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With the integration of a high penetration of wind power into distribution system, the ability to cope with the voltage fluctuation issue arising from the intensified uncertainty should be improved. Energy storage system (ESS) with effective control strategies keeps a key role in smoothing the voltage fluctuation caused by the uncertainty of wind turbine generators (WTGs). Considering the constraints of state of charge (SOC), charge-discharge efficiency and continuous operation of ESS, this paper proposes a novel approach to determine the minimum capacity of ESS to satisfy the voltage fluctuation limit based on unbalanced three-phase interval power flow. An unbalanced forward-backward sweep interval power flow considering the constraints of ESS and the voltage fluctuation limit is proposed. The interval model of uncertain WTG output is established on basis of wind speed fluctuation curve. The interval model of the practical ESS output is obtained on basis of the present SOC, nominal capacity and comprehensive efficiency. The output voltages of power flow can directly demonstrate the voltage fluctuation rate due to their interval forms. Therefore, a feedback mechanism is established between the output voltage fluctuation rate and the input ESS practical power, which is used to determine the minimum capacity of ESS to satisfy the fluctuation limit. A modified IEEE 33-bus system with high penetration of WTGs is implemented to test the proposed method. Results demonstrate the validity and effectiveness of the proposed approach.

To identify the DC bias state of transformers, a DC bias state identification method of transformer based on wavelet singular entropy is proposed in this paper. The vibration principle of transformers under DC bias has been analyzed. By combining continuous wavelet transform, singular value decomposition, and information entropy, the analysis method of wavelet singular entropy is proposed. The vibration signal of the transformer before and after DC bias is transformed by continuous wavelet transform, and then the wavelet time-frequency diagram is compared and analyzed. Furthermore, the DC bias state of the transformer is identified by wavelet singular entropy of vibration signal. The wavelet singular entropy under different states is in different numerical ranges, and the wavelet singular entropy of vibration signal under DC bias is significantly greater than that under no DC bias. The wavelet singular entropy effectively reflects the DC bias state of transformers. Then the proposed method is applied to a 500 kV transformer and a 220 kV transformer in the China Southern Power Grid. The results show that the proposed method can accurately and effectively identify the DC bias state of transformers.

This paper studied the influence of transformer inrush current on commutation failure prevention control of Ultra-high voltage DC (UHVDC) transmission system. An actual event occurred on Tianzhong UHVDC transmission project was analyzed with data from fault recorder. UHVDC transmission model with commutation failure prevention control was established in PSCAD/EMTDC, and then it was applied to replay the event. It is found that the frequent maloperation of commutation failure control and the fluctuation of DC power exist when inrush current occurred. An improved commutation failure prevention control strategy was proposed to avoid this phenomenon. Validity of the improved method was proved by the simulation results.

Abstract As states and utilities are moving to deep decarbonization, one challenge is to balance supply and demand on a longer duration. Despite great progresses, current battery storage technology can at best achieve daily balancing but is prohibitively expensive for monthly or seasonal balancing. This paper discusses the potential of a cross-seasonal balancing option, which is a cross-seasonal load shift by firms in the industrial sector (or seasonal demand flexibility).

The energy saving effect of low-pressure(LP) economizer of a 220MW unit was tested under multi-variable factors. Using the equivalent enthalpy drop method, the energy saving effect of LP economizer at different operational modes and different operating parameters has been calculated. When the higher water flow rate and inlet temperature of LP economizer is necessary, condensate water from NO.6 LP heater outlet should be used priority. Under the condition of 220MW, the optimal flow into LP economizer from NO.6 LP heater outlet is about 250t/h, the thermal efficiency of the unit could be increased 0.645%. If LP economizer inlet temperature is lower, the outlet of LP economizer should be connected to the outlet of NO.6 LP heater. And water temperature at LP economizer outlet should be slightly higher than the temperature of the NO.6 LP heater outlet. Otherwise, the energy saving effect will be decreased. Under the condition of 220MW, the thermal efficiency of unit increases 2.003%. For different LP economizer operation mode, there will be an optimal water returning location and optimal inlet flow rate.

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

Wave energy is an important renewable energy source. Previous studies of wave energy conversion (WEC) have focused on the maximum power take-off (PTO) techniques of a single machine. However, there is a lack of research on the energy and power quality of wave farm systems. Owing to the pulsating nature of ocean waves and popular PTO devices, the generated electrical power suffers from severe fluctuations. Existing solutions require extra energy storage and overrated power converters for wave power integration. In this study, we developed a master-slave wave farm system with rotor inertia energy storage; this system delivers self-smoothed power output to the grid and reduces the number of converters. Two control methods based on the moving average filter (MAF) and energy filter (EF) are proposed to smooth the output power of wave farms. RTDS simulations show that the proposed systems and control methods facilitate simple and smooth grid integration of wave energy. Keywords: Wave farm, Energy storage, Power smoothing, Power quality, Energy quality

Partial discharge (PD) ultrasonic detection is an early sign of the insolation defects of power transformers. The early diagnosis of PD requires the high sensitivity and reliability of ultrasonic sensing systems. For this purpose, a reformative PD ultrasonic sensing system based on phase-shifted FBG (PS-FBG) was demonstrated. By using PS-FBG as the ultrasonic sensing unit, the ultrasonic sensing system improved the response to the ultrasonic signal and overcame the electromagnetic noise. To compensate for the influence of temperature change on the ultrasonic sensing system, an automatic wavelength scanning demodulating method was carried out. The wavelength spanning strategy was optimized based on the principle of cross-correlation, in order to quicken the spanning. A PD detection test in the transformer oil was conducted, and the result shows that PS-FBG was 17.5 times more sensitive than PZT. Because of the better ultrasonic response, the proposed system was able to achieve the early diagnosis of insolation faults in a power transformer.

As fundamental equipment of advanced metering infrastructure (AMI), smart meters provide system wide visibility for distribution network. However, smart meter measurements often are not synchronized which may cause inaccuracy and divergence when directly applied to distribution system state estimation (DSSE). In order to achieve higher accuracy, the load variation between the near real-time measurements and real-time measurements which is defined as credibility are considered with the historical residential loads taken by high sample rate meters. According to the statistical result, the load variation is exponential distributed at the 95% confidence level which is utilized to update the measurements weight and 95% confidence intervals of state variables are described. A 116-node distribution network is selected to validate the proposed method. And, simulation results demonstrated its accuracy and sensitivity.