Large scale random and intermittent wind power integration will significantly increase the additional operation cost of power system, and the variation characteristics are complex and difficult to accurately quantify. Different from the existing work, this paper first proposes a double-layer clustering method for wind power fluctuation scenarios. Secondly, a production simulation model of new energy power system is established to generate a comprehensive, real and representative sample set of power system operation cost. Finally, a data-driven mapping model between wind power fluctuation and the operation cost of power system and thermal power unit is established by using deep neural network algorithm. The proposed method can accurately simulate the variation trend of power system operation cost in different seasons and wind power fluctuation scenarios. The results show that the simulation error of thermal power unit and total system operation cost can reach 3%-13% and 4%-18%. This work provides insights and policy guidance for improving the wind power consumption rate and power system cost.
Due to the increasing demand for sustainable energy, the DC microgrid with high penetration of power electronic devices is developing rapidly. The DC arc fault protection technique is essential to ensure the safety of DC microgrids. Although there are arc-fault detectors in the photovoltaic system, their misjudgment rate will increase when it is applied in the DC microgrid with a large number of power electronic devices. Furthermore, the cost will also increase when it is applied in the DC microgrid with many branches. In this paper, a series arc fault detection and location method based on state-space modeling analysis is proposed to realize arc fault detection and location in DC microgrids. Initially, the topology and operation principle of power converters in a DC microgrid is analyzed and the state-space model is developed. In this way, the equivalent circuit of a DC microgrid under normal operation and an arc fault can be established. Since the inductor current and the voltage of the output capacitor in a branch of a DC microgrid under normal operation and arc fault are different, they can be analyzed to determine the operating condition of the branches of a DC microgrid. Thus, the residual of inductor current and voltage of the output capacitor between theoretical analysis and practical measurement can be calculated to detect and locate the arc faults. Besides, the over-threshold values can be integrated to avoid the load transient influence on the arc fault detection. Hence, an arc fault detection and location technique based on state-space modeling and residual analysis is developed. The feasibility and high accuracy of this technique are verified by simulation and experiments in a typical DC microgrid composed of two branches.
Thermoelectric conversion can directly use thermal energy to generate electricity, a promising green waste heat recovery technology. It has been applied to medium- and low-temperature waste heat power generation in the industry. However, it is found that some components in the series thermoelectric module group are not working, and the power generation components are transformed into power consumption components due to the uneven temperature distribution of the heat source. The co-existence of the Seebeck effect and the Peltier effect appears in the circuit. In this study, the experimental bench with a thermoelectric module group, a heat source, and a water-cooling system was established, the power generation under different working conditions was tested, and the coexistence of the Seebeck effect and the Peltier effect was verified. The results show that the thermoelectric module group has excellent power generation performance when the temperature distribution of heat source is uniform. As the temperature of one heat source decreases, the output power curve is inversely proportional. When the heat source temperature difference exceeds 70K, there is an obvious inflection point in the output power curve, proving that the Seebeck effect and the Peltier effect exist simultaneously. This result has a guiding role in the practical application of thermoelectric conversion technology.
In order to achieve carbon peaking and neutrality goals, the photovoltaic integrated buildings with BIPV modules is more efficient and economical than other distributed solar energy systems. Since they are usually installed on the roofs of high buildings or big factories, BIPV modules have a high probability of being struck by lightning. In order to study the transient current and magnetic field distribution in the photovoltaic integrated buildings under lightning strike, this paper established a model of photovoltaic integrated buildings with BIPV modules in the FEM simulation platform. The simulation results show that the transient current distribution and the magnetic field distribution inside the photovoltaic integrated buildings are less and more uniform comparing to that of the common buildings without BIPV modules. Therefore, it is revealed that the building with BIPV modules will induce less threat of the lightning electromagnetic impulse to impact the sensitive electronic equipment inside the buildings.
Storage and transportation of hydrogen is a great challenge that restricts the applications of fuel cells. It is an effective solution to produce hydrogen onsite by using steam reforming of hydrocarbon fuels to avoid storing and transporting hydrogen directly. The steam reforming of diesel attracts a lot attentions recently because diesel is a widely used logistic fuel in heavy duty trucks and vessels. However, the diesel reforming is usually based on expensive noble metal catalysts which limits its commercialization. Moreover, the comprehensive influences of operating conditions on the reforming performances have not been thoroughly explained. The present work is devoted to experimentally investigate the steam reforming of n-hexadecane as a diesel surrogate over an in-house made non-noble metal Ni based catalyst. The co-impregnation method is used to washcoat the catalyst on a cordierite honeycomb monolith. Experiments are conducted to study the influences of steam to carbon ratio (S/C), weight hourly space velocity (WHSV) and reformer operating temperature (T) on the n-hexadecane conversion rate, mole fractions of gas species produced and the production flow rate of each species. An 8 h continuous operation confirms that the catalyst activity is table, yet the fuel conversion rate decreases with operating time because coke formation blocks the catalyst active sites.
Fuses play an important role in protection of the power and electrical components. This work is aimed at reducing the pre-arcing time of fuse by optimizing the fuse-element structure. A three-dimensional (3-D) finite element simulation model of fuse was established, and the temperature variation of fuse was simulated with the thermal-electric coupling method. On this basis, the optimal structure of fuse-element was determined and analyzed. The results show that temperature change rate and maximum temperature threshold of the fuse-element can be regulated through altering the structure of fuse-element. With the improvement of the fuse-element, the pre-arcing time can be reduced by approximately 4 ms. The simulation results can be used to guide the actual fuse design.
In this paper, an acoustic-electric combined detection method is proposed based on the acoustic and electrical signals of partial discharge. Firstly, based on the comparative analysis of ultra-high frequency and ultrasonic methods, a comprehensive detection scheme including ultra-high frequency sensor, ultrasonic sensor and high frequency current sensor is constructed, and the corresponding parameters of the sensor are set. Secondly, the acoustic-electric combined positioning method based on bisector method positioning and time difference positioning is proposed. Finally, through case analysis, the acoustic-electric combined detection method can quickly locate the discharge position, which verifies the effectiveness of this method.
Due to some characteristics of power energy, electric vehicles cannot achieve instantaneous energy supplements at present. Although the rapid charging technology takes a short time, it still cannot fundamentally solve the contradiction between ‘endurance mileage’ and ‘battery capacity’. At present, China State Grid and Southern Power Grid Corporation put forward the operation mode of electric vehicle charging and switching power stations. Taxi is an important part of urban public transport. In the construction and operation of electric taxi switching power stations, although a certain number of switching power stations are put into use, the research on the optimal capacity optimization planning of electric taxi switching power stations is still scarce, and thus the rational allocation of resources cannot be achieved in the construction and operation of distribution network switching power station. In order to solve this problem, this paper studies the capacity optimization of electric taxi switching stations by constructing a comprehensive cost model of switching stations. The main approach is to study the optimal battery capacity of the swapping station under the condition of different scales of service for electricity swapping and leasing, taking the maximum return of the swapping station as the optimization objective, taking into account the fixed investment cost and battery purchase cost in the construction and operation of the swapping station, and taking the price as the constraint condition.
With the development of renewable energy, gas-fired units are gradually replacing traditional coal-fired units, and the coupling between natural gas and power systems is also increasing. This paper proposes bidding strategies for energy supplier in multi-time scale power and gas coupled markets, which also offer ancillary services such as frequency regulation and spinning reserve. Firstly, there exists asynchronous clearing problems in power-gas coupled market, so using the forecasting power generation to clear the gas market. Secondly, taking the gas clearing value as upper limits of gas-fired units to participate in the day-ahead electricity and ancillary service joint market. Then, constructing clustering and scenario reduction models to calculate the unbalanced quantity brought by the uncertainty of renewable energy. The unbalanced quantity is finally cleared in the real-time market with reserve capacity. In case studies, taking the IEEE39-node power system and the Belgian 20-node natural gas system as examples to minimize the energy purchase cost of suppliers and achieve the optimal allocation of resources.
Power system with high penetration of renewable energy resources like wind and photovoltaic units are confronted with difficulties of stable power supply and peak regulation ability. Grid side energy storage system is one of the promising methods to improve renewable energy consumption and alleviate the peak regulation pressure on power system, most importantly, provide reliable power supply when needed. This study firstly proposed a power and capacity configuration model of grid side energy storage system considering power stability and economic factors. Secondly, certain operation strategies of energy storage peak-shaving and valley-filling are investigated, including the one charging/discharging mode and the multiple charging/discharging mode for the full performance of energy storage system and ideal peak-shaving and valley-filling effect. Finally, case study based on real load curves and power unit structure of a certain area showed that grid side energy storage under peak-shaving and valley filling operation mode effectively improves the stability of power supply and reduce the peak regulation pressure. A one charging two discharging power and capacity allocation project are proposed to demonstrate the effect of peak-shaving and valley-filling.
Since more renewable energy is being incorporated into the electrical grid, coal-fired power units must operate flexibly rather than as base-load. Sharp changes in the reheat steam temperature during flexible operation of the secondary reheat units can cause thermal stress on the reheater, resulting in tube breakage or unplanned shutdown. To avoid the risk of this issue occurring, the operating temperature of the reheater should be precisely monitored in real time. A novel long short-term memory (LSTM) for predicting metal temperatures is developed using real operating data obtained from a 1000 MW doublereheat power plant boiler. The soot blower action signals are added for more accurate prediction. The results indicate that the LSTM has adequate accuracy. The root mean square error for the test dataset is 0.861 ℃, the mean absolute percentage error is 0.105% and the Pearson correlation coefficient between the actual value and the predicted value is over 0.979.
As a priority industry in China to be included in the carbon trading system, the power industry has become the main body of carbon emission reduction. As a technology and market solution for aggregating distributed resources in the power grid, virtual power plants can further enhance the overall benefits of virtual power plants by aggregating electric vehicle resources to participate in the carbon market. This paper proposes an optimal scheduling method for virtual power plants to aggregate electric vehicles to participate in the carbon market. Electric vehicles are used as controllable loads and energy storage devices to participate in the optimal operation of virtual power plants, and to improve the economic benefits of virtual power plants participating in the electric energy market. The power plant participates in the certified emission reduction market by acting as an agent for electric vehicles, which improves the efficiency of electric vehicles participating in the carbon market. The analysis of an example shows that the advantages of traditional distributed resources and electric vehicles can be effectively complemented by the centralized optimal management of virtual power plants and simultaneous participation in electricity energy market and carbon market transactions, and the overall operation performance of virtual power plants can be improved.
As a clean and inexhaustible renewable energy, wind power attracts more and more attention. However, the intermittence and randomness of wind speed make the utilization of wind power challenging. These troubles can be alleviated via wind speed forecasting that can provide comprehensive future information about wind speed uncertainties. Compared with traditional point forecast methods, interval forecast is able to quantify uncertainties in renewable energy production effectively. In this paper, the idea of prediction intervals (PIs) is employed to capture the uncertainties of wind speed. Specifically, a novel interval forecast model based on long short-term memory (LSTM) neural networks is proposed to construct PIs with the lower and upper bound estimation (LUBE) method, which is a powerful nonparametric forecast approach. To tune the parameters of LSTM prediction model, the improved grid search algorithm is introduced as the optimization tool. The effectiveness of the proposed model and algorithm is demonstrated by a series of experiments based on a real world wind speed dataset, and the comparative results show the superiority of the model.
Compared with the traditional integrated energy system (IES), the IES with hydrogen as the energy storage medium combines electrolytic hydrogen storage and fuel cell cogeneration, which can achieve large-scale consumption of renewable energy and obtain considerable carbon emission reduction benefits and economic benefits. In the scenario of regional power supply, solid oxide fuel cell (SOFC) have attracted extensive attention due to their fuel flexibility and high reliability. However, as high-temperature fuel cells, their preheating process takes a long time, which reduces the flexibility of operation and scheduling. In contrast, proton exchange membrane fuel cell (PEMFC) have low operating temperature and good start-stop capability, but poor fuel adaptability. According to the operation characteristics of the two fuel cells, this paper proposes a hydrogen energy storage IES architecture combined with SOFC and PEMFC to play the complementary advantages of the two fuel cells. Firstly, the mathematical model of each equipment is established. Then the day-ahead optimal scheduling model of the system is put forward with economy as the optimization objective. Finally, the advantages of the proposed dual-fuel cells hydrogen energy storage IES are verified based on simulation analysis.
The unscented Kalman filter (UKF) has been widely used in power system dynamic state estimation (DSE), which provides a guarantee for the establishment of a high-quality database to store power network information. However, traditional UKF faces two main problems. First, the performance of traditional UKF will deteriorate due to the interference of non-Gaussian noise. Second, the traditional UKF estimator will be interrupted owing to the Cholesky decomposition of asymmetric positive definite matrix. To deal with these problems, this paper develops a square root UKF based on minimum error entropy with fiducial points (MEEF) criterion (SR-MEEF-UKF). The MEEF criterion inherits the common advantages of correntropy and error entropy, exhibiting robustness to outliers. At the same time, the kernel matrix in SR-MEEF-UKF is non-singular. Through performing DSE in the IEEE-14 bus system, the proposed SR-MEEF-UKF not only has excellent performance in non-Gaussian noise environments, but also has strong numerical stability.
The development of an online tool application that enables precise state estimate via synchrophasor processing tools to receive streams of sampled data from substation merging units via the IEC 61850-9-2 protocol is discussed in this study. This would make it possible for researchers to evaluate synchrophasor technology in a production setting in an entirely open source and transparent manner without the need for intrusive and expensive fixed wiring installations. Users of the platform can simulate a precisely modeled power grid thanks to the platform’s valuable infrastructure capabilities. Real-time simulation tools can be used in a range of applications, providing a wealth of potential for improved testing and prototype review, and this paper proposes a hardware/software integration to monitor state-estimation values and shared such data. The research proposes and employs a flexible methodology for commissioning OpenPMU devices, engaging with other virtual PMUs within the same simulation using Sampled Values to confirm measurement frames and assess estimation with generated data.
Power loss calculation and junction temperature analysis are of great significance to the thermal design in power electronics. In this paper, an electrothermal simulation analysis on GaN HEMT is performed based on the power loss calculation under actual working conditions including conduction loss and switching loss. By coupling the power loss and heat conduction process, the temperature distribution of the GaN HEMT, as well as the junction temperature, can be obtained by both the FEM thermal simulations and RC thermal model. The results show that the switching loss accounts for about 20% of the total power loss, far less than the conduction loss of 80% under the given working conditions. The RC thermal model based on the thermal resistances obtained by FEM simulations can predict the results of FEM simulations well for cases with different heat source distributions, while the model with original thermal resistances from thermal conductivity and size of layers cannot provide accurate predictions. The maximum junction temperature occurs in the case of concentrated distributed heat sources. This research is promising to provide valuable references for thermal management in GaN electronics.
DC microgrid technology has drawn extensive research attentions in recent years. It is regarded as a critical issue to obtain fast DC bus regulation and stability when feeding both different kinds of loads, including resistive loads and constant power loads (CPLs). A sliding mode control (SMC) design strategy is presented in this study for DC-DC converters with unknown CPLs. Firstly, disturbance observers are constructed to accurately reconstruct the multiple disturbances. By integrating disturbance compensations, the proposed composite controller is finally constructed for the converter system. Simulations are compared with existing integral sliding mode approaches to demonstrate the effectiveness of the presented controller. The presented approach is characterized by higher voltage tracking accuracy, better dynamic properties and stronger disturbance rejection ability in the presence of various working conditions.
With the continuous promotion of the "double carbon" strategy, the proportion of new energy power generation based on wind energy and solar energy is increasing. At the same time, it brings a series of power quality problems to the power grid. It is very important to study the causes of poor power quality caused by wind power and photovoltaic grid connection, and to formulate a scientific and reasonable comprehensive treatment plan for power quality. In this paper, the causes of grid harmonics, voltage fluctuation and flicker, voltage deviation, three-phase voltage imbalance and frequency deviation caused by the current wind power and photovoltaic grid connection are deeply studied. It is hoped to provide theoretical basis for the power system operators to take the optimal power quality prevention measures.
