Demand Response Participation Research Articles

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.

WITHDRAWN: Short term optimal scheduling of a virtual power plants integrated with electrothermal hybrid energy storage system using an effective adaptive multi-objective differential evolution algorithm

virtual power plants (VPPs) in the power distribution grid play an important role in balancing the power and heat generation and consumption in residential, commercial, and industrial sectors. This study proposes VPPs for risk-averse energy exchange of resources and customers in the presence of the hybrid storage system. An appropriate energy management program is presented to make a balance between generators and consumers in VPPs considering generation uncertainties including electricity demand and price forecasting in the electricity market and renewable energy uncertainties. Hence, monetary exchange methods such as day-ahead agreements are proposed for energy exchange among VPPs. The expanded exchange structure of the suggested framework simplifies the renewable energy's sources to the electricity market despite its small-scale and non-dispatchable features. Also, a novel model is presented for short-term programing of virtual power plant considering technical and economic uncertainties. Given that VPPs can enter the electricity market based on time-of-use (TOU) pricing strategy and participation in demand response (DR) program, minimizing the expected cost of energy generation and environmental pollution is considered as the objective function. A multi-objective efficient model of energy management employing the machine speed scaling mechanism is suggested. An effective adaptive multi-objective differential evolution (AMODE) algorithm is presented to solve the multi-objective optimization problem. AMODE usages a novel Fitness Evaluation Mechanism (FEM) using dynamic reference point and fuzzy association randomness study to evaluate solutions in the evolutionary population by implementing the conditional value at risk (CVaR) method for risk management. The simulation studies and sensitivity analysis results are evaluated to confirm the effectiveness of the proposed model on a VPP in the island and grid-connected modes with and without participation in DR program. Finally, the conceptual results are discussed.

Reliable energy management of residential buildings with hybrid energy systems

Distributed energy resources like photovoltaic (PV), fuel cells (FCs), and others are receiving more and more attention as a solution to the rise in residential energy consumption. In this paper, a reliability-based energy management model is proposed for residential buildings with local generation units. The proposed model studies the contingency analysis of the hybrid system of the residential buildings considering demand response (DR) programs under different participation rates of residents in DR. Along with accounting for the energy flows across the various components of the hybrid energy system, the analysis of PV power output utilization to sale of power to the main grid, battery charging, and demand supply are also taken into consideration. The proposed model aims to choose the best operating strategies while ensuring the system is well-functioning with energy supplement costs as the objective function to be reduced. In addition to the optimal electric and thermal balances, findings allow for the reasonable deduction of the PV module and fuel cell forms. A numerical example is implemented and examined to confirm the practicality of the suggested approach. From various viewpoints, the best operational techniques are determined and contrasted, where the findings are analyzed considering the reliable operation of the system. Applying DR with different participation rates has varying effects on energy management during contingency occurrences at off/on-peak hours. A 30% DR participation during off-peak hours reduces daily energy supplement cost by roughly one third. During on-peak hours, DR participation reduces energy supplement cost by over 76%.

Real-time under-voltage load shedding method considering integer-value load model and feeder participation in demand response

Real-time under-voltage load shedding method considering integer-value load model and feeder participation in demand response

Priority Analysis of Influence Factors for Electric Vehicle Demand Response Strategies

As the penetration of renewable energy continues to increase, the demand-side resources in the grid will become more and more important. Electric vehicles (EVs) account for a relatively large proportion of demand-side resources, but individual and social factors have been less considered in multifactorial studies affecting EV participation in demand response (DR), and the multiscenario DR process has not been adequately studied. Therefore, an EV demand response strategy considering the influence of multiple factors is proposed in this paper. Firstly, a multisource charging load characteristic model is constructed by analyzing the characteristics of EV charging behavior under multiple scenarios. Secondly, the DEMATEL-AISM method is used to analyze the degree of influence of personal and social factors on users’ charging behavior under complex social environments, and the dominant factors in each scenario are identified. Finally, based on the analysis of the dominant factors in multiple scenarios, an EV regulation strategy under the influence of multiple factors is developed to achieve peak shaving. The feasibility of the proposed method is verified through simulation cases. The simulation results reveal that the revenues of aggregators and users are improved, and the stability of the power system is enhanced.

Distributed energy storage operation optimization model considering demand response

With the rapid development of new energy, wind power, photovoltaic, geothermal and other energy sources are connected to the power grid on a large scale, showing the characteristics of system stability and reliability, complex safety characteristics, imbalance between supply and demand, and insufficient peak shaving capacity. As a flexible demand response resource, distributed energy storage can effectively promote the coordinated and stable operation of power supply and demand resources. Considering the economy and technology of distributed aggregators, an operation optimization model for their participation in demand response is constructed, and a distributed energy storage operation control strategy considering demand response is proposed, which can effectively realize peak load shifting.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configuration within the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

An investigation of the customer baseline load (CBL) calculation for industrial demand response participants –A regional case study from Iran

In today's power systems, performing demand response (DR) programs is considered a solution for sustainable operation, carbon reduction, and facilitating renewable energy integration. Currently in Iran, several DR programs are available to industrial customers. Moreover, the design of a market-based DR has recently been considered by the power system operator. In DR programs, customer baseline load (CBL) is used to determine the level of customers’ demand reduction and is key to the billing process; thus, the CBL calculation methodology can directly affect the efficiency of these programs. This paper investigates the current CBL calculation method in Iran's power system and, as its main contribution, proposes some effective methods to reduce the probable incentives for CBL manipulation. In order to show the effectiveness of the methods, a real study case is considered based on the historical load data of the industries in the Khorasan region.

Blockchain + IoT sensor network to measure, evaluate and incentivize personal environmental accounting and efficient energy use in indoor spaces

Electric demand flexibility in buildings is highly dependent on occupant behavior. Evaluating and incentivizing these behaviors can provide grid-responsive support and encourage demand response (DR) participation. To achieve these goals, we developed an infrastructure for connecting Internet of Things (IoT) sensors to a distributed ledger (blockchain network) for long-term monitoring of energy and environmental performance. This study presents a novel Blockchain + IoT paradigm for the building science research community, applied in a real-world application. This Blockchain + IoT Network (BIN) uses Raspberry Pi minicomputers as platforms for connecting sensors to a blockchain network, to provide and analyze real-time indoor environmental quality (IEQ), energy, and carbon intensity data. As part of the study, we propose various metrics to evaluate the environmental footprints of building users. Novel algorithms for normalizing energy usage and carbon intensity, with consideration of a variety of related environmental factors, are executed as smart contracts on the blockchain network. All measurements and the smart contract transactions are reported and visualized on live dashboards. The use of smart contract allocates tokens based on the reward algorithms to incentivize individuals’ energy conservation, and similarly to DR pricing, can help influence occupant consumption patterns towards carbon reduction goals. We further test the smart contract’s algorithm in relation to real sensor data we have collected in two case studies: single-unit households and carbon intensity in the energy market. The combination of proposed metrics translates measured sensor data into token awards, demonstrates upper and lower limits dictated by the grid generation mix profile, and indicates that there is the potential for load shifting to minimize carbon emissions without considering the scale of consumption.

Behavior-Aware Aggregation of Distributed Energy Resources for Risk-Aware Operational Scheduling of Distribution Systems

Recently there has been a considerable increase in the penetration level of distributed energy resources (DERs) due to various factors, such as the increasing affordability of these resources, the global movement towards sustainable energy, and the energy democracy movement. However, the uncertainty and variability of DERs introduce new challenges for power system operations. Advanced techniques that account for the characteristics of DERs, i.e., their intermittency and human-in-the-loop factors, are essential to improving distribution system operations. This paper proposes a behavior-aware approach to analyze and aggregate prosumers’ participation in demand response (DR) programs. A convexified AC optimal power flow (ACOPF) via a second-order cone programming (SOCP) technique is used for system scheduling with DERs. A chance-constrained framework for the system operation is constructed as an iterative two-stage algorithm that can integrate loads, DERs’ uncertainty, and SOCP-based ACOPF into one framework to manage the violation probability of the distribution system’s security limits. The benefits of the analyzed prosumers’ behaviors are shown in this paper by comparing the optimal system scheduling with socially aware and non-socially aware approaches. The case study illustrates that the socially aware approach within the chance-constrained framework can utilize up to 43% more PV generation and improve the reliability and operation of distribution systems.

Research on the Demand Response Model for the Peak-Shaving Auxiliary Service Market Based on Large Users

The current electricity market reform has become a historical trend. Among them, large enterprises and users will face profound changes in the characteristics of the energy market and the industry's operating environment. Through the feasibility analysis of large users' participation in demand response and based on the potential of large users to participate in demand response, this paper establishes a bidding model for large users in the peak shaving market. Finally, in combination with specific parameter settings, a case analysis of large industrial users participating in peak shaving market transactions was carried out to verify the feasibility of the built model. At the same time, the large user quotation strategy for each time under the case was obtained and analyzed.

Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings

• Introduces a novel demand flexibility (DF) benchmarking metrics framework • Allows benchmarking across geographic areas with different climates and grid needs • Enables empirical approaches to achieve understanding of key influential factors of DF • Framework tested with two benchmarking case studies of 132 office and retail buildings • Identified load shed intensity range and variability; influence of event duration and timing Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfill the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF ). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.

A charging plan generation method for EV clusters considering the operation of regional distribution network

The continuous development of large-scale electric vehicles (EVs) has provided abundant dispatchable resources for the power grid. However, the existing control methods are not enough to support the effective interaction of the EVs and the grid. This paper proposes a method for generating a charging plan of EV clusters considering the stable operation of the regional distribution network, which fully considers the load regulation requirements of the grid and the charging economy of the EV clusters. Combined with the improved Particle Swarm Optimization based on Genetic Algorithm, a day-ahead charging plan for multiple EV clusters can be figured out. It can be proved by the case analysis that, compared with the disordered charging behavior and the participation of demand response with peak and valley electricity price, the charging plan generated by the method proposed can reduce the comprehensive cost of EV charging while optimizing the operation parameters of the distribution network, and it can reduce the calculation dimension in the process of plan generation.

A hybrid probabilistic information gap decision theory based energy management of an active distribution network

The optimal operation of an active distribution network requires proper coordination between multiple entities like renewable energy sources, storage units, power electronic devices, demand response implementation, and network reconfiguration. In this context, this paper proposes optimal energy coordination and management scheme among various entities with different ownership in a reconfigurable active distribution network. The distribution system operator manages the coordinated energy procurement scheme from multiple sources to maximize the expected hourly profit of operation. Uncertainties of load, renewable generation, grid energy price, and the charging power of hybrid electric vehicles are modelled using a probabilistic “Hong’s 2m point estimate” approach. On the other hand, the uncertainty of the demand response is modelled using information gap decision theory. The optimization problem is solved in a hybrid framework encompassing probability and information gap decision theory. Simulation studies on a thirty-three-node distribution network validate the efficacy of the proposed method. With the proposed energy management and coordination scheme, the daily expected operating cost reduces by ∼6.66% while the daily expected energy loss reduces by ∼26.05%. Implementing a risk-averse strategy to hedge against the risk of uncertain demand response participation reduces the profit by ∼3.21% for a tolerance limit of 5%.

Robust optimal dispatch of distribution system considering carbon capture and demand response

Participation of carbon capture power plants and demand response in power system dispatch is an important mean to achieve the carbon-neutral goal. In order to take into account uncertainty of wind power and low-carbon economic operation of the distribution system, a two-stage robust optimal dispatch model of the distribution system considering carbon capture and demand response is proposed in this paper. In the day-ahead stage, the unit commitment plan and the price demand response (PDR) schedule are established. In the intra-day stage, the carbon related cost is included in the optimization objective. The strategies for the generators and the incentive demand response (IDR) are optimized under the worst-case output of wind power based on the results of the day-ahead stage. Karush-Kuhn-Tucker (KKT) conditions and the column-and-constraint generation (C&CG) algorithm are used to solve the proposed two-stage model. A revised IEEE 33-bus distribution system is used to verify the feasibility and effectiveness of the proposed model.

A reinforcement learning-based demand response strategy designed from the Aggregator’s perspective

The demand response (DR) program is a promising way to increase the ability to balance both supply and demand, optimizing the economic efficiency of the overall system. This study focuses on the DR participation strategy in terms of aggregators who offer appropriate DR programs to customers with flexible loads. DR aggregators engage in the electricity market according to customer behavior and must make decisions that increase the profits of both DR aggregators and customers. Customers use the DR program model, which sends its demand reduction capabilities to a DR aggregator that bids aggregate demand reduction to the electricity market. DR aggregators not only determine the optimal rate of incentives to present to the customers but can also serve customers and formulate an optimal energy storage system (ESS) operation to reduce their demands. This study formalized the problem as a Markov decision process (MDP) and used the reinforcement learning (RL) framework. In the RL framework, the DR aggregator and each customer are allocated to each agent, and the agents interact with the environment and are trained to make an optimal decision. The proposed method was validated using actual industrial and commercial customer demand profiles and market price profiles in South Korea. Simulation results demonstrated that the proposed method could optimize decisions from the perspective of the DR aggregator.

Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities

A power system dominated by renewable energy is one of the key measures for achieving carbon neutrality. Demand response (DR) is a promising flexible resource for alleviating the supply-demand matching of high-proportion renewable energy systems. With the application of modern technologies, the potential for residential DR is growing. Electricity price is the key to improving residential DR capacity. However, existing dynamic pricing programs may fail to motivate end-users to adjust demand based on fluctuations in wind and photovoltaic (PV) output. This study proposes a dynamic pricing model that combines the fluctuation characteristics of residential electricity demand and wind and PV output, and utilizes bi-level optimization to coordinately dispatch the flexible loads. A case study of smart residential community consisting of 200 households shows that dynamic pricing incentivizes residential consumers to shift flexible loads from morning and evening to noon or early morning, which effectively improves the degree of matching between wind and PV output and residential electricity demand. Moreover, bi-level optimization effectively alleviates the potential rebound peak caused by large-scale residential participation in DR and achieves a relatively flat net grid demand profile. Furthermore, the dynamic pricing can incentivize residential consumers to participate in DR by reducing electricity bills.

Day-ahead interval optimization of combined cooling and power microgrid based on interval measurement

The combined cooling and power (CCP) microgrid plays an important role in achieving the goal of dual carbon, while the uncertainty of renewable energy (RE) output and the equipment operation mode bring challenges to the operation optimization. To address this problem, a day-ahead interval optimization model based on interval measurement of CCP microgrid is proposed. First, a large number of scenarios of RE output are generated using Latin hypercube sampling. And the scenarios are reduced by the fast backward reduction method (FBRM). The reduced scenarios are combined with the extreme value theorem to obtain the RE output interval range. Then, the demand response (DR) models of electrical load and cooling load are established to achieve peak-cutting and valley-filling. And the optimization model of ice storage air conditioning (ISAC) with different connection modes is established. Furthermore, the day-ahead interval optimization model is constructed of the CCP microgrid. Finally, the results of the day-ahead optimization model for ISAC under different operation modes are analyzed. The simulation results show the accuracy and effectiveness of the proposed model. The interval radius of the proposed method is reduced by 29.408% and 57.409% compared to the traditional interval method and the original scenario interval method, respectively. And the upper limit of the interval is reduced by 519.9923¥, 1987.6423¥, respectively. In the optimization with the participation of DR and ISAC, the operation cost can be reduced. The economic and stable operation of the CCP microgrid can be realized.

Scheduling Online EV Charging Demand Response via V2V Auctions and Local Generation

Due to the enormous energy consumption and the wide geographic distribution, Electrical Vehicle (EV) charging stations are believed to have great potential in Emergency Demand Response (EDR) participation. However, EDR limits the electricity drawn from the power grid by the charging station, and can pose threats to satisfying EVs’ charging demand. In this paper, in order to complement the charging station’s energy supply to meet the dynamic EV charging demand, we formulate an online EV charging scheduling problem under EDR as a non-linear mixed-integer program, and propose a novel polynomial-time online algorithm and auction mechanism to jointly incentivize EVs with energy to sell their energy and utilize the charging station’s local generator to produce energy. Our approach conducts an auction in each single round based on a primal-dual method and ties these auctions over time to optimize the system’s long-term social cost, while accommodating the local generator’ on/off-state control, each EV bidder’s cumulative energy budget constraint, and the power grid’s EDR energy cap. Our approach achieves the economic properties of truthfulness, individual rationality, and computational efficiency simultaneously for each auction, and a parameterized-constant competitive ratio for the long-term social cost. By rigorous theoretical analysis and trace-driven experimental studies, the results exhibit that our approach outperforms multiple alternative algorithms regarding the social cost, attains the economic properties, and also executes efficiently in practice.

Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.