Typical Demand Response Research Articles

A Cloud–Edge Collaborative Multi-Timescale Scheduling Strategy for Peak Regulation and Renewable Energy Integration in Distributed Multi-Energy Systems

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management.

Study on Integrated Demand Response Strategy and System Coordination and Optimization of Park Integrated Energy System

The need for flexibility in integrated energy systems is progressively becoming more demanding as these systems expand. Three types of demand response are introduced. An integrated demand response strategy is developed to address the situation based on an analysis based on the fundamental model of the Park Integrated Energy System. Additionally, the coordinated optimization model of the Park Integrated Energy System is created to minimize operational costs while considering a variety of restrictions.

Research on optimal scheduling of integrated energy system in low-carbon parks based on demand response

Abstract The comprehensive energy system of the park is one of the effective ways to solve the problems of low utilization efficiency of comprehensive energy and difficulty in absorbing renewable energy. By coordinating the output of each unit to optimize the scheduling of the system, the operating cost of the system can be reduced to a certain extent, and the space for new energy generation to be connected to the Internet can be increased. Aiming at the characteristics of electric, heat and gas load demand of the integrated energy system of industrial low-carbon parks and the overall needs of low-carbon development, a research on the optimal scheduling of integrated energy systems in low-carbon parks based on demand response is proposed. By analyzing the structure and components of the integrated energy system of the low-carbon park, different types of demand response are modeled. On this basis, based on the comprehensive demand response of electricity and heat, the optimal dispatch model of the integrated energy system of the low-carbon park is constructed, and the model is solved to realize the optimal dispatch of the integrated energy system of the low-carbon park. The experimental resu lts show that the proposed method has lower operating cost of the integrated energy system, better economy of the integrated energy system and can effectively improve the power supply reliability and energy saving rate of the integrated energy system. The average system load rate of the proposed method is up to 98.7%, the average comprehensive energy utilization rate is up to 97.9% and the system operation cost is only 10343.1 yuan.

Residential load forecasting based on electricity consumption pattern clustering

In order to reduce the peak load on the power grid, various types of demand response (DR) programs have been developed rapidly, and an increasing number of residents have participated in the DR. The change in residential electricity consumption behavior increases the randomness of electricity load power, which makes residential load forecasting relatively difficult. Aiming at increasing the accuracy of residential load forecasting, an innovative electricity consumption pattern clustering is implemented in this paper. Six categories of residential load are clustered considering the power consumption characteristics of high-energy-consuming equipment, using the entropy method and criteria importance though intercrieria correlation (CRITIC) method. Next, based on the clustering results, the residential load is predicted by the fully-connected deep neural network (FDNN). Compared with the prediction result without clustering, the method proposed in this paper improves the accuracy of the prediction by 5.21%, which is demonstrated in the simulation.

Load-driven interactions between energy efficiency and demand response on regional grid scales

• We simulate bottom-up energy efficiency load impacts at building and grid scales. • Efficiency can interact differently with demand response on different scales. • We see no fixed relationship between efficiency and demand response; details matter. • Most often, there is competition between efficiency and demand response. • Strong complementarity is also possible, especially with controls-based efficiency. Energy efficiency (EE) has long been recognized as a source of value to the electricity grid. Especially with increasing penetration of variable renewable generation, demand response (DR) can also provide system value and support the evolving needs of the grid. Yet there has been little study to date of interactions between EE and DR that may complicate their grid impacts. In this study we perform bottom-up modelling of the interactive effects between EE and DR in buildings for three representative regions of the United States electricity grid. Leveraging new simulation tools that enable detailed modelling of the building stock, we synthesize system-level demand profiles for several scenarios representing different portfolios of EE measures. In each scenario, we couple the underlying building models with a database of DR-enabling technologies to estimate building-level DR capabilities and compute a system-level supply curve for DR. We assess the resulting EE and DR interactive effects based on an existing conceptual framework. The results show a complex relationship between EE and DR, with interactive effects whose size and direction can vary widely depending on the grid system, type of DR, and the framework level being considered. Most often, the overall effect is competition between EE and DR, but significant complementarity can also occur, especially when the EE portfolio includes controls measures. Our results suggest that EE and DR programs developed without considering interactive effects may erode the benefits of both resources, whereas a more integrated approach may yield increased benefits.

Metrics to describe changes in the power system need for demand response resources

Grid decarbonization efforts can benefit significantly from demand response (DR) resources. However, system-level changes that affect the net-load such as increased variable renewable energy (VRE) generation and widespread deployment of energy efficiency (EE) also affect the type, magnitude and timing of DR required to support the grid. In this study, we use publicly available system-level data to define seven metrics to assess how these changes affect system-level shed and shift DR needs. Specifically, there are four metrics for grid conditions when DR has the highest system value and three metrics for DR program design that were developed by considering the magnitude and temporal distribution of net-load. We also develop three stylized load shape profiles illustrating EE measure impacts and one high VRE generation profile to demonstrate the application of these metrics. The results confirm the robustness of the metrics to identify complex interactions between demand-side and supply-side resources that can affect the DR need. Widespread application of our metrics can help system planners and operators be cognizant of such interactions and identify the DR need for the system in a way that can be most valuable.

Matrix modelling and optimisation calculation method for large‐scale integrated We‐Energy

We-Energy (WE), as an important energy unit with full duplex and multi-energy carriers in the integrated energy system, uses the coupling matrix to connect the network-side and demand-side energy. However, the coupling matrix of WE is very hard to be formulated directly due to the complicated internal structure and the flexibility of operation mode. This paper proposes a multi-step method for the modelling of WE. According to the method, the conversion process in the WE can be separated into several steps and the WE model can be built by the coupling matrix in each step. Then, the WE model is extended by considering the renewable energy, location of storage and different types of demand response. Because of the non-linearity caused by the dispatch factors, the computational complexity increases greatly for solving the optimal scheduling issue of the WE. In order to reduce the computational burden, the variable substitution is added in the proposed modelling method. The results of simulation cases are presented to demonstrate the performance of the proposed modelling and calculation method.

Fairness algorithm for emergency demand response operation in distribution networks

As a response to increased power demand, electrical utilities have started to use Demand Response (DR) programs to improve the stability, efficiency and reliability of their networks. One type of DR, an Emergency Demand Response Program (EDRP), addresses stability challenges by sending curtailment orders to consumers associated with the program. There are different methods for calculating the amount of power that should be curtailed and how it should be divided among consumers. An important aspect of curtailment assignment is fairness, yet there is no agreed-upon manner of achieving fairness in curtailment allocation. This paper presents a new fairness-based EDRP for a residential area. The fairness is manifested in the algorithm by taking into account the number of times and the amount of energy a customer is asked to curtail for a predetermined period of time. The proposed algorithm is demonstrated and verified using simulations.

Robust Distribution System Expansion Planning Incorporating Thermostatically-Controlled-Load Demand Response Resource

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.

Classifying and modelling demand response in power systems

Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models.

Flexibility Optimal Dispatching for Power System Considering Demand Response

With the increasing penetration of renewable energy sources such as wind and solar power, higher requirements of flexible supply ability for power systems are put forward. In this context, an optimal dispatching method considering two types of demand response is proposed to improve power system flexibility. On the demand side, both the time-of-use (TOU) price and interruptible load are used to guide consumers to actively optimize the load curve, so as to relieve the pressure of the flexible supply of conventional units on the supply side. The demand response model consisting of TOU price and interruptible load is built. Then, taking the minimum cost as the objective function, the optimization model for power system dispatching is solved. Finally, the modified IEEE 30 bus system is taken as a case study. The results show that the consideration of two-type demand response in the optimal dispatching can improve the flexibility while ensuring economy, which verifies the effectiveness of the proposed method.

Blockchain‐based framework of power demand response in China

Demand response is recognized as an effective solution for eliminating power fluctuations and satisfying capacity constraints in power systems. A growing customer base equipped with energy storage and intelligent power meter on the demand side has resulted in the strong interest of China's power companies in demand response. However, most exiting demand response programs in China are based on a centralized framework, which is easier for management, but cannot support a large number of scattered small-scale users' participation effectively. Therefore, taking the desired features of blockchain technology, such as decentralization, trustworthiness, trackability, and immutability into consideration, the applicability of blockchain in demand response is analyzed. On this basis, a new blockchain-based framework of China's typical demand response programs is proposed, in which consensus mechanism, encryption algorithm, and smart contract of blockchain are applied to the process of invitation, bidding, and settlement in demand response. Furthermore, the development suggestions of China's demand response based on blockchain technology are put forward from the aspects of trading products, credit management, and platform construction at the end of this work.

Power system planning with high renewable energy penetration considering demand response

Electric system planning with high variable renewable energy (VRE) penetration levels has attracted great attention world-wide. Electricity production of VRE highly depends on the weather conditions and thus involves large variability, uncertainty, and low-capacity credit. This gives rise to significant challenges for power system planning. Currently, many solutions are proposed to address the issue of operational flexibility inadequacy, including flexibility retrofit of thermal units, inter-regional transmission, electricity energy storage, and demand response (DR). Evidently, the performance and the cost of various solutions are different. It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source. In this study, the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed. Two types of DRs, namely interrupted DR and transferred DR, were modeled. Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization. Clustered unit commitment constraints for accommodating variability of renewables were incorporated. Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.

Optimal sizing and operation for microgrid with renewable energy considering two types demand response

The integration of renewable energy sources into the power grid poses several challenges due to their variability in output power as they primarily depend on weather conditions. As a countermeasure, battery energy storage systems are introduced in order to mitigate output power fluctuations of the renewable energy sources. Moreover, demand response programs have been attracting huge attention as an effective mechanism for efficient energy management. It enables power suppliers to cope with the output uncertainty of renewable energy systems. First step, we propose an HRES design methodology that takes into account one year. In order to find the optimal power supply configuration, it is necessary to take into account the changes in load demand due to weather conditions and seasons for one year. However, the computational cost of simulating one year is huge and requires a rather long simulation time. Therefore, we propose a simplified simulation method that uses clustering to account for changes in load demand due to weather and seasonality in one year. Second, the first proposed method is used to propose a method for optimizing the HRES equipment capacity and operation schedule considering demand response. In this paper, mixed-integer linear programing is used as the optimization method. The proposed method considers two types of demand response and achieves cost reduction by controlling load demand in response to generated power fluctuations.

Assessments of data centers for provision of frequency regulation

There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation-based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. When chillers are deactivated, both strategies have a similar regulation capacity.

Demand response and smart technology in theory and practice: Customer experiences and system actors

Energy transitions change the relationships between technologies and human actors. Demand response (DR), the matching of demand to available electricity supply, is a relatively new activity, important for systems that rely on distributed renewable generation. Price-based DR is spreading among residential and small business customers, along with direct control of distributed and aggregated small loads, mostly thermal. In both types of DR, information and communication technology plays a part.This paper contributes to the debates on implementing electricity system transition and on adoption of smart technologies. Through analysis of a large-scale demonstration of highly-distributed smart thermal storage in three European countries, using mixed methods, it supports the claim that DR can usefully be seen as an outcome of interactions between technologies, activities and service expectations. Adopting a broad scope of enquiry to take in customer experiences of DR and the contributions from a range of actors, the paper shows how it was possible to achieve useful levels of aggregated DR in some circumstances. The quality and quantity of DR were influenced by customer experiences of taking part in the demonstration, which in turn were affected by three types of communication: connectivity, control and care. Outcomes also depended on contributions from a range of actors, especially the 'middle actors' who had some direct contact with both customers and programme leaders.Mainstream theory of DR has concentrated on deploying technologies, controls and price signals. The paper demonstrates how, in practice, effectiveness relates to social and organisational as well as technical and economic features of energy systems. It concludes with some implications for design and implementation of DR programmes, and for smart energy policy in general.

Multi-objective performance of smart hybrid energy system with Multi-optimal participation of customers in day-ahead energy market

Optimal energy consumption is one of the sustainable development issues in many countries to improve the economic and environmental indices in the energy sector. This paper presents a tri-objective optimal performance of a smart hybrid energy system (SHES) in the presence of customer's participation to optimally reshape the demand profile in the day-ahead energy market. Minimizing the operation costs and the emission pollution as well as maximizing the customer satisfaction level are considered as the objectives of this problem. The three types of demand response (DR) programs consisting of 1) demand curtailment, 2) demand shifting and 3) onsite generation program are considered for optimal scheduling of the electrical and the thermal energy consumption by the customers. The demand curtailment program is considered as the reserve for SHES and the Plug Electric Vehicles (PEVs) are taken into account as the onsite generation program. The uncertainties of energy and reserve prices are modeled using lognormal distribution function. The shuffled frog leaping algorithm (SFLA) is employed to solve the problem from which the non-dominated solutions are generated. Then, the best solution of the non-dominated solutions is selected by the hybrid approach of fuzzy method and the weight sum. To validate the mentioned approach, five case studies are investigated and the results demonstrate optimal scheduling of SHES with acceptable levels of operation costs, emission pollution and customer satisfaction.

A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response

The uncertainty of demand response (DR) will affect the economics of power grid dispatch due to the randomness of participating users’ intentions. According to the different working mechanisms of price-based demand response (PBDR) and incentive-based demand response (IBDR), the uncertainty models of two types of DR were established, respectively. Firstly, the fuzzy variable was used to describe the load change in PBDR, and the robust optimization theory was used to establish the uncertain set of the actual interruption of the interruptible load (IL). Secondly, according to the different acting speed of the two types of DR, they were deployed in the two-stage scheduling model with other output resources; then based on the fuzzy chance constrained programming theory and multi-stage robust optimization theory, the dispatch problem was transformed and solved by the bat algorithm (BA) and the entropy weighting method. Consequently, intraday running costs decrease with increasing confidence of day-ahead, but increase with day-in reliability, and the economics of the model were verified in the improved IEEE33 node system.

A Model for Multi-Energy Demand Response with Its Application in Optimal TOU Price

With the generalization of the integrated energy system (IES) on the demand side, multi-energy users may participate in a demand response (DR) program based on their flexible consumption of energy. However, since users could choose using alternative energy or transfer energy consumption to other time periods, obtaining response characteristics of this type of DR usually appears more complicated than traditional single-energy DR. To obtain the response characteristic, a response model for multi-energy DR, which reflects the relations between electricity (gas) response and time-of-use (TOU) electric prices, is proposed. The model is characterized by several coefficients which are associated with electric and heat efficiency. The model is obtained through the derivation process of optimizing user’s energy-using problem. Then, as a typical application of the response model, the TOU electric pricing for multi-energy users is able to be formulated by an interior point algorithm after giving the Kuhn-Tucker conditions of the optimal problem. Typical results of the optimal TOU pricing are further illustrated through the formulation on a PJM five-bus test system. It demonstrates that optimal TOU pricing can be effectively pre-calculated by the utility company using the proposed response model.

Effect of considering demand response program (DRP) in optimal configuration of combined heat and power (CHP)

ABSTRACT Cogeneration or combined heat and power (CHP) is the simultaneous production of thermal and electric energies. Usually, CHP is used in residential loads to supply both kinds of energies. To modify daily load curve and shift demands to off-peak times, demand response programmes (DRP) are introduced. By utilising DR programmes, the possibility of interruption can be reduced. In this paper, reliability indices are considered as system uncertainties. Furthermore, several types of demand response (DR) are employed in the objective function. The planning problem, in this paper, has been solved as an operational horizon. All of the simulations have been carried out using MATLAB software. Moreover, GAMS software is used to solve the optimisation problem. Furthermore, a GAMS tool called SCENRED is employed to reduce the number of scenarios and problem complexity.