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.

Agent-based simulation for symmetric electricity market considering price-based demand response

With the development of electricity market mechanism and advanced metering infrastructure(AMI),demand response has become an important alternative solution to improving power system reliability and effi-ciency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers’ response characteristics under time-of-use(TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly.Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles,which also helps to reduce the price.

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.

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.

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.

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.

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.

Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

Economic Analysis of Demand Response Incorporated Optimal Power Flow

Demand Response(DR)is one of the most cost-effective and unfailing techniques used by utilities for consumer load shifting.This research paper presents different DR programs in deregulated environments.The description and the classification of DR along with their potential benefits and associated cost components are presented.In addition,most DR measurement indices and their evaluation are also highlighted.Initially,the economic load model incorporated thermal,wind,and energy storage by considering the elasticity market price from its calculated locational marginal pricing(LMP).The various DR programs like direct load control,critical peak pricing,real-time pricing,time of use,and capacity market programs are considered during this study.The effect of demand response in electricity prices is highlighted using a simulated study on IEEE 30 bus system.Simulation is done by the Shuffled Frog Leap Algorithm(SFLA).Comprehensive performance comparison on voltage deviations,losses,and cost with and without considering DR is also presented in this paper.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.

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.

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.

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.

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.

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.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.