Demand Response Process Research Articles

Securing demand–response in smart grids against false pricing attacks

Two-way communication systems in smart grids help to engage consumers in demand–response programs, which can bring many benefits but also make smart grids vulnerable to cyber attacks. In this paper, a cyber attack which aims to disturb the demand–response process by injecting false electricity price signals is considered. A real-time pricing model where the operator has incomplete knowledge of the private demand–response behaviors of the customers is proposed. The vulnerability of the power system to false pricing attacks is analyzed by a Markov decision process, and the dynamic interaction between the attacker and the defender is modeled as a zero-sum Markov game where neither player has full information of the game model. For the solution of the Markov game, a model-free multi-agent reinforcement learning method is proposed to find the Nash Equilibrium policies for both players. The proposed method is applied to the IEEE 34 Node Test Feeder, in which the effect of the defense on mitigating the impact of the attack is demonstrated and different policies of the players given various resources are analyzed. The results shows that the studied cyber attack can cause a maximum of 1.96% unsatisfied load and the proposed defense measure can reduce the unsatisfied load by 50%–70% compared with the cases without defense measures.

Research on optimal dispatch model of power grid considering the uncertainty of flexible resource demand response on the residential side

AbstractResidential side flexible load participation in demand response (DR) has been widely used in recent years. However, there are uncertainties in the response willingness and response volume of residential customers, which have a non‐negligible impact on the security and economic dispatch of the power sector. This paper analyzes the uncertainty factors in the demand response process of residential users from the equipment load layer–user layer, establishes for the first time a refined model of residential users' participation in DR from the perspectives of participation uncertainty and response uncertainty, and further constructs a grid optimal dispatch model considering DR uncertainty. The experimental simulation results show that the model established in this paper is able to adjust the operation strategy according to the requirements of user comfort, enhance the enthusiasm of users to participate in DR, and at the same time reduce the dispatch security risk and economic loss caused by the uncertainty of residents' DR on the power grid.

Flexibility potential quantification and regulation measure comparison for the building air-conditioning system

Air-conditioning (AC) systems are the resources with the greatest flexibility potential for building demand response (DR). However, there is no unified method to quantify its flexibility under different DR measures, which results in difficulties in fully utilizing the DR potential and determining a proper regulation measure for the AC system operation strategies. To bridge this gap, the indicators of energy flexibility and power flexibility are firstly proposed in this study to compare three measures, active storage, passive storage, and temperature resetting, during the DR process of AC systems. According to the regulation measures with quantified flexibility potentials, different DR regulation measures are combined to explore the maximized stack flexibility. Based on a case study of an office building in Tianjin, the results show that among the different stacking strategies, stacking the active energy storage and temperature reset strategies produces the lowest operating cost, which is 13.8% and 22.5% lower than the stacking all three measures’ operating cost and total energy consumption, respectively. In the case of stacking the two measures, the highest energy flexibility is achieved when stacking the active and passive energy storage measure. If the temperature reset measure is further stacked, the energy flexibility can be increased by 15%.

Operation scheduling strategy of battery energy storage system with the integration of differenced power constraint factor

The battery energy storage system (BESS) as a flexible resource can effectively achieve peak shaving and valley filling for the daily load power curve. However, the different load power levels have a differenced demand on the charging and discharging power of BESS and its operation mode. For further improving the efficiency of BESS in a demand response process, the differenced power demand should be incorporated into the BESS operation schedule. Therefore, this paper proposes a BESS operation scheduling strategy to satisfy the differenced demand based on the control of the power constraint factor. Firstly, a boundary moving method is used to solve the charging and discharging thresholds of BESS to determine the optimal charging and discharging periods. Secondly, a charging and discharging demand degree model is constructed based on the hourly charging and discharging demand. Thirdly, a BESS scheduling model with a minimum standard deviation of daily load curve is established for enhancing the peak shaving and valley filling. Finally, the case study shows that the proposed strategy can further improve the efficiency of BESS participating in demand response, improve the smoothness of the load power curve, and decrease the peak-valley difference.

Optimal configuration and operation of the regional integrated energy system considering carbon emission and integrated demand response

Under the “carbon peaking and carbon neutrality” development strategy, in order to suppress load fluctuations and promote renewable energy consumptions in the regional integrated energy system involving concentrating solar power stations, a double-layer optimization model based on the improved non-dominated sorting genetic algorithm-II (NSGA-II) and mixed integer linear programming (MILP) is proposed. The upper layer completes the capacity configuration process based on multiple objectives to minimize the annual planning cost and the net emission of pollutants. The lower layer is designed to minimize the annual operating cost and optimize the output of the devices and the load curves through the participation of the integrated demand response process for flexible loads and the whole process of carbon emission including carbon capture, carbon utilization, and carbon trading mechanisms to obtain the best operating plan. The final results indicate that the participation of concentrating solar power stations can improve the level of coordinated optimization, and the improved NSGA-II is stronger than the conventional one in convergence ability. Besides, considering the whole process of carbon emission and integrated demand response is capable of decreasing the annual operating cost and net carbon emission to improve the economy and environmental protection of the system significantly.

Quantifying the capacity credit of IDC‐based demand response in smart distribution systems

AbstractWith the explosive growth of online services, the Internet Data Center (IDC) has become an emerging load demand . The power consumption of IDCs are believed suitable for demand response (DR) purposes due to its temporal‐spatial flexibility properties. To what extent IDCs could be used as candidate DR resources to provide capacity support are dependent both on the technical property and the willingness of data end‐users . To address the above issue, this paper presents a methodological framework for quantifying the potential value of IDC‐based DR in distribution grids. To achieve this, the concept of capacity credit (CC) is introduced and extended into the IDC scenario. The proposed framework explicitly considered the impacts of uncertainty of user participation intention and the reliability of basic information in the DR process. A novel Z‐number based price elasticity uncertainty model is introduced, and a more realistic evaluation of IDC capacity credit is obtained. Finally, the effectiveness of the proposed model and method is illustrated on a modified IEEE‐33 node network, and the obtained results verify the significance of IDC‐based DR in enhancing the adequacy of supply in distribution grids.

Electric Vehicle Charging Facility Configuration Method for Office Buildings

With the advent of advanced battery technology, EVs are gradually gaining momentum. An appropriate decision-making method for the number of charging piles is in need to meet charging needs, and concurrently, to avoid the waste of infrastructure investment. In this study, an optimal charging pile configuration method for office building parking lots is proposed. With the determination of the design period of charging facilities, a charging load prediction model is established under a collection of charging scenarios. Taking the average utilization rate of charging facilities and the average satisfaction rate of charging demand as the objective functions, the distribution of the optimal number of piles is obtained with the genetic algorithm. The benefits of the configuration method are also explored under the building demand response process. The results show that the optimal configuration of charging piles in office buildings with different volumes have similar characteristics. When the design period is 5 years and 10 years, the comprehensive indicator of the utilization rate of the charging facilities and the satisfaction rate of the charging demand can, respectively, be improved by 8.18% and 17.45%. Moreover, the reasonable scheduling strategy can realize the load regulation response with a maximum load transfer rate of 25.55%.

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.

Energy storage capacity competition-based demand response method in blockchain ancillary service market

With the development of new energy, the ancillary service demand of power grid is increasing. The published literature focuses on building a blockchain ancillary service market to ensure the safety and efficiency of ancillary service transactions. However, due to the high threshold and poor scalability, the existing blockchain market is hard to meet the demand of the response rate of ancillary services. Therefore, this paper proposes an energy storage capacity competition-based demand response method in blockchain ancillary service market. Firstly, a new market structure is proposed, which uses the Proof of Capacity (POC) as the consensus mechanism. The structure uses POC to realize the competition among users’ energy storage capacity. Secondly, the demand response process of ancillary service market is designed. During the transaction processes, the probability of users participating in the transaction is changed according to the users’ storage capacity. Dealing with different demands, the market chooses the user with the largest capacity or the combination of users to respond. The simulation shows that, compared with the traditional ancillary service market, the capacity competition-based market demand response rate is increased by 20%, the time to fulling all users’ capacity in the market is reduced by 2 h, and the call rate of user’s capacity is increased by 10%.

Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk

A combined heating and power (CHP) microgrid has high flexibility and economy, but the output of renewable energy is uncertain. Meanwhile, excessive flexible load adjustment in the demand response process will increase user dissatisfaction. In order to solve the above problems, this paper quantifies uncertainty with the conditional value-at-risk (CVaR) of relative disturbance. Additionally, a multi-objective optimal scheduling model that takes into account both the operating economy and the demand-side power consumption satisfaction is established. In order to solve the multi-objective mixed-integer nonlinear programming problem well, we propose an improved sparrow search algorithm (ISSA), which solves the problem that the sparrow search algorithm (SSA) is prone to low accuracy, insufficient in population diversity and easy to be trapped in local optimum. Combined with the non-dominated solution ranking method, ISSA has the ability of multi-objective optimization. Finally, simulation on a typical CHP microgrid is performed. The optimization results under different confidence levels and risk preference coefficients are compared and analyzed. When the risk preference coefficient is 0.1, 2 and 5, the minimum rotating reserve capacity is 75.17 kW, 82.83 kW, and 105.70 kW in the electric part and 40.08 kW, 59.89 kW, and 61.94 kW in the thermal part. The effectiveness of the proposed CVaR of relative disturbance is verified.

Cloud Computing Based Demand Response Management Using Deep Reinforcement Learning

Demand response is an effective way for ensuring safety and stabilization of power grid by maintaining the balance between the supply and the demand of power grid, and this article focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this article considers the overshoot temperature and its influence on demand response. First, a theoretical model of the heating and cooling processes of the electric water heater is established; second, the demand response process using electric water heaters is analyzed, including the influences of the physical parameters and the settings of electric water heaters on the demand response process; third, a model is established considering the demand response requirement, the comfort of owners of electric water heaters, and the electricity price, simultaneously; fourth, an optimization method based on deep reinforcement learning is proposed for demand response using electric water heaters. Meanwhile, the influence of parameters on the results of demand response is discussed in details. Experimental results show the effectiveness of the proposed method.

Demand response process assessment model: Development and case study assessment

ContextDemand response is a new study area and many countries intend to create roadmaps to activate their implementations. Smart Grid Maturity Model (SGMM) defines the characteristics and targets related to customer, but it does not adequately elaborate and provide a guiding perspective for demand response. ObjectiveIn addressing the gap mentioned, this article presents a Demand Response Process Assessment Model (DRPAM) as a fundamental proposal towards evaluating, implementing, and improving capabilities of customers’ demand response processes. MethodThe standard of ISO/IEC 33004 and the second maturity level of “Customer” domain in SGMM were referenced to develop the DRPAM. Literature studies and technical reports were examined to determine the key processes of the model, while Waterfall and Agile software development models and Action Workflow Loop were used to identify process practices. The set of key processes was defined, updated and verified using expert opinions gathered by Delphi study technique applied in five rounds. It was then used for an exploratory case study assessment in an organization that conducts research and development activities in this field. ResultsEight processes were identified with purpose, outcomes, base practices, work products (as inputs and outputs), and responsible roles of implementation. DRPAM is the first detailed model intended to reduce time and energy loss for demand response pilot studies of customers. The results of the case study assessment indicated that DRPAM was found satisfactory in identifying capabilities of demand response processes and of providing a roadmap for their improvement. As future work, the model will be validated by conducting multiple case studies.

Energy Pricing and Sharing Strategy Based on Hybrid Stochastic Robust Game Approach for a Virtual Energy Station With Energy Cells

In this paper, a novel energy pricing and sharing strategy is proposed to solve the energy management and market bidding problems of a virtual energy station (VES) in multi-carrier energy systems. First, the energy cell-tissue-based group interaction framework and the multiagent-based communication and control architecture are established in the VES. Second, the general model of an energy cell is built, in which the integrated demand response process and the automatic generation control mode of a combined heat and power unit are designed. Third, according to the classification of energy cells, the aggregation rules of the VES without the participation of third-party agents are proposed, and the internal trading mechanism of the VES led by a higher energy cell is designed based on a Stackelberg game. Moreover, a three-stage hybrid stochastic robust optimization strategy is proposed to address uncertainties in market prices and renewable energy output. Finally, the optimization model is linearized and solved by duality theory, KKT conditions, and column-and-constraint generation (C&CG) algorithm. Simulation results prove the rationality and effectiveness of the framework and business model for the VES.

Characteristics and control strategies of large-scale residential air conditionings for demand response programs

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.

A novel billing approach for fair and effective demand side management: Appliance level billing (AppLeBill)

Demand side management (DSM) is an important part of smart electrical grid studies due to high energy efficiency it provides. Effective DSM techniques are hinged on participation level of the customers and control manner of their loads so if there is no fair and appropriate management, customers will not embrace demand side management applications. Besides, the customers might not participate to the demand response process willingly if their comforts are not considered. Therefore, the DSM systems must consider fairness and customer’s comfort as well as distribution system objectives. Multifarious DSM systems have been proposed in the literature, however, the fair billing has not included as a parameter in most of these studies. This paper introduces a novel billing approach based upon appliance level billing (AppLeBill) for fair and effective demand side management. The proposed AppLeBill idea offers a billing strategy such that time-shiftable appliances (TSAs) in a residence may have a separate billing rate rather than a single billing rate for total electricity usage. The proposed AppLeBill concept includes three different schemes which are explained and simulated in order to compare each other in terms of their potential benefits.

A Simplified Regression Model for Dynamic Demand Response Process of Building Loads

This paper presents a simplified regression model for building dynamic DR processes based on the mechanism model to facilitate the timely analysis, prediction and optimization of aggregation DR characteristics of a large-scale diversified building load. First, numerous simulations based on the mechanism model are performed to obtain sample data of building DR under different external conditions. Second, the key factors influencing the dynamic demand response process of a building—namely, the climbing period, stable response period and recovery period—are analyzed. Finally, this paper analyzes the simulation error and calculation efficiency of the proposed simplified regression model. Furthermore, the calculation speed is sufficiently fast to meet the requirements of real-time scheduling.

A Knowledge-Based Energy Management Model that Supports Smart Metering Networks for Korean Residential Energy Grids

The various energy management technologies that are required in order to deliver effective energy demand responses have resulted from the integrated use of digital technologies with energy grids. Therefore, the core technologies for smart metering infrastructure are regarded as a key issue in the design of future energy grids. The proposed knowledge-based model that supports advanced metering networks is capable of estimating energy consumption according to the characteristics of residential buildings. The energy consumption data is analyzed according to the residential building's properties, which can significantly affect the energy consumption pattern. Therefore, appropriately designed models for energy consumption patterns with respect to identifying each energy consumption feature's potential impact can be applied to create smart metering networks for future energy grid environments. This study introduces a knowledge-based model that considers both the energy and building management profiles. Then, case studies for the estimation of the energy consumption are presented. The proposed model could be effectively utilized in managing the energy demand response process with respect to market prices and residential energy shortages, and it provides a good reference for designing energy demand response strategies in Korean residential energy grid environments.