Demand Response Events Research Articles

Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions

Energy communities enable close cooperation and collaboration among members (i.e., consumers, producers, and prosumers), where buildings, energy resources, and loads can be managed at the members' level while cooperating with the community. To influence and promote the community's members' interactions, models of energy sharing can be applied. This paper addresses the distribution of community-owned renewable energy among the community members' buildings. The proposed costless renewable energy distribution model considers the individual participation of each member in demand response events launched to promote the balance between consumption and generation inside the energy community. The distribution model can distribute renewable energy in a fair and costless approach, meaning that the energy receivers will not pay for the energy they receive. The proposed model uses the Shapley value to distribute renewable energy according to its members' individual contributions to the community. The proposed model is tested using a case study of an energy community, using a dataset for one summer month. The results show an energy cost reduction of 12.86 % for the community and an average energy cost reduction of 31.03 % for the community's members.

Do the customers remember? The fade-out effect from the demand response applied in the district heating system in Denmark

Buildings can deliver short-term thermal energy storage to energy systems. In district heating (DH) systems, it is mainly desk studies and simulations that reveal a large thermal flexibility potential. Knowledge from real-life case studies on how residents participate in demand management campaigns is crucial for the successful utilisation of buildings’ flexibility potential for minimizing bottlenecks in the daily operation of DH systems. In the field study including 72 single-family houses connected to the 3GDH network in southern Denmark, the demand response (DR) strategy “night setback” was applied for two heating periods. The houses were equipped with control and monitoring equipment, which allowed the deactivation of the heating system while monitoring the indoor temperature, so it does not drop below the defined value. The occupants controlled the DR events settings and could at any time stop utilisation of the night setback strategy (implicit participation in the DR). All 72 houses applied the night setback during both heating periods. Yet, the participation time decreased from 89% to 81%. The lowest participation rate was noted for the farm house, 60% and 9% of heating periods 1 and 2, respectively. In around 60% of the DR events, the night setback strategy was activated at 20:00.

Reinforcement learning-based demand response strategy for thermal energy storage air-conditioning system considering room temperature and humidity setpoints

Heating, ventilation, and air-conditioning systems significantly influence demand response (DR) actions. In the literature, many studies have investigated the reduction of sensible loads through DR events by increasing temperature setpoints while satisfying indoor comfort levels. However, less consideration has been given to latent loads. Therefore, proximal policy optimization reinforcement learning (RL) algorithms are adopted to simultaneously control indoor temperature and humidity (T&H) setpoints to reduce sensible and latent loads. An EnergyPlus-Python joint simulation platform was created for the temperature-humidity independent control system. DR strategies based on RL, active thermal energy storage, and time-of-use electricity prices are formulated to find the optimal indoor T&H setpoints, considering environmental constraints, comfort levels, and energy consumption. The results showed that in the optimal experimental strategies implemented based on negative feedback regulation, savings of 29.15 % on electricity consumption were achieved during DR periods and 46.11 % savings on the daily operating costs. Moreover, a simulation strategy based on feed-forward regulation was developed and found to reduce electricity consumption by 6.92 % during DR periods. Compared with a fixed T&H setpoint, the developed strategy meets the preferences of different groups of people for thermal comfort and increases the system's flexibility.

Potential quantification and impact factors analysis of energy flexibility in residential buildings with preheating control strategies

Demand response (DR) is an effective method for achieving energy flexibility. Preheating can store a certain amount of energy in the building while simultaneously reducing the heat load. The energy flexibility of a building during the heating season mainly depends on the types of DR events, the thermal performance of the building envelope, and weather conditions. Analyzing the impact of these factors on the preheating performance and exploring their interactions can reveal the potential for energy flexibility. This study proposes a simulation-based method to evaluate the energy flexibility of a residential building in Kitakyushu, Japan from the perspectives of energy efficiency, load and energy reduction. Rule-based control strategies are applied to heating, ventilation, and air conditioning systems. The results show that compared to preheating temperature of 24 °C, high insulation building at 25 °C achieves energy efficiency improvement of 3.5%–14.8% and load reduction increase of 1.6%–22.2%. Compared to low insulation building, preheating at 25 °C for 1 h increases energy efficiency in high insulation building by 6.2%–10.3%, with corresponding load reduction increase of 1.0%–19.1%. Extending the preheating duration improves load reduction but leads to heat loss. Preheating during heating system startup should be avoided as it produces the worst energy flexibility. Moreover, statistical analysis identifies key indicators for energy reduction: the inside surface temperature of the exterior wall (Pearson correlation coefficient = 0.820, standard regression coefficient = 0.924), followed by global horizontal radiation and building insulation performance.

Holistic Evaluation of Demand Response Events in Real Pilot Sites: From Baseline Calculation to Evaluation of Key Performance Indicators

Explicit demand response plays a significant role in the future energy grid transition, as it involves end consumers in smart grid activities and, at the same time, exploits the potential of flexibility, giving the opportunity to grid operators to accommodate a total amount of energy without the need to reinforce the grid infrastructure. For evaluating the successfulness of a demand response program, thus, evaluating its advantages, it is fundamental to have an accurate baseline curve consumption along with meaningful key performance indicators. In this work, we propose a novel way of calculating the baseline consumption using artificial intelligence techniques. In particular, regression models have been applied to a database of historical data. In order to present a complete evaluation of demand response programs, we present five key performance indicators (KPIs). The KPIs have been selected so as to depict the successfulness of the explicit demand response program. We suggest a novel way of evaluating two of the five KPI using a quantitative approach. We also apply the proposed methodology for baseline calculation and KPIs evaluation in a practical example: two pilot sites have been used and real-life scenarios of demand response events have been applied for this scope to actual nonindustrial consumers and especially residential consumers. The baseline has been calculated for these pilot sites and the KPIs have been evaluated for them. The presented results complete the picture of evaluating a real-life demand response program and show the effectiveness of the selected approach. The proposed schemes for baseline calculation and KPI evaluation can be used by the scientific community for evaluating future demand response programs, especially in the residential sector.

Multi-agent based distributed cooperative control of air-conditioning systems for building fast demand response

The utilization of air-conditioning systems for fast demand response is particularly valuable in addressing urgent power balance requirements, owning to their large electricity consumption and inherent energy flexibilities. Limiting the number of operating chillers has proven to be a cost-effective means for direct load control in many studies. However, the conventional feedback control fails to distribute the limited cooling supply evenly, resulting in inherent uneven temperature rises among individual air-conditioned spaces when using this direct load control. To tackle this issue, a distributed cooperative control approach is put forward in this research. The proposed control is achieved based on a multi-agent system (i.e., valve agents and damper agents) in a self-organizing way. Each agent performs on-site control using the collected information of its own and its neighbors in a distributed architecture. The validation tests demonstrate that the proposed control approach is capable of efficiently managing the uneven temperature increases in different zones/spaces of the building. During the demand response event, a significant reduction of 2,562 kWh electricity is achieved, accounting for 19.7% of the electricity consumption using the conventional control.

Online model-based predictive control with smart thermostats: application to an experimental house in Québec

This paper tests the impact of model resolution and structure on the performance of Model Predictive Control (MPC) implementation in an unoccupied research house in Québec equipped with smart thermostats. Two low-order models and a high-order multi-zone model were calibrated with measured data, with the structure of the multi-zone model being generated automatically during the calibration procedure. The three models were used to apply real-time MPC to an experimental house in Québec using the established dynamic tariffs for morning and evening peaks. MPC with any of the three models successfully preheated the house before the demand-response events, outperforming the reference reactive controller, reducing cost and thermal discomfort. The high-order multi-zone model performed the best, reducing average cost of electricity by 55% and high-price energy consumption by 71%, compared to the low-order models, which achieved cost reductions of 40% and 44% and energy consumption reductions of 48% and 54% respectively.

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m2), 10.0 W/m2, 11.1 W/m2, 7.1 W/m2, and 4.7 W/m2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. The comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

Assessment of the marginal technologies reacting to demand response events: A French case-study

Assessment of the marginal technologies reacting to demand response events: A French case-study

Development of energy aggregators for virtual communities: The energy efficiency-flexibility nexus for demand response

The implementation of load management and demand response programs is motivating utilities to propose demand-side management to incentivize customers to modify their energy consumption during critical events, providing energy flexibility to the grid. With the widespread use of distributed energy resources, energy aggregators have grown significantly to manage the portfolio of residential buildings. This paper presents a control-oriented implementation to define the energy flexibility potential of a portfolio of residential buildings within the same energy aggregator. The data-driven methodology combines the resistance-capacitance thermal network model of the households with building integrated photovoltaics/thermal and air source heat pump models to assess the modification of the baseline of power demand at the energy aggregator level. The optimization is performed through a model predictive control (MPC) framework for day-ahead predictions. Parametric analysis and scenario investigation are exploited to define the optimal configuration for both the households and the grid operator. The presented methodology provides insight for energy efficiency and flexibility about the operating and design of energy aggregators in a demand response program. A Canadian virtual community is used as a case study to estimate the performance of the presented methodology. The optimization of load management at the aggregator level is presented for a representative demand response (DR) event during the winter season for two different energy pricing structures. Numerical results show the potential of the proposed methodology to guide the design and operation of virtual clusters integrating renewable technologies towards energy efficiency, showing a reduction of 40% in total energy consumption and a peak reduction of around 32%.

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.

Resident-Centric Distributed Community Energy Management System

This study presents a resident-centric distributed community energy management system (CEMS). More specifically, the proposed resident-centric distributed CEMS allows residents to schedule their appliances autonomously, without the need to collaborate with the community and to consider whether their appliance scheduling is optimal from the perspective of the entire community. The central controller in the proposed CEMS will then determine a solution that is optimal for the entire community by dispatching the community's distributed energy sources according to the appliance scheduling of residents. In other words, the proposed distributed resident-centric CEMS allows residents to act autonomously while securing the collective goals of the community to a certain extent. In this paper, the collective goals of the community include participating in incentive-based demand response (IBDR) events at a specific time interval, and decreasing the total electricity cost of the community in response to time-varying electricity prices. The proposed distributed resident-centric CEMS is developed using the concept of distributed optimization and mixed-integer linear programming. Different types of public loads are incorporated into the proposed framework including stoppable and deferrable public loads. The simulation results show that the proposed framework dispatches power optimally.

DSO-Aggregator Demand Response Cooperation Framework towards Reliable, Fair and Secure Flexibility Dispatch

Unlocking flexibility on the demand side is a prerequisite for balancing supply and demand in distribution networks with high penetration levels of renewable energy sources that lead to high volatility in energy prices. The main means of fully gaining access to the untapped flexibility is the application of demand response (DR) schemes through aggregation. Notwithstanding, to extract the utmost of this potential, a combination of performance-, financial-, and technical-related parameters should be considered, a balance rarely identified in the state of the art. The contribution of this work lies in the introduction of a holistic DR framework that refines the DR-related strategies of the aggregator towards optimum flexibility dispatch, while facilitating its cooperation with the distribution system operator (DSO). The backbone of the proposed DR framework is a novel constrained-objective optimisation function which minimises the aggregator’s costs through optimal segmentation of customer groups based on fairness and reliability aspects, while maintaining the distribution balance of the grid. The proposed DR framework is evaluated on a modified IEEE 33-Bus radial distribution system where a real DR event is successfully executed. The flexibility of the most fair, reliable and profitable sources, identified by the developed optimisation function, is dispatched in an interoperable and secure manner without interrupting the normal operation of the distribution grid.

Rating and Remunerating the Load Shifting by Consumers Participating in Demand Response Programs

Effective and active consumers providing flexibility through Demand Response (DR) programs have three important aspects: rating each consumer according to previous participation, remuneration of that participation, and determining the rebound effect of consumption after the event. In this article, the authors design a rate to classify and select the proper participants for a DR event considering the context in which the event is triggered. The aggregator estimated the shifting of consumption to periods after the event is modeled, and the respective remuneration is estimated under different scenarios. This shifting can be done in several time frames in the future. The scenarios are developed to test the acceptable time range in which the load should be allocated according to the rebound effect. The results show that a higher time range can avoid huge peak consumption, optimizing the system operation with benefits for consumers, DSO, and the aggregator.

High resolution synthetic residential energy use profiles for the United States

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.

Incentive‐based demand response program with self‐reported baseline supported by blockchain technology

AbstractThis research presents a decentralised incentive‐based demand response (DR) program using blockchain technology. Consumers self‐report baseline to the system operator (SO), the smart contract confirms the validity of the data to execute transactions and finally the validators record the information on the blockchain network. During the DR event, a set of consumers are randomly selected to deliver the required load reduction. The signalled consumer who delivers the load reduction is rewarded, and the non‐called consumers who diverge from their reported baseline are penalised. The randomness of choosing the consumers and penalty function restrict the baseline inflation. Here, we create a blockchain network and deploy a smart contract on the Ethereum build platform. A DR event scenario is adopted with residential houses data sets, all consumers report their baseline information to the SO through the Internet and smart meter. The SO calls four users to deliver the essential load reduction according to the probability of choosing a consumer. The smart contract verifies the received information to start transaction execution. We use proof of authority mechanism to select validation nodes from the participants using voting system. They validate each block before adding it to the blockchain. Last, the monetary transactions settle in participants' wallets. Our results confirm that decentralised systems like blockchain can significantly improve transparency, openness, and customer participation in the DR program. Also contributes to the security and privacy of user information with a minimal investment in new infrastructure.

Precision and Accuracy Co-Optimization-Based Demand Response Baseline Load Estimation Using Bidirectional Data

Accurate baseline load estimation is critical for the compensation settlement of incentive-based demand response (DR). Baseline load estimation is different from load forecasting in evaluation metric and time scale. In terms of evaluation metric, Precision (i.e., the closeness between the estimated and the actual value) and Accuracy (i.e., lack of bias) are two aspects for assessing the performance of an estimation. Load forecasting usually focuses more on Precision and ignores Accuracy, while these two aspects are both important for baseline load estimation. In terms of time scale, load forecasting must be conducted in advance while baseline load estimation is an ex-post problem. However, baseline load estimation is usually formulated as a load forecasting problem in most existing studies, which can be further improved using the load data before and after the DR event. This paper proposes a Precision and Accuracy co-optimization based baseline load estimation framework to improve its performance. The proposed framework uses the load data not only before but also after the DR event day as the input features. An optimal weighted ensemble method is proposed to combine these input features, in which both Precision and Accuracy are considered into the objective function. The effectiveness and superiority of the proposed framework have been verified on a real smart meter dataset.

A Techno-Social Approach to Unlocking Vehicle to Everything (V2X) Integration: A Real-World Demonstration

The urgent transition towards electric vehicles (EVs) may potentially result in expensive capacity upgrades for power system operators (PSOs), however, the recent uptake in bidirectional EV charging, often referred to as "Vehicle to Everything" (V2X), may defer this need while simultaneously generating revenue for participating EV owners. Yet, there is a lack of real-world investigation to elicit (a) the willingness of EV owners to participate in V2X programs (b) the alignment of these programs within existing electricity markets, and (c) how V2X may be combined with other distributed energy resources (DERs) to realize maximum energy savings. As such, this paper proposes the design and implementation of a V2X program that is informed by the socio-technical preferences of EV owners, including convenience, revenue, and emissions savings. A survey is distributed to 124 EV owners to characterize their degree of willingness to participate in the program, which leads to the design of several EV session types that enable EV owners to arbitrage at their convenience, or participate in demand response (DR) events for higher revenue. Real-world tests at residential and commercial buildings in Ontario successfully demonstrate the facilitation of both arbitrage and DR, leading to increased revenue, decreased emissions, and the ability to participate in DR with other building DERs to reduce the building load by 30 kW. The proposed work contributes a novel attempt to demonstrate pathways to realize the potential offerings of V2X programs to benefit both EV owners, building owners, and PSOs.

Demand Response Event Participants Selection Using Classification Methods

The active consumers' role in the power and energy market is changing. To deal with the volatile behavior from the Distributed Generation (DG) resources, load flexibility must be provided from the demand side. So, Demand Response (DR) events are triggered. To reduce the response uncertainty, the authors proposed a Trustworthy Rate (TR) to classify the performance of the active consumers in a community. For comparison with previous works' approaches, the innovation from the present study introduces classification methods for deciding the best approach for selecting the DR program participants (load shifting) for specific contexts and reduction goals. From the results, the Decision Tree model created had a lower accuracy performance than the Artificial Neural Network one. To evaluate the effectiveness of the proposed model, the mean absolute error (MAE) was used by the authors.

Dynamic room temperature setpoints of air-conditioning demand response based on heat balance equations with thermal comfort model as constraint: On-site experiment and simulation

Demand response (DR) can alleviate the peak load of the grid and enhance its stability. The centralized controllability of heating, ventilation, and air-conditioning (HVAC) systems along with the inertia of the building makes it a potential efficient participant in DR events. In this regard, changing room temperature setpoints is considered a traditional DR strategy. Many studies have investigated the improvement of the indoor temperature settings during DR events, concentrating on the control and management aspects. However, less attention has been paid to the effect of temperature change on the indoor thermal balance factor and thermal comfort levels. In this work, a method based on heat balance equations combined with a thermal comfort model as a constraint is proposed for dynamically adjusting room temperature setpoint to tap the energy-saving potential of air-conditioning systems. According to the outdoor hourly temperature predicted by a radial basis function (RBF) neural network algorithm, combined with time-of-use (TOU) electricity price information, different thermal comfort models are established in DR and non-DR periods. Using this information, the optimal indoor hourly temperature setpoint is calculated using heat balance equations and a thermal comfort indicator. Physical experiments and EnergyPlus simulations are carried out to investigate and evaluate the proposed strategies. The results show that the dynamic temperature setpoint can save 2.8% on electricity consumption and 3.73% on operational costs compared to the fixed temperature setpoint scenario under the premise of ensuring thermal comfort.