Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

EV Response Capability Assessment Considering User Travel Demand and Cyber System Reliability

With the increasing penetration rate of electric vehicles(EVs),EV demand response holds great significance for promoting the optimal and secure operation of the power system.This paper proposes an EV response capability assessment method that considers EV users’travel demands and the reliability of the cyber systems integrated into both the power grid and the transportation network.A novel framework for an integrated cyber-power-transportation system is proposed for the first time,and a reliability model for the cyber system is provided.A method is further proposed to calculate the state of an EV when it is plugged in,considering the reliability of traffic guidance information and the reliability of the release of such information.The degree of relaxation in the EV charging demand is proposed to reflect the user’s travel demand,based on which the EV response capability can be assessed.Extensive test results on a cyber-power-transportation system containing RBTS BUS6 and the Beijing transportation network are conducted to show the efficiency of the proposed method.The impact of cyber reliability on the EV trip and response capability is analyzed.

考虑“响应-碳排放”责任耦合交易的IDR策略研究

综合需求响应(integrated demand response,IDR)作为区域综合能源系统(regional integrated energy system,RIES)维持供需平衡、实现分布式能源就地消纳的重要途径,已成为当下的研究热点。但现有IDR研究存在3个方面不足:综合能源服务商(integrated energy service provider,IESP)制定激励策略时,未考虑区域内用户的响应疲劳特性;在实施IDR的复杂场景下,未考虑区域IESP间的响应责任交易;忽略响应责任与碳排放责任的耦合关系。为此,首先分析用户响应意愿随响应次数的演化特性,通过引入响应疲劳函数实现对用户响应疲劳特性的有效建模;在此基础上,根据IESP的响应任务类型,设置多个IESP参与IDR的复杂场景。进一步,为实现各IESP参与IDR的经济性与低碳性,在考虑复杂场景下IESP间响应责任交易时的能源转移与碳排放责任转移基础上,建立响应责任-碳排放责任耦合交易机制。最终,采用基于自适应迭代步长的议价方法以确定最优交易价格。通过仿真算例验证所提模型的有效性:考虑用户响应疲劳特性的改进模型使用户的总收益提升27%;所提交易机制不仅使各场景下IESP的总成本分别降低15.8%、9.8%、94.1%,还使典型场景下IESP的碳排放量降低17.8%,提高IESP参与IDR的经济性与低碳性,实现IESP与用户的共赢。

Non-Cooperative Differential Game Based Energy Consumption Control for Dynamic Demand Response in Smart Grid

In this paper, we conduct research on the dynamic demand response problem in smart grid to control the energy consumption. The objective of the energy consumption control is constructed based on differential game, as the dynamic of each users’ energy state in smart gird can be described based on a differential equation. Concept of electricity sharing is introduced to achieve load shift of main users from the high price hours to the low price hours. Nash equilibrium is given based on the Hamilton equation and the effectiveness of the proposed model is verified based on the numerical simulation results.

Finite element modeling assumptions impact on seismic response demands of MRF-buildings

Recent seismic events have raised concerns over the safety and vulnerability of reinforced concrete moment resisting frame ＂RC-MRF＂ buildings. The seismic response of such buildings is greatly dependent on the computational tools used and the inherent assumptions in the modelling process. Thus, it is essential to investigate the sensitivity of the response demands to the corresponding modelling assumption. Many parameters and assumptions are justified to generate effective structural finite element（FE） models of buildings to simulate lateral behaviour and evaluate seismic design demands. As such, the present study focuses on the development of reliable FE models with various levels of refinement. The effects of the FE modelling assumptions on the seismic response demands on the design of buildings are investigated. the predictive ability of a FE model is tied to the accuracy of numerical analysis; a numerical analysis is performed for a series of symmetric buildings in active seismic zones. The results of the seismic response demands are presented in a comparative format to confirm drift and strength limits requirements. A proposed model is formulated based on a simplified modeling approach, where the most refined model is used to calibrate the simplified model.

Station-and-network–coordinated planning of integrated energy system considering integrated demand response

The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

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 operation of cold–heat–electricity multi-energy collaborative system based on price demand response

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

Transactive Demand Response Operation at the Grid Edge using the IEEE 2030.5 Standard

This paper presents a transactive demand response(TDR)scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture.A complete laboratory-based implementation provides the first(to our knowledge)realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers(IEEE)2030.5 standard,which addresses interoperability within a cybersecure smart energy profile(SEP)context.Verification is provided by a full system integration with commercial hardware using Internet Protocol(IP)-based(local area network(LAN)and Wi-Fi)communication protocols and transport layer security(TLS)1.2 cryptographic protocol,and validation is provided by emulation using extensive residential smart meter data.The demand response(DR)scheme is designed to accommodate privacy concerns,allows customers to select their DR compliance level,and provides incentives to maximize their participation.The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent(TA)and home energy management system(HEMS)agents.Customer response is handled by a multi-input multi-output(MIMO)fuzzy controller that manages negotiation between the customer agent and the TA.We take a multi-agent system approach to neighborhood coordination,with the TA servicing multiple residences on a common transformer,and use a reward mechanism to maximize customer engagement during the event-based optimization.Based on a set of smart meter data acquired over an extended time period,we engage in multiple TDR scenarios,and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22%under realistic conditions.

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 fromthe 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 configurationwithin 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.

Research on Multi-Objective Optimization Model of Industrial Microgrid Considering Demand Response Technology and User Satisfaction

In the process of wind power,coal power,and energy storage equipment participating in the operation of industrial microgrids,the stable operation of wind-storage industrial microgrids is guaranteed by considering demand response technology and user satisfaction.This paper firstly sorts out the status quo of microgrid operation optimization,and determines themain requirements for user satisfaction considering three types of load characteristics,demand response technology,power consumption benefit loss,user balance power purchase price and wind power consumption evaluation indicators in the system.Secondly,the operation architecture of the windstorage industrialmicrogrid is designed,and themulti-objective optimizationmodel of the wind-storage industrial microgrid is established with the comprehensive operating cost and user satisfaction as the target variables,and the corresponding solution method is mentioned.Finally,a typical wind-storage industrial microgrid is selected for simulation analysis,and the results showthat,(1)Considering the demand response technology,the comprehensive operating cost of the wind-storage industrial microgrid per day is 5292.63 yuan,the user satisfaction index is 0.953,and the wind power consumption rate reaches 100%.(2)By setting four scenarios,it highlights that the grid-connected operation mode is superior to the off-grid operation mode.Considering the demand response technology,the load curve can be optimized,and the time-of-use electricity price can be fully used to coordinate the operation of each unit,which enhances the wind power consumption capacity.The compromise solution of the system comprehensive operating cost and user satisfaction under the confidence level of 0.95 is obtained,namely(5343.22,0.94).(3)The frontier curve shows that in the process of model solving,it is impossible to optimize any sub-objective by changing the control variables,which proves that there is a close relationship between the comprehensive operating cost of the system and the confidence level,which can provide effective guidance for the optimal operation of industrial microgrids.

Fast Distributed Demand Response Algorithm in Smart Grid

This paper proposes a fast distributed demand response U+0028 DR U+0029 algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation U+0028 GaBP U+0029 solver. At the beginning of each time slot, each end-user U+002F energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption U+002F generation locally. The proposed demand response algorithm converges rapidly to a consumption U+002F generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user U+002F energysupplier estimates its energy consumption U+002F generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm. © 2017 Chinese Association of Automation.

Comprehensive Improvement of Marine Meteorological Service Capability by Taking Dem and as Traction:Investigation of Demand for Marine Services by Caofeidian Meteorological Bureau

To further improve the marine meteorological service capability of Caofeidian Meteorological Bureau, the staff of Caofeidian Meteorological Observatory recently visited Caofeidian Coal Port Co., Ltd., Jidong Oilfield Zhida Company, Tangshan Caofeidian LNG Degassing Station and other companies and further investigated the needs of professional marine meteorological services.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.