, rapidly decreasing the system power by 380~459 kW, with a maximum duration of up to 50 min, without significantly affecting the thermal comfort of indoor occupants. Additionally, the impact of existing control logic on the participation of chiller plants in the DR process is also discussed.

Passivity‐based event‐triggered frequency control in power system using dynamic pricing

AbstractThe integration of renewable energy resources in modern power systems promotes flexible demand response but poses challenges to power balance and frequency stability due to their intermittent generation. To address this problem, this study deals with the frequency control of power system in the presence of demand responses. The dynamic electricity pricing scheme enabling both demand response participants and suppliers to contribute to the frequency regulation via their own decision‐making process is proposed. The main concern in the controller design is the integration of physical and human systems on the same timescale, encompassing controllers based on frequency dynamics and dynamic pricing. Therefore, event‐triggered conditions are proposed to decrease the communication frequency while ensuring system stability, leveraging the passivity property of the system. Under the proposed event‐triggered conditions, the authors clearly demonstrate the asymptotic stability around the equilibrium point of the entire system. Furthermore, a numerical simulation using a four areas power network system is performed, confirming the effectiveness of the proposed control scheme and the stability of the system.

Power-controllable variable refrigerant flow system with flexibility value for demand response

With the increase of renewable energy sources in the power supply, the issues of balancing power supply and demand are becoming severe. Demand response, by altering the demand-side load state, has been proven as a good method to solve the supply and demand balance problem. Variable refrigerant flow system has great potential in demand-side loads and is poised to become the mainstay of demand response. However, the traditional temperature or compressor state control of variable refrigerant flow systems does not allow for better power regulation for the power system. To address this problem, this paper proposes a power-controllable variable refrigerant flow system with demand response flexibility value for power regulation. The demand response flexibility value is designed as a subjective indicator to satisfy the heterogeneous customer comfort requirement. The predictive mean vote as an objective indicator of customer comfort coordinates with the demand response flexibility value. A fuzzy control strategy for variable refrigerant flow systems is designed to balance power system requirements and customer comfort requirements. The experiment result demonstrates that the power-controllable variable refrigerant flow system can provide power regulation service to the power system with a maximum power regulation deviation of 3.67%. By guaranteeing consumer comfort, the higher the flexibility value, the shorter the consumer’s participation time in demand response. The demand response flexibility values can provide regulation configuration for demand response in practical engineering.

Optimal control strategy for building HVAC systems: Satisfying flexible demand response with different value-based selection

Growing renewable energy integration leads to significant fluctuations in electricity generation. To address this challenge, market-based mechanisms for dynamic carbon emission factors and electricity prices are emerging, particularly in regions like China pursuing carbon neutrality. Buildings, with HVAC systems as the largest consumers, require flexible Demand Response (DR) strategies to balance these fluctuations while maintaining occupant comfort. This paper proposes a hierarchical control framework for building HVAC systems. It optimizes operation for various objectives (minimizing energy consumption, reducing electricity costs, and lowering carbon emissions), while ensuring thermal comfort. The framework involves three modules. First is the time-based demand allocation, which uses genetic algorithms to optimize energy use based on future cooling demands, indoor temperature, and fluctuating electricity and carbon prices. It provides building-level temperature setpoints and electricity consumption plans. Second is the spatial indoor demand adjustment, which balances overall cooling load and temperature satisfaction across individual spaces, taking into account room-specific costs and difficulty confidents for temperature adjustments. This module assigns future temperature setpoints to each space. Third is HVAC system follow-up control, which Implements the optimized setpoints for real-time control of the HVAC system. The framework’s effectiveness is verified through application to a real commercial building project. Compared to a baseline scenario, the optimal electricity cost strategy achieved a 6.5% cost reduction, and the optimal carbon emission reduction strategy achieved an 8.2% reduction. Real-world control based on carbon emission optimization resulted in an actual year-on-year carbon emission reduction of 8.7%.

Robust optimization of community energy sharing considering source-load uncertainty and demand response

Community energy consumption is a crucial aspect of the overall societal energy consumption landscape. The allocation rate of distributed photovoltaic (PV) systems within communities is steadily increasing. However, managing and optimizing the consumption of PV resources while mitigating the impact of their inherent randomness and volatility, along with minimizing electricity costs, presents a significant challenge. This paper proposes a mechanism for community energy sharing that utilizes rooftop PV systems, energy storage systems, and bi-directional electric vehicles. To achieve the goal of finding the minimum cost of electricity in the worst scheduling scenarios, a two-stage robust optimization model is established. This model considers the two-sided uncertainty of source and load as well as flexible load demand response. The simulation outcomes prove the proposed method's efficacy in substantially mitigating residential electricity costs and enhancing PV utilization. Notably, during peak summer demand, a substantial 24.78% reduction in electricity costs was achieved, while PV utilization witnessed a significant 16.52% increase.

Virtual Power Plant Scheduling Model Considering the Power Consumption Satisfaction of Users with Different Flexible Loads

Abstract Aiming at the problem that customer satisfaction with electricity consumption will affect the implementation effect of demand response. This paper proposes a virtual power plant scheduling model considering the satisfaction of users with different flexible loads. Firstly, the flexible load is divided into three categories: reducible load, transferable load and substitutable load. By analyzing the factors that affect the satisfaction of electricity consumption, the satisfaction models of customers with different flexible loads are constructed respectively. Secondly, a virtual power plant scheduling model considering different flexible load demand response models is established. Finally, the scheduling model is simulated and solved based on MATLAB, and the validity of the proposed virtual power plant scheduling model considering the satisfaction of users with different flexible loads is verified.

Research on distribution–microgrid-coupled network demand response based on a multi-time scale

Under the background of “dual carbon” strategy, the integration of renewable energy adds volatility to the grid. Relying solely on generation-side resources for regulation is inadequate, necessitating a flexible demand response from diverse demandside resources. This paper employs a physical connection and information exchange between the distribution network and microgrids to leverage the advantages of centralized-distributed optimization. This establishes a coordinated demand response model between the distribution network and microgrids, gradually establishing a new type of distribution network that integrates interconnected grids and microgrids. This also necessitates the analysis of the response characteristics of various load resources within microgrids and the categorization and modeling of loads based on their response speeds. Additionally, a method for evaluating the multi-time scale schedulable capacity of microgrids is proposed. Finally, a coordinated demand response model between the distribution network and microgrids based on the schedulable capacity assessment is established. This model is validated through case studies, demonstrating its effectiveness. The coordinated demand response between distribution networks and microgrids enables them to operate in a collaborative and economically safe manner.

Flexible resource demand response scheduling strategy under 5G-V2X

Electric vehicles (EV) and 5 G base stations, functioning as flexible load resources, contribute significantly to the provision of ample reserve capacity for the power grid. The emergence of 5 G Vehicle-to-Everything (V2X) technology has fostered a coupling between EV and base stations, further enhancing their roles within the power grid. Building upon this foundation, this paper proposes a scheduling strategy for demand response in flexibility resources under the 5 G-V2X framework. Initially, the dispatchable load of EV is examined in conjunction with the traffic network. Subsequently, we consider the communication power consumption resulting from the connected vehicle model, enabling us to devise path planning and charging navigation strategies for EV. Lastly, we establish a scheduling decision model that aims to minimize both the operation costs of base stations and the variance in grid load. The simulation is validated using real-world road conditions in the primary urban area of a city. The findings illustrate that the proposed dispatching strategy not only achieves a mutually beneficial outcome for EV users and aggregators, without disrupting EV charging and discharging processes, but also enhances the capability of demand response, thereby ensuring reliable grid operation.

Coordinated Operation Strategy for Equitable Aggregation in Virtual Power Plant Clusters with Electric Heat Demand Response Considered

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of “source-load-storage” energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost-efficient operation strategy is proposed for VPP clusters. Initially, the influence of real-time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster-wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end-user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground-source heat pumps to restrict power output, thus achieving economical and low-carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario.

Optimal sizing and placement of battery energy storage system for maximum variable renewable energy penetration considering demand response flexibility: A case in Lombok power system, Indonesia

Indonesia is a tropical climate country with considerable renewable electrical energy source prospects, including photovoltaic (PV) and wind energies. Nevertheless, several variable renewable energy sources (VREs) have exhibited uncertain attributes and substantial reliance on natural conditions, leading to unstable load-related power supply risks. Hence, integrating battery energy storage systems (BESSs) with VRE generators is a dependable approach to bolster renewable energy generator applications on a large-scale grid while providing load demand flexibility. This study determined adequate sizing and placement of the BESS to achieve maximum VRE generator penetration while considering the demand response flexibility. Key indicators, including technical minimum load and system ramp capacity, were identified to achieve maximum penetration of thermal generators. This study also combined a unit commitment procedure with direct current optimal power flow (DC-OPF) as a novel approach to determine the maximum VRE penetration level. An optimization problem model concerning mixed integer linear programming (MILP) was subsequently employed in this study using the CPLEX solver in the general algebraic modeling system (GAMS). The study is based on the IEEE RTS-24 system modified and a real-life case study of the Lombok energy system in Indonesia. Results from the simulated Lombok power system highlighted that optimal sizing and placement of the BESS could lower system costs by 37.66%, 33.63%, and 22.26% compared to the current system conditions during the weekday, weekend, and the lowest day scenarios, respectively. The VRE penetrations of the generators were also higher than the current system conditions by 83%, 51%, and 39% during the weekday, weekend, and the lowest day scenarios, respectively.

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

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

Intelligent energy management of Microgrids with flexible demand response

This paper deals with the application of a mixed integer distributed ant colony optimization (MIDACO) algorithm as an energy management system for a microgrid represented by real time values and characteristics. The microgrid consisting of numerous sources of renewable energy such as solar photovoltaics, small hydro power plant and battery storage is also able to manage demand response in an intelligent manner so as to reduce the dependency on the utility grid during high peak conditions and also export power to the grid during lower load conditions.

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

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

A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning

Reducing carbon emissions has been a focus problem with the rapidly increasing building energy consumption. One solution is adopting more Renewable Energy Resources (RESs) for building energy supply. To overcome the intermittence of RESs, researchers paid efforts in flexible demand response based on centralized operation and model-based control, however, which get challenges for scalability and uncertain dynamic building systems. Moreover, few works have considered user willingness as an important part of human–machine interaction and user satisfaction. Thus, we propose a novel operation method called DC-RL for renewable building energy systems. DC-RL designs a distributed DC energy system, which is scalable, control-friendly, and provides users the willingness option for flexible operation. For energy control, DC-RL adopts a model-free deep reinforcement learning (DRL) algorithm Soft-Actor-Critic (SAC) to adjust demand to matching renewable supply with maintaining user satisfaction. We evaluate DC-RL on two real-life datasets. Compared to baselines, DC-RL improves energy saving and PV self-consumption by 38% and user satisfaction by 9%. DC-RL achieves near-zero-carbon buildings with 93% self-sufficiency rate and reduces up to 33% of battery dependency.

Graph Deep-Learning-Based Retail Dynamic Pricing for Demand Response

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily en-ergy consumption behaviors. It helps manage flexible de-mand response resources on peak load. However, it is insuf-ficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mecha-nism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers energy consumption behaviors under different prices. Then, to avoid unfair eval-uation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumers time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superior-ity of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

Utilization of Distinct HVAC Operation Modes to Improve Demand Response Flexibility in the Pharmaceutical Industry and Economic Analysis for Optimization by HOMER Software

Abstract In the pharmaceutical industry (PMI), the major portion of energy is consumed in heat ventilation air conditioning (HVAC) system; therefore, building energy management systems (BEMS) primarily focus on optimizing the energy consumption in HVAC systems. The two operation modes of HVAC, function mode (FM) and non-function mode (NFM), are descriptively explained with their role in improving the flexibility of demanded energy. Both modes are also exposed to hybrid optimization of multiple electric renewables (HOMER) software analysis from an economic perspective. Concerning net present cost (NPC) and cost of energy (COE) constraints, the FM/NFM of HVAC is preferable to the FM. This paper recognizes a comparative evaluation of several demand response (DR) alliances to deliver a comprehensive image of the suitability of DR alliances for different PMIs. Further, the paper also explores an innovative concept in the form of a control algorithm and discusses the relevant challenges and future opportunities. Moreover, the use of renewable energy systems (RESs) for enhancing energy management (EM) flexibility with the economy in the PMI or other industries is emphasized through DR alliances. This review study could be helpful to the PMI in terms of managing energy demand and also incorporating DR as an essential aspect of EM.

Demand Response Flexibility Potential Trading in Smart Grids: A Multileader Multifollower Stackelberg Game Approach

The value of demand response (DR) for improving the operational efficiency of smart grids has been widely recognized. To further explore the potential DR flexibility, this article presents a novel framework for DR resource trading between multiple service providers (SPs) and end customers, in which SPs are supposed to offer diversified incentives to a resource trading center (RTC) while anticipating responses from the demand side. Distinguished from previous studies, in this work each end customer is endowed the authority to simultaneously trade with multiple SPs with the intention of maximizing their net utilities. The trading process between SPs and customers is demonstrated by a multileader multifollower Stackelberg game, due to its superiority of modeling problems with hierarchical decision-making behaviors. To solve the formulated game, an exact potential function is proposed to help determine the unique Nash equilibrium (NE) among SPs, which serves as the cornerstone for further deriving the Stackelberg equilibrium (SE), i.e., the ultimate outcome of the resource trading process. Simulation results verified that the DR flexibility was significantly increased compared with the case when there is only one SP responsible for the entire trading on behalf of all the end customers. Moreover, the proposed approach is also able to bring extra benefits for both SPs and customers in terms of net profit and net utility value.

Improving flexible optimal scheduling of virtual power plants considering dynamic line rating and flexible supply and demand

Global warming, air pollution, and the need for energy has led to the growth of renewable energy sources (RESs) around the world. Despite the fact RESs offer less expensive energy to customers, uncertainty in their output power, along with other sources of uncertainty inherent in power systems operations such as market price and load forecast, creates an imbalance between demand and supply. The virtual power plant (VPP) has been recognized as a solution that can integrate RESs into the power system to help deal with uncertainty. VPP operators require a flexible power system to address the supply and demand imbalance by rescheduling energy resources. In this study, coordinated scheduling of flexible resources in the form of a VPP participating in a day-ahead (DA) market is presented. Flexible resources and components include fast ramping conventional generators (CGs), battery energy storage systems, dispatchable wind turbines equipped with compressed air tanks, lines equipped with dynamic line rating (DLR) technology, and a flexible demand response program. In addition, flexibility indices (FI) are provided for each flexible component to capture each component’s impact on system flexibility. The proposed model is a mixed integer linear program (MILP) implemented on the IEEE standard 33 bus system. The result of the study show that the system equipped with DLR technology increases VPP profit when compared to the system with static line rating by 23%. The 24-hour average system’s FI indicated an increase in operational flexibility by the proposed model. Finally, the energy generated by each flexible resource is compared for different cases. The results indicates an increase in power sold to the upstream network by the VPP and a decrease in the need for load shedding.

Optimization scheduling of a wind–photovoltaic–gas–electric vehicles Community-Integrated Energy System considering uncertainty and carbon emissions reduction

Coupling renewable energy power generation, electric vehicles, combined cooling heating and power system, and energy storage system is a new way for Community-Integrated Energy Systems (CIES) to shift to a low-carbon, highly efficient, environmentally friendly society, which is the key task in coordinating flexible demand response with multiple uncertainties in renewable energy. To this end, an integrated demand response program is used to explore the potential interaction ability of electricity-gas-heat-cool-Electric Vehicles flexible loads in a scheduling model based on chance-constrained programming, based on which an integrated demand respond-enabled CIES with uncertain renewable generation was built, meanwhile, a ladder-type carbon trading mechanism was designed, and then the original model was transformed into a solvable mixed-integer linear programming model using Sequence Operation Theory and linearization methods. Furthermore, the objective function was constructed by using the deviation preference optimization strategy, daily comprehensive cost and daily carbon emissions were introduced to reflect the decision-makers’ different optimization preferences by using weight coefficients as double optimization objectives. Finally, the simulation shows that CIES operation can effectively balance economy and reliability by setting an appropriate confidence level for the spinning reserve constraint, meanwhile, the comparative result shows that EV charging in the CIES has a very significant environmental benefit in carbon dioxide emissions reduction, while the number of EVs exceeds 500, EV charging in the CIES starts to show a better economic benefit.