The IES dynamic time-scale scheduling strategy based on multiple load forecasting errors

Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.

Fault Diagnosis Technology Based on Model Driven

Fault diagnosis is an important application of the power grids monitoring system. Under the situation of continuous development of smart grid, it brings new challenges to the fault diagnosis technology. A fault diagnosis expert system based on model driven approach is proposed in this paper. And the corresponding fault modeling technology based on Fault Logic Description Language (FLDL) is described step by step. Practices show that this system could meet the requirements of processing fault alarm information rapidly and reliably by operator.

Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.

An Efficient Multi-area Networks-Merging Model for Power System Online Dynamic Modeling

To improve accuracy and efficiency in power systems dynamic modeling,the distributed online modeling approach is a good option.In this approach,the power system is divided into sub-grids,and the dynamic models of the sub-grids are built independently within the distributed modeling system.The subgrid models are subsequently merged,after which the dynamic model of the whole power system is finally constructed online.The merging of the networks plays an important role in the distributed online dynamic modeling of power systems.An efficient multi-area networks-merging model that can rapidly match the boundary power flow is proposed in this paper.The iterations of the boundary matching during network merging are eliminated due to the introduction of the merging model,and the dynamic models of the sub-grid can be directly“plugged in”with each other.The results of the calculations performed in a real power system demonstrate the accuracy of the integrated model under both steady and transient states.

Operational optimization of a building-level integrated energy system considering additional potential benefits of energy storage

As a key component of an integrated energy system(IES),energy storage can effectively alleviate the problem of the times between energy production and consumption.Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems.This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system(BIES)considering additional potential benefits of energy storage.Based on the characteristics of peak-shaving and valley-filling of energy storage,and further consideration of the changes in the system’s load and real-time electricity price,a model of additional potential benefits of energy storage is developed.Aiming at the lowest total operating cost,a bi-level optimal operational model for day-ahead operation of BIES is developed.A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation.The results demonstrate that the model can exploit energy storage’s potential,further optimize the power output of BIES and reduce the economic cost.

Subsynchronous resonance and its mitigation for power system with unified power flow controller

Control strategy of unified power flow controller(UPFC)utilizing dq decoupling control is deduced in this paper,which can closely follow the control orders of the active and reactive power.The subsynchronous resonance(SSR)characteristics of a series compensated system equipped with UPFC are studied,and the results reveal that SSR characteristics of the system may vary significantly with UPFC in service or not.Consequently,supplementary subsynchronous damping controller(SSDC)for UPFC is proposed and investigated,and the effectiveness of the proposed SSDC is verified by damping torque analysis and time domain simulations.

Line Aging Assessment in Distribution Network Based on Topology Verification and Parameter Estimation

The aging of lines has a strong impact on the economy and safety of the distribution network.This paper proposes a novel approach to conduct line aging assessment in the distribution network based on topology verification and parameter estimation.In topology verification,the set of alternative topologies is firstly generated based on the switching lines.The best-matched topology is determined by comparing the difference between the actual measurement data and calculated voltage magnitude curves among the alternative topologies.Then,a novel parameter estimation approach is proposed to estimate the actual line parameters based on the measured active power,reactive power,and voltage magnitude data.It includes two stages,i.e.,the fixed-step aging parameter(FSAP)iteration,and specialized Newton-Raphson(SNR)iteration.The theoretical line parameters of the best-matched topology are taken as a warm start of FSAP,and the fitted result of FSAP is further renewed by the SNR.Based on the deviation between the renewed and theoretical line parameters,the aging severity risk level of each line is finally quantified through the risk assessment technology.Numerous experiments on the modified IEEE 33-bus and 123-bus systems demonstrate that the proposed approach can effectively conduct line aging assessment in the distribution network.