Demand Response Potential Research Articles

Non-intrusive multi-label load monitoring via transfer and contrastive learning architecture

To achieve the goal of peaking carbon emissions globally and carbon neutrality, smart energy management is a promising way to boost energy conservation and estimate the residential potential for regional demand response, among which the non-intrusive load monitoring technique is highlighted due to its effectiveness on the residential side. However, the identification of multi-label appliance switching operations is still a challenge in this field, which may critically affect the total identification results due to the few-shot learning problem and the complicated overlap of features belonging to different appliances. Therefore, this paper proposed a transfer and contrastive learning architecture to identify multi-label appliances effectively. In the first stage, Gramian angular field encoding is implemented to visualize power sequences to highlight the correlation between timestamps and enhance the feature extraction efficiency. Secondly, a contrastive learning architecture is implemented to learn the general distinguishing features between samples of different labels, and density-based spatial clustering of applications with noise clustering is utilized to detect multi-label samples. Thirdly, transfer learning is utilized to enhance the multi-label identification capacity of contrastive learning structures based on the existing trained model. Finally, the effectiveness of the proposed algorithm is verified through two low-frequency non-intrusive load monitoring public datasets and real-world measurements from a pilot project in China. The results show that the proposed architecture can achieve the efficacy of deep features extraction and few-shot learning in identifying multi-label appliance switching operations.

Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy

Large-scale demand response (DR) is a promising solution to mitigate the problem of renewable energy (RE) curtailment with the rising proliferation of RE in both the transmission system (TS) and distribution system (DS). However, the current DR schemes face significant limitations when applied at a large scale, and the lack of consideration of TS and DS networks stymies the potential of large-scale DR in facilitating RE accommodation. To address these gaps, this paper proposes a novel hierarchical DR scheme based on locational customer directrix load (LCDL) that takes into account both TS and DS, involving interactions among tri-layer entities: the transmission system operator, distribution system operators, and customers. Firstly, the concept of LCDLs is proposed to characterize the desired load profiles at various locations, considering the constraints of both TS and DS networks. Subsequently, transmission-level and distribution-level LCDLs are formulated respectively and leveraged to induce the load reshaping of flexible resources at pertinent locations, thereby unlocking the deliverable flexibilities over TS and DS. Furthermore, the collaborative interaction among the three layers is depicted by a two-loop Stackelberg game, and a distributed algorithm is presented to achieve an equilibrium solution without compromising the privacy of the respective entities. Case studies testify that the proposed DR scheme significantly enhances the capacity to accommodate RE in both TS and DS, maintains economic balance in the trading process of DR services, and benefits all involved entities. Conducted on a practical city power grid with a substantial share of RE and DR customers, the simulation test validates the scalability of the proposed DR scheme and results show that with a 10% increment of DR customers in one DS, the RE curtailment rate could be reduced by 3% and 15% for TS and DS, respectively.

Effect of price and income on the demand for sugar-sweetened beverages in Nigeria: an analysis of household consumption data using an almost ideal demand system (AIDS)

ObjectivesTo estimate the own-price, cross-price and income elasticities for carbonated soft drinks (CSDs), malt drinks, chocolate powder, sachet water and sugar in Nigeria. These elasticities can be used to estimate...

Would employees accept curtailments in heating and air conditioning, and why? An empirical investigation of demand response potential in office buildings

Support for demand response has mostly been investigated in the context of private households. We extend the examination of support to organizational contexts. Specifically, we analyze employee support for a scenario in which employees are faced with temporary heating or air conditioning curtailments in their workplace. We also examine the factors underpinning support, including socio-demographic and workplace characteristics and psychological motivations and beliefs. Our empirical results are based on a sample of 551 employees from different organizations who work in air-conditioned offices. Results indicate that employees generally support corporate demand response programs and are willing to accept small deviations from their ideal office temperature, but the level of support is heterogeneous across employees. Subsequent modeling of potential underlying factors shows that socio-demographic or workplace-related characteristics only play a minor role in explaining these variations. In contrast, psychological factors, including environmental motivations and perceived corporate environmental responsibility underpin support, and specific beliefs about the consequences of participation can directly explain support as well. Moreover, personal norms emerged as an important antecedent of program support. Policy makers and organizations can use these insights to design effective demand response programs that address the relevant drivers and barriers of employee support.

Optimal control method of active distribution network considering soft open point and thermostatically controlled loads under distributed photovoltaic access

To achieve the flexible operation of the active distribution network (ADN), an optimal control method for the ADN considering the soft open point (SOP) and thermostatically controlled loads under distributed photovoltaic (PV) access is proposed. First, the mathematical model of the SOP and thermostatically controlled loads are constructed based on the flexibility of the SOP and the thermal storage characteristic of thermostatically controlled loads, respectively. Then, the mathematical model of the ADN considering the SOP and thermostatically controlled loads are further constructed. The optimal control of the ADN is realized by adjusting the transmission power of the SOP and the switching state of the electric water heater. Finally, the optimal operation analysis of the SOP and thermostatically controlled loads under various control methods is carried out, and the impact of the SOP and thermostatically controlled loads on the economic and secure operation of the ADN is further investigated. The results show that the proposed method taps the demand response potential of thermostatically controlled loads while satisfying the water consumption habits of different customers, and utilizes the network reconfiguration with the SOP to further improve the security and economy of the ADN operation.

Multi-objective residential load dispatch based on comprehensive demand response potential and multi-dimensional user comfort

Multi-objective residential load dispatch based on comprehensive demand response potential and multi-dimensional user comfort

Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples.

From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company

Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while balancing the electricity grid. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company’s MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of electrical industrial forklifts, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal, electrical, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19%–80% depending on electricity and carbon removal prices), total annual costs, operational carbon footprint, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.

Building Energy Flexibility Assessment in Mediterranean Climatic Conditions: The Case of a Greek Office Building

The EU energy and climate policy has set quantitative goals for decarbonization based on the energy efficiency and the evolution of energy systems. The utilization of demand side flexibility can help towards this direction and achieve the target of higher levels of penetration in regard to intermittent renewable energy production and carbon emission reduction. This paper presents a simulation-based assessment of thermal flexibility in a typical office building in Greece, which is a representative Mediterranean country. The use of variable speed heat pumps coupled with hydronic terminal units was evaluated. The research focused mainly on the evaluation of energy flexibility offered by energy stored in the form of thermal energy by utilizing the building’s thermal mass. The demand response potential under hourly CO2eq intensity and energy prices was investigated. The flexibility potential was evaluated under different demand response strategies, and the effect of demand response on energy consumption, operational costs, CO2eq emissions and thermal comfort was analyzed and discussed. The results showed that both control strategies based on both the CO2eqintensity signal and spot price signal have, in some cases, the potential for cost and emission savings, and in other cases, the potential to depreciate in terms of emissions and cost the increase of energy consumption due to load shifting.

A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data

The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading.

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.

Survey- and simulation-based analysis of residential demand response: Appliance use behavior, electricity tariffs, home energy management systems

Residential demand response (DR) aims to stabilize the electricity grid by utilizing the flexibility of end-users. To this end, end-users are offered time-varying electricity prices and incentivized for load shifting. End-users can maximize bill reduction through automated load shifting using home energy management systems (HEMSs). Since HEMS is a new technology, the future DR potential of its mass use is unknown. Here, surveys can be very useful for gaining insight into future behaviour and preferences in using HEMS. Therefore, the objective of this study is twofold: (1) to understand appliance use behaviour, electricity tariff perception, and tendency towards HEMS-based DR participation, through a survey. And then, (2) to simulate the DR potential by entering survey responses into a HEMS optimization tool. The results show that 78% of the respondents are willing to engage in HEMS-based DR. This provides the potential to reduce the peak period consumption by 33%. However, the average bill savings achieved by HEMS owners is only 6.7%, which can hinder reaching this potential. Still, 21% of the HEMS owners save more than 10% on their bills. 8% save over 15%, and 3% over 20%. These can be the target audience of the future HEMS market and DR campaigns.

Chlor-alkali Process with Subsequent Polyvinyl Chloride Production─Cost Analysis and Economic Evaluation of Demand Response

Demand response is a viable option to cope with the increasing demand for flexibility of an energy system based on renewables. Several studies have shown that the electricity-intensive chlor-alkali process has high potential. Previous research primarily focused on process engineering to determine technological flexibility potentials or optimal production scheduling. However, these potentials can only be realized if they are economically viable. This requires knowledge of the costs of a demand response, including their causes and dependencies, as well as an understanding of the necessary market conditions. Using a business management approach, we examine the economically viable demand response potentials. The cost is based on real data and the economic evaluation of marketing flexibility on a day-ahead market. The results lead to conclusions about relationships between costs and performed load change and the economic limit of demand response. Significant costs like accelerated membrane aging or anode degeneration depend on various load change parameters. These conclusions can be used to implement economic conditions in models for dispatch and scheduling or bidding strategies concerning the chlor-alkali process and similar electrochemical processes. We demonstrate that the chlor-alkali process with subsequent PVC manufacturing is economically viable under current market conditions.

Automation architecture for harnessing the demand response potential of aqueous parts cleaning machines

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory.

Physics-informed neural networks for building thermal modeling and demand response control

Buildings energy consumption constitutes over 40% of the total primary energy consumption, and buildings can provide potential energy flexibility for the grid. In the grid with high penetration of renewable energy, a control-oriented model with accurate prediction and prompt response is crucial for better load management. However, developing such a model is still a big challenge for grid-integrated buildings due to the limitations of current modeling approaches. Physics-based models commonly represent a specific building while customization for each building remains a daunting task; meanwhile, data-driven models, though easier to develop, require a large amount of data and are not well generalized for other buildings. Thus, we propose a novel model based on a physics-informed neural network dubbed as PhysCon, which combines the interpretable ability of physical laws and the expressive power of neural networks for control-oriented demand response of grid-integrated buildings. Specifically, a Resistance-Capacitance (2R2C) thermal model is adopted to express the knowledge of building physics, and a full-connected neural network is developed to learn the information from actual data. We demonstrate the prediction performance of PhysCon using actual building physics parameters and on-site measured data from an experimental platform that contains two detachable rooms. Two different internal thermal mass scenarios, namely light and heavy mass, were designed to demonstrate the power of PhysCon for buildings with different levels of demand response potentials. The results show that PhysCon outperforms the purely data-driven model in predicting the room temperature and thermal load demand for both light and heavy buildings. Moreover, building thermal mass is an efficient and free demand response resource for grid-integrated buildings. The proposed PhysCon model can be conveniently and efficiently deployed in the energy management system of buildings, owing to its simplicity and generalization.

Methodology to determine residential heating-based flexibility potential in electricity distribution networks

Demand response will be a valid tool to operate an electrical power system. A form of demand response is heating-based load control, which constitutes a significant proportion of households’ demand response potential. In this paper, the heating-based demand response is considered to provide flexibility for the operation of electricity distribution networks. The aim of the paper is to present a methodology to analyze the flexibility potential of the electric heating systems of residential electricity end-users to affect loading in electricity distribution networks. The methodology provides, with broad background information of buildings, electricity use, and electricity networks, an analysis of the effects of flexible heating loads on electricity distribution networks. The presented methodology is tested with verification data from a sparsely populated area in Finland. The verification analyses demonstrate the benefits and challenges of flexibility at the component level in an electricity distribution network. • Methodology to evaluate flexibility of residential electric heating without effect on comfort of living. • Assessment of effects of flexible electric heating on electricity distribution system. • Combination of large amount of electricity consumption data, building data, and electricity distribution network data for the verification analysis. • Hourly model to assess the effects on the electricity distribution system in the utility scale.

Real-time pricing method for VPP demand response based on PER-DDPG algorithm

Through advanced information communication and management system, virtual power plant (VPP) can realize the aggregation and coordination optimization of distributed energy, energy storage system, controllable load and other distributed energy resources. However, when making real-time price decisions according to users' demand response (DR) characteristics, the optimization effect of VPP is still limited by the evaluation accuracy of users’ DR potential and the computational burden of continuous decisions. By combining gate recurrent unit (GRU) and attention mechanism (AM), Neural Turing Machine (NTM) can extract users' response features in different environments and improve the accuracy of evaluating DR potential. Subsequently, based on the evaluation results, a deep deterministic policy gradient (DDPG) algorithm relying on prioritized experience replay (PER) is used to formulate a real-time electricity price plan. Ultimately, VPP achieves multi-objective optimization through DR management, which helps to increase the consumption amount of renewable energy resources, smooth its power fluctuation, and reduce users' electricity cost. Case study results show that the proposed method can improve the accuracy of the DR potential evaluation, reduce the response deviation to about 3%, and enhance the real-time decision calculation efficiency by 17%, which helps to optimize the smooth consumption of renewable energy.

Estimating cooling demand flexibility in a district energy system using temperature set point changes from selected buildings

Energy demand flexibility from buildings remains a largely untapped resource in electric power systems, in spite of its potential to be a low-cost substitute for investments in rarely used generation capacity. We develop a general methodology to estimate the aggregate cooling demand response for a large number of co-located buildings to thermostat temperature set point increases using empirically estimated building-level demand reductions in a subset of these buildings. For this subset of buildings, estimates were previously computed from real-world experimental data. The response of each remaining building is estimated as a different weighted sum of the empirical estimates, where the weights depend on observable characteristics of the buildings. We apply our method to a district energy system at a university campus that is roughly equivalent to a city of 30,000 people. Cooling is produced at a central energy facility with electric heat pumps and distributed to 124 commercial buildings through a chilled water loop. The response of six of these buildings to 1.1 °C (2 °F) daily temperature set point adjustments was previously estimated. Our methodology provides estimates for all 124 buildings and an estimate of the campus-wide demand response potential by leveraging a dataset including both structural (e.g. age, square footage) and operational (cooling loads and types of building operation) features for the full set of buildings. We estimate a 13.47% reduction in the campus energy system capacity needs under a 1.1 °C daily set point increase in all campus buildings during the 10 highest system demand days in 2020. On the highest demand day of 2020, we find that our predicted demand reduction could provide services equivalent to those provided by a lithium-ion battery with $4.6–$8.0 million installation cost at current prices and a storage capacity of 35.6–52.6 MWh.

Assessment Method for Demand Side User Response Potential based on Game Theory and Information Value

An accurate assessment of user demand response potential is helpful for power companies or load aggregators to rationally call demand-side resources, improve the implementation effect of demand response, and reduce the peak-valley load difference of power systems. This paper proposes a new approach based on game theory and information value to evaluate the response potential of demand-side users. Firstly, the evaluation index system of user response potential is established based on user history behaviors, user basic information, aggregator’s incentive to users, and user’s information value. Secondly, based on game theory, the total of subjective and objective weights is integrated, and the game solution of evaluation index weight is obtained. Finally, the user potential assessment results are quantified based on the fuzzy optimization set pair analysis theory to achieve an accurate online assessment of the demand-side user response potential.

Smart power consumption in energy digital economy: A perspective of the value co-creation mechanism

Residential load is one of the important components of the seasonal peak load of the power grid, and it is increasing each year, with a huge demand response potential. With the development of the energy digital economy, the demand response of the new power system shows the characteristics of multi-stakeholder participation. The development mode based on the value co-creation has become a prominent support for market-oriented reform, and the need for the promotion of smart electricity use is increasingly prominent. In order to realize the in-depth exploration of residents’ demand response potential and the sustainable development of “value co-creation” of smart electricity consumption with the participation of multi-stakeholders, this study adopts both the social network analysis method and the counterfactual analysis approach to reveal the general characteristics of agents promoting the residents to participate in the value co-creation of smart electricity positively. The results show that 1) for social network, both the absolute resource advantage and the structural hole have obvious positive guidance on the agent; however, the incentive effect of the relative resource advantage is not significant; 2) for individual nodes , the role positioning of each agent has obvious guiding function for realizing the value co-creation; and 3) for interrelationship among main agents, the functional relationship has a significant degree of interdependence.