Demand Response Potential Research Articles

Assessments of demand response potential in small commercial buildings across the United States

Buildings are responsible for more than 73% of the total electricity usage in the United States and have a significant contribution to peak electrical demand of the power grid. Thus, proper building demand management could improve the robustness and efficiency of grid operation and could also result in economic benefits for building owners. Almost half of the building electricity usage occurs in the commercial building sector where 60% of the installed cooling capacity is attributed to small commercial buildings employing packaged air-conditioning systems (e.g., rooftop units). Thus, understanding the demand response potential and economic benefits for the small commercial sector is of great importance. In particular, the effect of climate, building type, and utility structure on demand response potential is valuable information for building managers in identifying the most beneficial demand response strategies and for utilities in developing better utility structure rate incentives to achieve demand reduction. This article presents a simulation-based assessment approach and highlights the key assessment results for three demand response strategies—thermostat, shade, and lighting controls—across a range of building types, vintages, locations, and utility rate structures. The control performance is evaluated in terms of utility cost savings, peak demand reductions, and indoor comfort impacts in comparison to a baseline night setup strategy. The tradeoff between cost savings and comfort is also explicitly investigated. The assessment results show that the demand response controls lead to slightly worse indoor comfort compared to the baseline control, with an increase in the median percentage of people dissatisfied from 5.7% to 8.1%. However, significant demand reductions and cost savings could be achieved across all tested cases with the magnitudes dependent on the weather condition and building type. Annual electricity cost savings of 5 to 25 cents/ft2 (54 to 269 cents/m2) were demonstrated for time-of-use energy rates with demand charges, whereas the savings were relatively small, below 5 cents/ft2 (54 cents/m2) for most cases, when buildings subscribed to flat rates without demand charges.

Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems

Using an integrated demand-supply optimization model, this work investigates the potential for flexible space heating demand, i.e., demand response (DR), in buildings, as well as its effects on the heating demand and the operation of a district heating (DH) system. The work applies a building stock description, including both residential and non-residential buildings, and employs a representation of the current DH system of the city of Gothenburg, Sweden as a case study. The results indicate that space heating DR in buildings can have a significant impact on the cost-optimal heat supply of the city by smoothing variations in the system heat demand. DR implemented via indoor temperature deviations of as little as +1 °C can smoothen the short-term (daily) fluctuations in the system heating demand by up to 18% over a period of 1 year. The smoothening of the demand reduces the cost of heat generation, in that the heat supply and number of full-load hours of base-load heat generation units increase, while the number of starts for the peaking units decreases by more than 80%. DR through temperature deviations of +3 °C confers diminishing returns in terms of its effects on the heat demand, as compared to the DR via +1 °C.

HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response

Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.

Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network

Electric vehicles (EVs) can be regarded as a kind of demand response (DR) resource. Nevertheless, the EVs travel behavior is flexible and random, in addition, their willingness to participate in the DR event is uncertain, they are expected to be managed and utilized by the EV aggregator (EVA). In this perspective, this paper presents a composite methodology that take into account the dynamic road network (DRN) information and fuzzy user participation (FUP) for obtaining spatio-temporal projections of demand response potential from electric vehicles and the electric vehicle aggregator. A dynamic traffic network model taking over the traffic time-varying information is developed by graph theory. The trip chain based on housing travel survey is set up, where Dijkstra algorithm is employed to plan the optimal route of EVs, in order to find the travel distance and travel time of each trip of EVs. To demonstrate the uncertainties of the EVs travel pattern, simulation analysis is conducted using Monte Carlo method. Subsequently, we suggest a fuzzy logic-based approach to uncertainty analysis that starts with investigating EV users’ subjective ability to participate in DR event, and we develop the FUP response mechanism which is constructed by three factors including the remaining dwell time, remaining SOC, and incentive electricity pricing. The FUP is used to calculate the real-time participation level of a single EV. Finally, we take advantage of a simulation example with a coupled 25-node road network and 54-node power distribution system to demonstrate the effectiveness of the proposed method.

Review and assessment of the different categories of demand response potentials

Demand Response (DR) is a well-known concept which has been recognized as an increasingly valuable tool to provide flexibility to the power system, to support the integration of Variable Renewable Energy (VRE) resources and to manage the grid more efficiently. In recent years, there have been a growing number of publications focusing on the estimation of different categories of DR potentials (e.g. theoretical, technical, economic, and achievable) using different methodologies and assumptions in each research study. The contribution of the present study is twofold. Firstly, a literature review is undertaken focusing specifically on the categorization of the scientific approaches used to estimate the different categories of DR potentials. To the best of authors' knowledge, a general procedure for the estimation of each DR potential category is still missing. Therefore, a novel user-friendly and step-by-step theoretical framework for the determination of the different categories of DR potentials is presented. Findings of this study reveal that literature has extensively focused on the estimation of the technical DR potential followed by the economic, theoretical and achievable potentials respectively. A lack of understanding of the different categories of DR potentials was also identified, which sometimes have been unduly used in the literature. The proposed framework is supported on a small sample of numerical approaches and equations which results in a structured approach to bringing consensus to the DR potential assessment.

Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model

Realizing the full potential of plug-in electric vehicle (PEVs) in power systems requires the development of business models for PEV owners and electric vehicle aggregators (EVAs). Most business models neglect the significant economic potential of PEV demand response. This paper addresses this challenge by proposing a novel business model to optimize the charging energy of PEVs for maximizing the owners’ profits. The proposed business model aims to overcome the opportunity cost neglect for PEV owners, whose charging energy and charging profiles are optimized with full consideration of the demand curves and market conditions. Lagrangian relaxation technology is used for the relaxation of the constraint of satisfying the charging demand, and as a result, the optimization potential becomes greater. The bidding/offering strategy is formulated as a two-stage stochastic optimization problem, considering the different market prices and initial and target state of energy (SOE) of the PEVs. By case studies and analyses, we demonstrate that the proposed business model can effectively overcome the opportunity cost neglect and increase the PEV owners’ profits. Furthermore, we demonstrate that the proposed business model is incentive-compatible. The PEV owners will be attracted by the proposed business model.

Evaluation Method for the Hourly Average CO2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

This work introduces a generic methodology to determine the hourly average CO2eq. intensity of the electricity mix of a bidding zone. The proposed method is based on the logic of input–output models and avails the balance between electricity generation and demand. The methodology also takes into account electricity trading between bidding zones and time-varying CO2eq. intensities of the electricity traded. The paper shows that it is essential to take into account electricity imports and their varying CO2eq. intensities for the evaluation of the CO2eq. intensity in Scandinavian bidding zones. Generally, the average CO2eq. intensity of the Norwegian electricity mix increases during times of electricity imports since the average CO2eq. intensity is normally low because electricity is mainly generated from hydropower. Among other applications, the CO2eq. intensity can be used as a penalty signal in predictive controls of building energy systems since ENTSO-E provides 72 h forecasts of electricity generation. Therefore, as a second contribution, the demand response potential for heating a single-family residential building based on the hourly average CO2eq. intensity of six Scandinavian bidding zones is investigated. Predictive rule-based controls are implemented into a building performance simulation tool (here IDA ICE) to study the influence that the daily fluctuations of the CO2eq. intensity signal have on the potential overall emission savings. The results show that control strategies based on the CO2eq. intensity can achieve emission reductions, if daily fluctuations of the CO2eq. intensity are large enough to compensate for the increased electricity use due to load shifting. Furthermore, the results reveal that price-based control strategies usually lead to increased overall emissions for the Scandinavian bidding zones as the operation is shifted to nighttime, when cheap carbon-intensive electricity is imported from the continental European power grid.

Optimal sizing of autonomous hybrid PV system with considerations for V2G parking lot as controllable load based on a heuristic optimization algorithm

Hybrid energy systems such as PV-battery-diesel have been identified as an effective option to solve the power-supply problem and, therefore, the optimal sizing of such hybrid PV systems becomes important. Also, activities related to the demand side of the power system such as demand response programs are interested and, electric vehicle to grid (V2G) parking lot as a controllable load unit is recently used with demand response potential through charging and discharging V2Gs according to electricity price variations. The goal of this study is to develop and examine a heuristic optimization algorithm to solve the optimal sizing for hybrid PV-battery-diesel system with considerations for V2G parking lot as a controllable load. Simulation results show that utilization of V2G parking lot can reduce total cost for optimal sizing of hybrid PV-battery-diesel system by 5.21%.

Industry, flexibility, and demand response: Applying German energy transition lessons in Chile

Demand side management (DSM) contributes, among its other purposes, to provide stability and flexibility to energy systems that include a high proportion of variable renewable energy in pioneer countries and those which recently began their energy transition. Among DSM policies, implementation of measures and mechanisms for a demand response (DR) is not a trivial matter. This study presents a review and comparative analysis of the major energy policy developments in Germany and Chile. Based on i) the relevance of the industry sector in both countries, ii) the difficulty in implementing the regulatory framework and iii) the importance given to the industrial customers in the German legislation and DR literature, the following analysis will focus on both medium and large industrial customers. The goals are to review the challenges in untying the full DR potential of the productive industry and to provide recommendations for the promotion of DR in Chile. Emphasis is placed on the lessons learned in fitting the wide potential of DR in the German electricity markets. A review of the successes and failures of the German experience reveals the potential risk that poor policy implementation can have in both the short and long term.

Intrinsic limitations of load‐shifting response dynamics: preliminary results from particle hopping models of homogeneous density incompressible loads

In this study, end-use storage loads with elasticity constrained by a time window are modelled by a particle hopping cellular automaton. The automaton model is introduced and parameterised to obtain results on the ideal load-shifting response. Simulation is used to analyse the intrinsic limitations of load-shifting ramping capabilities. New concepts are introduced: load particle velocity and particle density. These concepts are used to advance formal hypotheses on the ramping limitations. Hypotheses are tested against experimental results to conclude about the underlying potential of load-shifting demand response.

Smart home energy optimization with incentives compensation from inconvenience for shifting electric appliances

Demand management programs help utilities to reduce the load demand during high wholesale electricity price or system peak periods to maintain the system reliability and operational security. The challenge here is to properly address the customers’ inconvenience to encourage them to participate and meanwhile satisfy the required demand reduction efficiently. To this end, we propose a new incentive-based demand management scheme for schedulable appliances in a residential community. The proposed scheme is designed to provide scalability to the system. Different from existing literature, a new compensation scheme is adopted for the shifting of task-based appliances based on the level of inconvenience. The potentials of the schedulable appliances to contribute during the demand reduction event are assessed with a modified demand response potential. In this approach, the utility sends command and consumption limit to several controllers to perform the energy optimization. Participating customers are benefited with financial compensation along with minimized privacy concerns in the proposed centralized approach. Considering different demand reduction events within a month, the electricity bill with the proposed scheme is compared with the electricity bill from time-of-use (TOU) tariff-based optimization scheme and also with the base case (i.e., without any energy optimization scheme). Comparison of monthly electricity costs shows that the proposed scheme can save 11.3% on average more than the base case, and also the proposed scheme can save 6.2% on average more than the TOU tariff-based optimization.

Large-scale demonstration of precise demand response provided by residential heat pumps

Demand response can be an effective means for power system operators to compensate fluctuating renewable generation, to avoid grid congestion, and to cope with other contingencies. Buildings equipped with electric heating systems can provide demand-response services because their electricity consumption is inherently flexible due to their thermal inertia. This paper reports on the results of a large-scale demand-response demonstration involving a population of more than 300 residential buildings with heat pumps. Based on a procedure to autonomously estimate the electric flexibility of individual systems from energy meter data and outdoor air temperature measurements, we show how the aggregate demand-response potential of the systems can be quantified and predicted. The results of various experiments illustrate that load reductions of 40–65% of the total load can be achieved by throttling the heat pumps, and that these load reductions can be delivered precisely with a median absolute percentage error of below 7%. In addition, a rebound damping strategy is proposed that was shown to reduce the peak rebound power by 50% in practice.

Demand response of space heating using model predictive control in an educational office building

The building sector plays a remarkable role in decreasing of the overall global CO2 emissions since as much as 30% from the total global CO2 emission are generated in buildings. Demand response provides one possibility to tackle the problem. It can be used to decrease CO2 emissions in entire energy system in addition to providing energy cost savings for building owners and energy companies. In this study, the demand response potential was estimated in an educational office building that was heated by district heating. The potential was defined in respect of energy cost savings, energy flexibility and thermal comfort. Model predictive control was developed, which utilized the dynamic hourly district heating prices. The MPC algorithm written in the Matlab software, predicted the future heating demand while the optimization algorithm NSGA-II minimized the heating energy cost, maximized the energy flexibility and maintained acceptable thermal comfort by changing the space heating temperature setpoints. The operation of the MPC algorithm was tested in the IDA ICE 4.8 simulation software. As a result, the annual district heating energy costs could be reduced by 4.2% compared to the reference case with constant space heating temperature setpoint of 21 °C. The maximum flexibility factor attained was 14%. Acceptable level of thermal comfort was maintained throughout the simulation time.

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population

Residential air conditionings (RACs) can be seen as one of the important flexible demand resources controlled to provide balance services for the power system due to their spatial thermal storage feature. However, such control on RACs will bring out the negative impact on the comfort level of residents inevitably, affecting their proactivity in demand response (DR). So, a novel two-stage control model of large-scale RACs is proposed to participate in DR considering different comfort sensitivity of various residents. The comfort sensitivity index is developed in terms of daily operation time and daily power consumption per area of RACs, classified by K-means clustering. Besides, the DR potential of RACs is established with the adjusted temperature setpoint, where the temperature setpoint is determined by the comfort sensitivity index. Moreover, a two-stage control model of large-scale RACs is proposed comprised of load response and recovery, where the corresponding control strategies at each stage are proposed. Simulations of the proposed method have been performed on residential 20,000 RACs to validate its effectiveness in Changzhou, China. The outcome reveals that the strategies could maximize the DR potential of RACs and ensure the stable recovery of RACs under the premise of meeting the comfort demand of differentiated population.

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.

Prioritizing the effectiveness of a comprehensive set of demand response programs on wind power integration

The environmental targets set by power sectors throughout the world are the main drivers toward increasing the share of variable renewable energy sources (VRESs). Growth of VRESs will lead to a higher demand for operational flexibility due to their stochastic nature. Traditionally, conventional generation units provide the major share of additional required flexibility that may result in a higher depreciation. Motivated by this challenge, this paper investigates the potential of Demand Response (DR) as an emerging alternative in systems with significant amounts of wind power. To this end, a comprehensive set of DR programs including tariff-based, incentive-based and combinational DR programs are considered in a stochastic network-constrained market clearing framework. Afterwards, various DR programs are prioritized taking into account the system operator’s economic, technical, and environmental desires. Moreover, the sensitivity of different DR programs into customer’s price elasticity of demand as well as the participation level are evaluated by means of several sensitivity analyses. The obtained results can provide a guideline for the system operators to opt the most effective DR program.

The effect of including hydronic radiator dynamics in model predictive control of space heating

Existing simulation-based studies on applying model predictive control (MPC) schemes for space heating operation to enable demand response (DR) make use of linear models for the heating system, usually by assuming convective electrical baseboard heaters. However, buildings connected to district heating networks are typically equipped with hydronic heat emitters, such as radiators, that behave nonlinear. This paper therefore investigates the effect of including the nonlinear dynamics of a hydronic heat emitter on the DR potential of MPC for space heating. Furthermore, the performance of a practical two-level control approach suitable for real application, in which a heating setpoint was determined by a linear MPC and communicated to a conventional proportional integral controller, was investigated. The simulation framework for the investigation was based on the application of an experimentally obtained hydronic radiator model applied in different co-simulation setups, featuring a model of a poorly and a highly insulated apartment, respectively. The results indicated that inclusion of the nonlinear thermal effects of hydronic radiators did not significantly affect the DR performance when compared to the results of an MPC scheme controlling convective electrical baseboard heaters. In general, both setup achieved operational cost savings of approx. 5% and 18% in an existing and retrofitted building, respectively, while restricting the amount of thermal comfort violations to a limited extent. This suggests that results obtained in previous studies featuring electrical baseboard heaters also apply to buildings equipped with hydronic heating systems, and that future simulation-based studies and practical implementation of MPC for space heating can continue to rely on the use of far less computationally demanding linear control-models. Furthermore, the results suggest that the two-level control scheme seems like an appropriate control setup suitable for real applications.

Guest Editorial Special Section on Industrial and Commercial Demand Response

The eleven papers in this special section focus on the industrial and commercial potential of demand response (DR). Customers from this non-residential market base have great potential in providing flexibility for power systems through diverse demand response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if onsite control, information, and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation, and air conditioning systems. In the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral.

Estimating demand response potential under coupled thermal inertia of building and air-conditioning system

Taking advantage of the thermal inertia of a building to shift the cooling load during a demand response (DR) period is an important strategy for commercial buildings. However, researchers have consistently ignored the effects of the thermal inertia of air-conditioning systems on the DR potential of buildings. This study focused on the coupling effect of a building and an air-conditioning system. This paper describes the construction of a coupled thermal inertia model of a building and an air-conditioning system, which was validated experimentally; analyzes the accuracy of the thermal inertia of a building solely, as well as the accuracy of both the building and air-conditioning system together by comparing the model and experiment results; and quantifies the DR contributions of the thermal inertia of both the building and the air-conditioning system. A considerable demand reduction effect of the thermal inertia of the air-conditioning system was found, and the results of coupling the thermal inertia of both the building and air-conditioning system showed greater accuracy.

Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context

This article presents a case study on a demand response (DR) pilot project dealing with the application of DR in a grocery store with the utilization of refrigeration equipment as energy storage and photovoltaics (PV) as an energy source. DR has recently gained increased interest due to the growing penetration of intermittent renewable energy requiring flexibility in power consumption. The smart power grid enables the introduction of novel solutions to increase flexibility and the entrance of new actors into the markets. Developing new solutions for the mainstream markets requires experimentation in real-life settings serving the development of technological capabilities, necessary policies and regulation, and user and market needs, as well as adaptation of and to infrastructure and maintenance systems. Our case study on a DR pilot in a grocery store in Northern Finland focuses on how the project contributes to knowledge on the potential for DR and scaling up. It was found that energy efficiency, DR, and self-generated PV power can be aligned and even enhance the potential for DR. While mature technologies exist, applications and installations have not yet been standardized to enable rapid scaling up, and current DR market rules and practices fail to accommodate for small electricity consumers.