Research on Evaluation of Multi-Timescale Flexibility and Energy Storage Deployment for the High-Penetration Renewable Energy of Power Systems

With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is uncertain,and thus flexible regulation for the power balance is highly demanded.Considering the multi-timescale output characteristics of renewable energy,a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper.Through the multi-timescale decomposition algorithm on the basis of mathematical morphology,the multi-timescale components are separated to determine the flexibility requirements on different timescales.Based on the obtained flexibility requirements,a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation.This optimization model can allocate corresponding flexibility resources according to the economy,flexibility and reliability requirements of the power system,and achieve the trade-off between them.Finally,case studies demonstrate the effectiveness of our model and method.

Study on Probability Distributions of Multi-Timescale Aerosol Optical Depth Using AERONET Data

The probability distribution analysis is per-formed for multi-timescale aerosol optical depth (AOD) using AErosol RObotic NETwork (AERONET) level 2.0 data.The maximum likelihood estimation is employed to determine the best-fit probability density function (PDF),and the statement that the fitting Weibull distribution will be light-tailed is proved true for these AOD samples.The best-fit PDF results for multi-site data show that the PDF of AOD samples with longer timescale in most sites tends to be stably represented by lognormal distribution,while Weibull distribution is a better fit for AOD samples with short timescales.The reason for this difference is ana-lyzed through tail characteristics of the two distributions,and an indicator for the selection between Weibull and lognormal distributions is suggested and validated.The result of this research is helpful for determining the most accurate AOD statistics for a given site and a given time-scale and for validating the retrieved AOD through its PDF.

Multi-timescale Collaborative Optimization of Distribution, Distributed Generation and Load in Microgrid

The distribution loads, output of distributed generations (DGs) and dynamic power price present obvious time-sequence property, the typical property is studied in this paper. The model of microgrid (including adjustable load, DGs, storage and dynamic power price) is studied. A multi-timescale collaborative optimization model is built towards microgrid;main measures in different timescale optimization are realized. An improved adaptive genetic algorithm is used to solve the optimization problem, which improved the efficiency and reliability. The proposed optimization model is simulated in IEEE 33 node system;the results show it’s effective.

MULTI-TIMESCALE VARIATIONS OF SOMALI JET AND ITS RELATION WITH PRECIPITATION IN CHINA

Based on the ERA reanalysis winds data, the multi-time scale variations of Somali jet are analyzed synthetically. The jet's influences on rainfall in China on interannual, interdecadal and sub-monthly scales are also studied using correlation and composite analyses. The results demonstrate that the interdecadal variations of the jet are significant.The Somali jet became weaker in the 1960 s and became the weakest in the early 1970 s before enhancing slowly in the late 1970 s. Moreover, the relation between the Somali jet and summer precipitation in China is close, but varies on different timescales. Preliminary analysis shows that the intensity variations in May and June during the early days of establishment are well correlated with summer precipitation in China. The Somali jet intensity on the interdecadal scale is closely related with interdecadal variations of the precipitation in China. Regardless of leading or contemporaneous correlation, the correlations between the Somali jet intensity and the rainfall in northern and southern China show obvious interdecadal variations. Moreover, the link between the anomalies of the jet intensity in May-August and precipitation evolution on synoptic scale in China is further studied. China has more rainfall with positive anomalies of the Somali jet but less rainfall with negative anomalies during the active period of the jet. The influence of positive Somali jet anomalies on China precipitation is more evident.

Multi-timescale Modeling and Dynamic Stability Analysis for Sustainable Microgrids:State-of-the-art and Perspectives

The increasing trend for integrating renewable energy sources into the grid to achieve a cleaner energy system is one of the main reasons for the development of sustainable microgrid(MG)technologies.As typical power-electronized power systems,MGs make extensive use of power electronics converters,which are highly controllable and flexible but lead to a profound impact on the dynamic performance of the whole system.Compared with traditional large-capacity power systems,MGs are less resistant to perturbations,and various dynamic variables are coupled with each other on multiple timescales,resulting in a more complex system instability mechanism.To meet the technical and economic challenges,such as active and reactive power-sharing,voltage,and frequency deviations,and imbalances between power supply and demand,the concept of hierarchical control has been introduced into MGs,allowing systems to control and manage the high capacity of renewable energy sources and loads.However,as the capacity and scale of the MG system increase,along with a multi-timescale control loop design,the multi-timescale interactions in the system may become more significant,posing a serious threat to its safe and stable operation.To investigate the multi-timescale behaviors and instability mechanisms under dynamic inter-actions for AC MGs,existing coordinated control strategies are discussed,and the dynamic stability of the system is defined and classified in this paper.Then,the modeling and assessment methods for the stability analysis of multi-timescale systems are also summarized.Finally,an outlook and discussion of future research directions for AC MGs are also presented.

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.

Study on multi-timescale operation of hydrogen-containing energy coupled with transportation system

With the widespread attention on hydrogen energy,the operation optimization of the coupling system of hydrogen energy and transportation has become a critical issue.Therefore,this study develops a hydrogen-containing energy transportation coupling system for the system collaborative operation framework,operation mode and equipment modelling of the system.Furthermore,a medium-to long-term operation optimization model and a short-term operation optimization model were constructed considering the differences in the operation of coupled systems at different timescales and operating costs as the objective function and power balance and system equipment as constraints.In the medium-and long-term operation optimization model,the planning scenario reduction method is used to reduce the wind power generation scenario.In the short-term operation optimization model,a multivariate uncertainty model is constructed to represent the uncertainty in the coupling system.Subsequently,the solution method of the model is proposed.Finally,a coupled system is simulated to verify the effectiveness of the model.(i)When the initial scene set is 600,the typical scene reduction method using Latin hypercube sampling and the Wasserstein distance can reduce operating costs by 7.60%and 9.49%compared with K-means reduction and K-media reduction methods.(ii)The sensitivity coefficients of hydrogen sales price,electricity sales price and maintenance rate to operating costs are-0.031%,-1.009%and 0.0105%,respectively.(iii)Considering multiple uncertainties can help make optimal decisions based on the overall consideration of disadvantage scenarios,thereby reducing system operating costs.

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic(PV)power generation has highly penetrated in distribution networks,providing clean and sustainable energy.However,its uncertain and intermittent power outputs significantly impair network operation,leading to unexpected power loss and voltage fluctuation.To address the uncertainties,this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch(MTAAR-RPD)method to reduce the network power losses as well as alleviate voltage deviations and fluctuations.The MTAAR-RPD aims to coordinate on-load tap changers(OLTCs),capacitor banks(CBs),and PV inverters through a three-stage structure which covers multiple timescales of“hour-minute-second”.The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min.The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time.The three stages are coordinately optimized by an affinely adjustable robust optimization method.A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively.The proposed method is verified through theoretical analysis and numerical simulations.

Model Predictive Current Control with Adaptive-adjusting Timescales for PMSMs

A model predictive current control(MPCC)with adaptive-adjusting method of timescales for permanent magnet synchronous motors(PMSMs)is proposed in this paper to improve the dynamic response and prediction accuracy in transient-state,while lessening the computational burden and improving the control performance in steady-state.The timescale characteristics of different parts of MPCC,such as signal sampling,prediction calculation,control output,model error correction,are analyzed,and the algorithm architecture of MPCC with multi-timescale is proposed.The difference between reference and actual speed,and the change rate of actual speed are utilized to discriminate the transient state of speed change and load change,respectively.Adaptive-adjusting method of control period and prediction stepsize are illustrated in detail after operation condition discrimination.Experimental results of a PMSM are presented to validate the effectiveness of proposed MPCC.In addition,comparative evaluation of single-step MPCC with fixed timescale and proposed MPCC is conducted,which demonstrates the superiority of proposed control strategy.

The tail risk safe haven property of China's energy futures against US market implied volatility

This paper analyses the tail risk contagion of US market implied volatility(USIV)on China's energy futures(CEF)markets,exploring how to utilize operations in the CEF to achieve a safe haven.Leveraging CEF characteristics to simultaneously take both long-/short-positions and engage in long-/short-run investment horizons,this paper defines eight different CEF safe haven attributes to counteract the tail risk of extreme increases in USIV.Using trading data from March 27,2018,to October 30,2023,the empirical results show that,first,in the analysis of the entire sample period,China's coking coal futures can serve as a weak safe haven,aiding long-position investors in mitigating the tail risks associated with US gold and stock market implied volatility.Coking coal futures also assist short-position investors in countering US stock market implied volatility tail risk.Second,in the sub-period analysis,the safe haven attributes of CEF exhibit strong heterogeneity and asymmetry across different periods.Finally,the time span during which CEF exhibits a particular safe haven attribute does not persist for an extended period.

A New Method for Estimating Lithium‑Ion Battery State‑of‑Energy Based on Multi‑timescale Filter

Accurate estimation of the state-of-energy(SOE)in lithium-ion batteries is critical for optimal energy management and energy optimization in electric vehicles.However,the conventional recursive least squares(RLS)algorithm struggle to track changes in battery model parameters under dynamic conditions.To address this,a multi-timescale estimator is proposed.A variable forgetting factor RLS approach is used to determine the model parameters at a macro timescale,and the H infinity filter is utilized to estimate the SOE at a micro timescale.The proposed algorithm is verified and analyzed and shown to have accurate and robust identification of battery model parameters.Finally,experiments under dynamic cycles demonstrate that the proposed algorithm has a high level of accuracy for SOE estimation.

Projection of IPCC AR4 Coupled Climate Models for Multitimescale Temperature Variation in Yunnan: A Case Study on Southwest Yunnan from 1960 to 2050

Based on daily mean temperature records from 1961 to 2007 at 20 meteorological sites in Southwest Yunnan, and the surface temperature simulated by IPCC AR4 Climate Models, a quantitative examination was undertaken into the characteristics of multi-timescale temperature （AMT, DMT and WMT） variation in Southwest Yunnan. The simulation abilities of the models were also evaluated with the normalized root mean square error （NRMSE） and Mann-Kendal test statistic methods. Temperatures show remarkable increasing trend from 1961 to 2007, with the Mann-Kendall test statistic passing 95% confidence verification. The result of the NRMSE analysis shows that the simulated temperature anomaly variations are more similar to observed ones especially for AMT and DMT, and the projected result （anomalies） of IPCC AR4 climate models can be used for predicting the trends in multi-timescale temperature variation in Southwest Yunnan in the next 40 years under the three emission scenarios, which has better simulating effect on AMT and DMT than WMT. Over the next 40 years the temperature will continue to rise, with annual mean temperature showing a more remarkable rising trend than that of the dry and wet seasons. Temperature anomalies exhibit different increasing rates under different emission scenarios： During the 2020s the rising rates of multi-timescale temperature anomalies in a high greenhouse gases emissions scenario （SRESA2） are smaller than those under a low emission scenario （SRESB1）. Except that, the rate of increase in temperature anomalies are the highest in the intermediate emissions scenario （SRESA1B）, followed by those in SRESA2, and those in low emissions scenario （SRESB1） are the lowest. The reason of different simulating effects on WMT from AMT and DMT was also discussed.

Robust Unit Commitment for Large-scale Wind Generation and Run-off-river Hydropower

Increased penetration of large-scale renewable sources such as wind and solar power creates additional constraints for power system maintenance,reliability and security.The intermittent and fluctuation characteristics of renewable energy require robust operation methodologies.Co-optimized scheduling of multiple energy resources is regarded as an efficient way to accommodate renewable energy.As the second largest source of electricity in the world,hydro power is clean and has the merits of ease of regulation,low cost and high flexibility.In this regard,it can play an important role in mitigating the uncertainty of wind power.This paper proposes a robust wind-hydro-thermal unit commitment(UC)model that provides reliable day-ahead unit commitment decisions.The hydro units are operated in longer time scales than the thermal units,so as to capture reservoir operations.The hydro production capability curve is linearized by using variable separation and the piecewise linear(PWL)function,leading to a mixed integer linear programming(MILP)problem that can be solved using mature software such as CPLEX.Numerical tests on an IEEE 39-bus system demonstrate the effectiveness of the proposed model to increase system flexibility in accommodation of uncertain wind power.