Demand Response Control Strategies Research Articles

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy.

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

Benefits through Space Heating and Thermal Storage with Demand Response Control for a District-Heated Office Building

Demand response techniques can be effective at reducing heating costs for building owners. However, few studies have considered the dynamic marginal costs for district heating production and taken advantage of them for building-level demand response. In this study, a district heating network in the Finnish city of Espoo was modeled to define dynamic district heat prices. The benefits of two demand response control approaches for a Finnish office building, the demand response control of space heating and a thermal energy storage tank, were evaluated by comparing them to each other and utilizing them together. A 5 m3 storage tank was installed in a substation of a conventional high-temperature district heating network. A new demand response control strategy was designed to make the most of the storage tank capacity, considering dynamic district heat prices and the maximum allowed return water temperature. The results indicate that the demand response control of space heating and the storage tank cut district heat energy costs by 9.6% and 3.4%, respectively. When employing the two approaches simultaneously, 12.8% savings of district heat energy costs were attained. Additionally, thermal energy storage provides more potential for peak power limiting. The maximum heating power decreases by 43% and the power fee reduces by 41.2%. Therefore, the total cost, including the district heat energy cost and the power fee, can be cut up to 22.4% without compromising thermal comfort and heat supply temperatures to ventilation systems.

Demand response strategy study of a radiant roof cooling system based on the thermal inertia of the building envelope

There is an imbalance between supply and demand in the power system. Implementing demand response control strategies for air-conditioning systems is beneficial to optimize the allocation of power resources. Here, we use two single strategies and a combination strategy for the radiant roof cooling system: passive energy storage, global temperature reset, and the passive energy storage-global temperature reset combination strategy to implement demand response control, all of which achieve peak load reduction or shifting by changing the indoor controlled parameters. Based on the thermal inertia of the building envelope, we utilize a TRNSYS model to analyze the performance of three demand response strategies of radiant roof cooling systems in terms of thermal comfort, energy consumption, operating costs, and peak load shifting rates. The findings reveal that implementing demand response strategies can reduce the operating energy consumption of radiant roof cooling systems and facilitate peak load shifting. Among them, the combined response strategy shows the best peak load transfer effect, with a transfer rate of 19.84% and a better operating economy. Meanwhile, we find that the outdoor temperature affects the implementation of demand response strategies for the radiant roof cooling system based on the thermal inertia of the building envelope. Practical application The study has significant application value in the following aspects: Implementing a demand response strategy for the radiant roof cooling system, based on the thermal inertia of the building envelope, can reduce operational energy consumption and achieve peak load shifting. This approach effectively addresses the issue of supply-demand imbalance in the power system. The application of the work could facilitate improved operational energy efficiency, contributing to emissions reduction goals and optimizing the use of intermittent renewable energy systems in power grids.

A dynamic demand response control strategy for isolated microgrid with primary frequency regulation

Electric water heaters (EWHs) are considered as suitable devices for demand response (DR) load control and can be aggregated to participate in primary frequency regulation services for microgrids. However, achieving accurate frequency control of the microgrid through the load response of EWHs while reducing frequency rebound remains a challenge. This paper proposes a dynamic DR load control strategy for primary frequency regulation in microgrids that utilizes EWHs as responsive loads. First, an aggregated model is developed to aggregate EWH clusters based on their physical characteristics. Then, the coefficients of the local frequency curve equation are obtained using the least squares method to predict the nadir of frequency fluctuations. The maximum frequency deviation is calculated based on the nadir of the frequency. Furthermore, an EWH cluster switch control strategy is established based on the EWH temperature and the maximum frequency deviation to determine the switching sequence of EWHs. The simulation results point out that the control algorithm proposed in this study is capable of accurately regulating the frequency of the microgrid within the aggregated capacity range of the EWHs while also the user’s comfort is almost unaffected.

Heat pump water heater enhanced with phase change materials thermal energy storage: Modeling study

A promising solution to improve the first hour rating (FHR) of a heat pump water heater (HPWH) involves employing a secondary tank which contains phase change material (PCM) capsules. To better understand the influence of PCM thermal storage on the HPWH operational performance, a dynamic model was developed to simulate and analyze the behavior of a newly developed HPWH technology that incorporates PCM storage into a standard HPWH to optimize key parameters such as the uniform energy factor and FHR. Mathematical models of several key components of the proposed HPWH-PCM integrated thermal energy storage (TES) system, e.g., water heater tank, PCM TES tank, evaporator, compressor, and expansion valve, have been elaborated. Also, a model-based control co-simulation platform was developed to integrate a embed PCM storage HPWH dynamic model with a control model for better supporting control design, analysis, verification, and validation. The model accuracy has been validated through comparing simulation results with lab test results, with a mean average percentage error of <5.5% for most of the selected performance variables. In addition, using the developed co-simulation platform, the demand response control strategy was studied to evaluate the load flexibility of the combined HPWH-PCM storage system by shifting system power usage to outside of the 3.5 h peak load period.

Global sensitivity analysis of fan coil air conditioning demand response—A case study of medium-sized office buildings

Air-conditioning demand response has drawn more attention as a means of reducing the ever-increasing peak load of the utility grid. However, the parametric analysis of the demand response performance is still insufficient, given a large number of design parameters of buildings, air-conditioning systems, climate zones, and demand response scenarios. In this study, four different sensitivity analysis methods, including the standardized regression coefficient method, the partial correlation coefficient method, the Morris method, and the extended Fourier amplitude sensitivity testing method, were adopted to screen the most important design parameters in determining the peak load, demand shedding intensity, and demand response potential of a medium-sized office building equipped with a fan-coil air conditioning system under the demand response control strategies of different global temperature adjustments. The results show that the four sensitivity analysis methods presented similar results of parameter screening, even with different degrees of accuracy and computational costs. For example, all the sensitivity analysis methods identified the occupied area per capita, fresh outdoor air per person, internal thermal mass, the safety factor of chiller capacity, and coefficient performance of chillers as the critical parameters for the demand shedding intensity. In addition, the critical parameters have similar rankings for their importance under different adjustment parameters, such as setpoint changes and duration time, in a demand response event. For critical parameters, their degrees of influence on demand shedding intensity are affected by climate zones, and the variation of coefficient for their sensitivity analysis indicators could be 20% higher than the least influential parameters. The study would benefit the design of air conditioning systems with load flexibility and prediction model simplification, etc.

Temperature Control Load Demand Response Control Strategy Considering User's Willingness Difference

Considering the pressure of communication facilities, the difference in willingness to participate, privacy protection, and other factors, a temperature control load demand response control strategy based on signal broadcasting considering the difference in user willingness is proposed. The control strategy is divided into the aggregator side and the local temperature control load unit side and its response index are sent by the aggregator to all temperature control load units in the form of signal broadcasting. In this control strategy, considering the variety of temperature control load units and different temperature control intervals, a universal temperature normalization value is established. The user participation willingness coefficient is introduced, and the expression of the actual participation probability of the temperature control load is constructed. According to the comparison result with the random number, whether to start or stop the temperature control component is selected. Finally, the overall application flow of temperature control load demand response is designed, and the effectiveness of the strategy is verified by MATLAB simulation.

Demand response with heat pumps: Practical implementation of three different control options.

Three case studies of different heat pump demand response control strategies in real homes are presented. All three households reduced their electricity consumption during a peak period but delivered unintended consequences where the heat pump's logic did not correspond to the demand response requirements. This study highlights that the implementation of heat pump demand response to support electricity system operation requires a clear definition of electricity system need as well as practical demand response mechanisms to be integrated into heating system design.

Optimal Control Strategies for Demand Response in Buildings under Penetration of Renewable Energy

The penetration rates of intermittent renewable energies such as wind and solar energy have been increasing in power grids, often leading to a massive peak-to-valley difference in the net load demand, known as a “duck curve”. The power demand and supply should remain balanced in real-time, however, traditional power plants generally cannot output a large range of variable loads to balance the demand and supply, resulting in the overgeneration of solar and wind energy in the grid. Meanwhile, the power generation hours of the plant are forced to be curtailed, leading to a decrease in energy efficiency. Building demand response (DR) is considered as a promising technology for the collaborative control of energy supply and demand. Conventionally, building control approaches usually consider the minimization of total energy consumption as the optimization objective function; relatively few control methods have considered the balance of energy supply and demand under high renewable energy penetration. Thus, this paper proposes an innovative DR control approach that considers the energy flexibility of buildings. First, based on an energy flexibility quantification framework, the energy flexibility capacity of a typical office building is quantified; second, according to energy flexibility and a predictive net load demand curve of the grid, two DR control strategies are designed: rule-based and prediction-based DR control strategies. These two proposed control strategies are validated based on scenarios of heating, ventilation, and air conditioning (HVAC) systems with and without an energy storage tank. The results show that 24–55% of the building’s total load can be shifted from the peak load time to the valley load time, and that the duration is over 2 h, owing to the utilization of energy flexibility and the implementation of the proposed DR controls. The findings of this work are beneficial for smoothing the net load demand curve of a grid and improving the ability of a grid to adopt renewable energies.

An open-source virtual testbed for a real Net-Zero Energy Community

• An open-source virtual testbed in Modelica for studying Net-Zero Energy Communities. • Physics-based and data-driven models are both considered to provide flexibilities. • All the models are validated against real measurement. • A software framework for optimizing the design and operation with the testbed. • An comprehensive evaluation on a demand-response control strategy with this testbed. Net-Zero Energy Communities (NZECs) are critical to assuring the sustainability and resilience of modernized power systems. System modeling helps overcome technical challenges in designing and operating NZECs. In this paper, we present an open-source NZEC virtual testbed in Modelica based on a real NZEC in Florida, USA. This testbed consists of two sets of models: (1) higher-fidelity physics-based models that consider the interaction between subsystems of the studied NZEC and capture fast dynamics, and (2) lower-fidelity data-driven models that require fewer resources to establish and/or run. All models are validated against measurements from this real NZEC. In addition, this testbed includes a simulation framework that streamlines the processes for simulation and thus allows the use of developed models to form a virtual testbed. To demonstrate the usage of the virtual testbed, a case study is conducted where a building-to-grid integration control is evaluated via simulation. The evaluation results suggest that the tested control significantly smooths the power draw of the studied community and does not sacrifice thermal comfort to a great extent.

Demand Side Flexibility Potential and Comfort Performance of Non-Residential Buildings

The need to create and maintain a sustainable indoor environment is now more than ever compelling. Both the legislation framework concerning the energy performance of buildings, as determined in its evolution through the EU Directives 2010/31/EU, 2012/27/EU and 2018/844/EU, and the European strategic plans towards green buildings, denote the need of sustainability and comfort of indoor environment for the occupant. Moreover, the EU Directive 2018/2001 sets the renewable energy target of at least 32% for 2030, denoting that the high renewable energy sources penetration level leads to challenges in the design and control of power generation, transmission and distribution. Demand side management may be able to provide buildings with the energy flexibility needed, in order to utilize the intermittent production of Renewable Energy Sources in a much more efficient and cost-effective way. The flexibility potential of installed building systems is investigated, while considering the effects on the indoor environment conditions and the perceived comfort. The implemented Demand Response (DR) control strategy shifts loads by changing heating system set point temperatures, based on market clearing prices of the day ahead market. The results indicated a reduction in energy consumption and energy costs, while maintaining indoor environment quality at satisfactory levels.

A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids

Ensuring the power balance and reliability of power grids is an increasing challenge due to the increasing involvement of intermittent renewable power generations. The use of existing heating, ventilation and air-conditioning (HVAC) systems in buildings has attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. However, the energy flexibility of buildings is not consistent, and the capacity available for frequency regulation service changes over time due to the changes of working conditions. In this study, a hierarchical optimal control strategy, consisting of a regulation bidding controller and a power use following controller, is proposed. It optimizes the power use baseline and regulation capacity, and controls HVAC systems to provide qualified frequency regulation service, considering the tradeoff between financial reward (regulation capacity) and thermal comfort while satisfying the operating constraints of HVAC systems. The proposed control strategy is validated on a simulation test platform. Results show that the strategy can maximize the use of regulation capacity provided by HVAC systems while ensuring the indoor environment control quality under a given guarantee rate.

Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage

Heating, ventilation, and air conditioning (HVAC) systems, combined with the internal thermal mass of buildings, have been deemed to be promising means of providing demand response (DR) resources, particularly for buildings with active energy storage systems. DR resources, such as peak-load reduction potential, can provide grid-responsive support resulting in a high degree of grid involvement and high flexible electricity demand. In the DR field, the potential of HVAC load flexibility has been considered in buildings. In the future smart buildings, it is important to take advantage of demand-side resources to achieve real-time energy supply–demand balance sustainably. In this context, DR potential and characteristics of buildings play a pivotal role in DR programs. However, few studies have investigated the internal thermal mass’s heat release and DR characteristics of buildings. Thus, a systematic experiment is conducted to study the DR potential and characteristics of internal thermal mass and active storage systems. The DR resources include the passive cooling storage from furniture, building envelope and an active water storage tank. Two DR control strategies, including pre-cooling and temperature resetting, are analyzed in this study. The experimental results show that the strategies are effective for short-term (0.5 h) and intermediate-term (2 h) DR programs. For a long-term DR program, active energy storage technology such as a water storage tank is required to satisfy the occupant's comfort requirements. Hence, we conclude that passive thermal mass and active storage systems should be simultaneously considered in practical DR programs for better DR implementation.

Characteristics and control strategies of large-scale residential air conditionings for demand response programs

Residential air conditioning (RAC) loads have a great potential to participate in demand response (DR) program. This paper studies large-scale RAC loads participating in DR, including modeling, parameters identification, DR characteristics and control strategies. Firstly, aggregate model of large-scale RACs is established according to the buildings' performance of heat storage and insulation, avoiding calculation of single RAC model. Then, parameters of the aggregate model are identified based on RACs' power and outdoor temperature. Based on the aggregate model, DR characteristics of RACs are analyzed, including dynamic relationship among power, outdoor and indoor temperature, and potential of DR combined with users' comfort. Thirdly, the DR control strategies adapted to large-scale RACs are established by adjusting temperature set-points. The DR strategies consider users' comfort and calculate the control signals of each RAC according to the DR power, including adjustment temperature and adjustment time, which are sent to each RAC for execution. In the DR process, the control center does not need to obtain the users' indoor temperature, which is conducive to protecting the users' privacy. DR strategies of RACs when control degree within/beyond DR potential are both proposed, and load recovery control strategy is also introduced. Finally, the effectiveness and accuracy of the proposed model and DR control strategies are verified by simulation results.

A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications

The power grid is facing the critical issue concerning the power imbalance. To address the issue, demand response programs are increasingly deployed to encourage the end-users to change their load profiles. For buildings, the existing fast demand response strategy has been demonstrated effective in performing quick response to the grid request by reducing the power demand. However, the existing fast demand response methods require a physical flowmeter to be installed in each air handling unit. While in most of the existing commercial buildings, flowmeters are rarely installed in individual air handling units due to the high initial cost. As a result, the existing fast demand response method may not be applicable in these commercial buildings. Thus, this paper presents a virtual sensor based self-adjusting control strategy for fast demand response of building heating, ventilation and air-conditioning system. A virtual flowmeter model is first developed to estimate the water flow rate in each air handling unit. Based on the virtual flowmeter model, a self-adjusting water flow supervisor, in which the online self-adjusting method is integrated to reduce efforts in parameter identification, is then developed to achieve a balanced water flow distribution among different air handling units. The performances of the proposed control strategy have been tested and evaluated on a simulated system. The results show that the virtual flowmeter model has good accuracy for estimating the water flow rate in AHUs. The proposed control strategy can achieve significant and quick power reduction and meanwhile address the hydraulic imbalance problem.

Contracted energy flexibility characteristics of communities: Analysis of a control strategy for demand response

Increasing energy system flexibility through demand-side measures will help meet challenges brought by the transition to a low-carbon energy system. Through participation in demand response programmes, buildings can act as sources of contracted flexibility. Contracted flexibility, in this work, is defined as energy flexibility that is supplied to fulfil a set of contractual terms that define when and how demand modifications are delivered and under which incentives or penalties. This paper identifies the factors affecting contracted energy flexibility potential of homes implemented with a model-predictive control strategy designed to deliver a simplified but yet generalisable incentive-based demand response scheme. The control strategy was implemented in centralised and naive-decentralised architectures using co-simulations to observe interaction of the controller with an English community of 30 homes fitted with air-source heat pumps. The results showed that the control strategy was able to deliver sustained demand reductions without violating comfort by preheating the homes prior to demand response periods, if conditions were suitable. Preheating the homes increased overall energy consumption and, in some cases, caused a peak in electricity demand prior to the DR period. Modifying factors of control operation, like the coordination strategy, magnitudes of penalties, control constraints and notice period between call for demand reduction and its delivery, were shown to affect the ability to deliver demand reductions. The contracted flexibility potential of the community was shown to be characterised by the buildings and their systems, the physical and contractual environment, and behaviour and preferences of the occupants.

Demand response potential of district heating and ventilation in an educational office building

This study examines the influence of demand-response control strategies on thermal conditions, indoor air CO2 concentration, and heating energy cost and consumption in an educational office building heated by a district heating system in a cold climate. The real-time pricing-based demand response is applied for space heating, heating of ventilation, and adjustment of airflow rates. The ventilation analysis covers both constant and variable air volumes systems. The applied demand-response algorithms regulate room air temperature set-points for space heating, temperature set-point for supply air, and CO2 set-point adjusted with the variable air volume ventilation system. The accepted room air temperature range was 20–24.5 °C and CO2 concentration within 800–1200 ppm. This study was conducted with the validated dynamic building simulation tool IDA ICE. The results illustrate that the maximum yearly savings by demand response of space heating and ventilation with the constant air volume ventilation system are around 3 and 6% for the heating energy consumption and heating energy cost, respectively. For the variable air volume system, the heating energy consumption, heating energy cost, electricity consumption, and electricity cost saved by demand-response control can be up to 8, 11, 9, and 2%, respectively.

Trajectory Tracking With an Aggregation of Domestic Hot Water Heaters: Combining Model-Based and Model-Free Control in a Commercial Deployment

Scalable demand response of residential electric loads has been a timely research topic in recent years. The commercial coming of age or residential demand response requires a scalable control architecture that is both efficient and practical to use. This work presents such a strategy for domestic hot water heaters and present a commercial proof-of-concept deployment. The strategy combines state of the art in aggregate-and-dispatch with a novel dispatch strategy leveraging recent developments in reinforcement learning and is tested in a hardware-in-the-loop simulation environment. The results are promising and present how model-based and model-free control strategies can be merged to obtain a mature and commercially viable control strategy for residential demand response.

A Multi-Communication-Based Demand Response Implementation Structure and Control Strategy

Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to achieve wide-area DR control without using an expensive metering infrastructure. Multi agents are introduced with respective control strategies to implement multi-time-scale control in a power system. In order to support quick DR strategies, a novel smart terminal design for the proposed DR system is described with functions of local parameter detection and action. The practicality of the proposed system was validated on a developed hardware-in-loop co-simulation platform.