Demand Response Capability Research Articles

Enhancing demand response and heating performance of air source heat pump through optimal water temperature scheduling: Method and application

Air source heat pump (ASHP) is a key technology for the electrification of building heating, and its participation in demand response (DR) has important implications for reducing the grid peak load. However, current DR strategies mainly focus on changing the setpoint of indoor temperature, using the building’s thermal mass as a passive thermal storage, while the impacts of water temperature on the performance of ASHP units are often overlooked. Therefore, this study proposes an optimal water temperature scheduling (OWTS) method to respond time–of–use tariffs. It is developed based on a room thermal dynamic prediction model and an ASHP power consumption prediction model, optimizing supply water temperature schedule with objectives of maintaining indoor thermal comfort and reducing heating operating costs. The effectiveness of the OWTS method was tested on a field ASHP system in Beijing, and compared with two benchmark methods. Results demonstrate that the application of the OWTS method can enhance the DR capability of the ASHP heating system, with the average value of Flexibility Factor increasing from 0.134 to 0.728, and can reduce the heating operating costs by 20.8%–43.0%. This study thereby offers a promising method for ASHP heating systems to effectively participate in DR.

Optimal utilization of frequency ancillary services in modern power systems

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Collaborative optimization of distribution network and 5G base stations considering its communication load migration and energy storage dynamic backup flexibility

5G base stations have experienced rapid growth, making their demand response capability non-negligible. However, the collaborative optimization of the distribution network and 5G base stations is challenging due to the complex coupling, competing interests, and information asymmetry among different stakeholders. In this paper, a distributed collaborative optimization approach is proposed for power distribution and communication networks with 5G base stations. Firstly, the model of 5G base stations considering communication load demand migration and energy storage dynamic backup is established. Afterward, a collaborative optimal operation model of power distribution and communication networks is designed to fully explore the operation flexibility of 5G base stations, and then an improved distributed algorithm based on the ADMM is developed to achieve the collaborative optimization equilibrium. Finally, the effectiveness of the proposed distributed collaborative optimization model is validated by a modified IEEE 33-bus power distribution and communication networks with 5G base stations.

A Stackelberg game-based approach to load aggregator bidding strategies in electricity spot markets

Load aggregators (LA) can aggregate the users with adjustable load capacity such as shared energy storage into a scaled resource in a package, and solve the problem of insufficient demand-side management by participating in the competition of electricity spot market. It is gradually becoming an emerging way to mobilize adjustable loads and improve load demand response capability. But it takes a set of economical and reliable bidding strategies to guide LA's participation in the spot market. This paper first describes the framework of the LA and the functions of the stakeholders in the system, as well as the trading mechanisms for their participation in the spot market, proposing a two-stage determination model of pricing and quantification; Secondly, a Stackelberg game model is developed, combining the operational constraints of different players, with the trading center as the leader and the LA and generators as the followers; Finally, four scenarios are designed to verify the validity of the model, and the results show that the proposed LA trading strategy reduces the purchasing cost by 16.53 % and increases the revenue by 12.66 %. The introduction of the Stackelberg game model ensures the sustainability of the strategy and provides an innovative idea for the trading strategy of customer-side in the spot market.

A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.

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.

Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

This paper introduces a novel model for optimizing microgrid systems by integrating multi-purpose renewable energy (MEM) and cutting-edge technologies, including electric vehicles (EVs). The proposed MEM demand response programs encompass various energy sources such as wind energy, multi-carrier energy storage technologies, boilers, combined heat and cooling units, EVs, P2G (power-to-gas), and demand response capabilities. The primary objective is to minimize the total operational cost of the microgrid system. A distinctive aspect of the proposed method lies in considering the prices of all energy carriers as unknown variables. Market prices are integrated into the modeling process, incorporating scenarios with reasonable probabilities and taking into account demand-side management programs. Moreover, the model allows for customizable programming of different parts of the multi-energy microgrids, with a focus on maintaining convexity principles for the operating area of each CHP (combined heat and power) unit. To tackle the unique complexity of this optimization problem, a developed blue whale optimization algorithm is proposed. This algorithm builds upon the main whale optimization algorithm (WOA), a promising population-based optimization approach. However, the effectiveness of WOA heavily relies on the careful setting of exploration and exploitation parameters, which may lead to being trapped in local optima. To address this challenge, the paper introduces a self-adaptation modification based on wavelet theory to enhance the WOA's performance. The proposed model and optimization method are thoroughly evaluated through simulation studies for different scenarios, showcasing their efficacy in achieving cost-efficient and sustainable microgrid operation. The combination of MEM and emerging technologies like EVs, along with the improved optimization algorithm, marks a significant contribution to advancing the planning and operation of microgrid systems towards low-cost, reliable, and environmentally-friendly energy solutions.

What influences industrial enterprises’ willingness of demand response: A survey in Qinghai, China

Although demand response (DR) is an important means for solving the stability and reliability challenges of renewable energy (RE) power systems, it remains unclear how DR can be widely applied in industrial enterprises, particularly in areas with a high proportional RE system and industrial energy consumption that do not apply any DR programs. The objective of this study is to understand the influencing factors of industrial enterprises' willingness to participate (WTP) in DR in these areas, including one economic factor and three noneconomic factors. This study performs a questionnaire survey in Qinghai, and a total of 556 industrial enterprises completed valid questionnaires, these data are empirically analyzed through Structural Equation Model. The results demonstrate enterprises' DR awareness degree is the most significant predictors of the WTP in DR. Meanwhile, enterprises' electricity consumption level also had a significant impact on the WTP in DR rather than enterprises' potential DR capabilities and the expected minimum compensation. Finally, the study suggests feasible recommendations, such as increasing enterprises' ability of analyzing potential DR, directly encouraging enterprises to participate in DR, designing compensation standards and adopting differentiated DR according to enterprises’ actual DR potential.

Integrated energy system optimization and scheduling method considering the source and load coordinated scheduling of thermal-storage electric boilers and electric vehicles

The northern regions of China face the challenges of the mismatch of the power supply and demand, as well as serious wind curtailment issues, caused mainly by the limitation of the “with heat to determine electricity” mode for combined heat and power generation during the winter season. To further absorb the surplus wind power and alleviate restrictions, a comprehensive energy system optimization method for parks based on coordinated scheduling between sources and loads is proposed in this paper. First, the implementation of a heat-storage electric boiler on the source side further achieves the decoupling of heat and power. Second, an optimized scheduling method for electric vehicles combining incentive scheduling and orderly scheduling is proposed on the load side, which helps flatten the load curve. Finally, a tiered carbon trading mechanism is introduced and a community integrated energy system (CIES) optimization scheduling model is established with the aim of minimizing the total cost of the CIES, and the problem is solved using the CPLEX commercial solver. The simulation results indicate that the overall system efficiency is significantly improved through the coordinated scheduling of power sources and loads. Specifically, the integration rate of wind power increases by 3.91% when compared to the sole consideration of the integrated demand response. Furthermore, the peak shaving and off-peak filling effect is considerably enhanced compared to the utilization of only thermal-storage electric boilers. Additionally, the implementation of coordinated scheduling leads to a reduction in the total system cost by 2764.32 yuan and a decrease in total carbon emissions by 3515.4 kg. These findings provide compelling evidence that the coordinated scheduling of power sources and loads surpasses the limitations of thermal power units, strengthens the demand response capability of electric vehicles, and enhances the economic benefits of the CIES.

Unlocking ventilation flexibility of large airport terminals through an optimal CO2-based demand-controlled ventilation strategy

Large airport terminals consume significant energy due to their extensive ventilation demands. Optimizing the operation of ventilation systems is a crucial aspect of achieving greater energy flexibility and efficiency. In this study, we proposed an optimal CO2-based demand-controlled ventilation (DCV) strategy that utilizes the large indoor space of airport terminals to shift ventilation loads, reduce operating cost, and enable demand response (DR) programs. The effect of air infiltration on ventilation demand is considered in the DCV strategy through on-site CO2 measurement. A passenger dwell model is integrated to estimate the occupancy of airport terminal based on flight schedules for dynamic ventilation control. Considering the DR signal and time-of-use tariff, an advanced evolutionary algorithm is employed to locate optimal ventilation solutions that deliver better economic performance and energy flexibility while meeting indoor air quality constraints. Using an airport terminal in Beijing, China, as a testbed, the superior performance of the proposed DCV strategy was demonstrated against a baseline strategy with a fixed outdoor air supply rate under three scenarios. Simulation results showed that the proposed DCV strategy can reduce peak energy consumption by up to 60.9% and operating costs by up to 48.6% in different scenarios. Moreover, the DCV strategy can provide a flexible trade-off between the overall energy efficiency and DR capability in the adjustable DR scenario. This study revealed great opportunities for improving the flexibility of ventilation in large-space buildings and provided an instructive case study.

Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk

--This paper selects the whole microgrid system as the master and renewable energy, energy storage, and load as the game's slave. It builds a master-slave game optimization model for coordinating the microgrid's source-network-load-storage. The master's goal in the microgrid game is to minimize the overall operation cost. The slave in the renewable energy game aims to minimize the operation cost of renewable energy while considering penalties for wind and PV curtailment. The slave in the energy storage game focuses on optimizing energy storage regulation performance and considers overcharge/discharge risks. Meanwhile, in the load game, the slave aims for the highest power quality and improves load power quality as much as possible while preserving demand response capability to enhance system flexibility. The case analysis shows after 45 games, all game participants can achieve Stackelberg equilibrium, effectively balancing multi-party benefits and improving safety and regulation capabilities of energy storage operations. The microgrid operation scheme based on this method maintains the existing microgrid operation mode and ensures safe and stable system operation.

Two-stage robust optimization strategy for spatially-temporally correlated data centers with data-driven uncertainty sets

Data center buildings (DCBs) in data center parks consume significant and ever-growing amounts of electric power for processing tremendous data workloads in a modern city. The workloads are divided into two types: interactive workloads (IWs) with spatially transferrable characteristics and batch workloads (BWs) with temporally shiftable characteristics. The unique spatially-temporally workloads show great demand response capability, enabling reducing the electricity cost of DCBs. However, the source-load uncertainties such as the number of arriving workloads, and photovoltaic output affect the cost-efficient operation of DCBs in the electricity market. Inadequate server numbers during worst-case scenarios may result in the inability to fulfill user contracts. This paper presents a model for DCBs that incorporates battery energy storage systems, photovoltaic generation, and electrical loads for processing spatially-temporally correlated workloads. Considering multiple uncertainties, a two-stage robust optimization strategy is proposed to coordinate workload and power for DCBs. To accurately determine the boundaries of the uncertainty sets, a data-driven method using the 1-Wasserstein metric is adopted. This method is reformulated into a distributionally robust chance-constrained programming model. The nested column-and-constraint generation (C&CG) method is used to resolve the established two-stage robust optimization problem with mixed-integer recourse. Finally, case studies verify the effectiveness of the proposed method.

Game Theory Based Optimal Pricing Strategy for V2G Participating in Demand Response

Aggregators, as the interface of electric vehicles (EVs) and distribution system, are the market agents of EVs to participate in demand response (DR) and other balancing services. Aggregators can enlarge its benefit room from vehicle-to-grid (V2G) by providing flexible V2G price to EV users as long as they can secure a certain amount of V2G energy. There is a need to study Aggregator's V2G pricing strategy due to the fact that distribution companies, aggregators, and EV users have their own independent benefit maximization objectives as independent entities, and the decision variables of different entities interact and restrict each other. In this study, an optimal V2G pricing strategy for each aggregator is obtained by using Stackelberg game during the gaming of the involved multiple entities. The benefits of aggregator and EV users are set as the game factors, and benefit models of EV user's and aggregator's are established with consideration of the historical charging cost, and users' inconvenience cost. A simulation of V2G price for 750 EVs and 3 aggregators is carried out, and the impact of the market volume of EVs on each aggregator under this pricing strategy is studied. Simulation results show the feasibility of the proposed model and the interaction among the randomness of driving behavior and the market volume of EV groups, loading level of the distribution system, V2G strategies, as well as the DR capabilities.

A two-stage demand response strategy for multiple scenarios based on deviation compensation

Integrated demand response (IDR) is an integral part of the park-level integrated energy system (PIES). Most existing IDR strategies depend heavily on explicit forecasts of future uncertainties. Due to the randomness of wind power generation and load demands, these approaches are limited by forecasting response time or accuracy. A two-stage demand response strategy for multiple scenarios based on deviation compensation is proposed to consider prediction uncertainties arising in distributed wind power generation and multi-energy load. In the day-ahead stage, considering the impact of energy prices on the economy of PIES operation, it is proposed to divide PIES operation scenarios based on energy prices. It is also proposed to develop a day-ahead response plan considering the demand response capability of the load side. The real-time stage considers the uncertainty of wind power and load forecasting as well as the real-time response speed of the strategy, and proposes to use deviation compensation to change some day-ahead plans in order to solve the uncertainty of prediction. Finally, considering the synergy between energy storage and demand response strategies, the above demand response strategy is embedded into multi-objective Harris Hawk optimization (MOHHO) to work out the energy storage capacity. The simulation results show that the strategy considers multiple operational scenarios, is more economical, and only changes a portion of the day-ahead plan in the real-time phase, so it has faster real-time response speed.

Coordinative Optimization Between Multiple Data Center Operators and a System Operator Based on Two-Level Distributed Scheduling Algorithm

Data Centers (DCs) have been playing a significant role in demand response (DR) programs in recent years due to their considerable DR capability. DCs are in the charge of the data center operator (DCO), who is responsible for making a reasonable allocation of computing tasks to provide DR resources. To relieve the transmission pressure of power systems, the system operator (SO) encourages DCOs to participate in the DR programs. To maximize the total welfare, a detailed DR scheduling model of DCOs and SO is proposed for the coordinative optimization. Considering the privacy issue of DCOs, a two-level distributed scheduling algorithm based on the alternating direction multiplier method (ADMM) is designed for privacy protection and distributed autonomy. Simulation results show that the proposed coordinative optimization algorithm can effectively realize the maximization of total social welfare with data privacy protection. For a power system with multiple DCOs, reasonable scheduling of DCO’s DR resources can reduce the peak-valley difference of system loads reliably and economically.

A Cautionary Note on Using Smart Plugs for Research Data Acquisition

The availability of low-cost smart plugs that measure and control loads has encouraged their use as a research tool to disaggregate end-user loads. While there has been extensive research on the cybersecurity implications, and demand response capabilities, of smart plugs, there has been little investigation of their primary function: Measuring loads. In this study, we analyze the accuracy of power and energy measurements reported by 5 smart plugs from different manufacturers, with a specific focus on the accuracy of the meters when load levels change rapidly, as is often the case with power electronic loads. The study included Belkin, TP-Link, Etekcity, Emporia, and Sonoff smart plugs, with loads switching at frequencies of 0.01Hz to 2Hz, in addition to the steady-state loads often used for testing these devices. Data indicate significant errors in both energy and power measurement when used to monitor highly variable loads. While maximum energy measurement error for the Belkin product never exceeded 1.7%, maximum energy measurement errors were 54–100% for the other 4 brands. Power measurement accuracy was highly dependent on load variability, with all units exhibiting substantial errors when loads varied, even at rates as low as 0.01-0.05 Hz. All units also exhibited reporting delays of 3–6 seconds, with some delays as high as 20 seconds. The large, load-dependent, errors, call into question the use of these devices for research data acquisition, particularly for monitoring highly variable power electronic loads.

Event-driven demand response control of air-conditioning to enable grid-responsive buildings

Fast demand response capability of a grid-responsive building is of particularly high value to meet urgent power balance needs of power grids. In this study, an event-driven control strategy is proposed to effectively unlock building energy flexibility for fast demand response using building air-conditioning systems. The proposed control strategy can properly distribute the chilled water among different zones in a building to allow uniform thermal comfort compromise. Validation tests are conducted and the results show that the limited cooling supply can be distributed properly and the same indoor air temperature profiles can be achieved eventually among the indoor spaces. The power demand of the chiller plant is reduced by 170 kW (5%) by adopting the proposed event-driven control, while achieving the same comfort compromise as existing time-driven control. The average accumulated valve travel distance is also reduced by 54.6%, reducing the wear and tear of the AHU valves significantly.

Multi-level distributed demand response study for a multi-park integrated energy system

Current studies of integrated demand response (IDR) across multiple campuses often use centralized, unified scheduling with individual campuses as the object of analysis, which ignores the independent autonomy of users within the park. To this end, this paper proposes a multi-level distributed demand response model for a multi-park integrated energy system, which is solved using a combination of the particle swarm optimization algorithm and mixed integer linear programming software. First, based on the existing two-part tariff, an energy management system is introduced to construct a park integrated demand response market based on a noncooperative game. Second, load aggregators are used as a link to form an optimized alliance of multiple parks, and the overall economy of the coalition is optimized through a cooperative game of multiple parks. The model allows for coordination and complementarity based on zonal autonomy, maximizing economic efficiency and exploring the demand response capability of users. Finally, an arithmetic simulation is carried out with a scenario of a day-ahead electricity peaking auxiliary service market, and the results verify the feasibility and economics of the designed model in a scenario where multiple parks participate in integrated demand response.

Waste heat reutilization and integrated demand response for decentralized optimization of data centers

In recent years, the energy consumption of data centers (DCs) has increased rapidly. Since the demand response (DR) capability of DCs is considerable, DCs play an important role in DR programs. In this work, we further focus on the DCs' integrated demand response (IDR) capability which owes to its temporally and spatially shiftable electricity and heat. To make full use of waste heat retained in the coolant, we design a waste heat recovery process for waste heat reutilization. Moreover, we propose a decentralized optimization algorithm based on the alternating direction multiplier method (ADMM) to protect the inner sensitive data of DCs. Numerical results show that energy purchase costs can be effectively reduced for DCs with IDR capability. It is also proved that the reutilized waste heat constitutes an important part of the heat supply. Besides, the convergence performance of the proposed algorithm is verified based on large quantities of simulations.

Storage and demand response contribution to firm capacity: Analysis of the Spanish electricity system

Provision of firm capacity will become a challenge in power systems dominated by renewable generation. This paper analyzes the competitiveness and role of battery storage, six types of pumped-hydro storage, open cycle gas turbine (OCGT), and demand response (DR) technologies in providing the firm capacity required to guarantee the security of supply in a real-size power system such as the Spanish one in horizon 2030. The paper contributes with detailed and realistic modeling of the DR capabilities. Demand is disaggregated by sector and activities and projected towards 2030, applying a growth rate by activity. The load flexibility constraints are considered to ensure the validity of the results. A generation operation planning and expansion model, SPLODER, is conveniently upgraded to properly represent the different storage alternatives addressed in the paper. The results highlight the importance of considering demand response for evaluating long-term firm capacity requirements, showing a non-negligible impact on the investment decisions on the amount of firm capacity required in the system and the optimal shares of wind and solar PV renewable generation. Results also show the dominance of cost-competitiveness of pumped hydro and OCGTs over batteries. Additionally, capacity payments are required to support firm capacity providers’ investments.