Investigation and Research on the Potential of Resident User Demand Response Based on Big Data

In the process of demand response execution, the incentive intensity directly affects the demand response potential of energy-intensive enterprise. Taking the silicon carbide enterprise as an example, firstly, the production process of silicon carbide and the adjustment characteristic of key equipment smelting furnace is analyzed. Then, a refined cost model that considers various costs and benefits of demand response is established, and the optimization goal is to minimize the operating cost of the enterprise. Finally, the influence of different incentive subsidy on the user's demand response potential is analyzed. The research results can support demand response incentive subsidy price setting and demand response potential assessment.

The demand response potential in copper production

Smart meter data provides rich information on customers’ energy consumption behaviors, which can be a valuable resource for evaluating customers’ demand response (DR) potential and thus can inform the distribution system expansion planning (DSEP). This paper presents a novel DSEP framework incorporating a data-driven model for a particular type of incentive-based DR called the thermostatically-controlled-load DR. The heterogeneity in individual customers’ DR potential is considered in the DSEP by leveraging the healing, ventilation, and air conditioning (HVAC) load information extracted from smart meter data. The relationship between the DR incentive and the customer participation rate is also considered so that incentives can be optimally designed for customers with different DR potentials in a differentiated way. The overall DSEP problem is formulated into a robust optimization framework to address the uncertainties from the load demands, renewable energy generations, and DR resources. Case studies show that the proposed DSEP model can substantially reduce the total expansion cost over conventional planning paradigms, demonstrating the positive role of the proposed data-driven DR model in DSEP.

As an intermediary between residential customers and system operators, load aggregators (LA) are responsible for integrating the residential customers' demand response (DR) potential and enabling their transactions in the day-ahead market. Profit of the market trading process mainly relies on the pre-estimated achievable DR potential, which puts forwards the necessity of its accurate forecasting. Therefore, this paper proposes a probabilistic forecasting model to forecast the day-ahead achievable DR potential at the aggregated level under an incentive-based demand response (IBDR) program. Firstly, we attempt to establish the customers' DR potential, during which a home energy management system (HEMS) is introduced to implement load adjustment for electrical appliances. Secondly, several features that may affect the DR potential are extracted, among which the more relevant ones are selected through the support vector machine recursive feature elimination (SVM-RFE) method. Finally, based on these selected features, a support vector machine (SVM) method is adopted to establish the DR potential point forecasting model, and then the probabilistic forecasting model of the aggregated DR potential is established through the superimposition of the point forecasting results and the corresponding error distribution, the latter could be estimated by the non-parametric kernel density (NKDE) method. Case studies show that a good performance could be achieved by the proposed probabilistic forecasting model and the feature selection process could significantly improve the forecasting accuracy.

The uncertainty of power load is one of the important research directions in demand response uncertainty. Accurate and effective power system load forecasting is an important prerequisite for ensuring the safety, stable operation, and normal production of the power grid. To improve the accuracy of short-term load forecasting in power systems under demand response scenarios, this paper proposes a Transformer load forecasting method that considers demand response potential. Firstly, the change law of response uncertainty with electricity price difference and consumer psychology principles are used to quantify the power demand response results under different probability conditions. Then, Transformer neural networks are used to extract features from user historical load, temperature, electricity price, and other time series data. Finally, a multi-head self-attention mechanism is used to pay attention to the structural relationship between time series data, analyze the importance of input variables at each historical moment on the current load, and achieve high-precision prediction of user load and demand response potential. This article takes industrial users as an example to predict the power load and demand response regulation power of the general component manufacturing industry. Through comparative analysis with actual data, the effectiveness of the proposed method is verified. Compared with other existing methods, the Transformer model that considers demand response performs well in power load forecasting, providing a certain theoretical basis for evaluating the potential of demand response. The subsequent work will study the characteristics of electric, hot, and cold loads and their coupling relationships under the difference of electricity prices, and improve the forecasting performance of user loads and Demand Response Regulation power, so as to reduce the power generation and operation costs of the grid.

To address the problem that the existing demand response schemes and electricity consumption optimization strategies can hardly adapt to the increasingly complex and diverse user demand, this paper proposes an incentive-based demand response scheme definition method based on user behavior analysis to deeply explore the user demand response potential. Firstly, this paper analyzes characteristics of the users the of comprehensive baseline suitability portrait of demand response with considering user daily load pattern event probability characteristics, user baseline demand response fitness and user daily load pattern baseline capacity, and classifies users by using spectral clustering. Secondly, based on the results of the above user classification, the selection mechanism of user response scheme definition is established based on medium- and long-term, short-term and comprehensive user incentive demand response fitness evaluation, and the users suitable for participating in incentive-based demand response are selected. Finally, the analysis of arithmetic examples shows that the user classification model proposed in this paper can effectively distinguish the potential demand response resource capacity level in the daily electricity load of residential customers, and the proposed dual time scale demand response potential user selection mechanism can improve the effectiveness in terms of decision accuracy.

Practical demand response potential evaluation of air-conditioning loads for aggregated customers

Demand Response Potential: Available when Needed?

Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads

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.

Energy efficiency (EE) and demand response (DR) resources provide important utility system and ratepayer benefits. At the same time, the rapid change in the amount and type of variable renewable energy, like solar and wind, is reshaping the role and economic value of EE and DR, and will likely affect time-dependent valuation of EE and DR measures. Utilities are increasingly interested in integrating EE and DR measures and technologies (as well as other distributed energy resources) as a strategic approach to improve their collective cost-effectiveness and performance. However, the specific EE and DR features that may be best integrated, the interplay between changing EE and DR resource potential, and the resulting utility system impacts, are not well understood. We develop a framework to identify the EE and DR attributes, system conditions, and technological factors that are likely to drive interactions between EE and DR. We apply the framework to example measures with different technology specifics (e.g., presence of controls, building type, and targeted end use) in the context of different system conditions (e.g., peak demand, load-building periods during high renewable generation output). Ultimately, the framework defines EE and DR interactions not only by the change in discretionary load (i.e., DR potential) but also by the change in likelihood of participation in EE and DR programs—as well as the change in system need for, and the overall availability of, EE and DR resources. The framework is intended to improve the integration of EE and DR in utility operational and planning activities.

Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

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

Author(s): Homan, Gregory K.; Aghajanzadeh, Arian; McKane, Aimee | Abstract: During periods of peak electrical demand on the energy grid or when there is a shortage of supply, the stability of the grid may be compromised or the cost of supplying electricity may rise dramatically, respectively. Demand response programs are designed to mitigate the severity of these problems and improve reliability by reducing the demand on the grid during such critical times. In 2010, the Demand Response Research Center convened a group of industry experts to suggest potential industries that would be good demand response program candidates for further review. The dairy industry was suggested due to the perception that the industry had suitable flexibility and automatic controls in place. The purpose of this report is to provide an initial description of the industry with regard to demand response potential, specifically automated demand response. This report qualitatively describes the potential for participation in demand response and automated demand response by dairy processing facilities in California, as well as barriers to widespread participation. The report first describes the magnitude, timing, location, purpose, and manner of energy use. Typical process equipment and controls are discussed, as well as common impediments to participation in demand response and automated demand response programs. Two case studies of demand response at dairy facilities in California and across the country are reviewed. Finally, recommendations are made for future research that can enhance the understanding of demand response potential in this industry.

The rural light industrial load is an important regional resource that could interact with the power grid and participate in demand response. Productive loads account for the majority, with the characteristics of aggregation, need for human assistance, and single electrical equipment. Exploiting the schedulable potential of productive loads could provide demand response services for power systems. This paper establishes a model considering characteristics of productive loads, including time constraints, production characteristics constraints, and so on. Taking liquor-brewing industry as an example, capacity and cost evaluation of demand response method for light industry clusters is proposed. Based on the actual load data of several townships in Guizhou, China, cases were conducted to verify that liquor-brewing industry clusters could provide the capacity of demand response. As capacity increases, the cost of participating in demand response increases, and even the opportunity cost caused by production losses needs to be considered. The proposed evaluation method could guide users' demand response behavior.