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.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Demand Flexibility of Residential Buildings:Definitions,Flexible Loads,and Quantification Methods

This paper reviews recent research on the demand flexibility of residential buildings in regard to definitions,flexible loads,and quantification methods.A systematic distinction of the terminology is made,including the demand flexibility,operation flexibility,and energy flexibility of buildings.A comprehensive definition of building demand flexibility is proposed based on an analysis of the existing definitions.Moreover,the flexibility capabilities and operation characteristics of the main residential flexible loads are summarized and compared.Models and evaluation indicators to quantify the flexibility of these flexible loads are reviewed and summarized.Current research gaps and challenges are identified and analyzed as well.The results indicate that previous studies have focused on the flexibility of central air conditioning,electric water heaters,wet appliances,refrigerators,and lighting,where the proportion of studies focusing on each of these subjects is 36.7%,25.7%,14.7%,9.2%,and 8.3%,respectively.These flexible loads are different in running modes,usage frequencies,seasons,and capabilities for shedding,shifting,and modulation,while their response characteristics are not yet clear.Furthermore,recommendations are given for the application of white-,black-,and grey-box models for modeling flexible loads in different situations.Numerous static flexibility evaluation indicators that are based on the aspects of power,temporality,energy,efficiency,economics,and the environment have been proposed in previous publications,but a consensus and standardized evaluation framework is lacking.This review can help readers better understand building demand flexibility and learn about the characteristics of different residential flexible loads,while also providing suggestions for future research on the modeling techniques and evaluation metrics of residential building demand flexibility.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use(TOU)strategy can effectively improve the energy consumption mode of customers,reduce the peak-valley difference of load curve,and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First,this study proposes a period partitioning model,which is based on a moving boundary technique with constraint factors,and the Dunn Validity Index(DVI)is used as the objective to solve the period partitioning.Second,a control strategy for the curtailable flexible load is investigated,and a TOU strategy is utilized for further modifying load curve.Third,a price demand response strategy for adjusting transferable load is proposed in this paper.Finally,through the case study analysis of typical daily flexible load curve,the efficiency and correctness of the proposed method and model are validated and proved.

Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability.The flexibility of demand response(DR)resources has become a valuable solution to this problem.However,existing research indicates that problems on flexibility prediction of DR resources have not been investigated.This study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate prediction.The prediction can generate the size and maintenance time of the aggregated flexibility.The accuracy of the flexibility prediction results was verified through simulations of case studies.The simulation results show that under different maintenance times,the size of the flexibility changed.The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.

Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics,energy-related occupant behavior,and the benefits of different stakeholders.A pilot test was conducted for a typical household.According to the monitored appliance-level data,operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods.Moreover,a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data.Occupant behavior of appliance usage,such as daily turn-on times,turn-on moment,duration of each operation,preference of temperature setting,and flexibility window,were determined by the developed data-mining approach.Based on the established flexible load models and the identified occupant energy usage behavior,a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs,occupants’dissatisfaction,CO_(2) emissions,and the average ramping index of household power profiles.The model was solved with the assistance of the NSGA-III and TOPSIS methods.Results indicate that the proposed framework can effectively optimize the operation of household flexible loads.Compared with the benchmark,the daily electricity costs,CO_(2) emissions,and average ramping index of household power profiles of the optimal plan were reduced by 7.3%,6.5%,and 14.4%,respectively,under the TOU tariff,while those were decreased by 9.5%,8.8%,and 23.8%,respectively,under the dynamic price tariff.The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice.

Stochastic Security-constrained Unit Commitment Considering Electric Vehicles, Energy Storage Systems, and Flexible Loads with Renewable Energy Resources

In this paper, a new formulation for modeling the problem of stochastic security-constrained unit commitment along with optimal charging and discharging of large-scale electric vehicles, energy storage systems, and flexible loads with renewable energy resources is presented. The uncertainty of renewable energy resources is considered as a scenario-based model. In this paper, a multi-objective function which considers the reduction of operation cost, no-load and startup/shutdown costs, unserved load cost, load shifting, carbon emission, optimal charging and discharging of energy storage systems, and power curtailment of renewable energy resources is considered. The proposed formulation is a mixed-integer linear programming(MILP) model, of which the optimal global solution is guaranteed by commercial solvers. To validate the proposed formulation, several cases and networks are considered for analysis, and the results demonstrate the efficiency.

Simulating dispatchable grid services provided by flexible building loads:State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.

Real-time Energy Management of Grid-connected Microgrid with Flexible and Delay-tolerant Loads

For optimal operation of microgrids,energy management is indispensable to reduce the operation cost and the emission of conventional units.The goals can be impeded by several factors including uncertainties of market price,renewable generation,and loads.Real-time energy management system(EMS)can effectively address uncertainties due to the online information of market price,renewable generation,and loads.However,some issues arise in real-time EMS as batterylimited energy levels.In this paper,Lyapunov optimization is used to minimize the operation cost of the microgrid and the emission of conventional units.Therefore,the problem is multiobjective and a Pareto front is derived to compromise between the operation cost and the emission.With a modified IEEE 33-bus distribution system,general algebraic modeling system(GAMS)is utilized for implementing the proposed EMS on two case studies to verify its applicability.

Local Energy and Planned Ramping Product Joint Market Based on a Distributed Optimization Method

High penetration of renewable energy generation(REG)in the distribution system increases both the power uncertainty at a given interval and the power variation between two intervals.Reserve markets addressing power uncertainty have been widely investigated.However,there is a lack of market mechanisms regarding the power variation of the load and REGs.This paper thus defines a planned ramping(PR)product to follow the net load variation and extends the local energy market to include the trading of PR products.Players are economically compensated for their PR products.Bidding models of dispatchable generators and flexible load aggregators in the joint market are investigated.To solve the market problem in polynomial time,a distributed market clearing method is developed based on the ADMM algorithm.The joint market is tested on a modified IEEE 33-bus system.It verifies that introducing the PR market can encourage flexible loads to provide more PR service to accommodate the net load variation.As such,the ramping cost of dispatchable generators is reduced by 29.09%in the test case.The planned energy curtailment from REG is also reduced.The computational efficiency of the proposed distributed clearing method is validated by comparing it with a centralized method.