In order to evaluate the possibility of the ‘death spiral’ operation dilemma faced by the retailers with the high level penetration of distributed photovoltaic (PV) under the background of carbon neutrality, and to analyze the key factors that may lead to the ‘death spiral’, the system dynamics approach is applied for modeling. First, the model of customer-side distributed PV penetration guided by market conditions such as sales tariff is established. Then, a model of surplus of retailers is established based on the negative feedback relationship between distributed PV penetration level and the surplus of retailers. The case study evaluates the sensitivity effects of factors such as the generation of distributed PV and wholesale electricity prices on the surplus of retailers. The results show that the surplus of retailers tends to decrease slowly in the mid-long term. An extreme scenario may cause the ‘death spiral’ of retailers in which multiple factors such as transmission and distribution volumes, wholesale tariffs, and maintenance costs change significantly at the same time.
Miniaturization, lightweight, and high power density are development trends of vehicle-mounted inverters. The existing vehicle mounted single-phase inverter with DC 110 V input voltage adopts two-stage independent modulation between Boost circuit and full bridge inverter circuit, which has the problems of low efficiency and low power density. To solve these problems, a two-stage cooperative modulation between Boost circuit and full-bridge inverter circuit is studied. According to the relationship between the absolute value of input voltage and output voltage, the Boost circuit and the full-bridge inverter operate with different modes in the proposed method, which optimizes the insulated gate bipolar transistor(IGBT) switching state and diode on-off state to reduce the loss of the inverter and improve the efficiency of the inverter. At the same time, the output harmonics of the full-bridge inverter circuit can be reduced to decrease the size of the filter and improve the power density of the inverter. A 2.75 kV·A prototype is designed and fabricated. Experiments are conducted to verify the correctness and feasibility of the proposed method.
The proportion of renewable energy in the new power system is further increased, and the grid connected capacity of photovoltaic units has a trend of obvious improvement. The dynamic behavior of the photovoltaic (PV) power generation system at different permeabilities has a significant impact on the load characteristics of the power grid. However, the complex dynamic model of photovoltaic power generation grid connection and the large number of parameters to be identified increase the difficulty of practical application of the model. Therefore, a dynamic discrete equivalent model of the PV power generation model based on the physical model of the PV power generation model is established, and the parameters of the dynamic discrete equivalent model for the PV power generation model are obtained. The IEEE 14-bus system, which is subject to various PV permeabilities, is adopted to verify the superb dynamic characteristics of the proposed discrete equivalent model for the PV power generation in power system simulations. The pertinent simulation results show that the dynamic discrete equivalent model of the PV power generation system can accurately describe the dynamic characteristics of the PV power generation system with a high accuracy and an easy identification performance.
With the increasing scale of electric vehicles (EVs), the adaptive management of its charging behavior becomes an urgent problem to be solved. From the point of view of charging service provider, an online management algorithm for EV charging is proposed based on the Lyapunov optimization theory under the random environment in this paper, considering renewable sources energy, storage equipment, time-varying electricity price, and the tolerable delay of EV, with an aim of maximizing the benefits of charging service providers (i.e., minimizing the cost of electricity purchased). The performance of the proposed algorithm is analyzed to verify that it can achieve near-optimal optimization results without any a priori statistical information about the system inputs (renewable energy generation, charging demand, and time-varying electricity price). The simulation results show that the proposed algorithm can effectively reduce the economic cost by 27.3% compared with the benchmark algorithm.
There are abundant biomass resources in China’s rural areas, which can be converted into biogas energy through fermentation systems. However, the rewards of the investments of the pure biogas projects is poor because biogas is a cheap resource. This paper proposes a 100% renewable grid-connected wind-solar-biogas integrated energy system which utilizes the complementarity between solar energy, wind energy, and biogas to provide users with biogas and electricity. The battery-like characteristics of biogas are modeled based on the microbial fermentation kinetic model and the temperature-sensitive characteristics of biogas fermentation. In addition, the demand-side response is considered to further increase the flexibility of the system, and the time-of-use electricity price is used to save power purchase costs, thereby minimizing investment costs and annual operating costs. Case studies show that the wind-solar-biogas micro-energy network can effectively reduce the total investment cost by 3% to 9% while increasing the benefit by 1.27 to 2.40 times.
In order to develop an accurate model of proton exchange membrane fuel cell (PEMFC), it is essential to exactly identify unknown parameters in PEMFC. However, parameter identification of PEMFC is a multi-variable, multi-peak, and strongly coupled nonlinear optimization problem, of which traditional parameter identification methods often fail to achieve satisfactory results. In addition, noises generated under different operation conditions will hinder meta-heuristic algorithms (MhAs) to obtain accurate parameters. To handle these thorny obstacles, extreme learning machine based MhAs (ELM-MhAs) are proposed for PEMFC parameter identification, which can achieve denoising through ELM. ELM is used to train data to reduce or eliminate noises and provide more accurate and reliable fitness functions for MhAs, thus ensuring the accurate identification of PEMFC parameters by MhAs. To verify the feasibility and effectiveness of this strategy, 25 groups of voltage-current data are processed without denoising, with Bayesian regularization neural network (BRNN) denoising or with ELM denoising under two conditions—low temperature and low relative humidity; high temperature and high relative humidity, respectively. Subsequently, parameter identification results of six MhAs and a Levenberg-Marquardt backpropagation of different data are thoroughly compared. The simulation results indicate that ELM can significantly reduce the impact of noise on the data, while effectively improving the parameter identification accuracy of MhAs, compared with no denoising and BRNN denoising.
The time resolution of scheduling plan refers to the length of each time interval in the scheduling plan. With the increasing proportion of climate-sensitive renewable energy sources (RES), the volatility of net load during the dispatch interval is significantly enhanced, resulting in an insufficient system climbing capacity and abnormal frequency. Therefore, the setting of time resolution at different renewable energy penetration rates becomes an urgent problem to be solved at present. A global sensitivity-based day-ahead dispatch time resolution selection optimization method is proposed to select the time resolution for different volatility grids. To achieve a balance between the degree of refinement and the accuracy of load prediction, the global sensitivity analysis method based on Sobol' method and sparse chaos expansion is used to evaluate the impact of net load volatility and uncertainty on dispatch at different time resolutions, and an appropriate time resolution is chosen to optimize the scheduling effect. The analysis and simulation results show that the choice of time resolution is mainly determined by the net load fluctuation rate. The appropriate time granularity is chosen according to the net load fluctuation rate, which minimizes the unbalanced power, and the purpose of improving the optimal scheduling effect and reducing the dispatching cost is achieved.
There are many terminals in the central air-conditioning system of which load demands vary frequently. Although conventional proportional integral derivative control or fixed parameter control can meet the load demand, there is a problem of energy waste caused by excess cooling. This paper proposes a central air-conditioning system energy-saving control method based on an improved sparrow search algorithm (ISSA) for the air-water system in the central air-conditioning system. The ISSA applies t-distribution to strengthening the search ability and enables the individual to learn from the best group based on the roulette wheel selection, which enhances the ability of the algorithm to jump out of the local optimum and improves the accuracy and stability of control parameters effectively. For the 12 test functions, most of the optimization accuracy and stability have been improved by more than 2 orders of magnitude. Compared with the original control strategy, the ISSA has shown a good energy-saving potential for energy optimization of air conditioning subsystems, reducing energy consumption by 25.13%. The feasibility of the ISSA in actual engineering problems has also been verified.
DC distribution network is the main development direction of the power distribution system. Due to the influence of electronic equipment and other factors, the system is very easy to produce second harmonics, which seriously affects the stability of the system and the safety of electrical equipment. Multi-filter collaborative filtering in DC distribution network has become one of the methods to control the second harmonic. However, due to the coupling interference between the filters, the filtering effect is affected by multiple factors. This paper establishes a Norton equivalent grid-connected model of multiple filters and proposes a method for analyzing the interaction mechanism of multiple filters based on the relative gain matrix theory. This method establishes the matrix relationship between the change in the output current of the filter and the change in the harmonic source current and analyzes the influence of the grid parameters and controller parameters in the multi-filter grid-connected system on the filtering effect of the filter, and proposes a reasonable parameter selection method. Finally, this paper establishes a DC distribution network model with multiple DC filters in PSCAD/EMTDC, and simulates the parameters affecting the filtering effect and verifies the rationality of the analysis in the scenario of multiple harmonic sources. In addition, it builds a semi-physical simulation model in RT-LAB to further verify the effectiveness of the method.
Under the vision of carbon neutrality, carbon dioxide emission allowance targets are gradually decreasing, and clean power sources will penetrate in an ultra-high proportion. The traditional distribution grid dispatching model needs to solve the problems of carbon emission compliance and strong intermittent leveling of clean power sources. Based on the analysis of the coupling relationship between carbon emission index and the economic cost of electric power, this paper proposes a novel dispatching model for the future state distribution grid with carbon and electric power couping, proposes an optimal dispatching strategy for distribution grid based on the second-order cone planning model for several scenarios of increasing carbon emission cost of system operaion, and verifies the effectiveness of the proposed method in the improved IEEE 33-node system. The example results show that the output and power generation cost of each distribution network change after considering the carbon emission index.
In order to solve the problem of battery bulging and capacity fading, this paper proposes an innovative battery capacitor structure and the related preparation process. This method integrates the physical energy storage method of the capacitor and the chemical energy storage method of the energy storage battery. In the preparation process, a novel technology of columnar lithium-ion battery soaking is adopted, which improves the soaking efficiency and reduces the internal moisture of the battery. The related performance tests show that the capacity retention rate of the new lithium-titanate battery can reach 92.5% after 9 548 cycles, and the battery capacity can be maintained above 75% at a low temperature. The proposed method provides an effective means for improving the performance of the lithium-titanate battery.
In order to accelerate the rapid and economic low-carbon transformation of the power-gas system, a multi-objective stochastic optimization programming model for the whole equipment of the power-gas system was established, which comprehensively considered the economic cost and carbon emissions. First, the mathematical model of the electric-gas network and related equipment was established, and the uncertainty characteristics of the electric and gas loads and photovoltaic output were analyzed by using the scenario method. Next, a mixed-integer quadratically constrained programming (MIQCP) model considering the economic cost and carbon emissions of the system was established. An overall planning was made for power feeders, gas network pipelines, substations, gas distribution stations, gas units, power-to-gas devices, photovoltaic, and energy storage devices. Finally, a numerical example was built to verify the feasibility and effectiveness of the model. The results show that the model can fully consider the coupling relationship between power-gas network lines and a variety of comprehensive energy equipment under different weight choices of objective function, and obtain the overall optimal planning scheme.
To solve the problems of large thermoelectric coupling in combined heat and power, high carbon emission of thermal power units, and insufficient resource flexibility on the load side in cogeneration units, a low-carbon dispatching model is established for the electricity-heat system. First, heat storage and carbon capture equipment is added on the source side while the demand response of electricity price and the heat load inertia of heating buildings are considered on the load side. Then, the sum of unit operating cost, carbon transaction cost and wind abandonment penalty cost are taken as the objective function with relevant constraints and solved by calling Gurobi solver. Finally, a comparative analysis of the economic cost, wind power consumption, and carbon emission rate of the system in different cases is conducted, which shows that the dispatching strategy proposed in this paper can improve the wind power consumption capacity while taking economy and low carbon emission into account.
With the growing penetration of renewable generation, the inertia and frequency support ability of the renewable-dominated power system are continuously reduced, which leads to frequency collapse when the system is disturbed. Therefore, it is of great significance to promptly and accurately evaluate the power shortage after a major disturbance to fill the power shortage quickly. This paper proposes an online estimation approach of power shortage in power system based on deep convolution and long-short term memory composite neural network driven by local frequency measurement data. First, since using the synchronous measurements to obtain the frequency of center of inertia (COI) cannot adapt to the rapidity of online estimation, this paper employs the local frequency measurements to estimate the COI frequency, avoiding the delay effect caused by the complex communication. Then, it designs a deep composite neural network to mine the correlation information between massive frequency data and power shortage. Finally, it tests the simulations on a 39-bus system to verify the effectiveness of the proposed approach. The results demonstrate that the proposed approach is effective and fast.
Integrated energy system (IES) has become the research hotspot of the energy system due to the characteristics of multi-energy joint coordination and energy efficiency. Because of the complex structure, control, and fault characteristics of IES, it is difficult for traditional protection principles and schemes to adapt to system requirements. This paper first analyzes the structural characteristics and control characteristics of IES, and studies the fault characteristics of the core power part based on its characteristics. Then, based on the fault characteristics used in the existing protection principles, it classifies and analyzes domestic and foreign research, improvement status, and protection applicability. Finally, it discusses and prospects the research and development direction of IES line protection principles and schemes.
To improve the stability of solar heating and reduce the high cost of air source heat pump heating, the idea of air source heat pump assisted solar stable heating was proposed. A solar vacuum tube water heater-air source heat pump system was developed and bulit in Weiling Township, Qilihe District, Lanzhou, Gansu Province. The performance of the system was compared to analyze the heat collection efficiency, heat pump coefficient of performance (COP), solar energy guarantee rate, and energy efficiency ratio under sunny, overcast, and cloudy conditions. The results show that the effective heat obtained by solar energy under sunny, overcast, and cloudy conditions is 75.5 kW·h, 4.1 kW·h, and 49.2 kW·h respectively, the system heat collection efficiency is 61.3%, 26.6%, and 55.2%, the average coefficient of performance(COP) of the solar heat pump is 3.6, 3.4, and 3.6, the average COP of the air source heat pump is 0, 2.9, and 3.1, the actual heat supply of the system is 113.4 kW·h, 125.9 kW·h, and 124.8 kW·h, the system power consumption is 33.4 kW·h, 50.5 kW·h, and 42.7 kW·h, the system solar energy guarantee rate is 66.6%, 3.3%, and 39.4%, and the system energy efficiency ratio is 3.4, 2.5, and 2.9 respectively. The research results prove that the solar vacuum tube collector-air source heat pump system is feasible for heating and provide a new way for heating in cold areas.
Fully tapping into the role of user side regulation helps reduce the energy cost of integrated energy system (IES). Demand response (DR) and electric vehicle (EV) as schedulable resources on the user side are important regulation means for optimal scheduling of IES. However, in the actual operation process, due to the influence of load aggregator (LA) economic incentives and EV travel, the economic impact of the uncertainty of user side DR on IES cannot be ignored. Based on this, this paper proposes an IES optimal operation model considering the robust stochastic optimization of EV and the participation of LA which considers the energy purchase cost of IES from the superior network and the economic loss cost of LA. First, the response rate model and EV uncertainty model based on economic incentive are constructed. Then, the robust optimization model of EV is built, and the load demand of EV travel uncertainty is analyzed. Finally, a simulation example is given to analyze the impact of user DR uncertainty and EV uncertainty on IES operation economy and power balance. The simulation results show that considering the uncertainty of DR and EV can optimize the economic operation of IES and reduce the economic loss of LA and the total cost, which verifies the effectiveness and economy of the proposed models.
With the gradual advancement of the market-oriented process of distributed generation, it is difficult to accurately distinguish the use degree of power grid assets by prosumers via pricing method of uniform calculation of network usage charge according to user access voltage. Therefore, this paper proposes a calculation method of network usage charge suitable for market-oriented trading of distributed generation. The characteristics of the peer-to-peer (P2P) trading model and the community-based (CB) trading model in distributed generation market are discussed from the perspective of prosumers. Meanwhile, the power trading models of the P2P model and the CB model are constructed. The optimal power flow model based on second-order cone relaxation is used to determine the distribution of power flow in distribution network, and the distribution locational marginal price is calculated with the economic significance of dual multiplier. Considering the transitivity of dual multipliers, calculation models of the network usage charge of the P2P trading model and the CB trading model are established by coupling the power trading model and the optimal power flow model. The limitations of the CB trading model are analyzed, and the Shapley value method is used to realize the fair allocation of network usage charge according to marginal contribution. By using the improved IEEE15 bus and IEEE123 bus test systems, the availability and feasibility of the proposed calculation method of network usage charge in distributed generation market are verified.
The access of a high proportion of renewable energy has posed new challenges to the supply reliability of the power system. The system must have sufficient capacity credit to cope with the output fluctuation and randomness of renewable energy. Due to the nonlinear relationship between energy storage capacity credit and power planning results, it is difficult to establish accurate capacity adequacy constraints for traditional power planning methods. Therefore, a generation expansion model is established, in which thermal power, renewable energy, energy storage, and demand response resources are incorporated, with the full-year hourly production simulation to ensure adequate operation flexibility and improved capacity adequacy constraint to incorporate the capacity value of energy storage and demand response resources. An iterative algorithm is designed to solve the nonlinear problem of energy storage capacity credit, and the validity of the model is verified by some regional grid in China. The results show that in the high-proportion renewable energy system, the system capacity is surplus, and the main factor affecting the system cost is the flexibility constraint. The introduction of a small amount of demand response resources can greatly reduce the system cost, which provides new ideas for power system planning at a high proportion of renewable energy.
Serious earthquake disasters not only cause power outages in distribution network, but also destroy transportation networks, which hinders the transportation of resources for restoration of distribution network and slows down the restoration. This paper proposes an improved resilience evaluation method and a resilience enhancement strategy of distribution network considering the effects of seismic attack on transportation networks. First, a seismic attack model is established to describe the relation between earthquake disasters and failure probability of transportation-distribution networks based on peak ground acceleration. The impact of earthquake disasters on transportation-distribution networks is quantified, and the failure scenarios are generated. Then, a resilience evaluation index is proposed by introducing the waiting time for road repair of emergency repair teams. Afterwards, a bi-level optimization model for distribution network restoration considering the fault line repair, the road repair, and the emergency resource scheduling is established and solved. The upper layer aims at the minimum power loss load, while the lower layer takes the minimum waiting time of the repair team as the goal. Finally, case studies on a coupling example of a 12-node transportation network and an IEEE 33-node distribution network verify the feasibility of the improved resilience index and the effectiveness of the proposed method. The results show that the resilience index considering seismic attack on transportation networks is accurate, and the restoration strategy can effectively enhance the resilience of distribution network in earthquake disasters.
With the increase of the penetration rate of distributed generation (DG), lack of flexibility of distribution network has become increasingly prominent. This paper focuses on electric energy router (EER), and analyzes its impact on the flexibility of distribution network. First, the index of the transfer flexibility of distribution network is proposed to represent one aspect of distribution network flexibility. Then, the linear model of the EER and its related constraints are analyzed, and an optimization model of the transfer flexibility of the active distribution network with the EER model is constructed. Finally, based on the 94-bus alternating current distribution network, different scenarios are designed to analyze and compare the effects of different DG operation control modes and different parameters of EER on the transfer flexibility of distribution network. The effectiveness of EER in improving the transfer flexibility of distribution network is verified.
Energy is an important component of urban carbon emissions. Assessing the peak of urban energy carbon is a necessary means to implement the national “double carbon” strategy. For this reason, this paper proposes an energy carbon peaking assessment method based on Mann-Kendall trend test for carbon emission of urban energy. By constructing a carbon monitoring system covering elements such as energy carbon emissions, clean energy generation, and transportation electric energy substitution, the total energy carbon emissions of the city are calculated by combining historical data. In view of the seasonality and randomness of energy carbon emissions, the Mann-Kendall trend test was used to establish a model for determining urban energy carbon peaking and to measure regional carbon emissions in different periods. Taking an administrative region in Shanghai as an example, the peak status of energy carbon in this region is judged from the perspective of year and quarter. The results show that based on the annual data, the region has reached its peak energy carbon in 2020. Based on quarterly data, peak energy carbon has been achieved in summer and autumn, while spring and winter are still in plateau. The methods proposed in this paper can be used to assess the carbon peak status in the city, and provide a reference for examining the carbon peak process in other provinces and cities.
Driven by the carbon peaking and carbon neutrality goals, the power system is transforming to the new structure which is dominated by renewable energy and is facing a new supply-demand balance situation. Pumped storage, as the most mature energy storage technology at present, can provide flexible resources with different time scales to ensure the safety of the power system and promote the consumption of renewable energy. However, the operation strategy and cost sharing mechanism of the pumped storage station (PSS) are not clear, which hinders its further development under the new situation. In this context, the technical characteristics and functions of PSS are sorted out first. Then, the investment cost model is established from the perspective of the whole life cycle. After that, the evolution path of pricing mechanism and cost sharing mode are described in view of the different stages of electricity market development, providing a feasible scheme for the marketization of PSS. Finally, the future development of PSS is summarized and prospected.
During the production of gas diffusion layer (GDL), hydrophobic binding treatment and assembly compression lead to changes in pore structure and permeability. In this paper, a GDL model based on stochastic reconstruction is developed with binder and inhomogeneous compression. Single-phase flow of gas is simulated by utilizing the Lattice Boltzmann method and the effect of binder and compression on pore structure and permeability of GDL is explored. The results show that both the binder and the compression cause porosity to decrease and small-scale pore volume to increase, leading to the shrink of permeability. The change is basically consistent with the theoretical relationship between porosity and permeability. Moreover, the decrease of permeability caused by compression is higher than that caused by binder when porosity is similar.
In this paper, large-scale energy storage system(ESS) is taken as the research object to conduct study of business models on the participation of ESS in electricity spot market with liberalization. First, based on the typical market clearing mechanism at home and abroad, the clearing method, clearing calculation process and so on in day-ahead market and real-time balance market are analyzed, and a joint clearing mechanism suitable for large-scale ESS to participate in the spot market is proposed, including bidding method, billing method and clearing method, etc. Then, in order to fully explore the market value and other added value of large-scale ESS, to enhance cluster effect and to solve the problem of idle ESS capacity, business models suitable for large-scale ESS to participate in the spot market are proposed, including independent (single investment entity, single service model), alliance (diversified investment entities, single service model), and shared (diversified investment entities, diversified service models) models. The game relationship in the market transaction chain is analyzed, of which the electric energy value, the ancillary service value, and other added value are quantified. On this basis, a bilevel clearing model paradigm for ESS to participate in the spot joint market of different business models is constructed based on the master-slave game. In the upper-level model, large-scale ESS is the leader to participate in market competition with the goal of maximizing profits, while the dispatching and trading center in the lower-level model are followers to jointly clear the market with the goal of maximizing social welfare. Finally, the validity and feasibility of the proposed business models are verified by taking typical transaction scenarios as examples based on the improved IEEE30 node system.
Electromagnetic detection is the main method of marine oil and gas resources exploration, and marine electromagnetic transmitter is the key equipment of marine electromagnetic detection system. At present, when the underwater towed body of the marine electromagnetic transmitter operates for a long time, the switch devices will be damaged. First, the commutation process of the transmitting bridge with unidirectional controlled-source circuit is analyzed. It is found that the feedback energy of the transmitting dipole parasitic inductor makes the secondary bus generate impulse voltage, which increases the voltage stress of the switch devices. Then, the operating mode of bidirectional controllable source circuit is analyzed, and a dual variable decoupling control strategy is proposed. Based on the model established at the front and back of the transformer, the original coupling nonlinear system is globally linearized into two single input single output systems, so as to obtain the functional relationship of the sliding mode controller. The simulation and experimental results show that the designed controllable source circuit can significantly reduce the impulse voltage of bus capacitor and the voltage stress of switch device, and improve the dynamic performance and efficiency of the system.
With the increasing penetration of wind power in power grids, it is necessary for wind storage joint farms to participate in power grid frequency modulation to maintain frequency stability of the power grid. By analyzing the mechanical characteristics of the wind turbine and the operation characteristics of the energy storage system, this paper determines the adjustability of the wind turbine power output in the pitch angle load shedding operation mode, and proposes a control strategy for the wind farm with an energy storage system to participate in the secondary frequency regulation of the power grid based on model predictive control (MPC). It establishes a prediction model for pitch angle control of the wind farm and an electrochemical energy storage system, optimizing the active power output of the wind turbine and the energy storage system, and better reducing the wind energy loss based on frequency regulation. The pitch angle control is further corrected based on the difference between the active power command value of the superior system and the actual power output of the wind turbine, so that the wind turbine can better track the power command value of the superior system during secondary frequency regulation, quickly respond to the frequency changes, reduce the dynamic frequency deviation, avoid load rejection due to too low frequency drop, and complete the task of secondary frequency regulation. The simulation results show that under the control strategy proposed in this paper, the controllable secondary frequency regulation ability of the wind turbine and the characteristics of fast response and accurate tracking of the energy storage system are comprehensively considered, the active power command issued by the superior system is better tracked, and the task of the wind farm including the energy storage system participating in the secondary frequency regulation is realized.
As an important manifestation of the energy Internet, the integrated energy system improves the energy utilization rate. However, it also brings more risks due to the high coupling and the large difference in the response speed between the various systems. From the perspective of system security, it becomes crucial to accurately identify the weak links in the system and evaluate the robustness of the system. Therefore, a robustness evaluation method combining variable step size simulation and improved entropy weight method is proposed in the complex network environment. First, the structure of the integrated energy system is introduced and the coupling links of the system are further explained. Then, the robustness indicators including network damage degree and connectivity factor are proposed, and a variable step according to the difference of the response time of different systems is adopted. Based on the simulation results, an improved entropy weight method is proposed, and a more objective evaluation method is constructed. Finally, the superiority of the evaluation method is verified by a case study.
With the increasing demand for energy storage charging stations, many energy storage systems utilize lithium batteries as the major carriers. However, due to frequent charging and discharging at high power levels, the cycle life of lithium batteries is greatly reduced, which increases the energy storage costs. Given the longevity of supercapacitors, a supercapacitor-lithium hybrid energy storage system has been developed to effectively extend the lifespan of lithium batteries and reduce both investment and operational costs of energy storage charging stations. Based on the dual-stage active topology, a hybrid energy storage system combining supercapacitor-lithium is proposed. Under mild load conditions, two supercapacitor modules are alternatively charged by the lithium battery. Then, the supercapacitor modules are discharges when high power demands are encountered. Accordingly, based on working conditions of the charging pile, a multi-stage strategy, integrating state-of-power estimation and programming, is proposed to optimize the power distribution, smooth the power fluctuation of the lithium battery, and protect the lithium battery. The simulation results show that compared with the lithium batteries only energy storage and the traditional full active topology energy storage, the dual-stage active topology energy storage significantly improves the cycle life of lithium batteries.
Urban cities are the main force of energy consumption and carbon emission. In the context of “dual carbon”, promoting low-carbon transformation of urban energy systems has become the top priority of urban planning. However, while the share of renewable energy output increases, the requirement for system flexibility also increases. To this end, an urban energy system expansion planning model that accounts for both long-term and short-term uncertainties is proposed. Multiple forms of energy, including electricity, gas, and heat are encompassed in this model. At the planning level, uncertainty and operational flexibility during the real-time operation stage are estimated, and a stochastic optimization approach is employed for solving. The capacity expansion of renewable energy generators and energy hubs (EHs) is considered by the model, with the imposition of carbon emission quota constraints to ensure the attainment of carbon emission reduction targets. The results show that the model can effectively improve the economy of urban energy system and the rate of consumption of renewable energy, and can meet different carbon-emission reduction requirements.
In order to achieve the dual carbon goal of “carbon peak and carbon neutrality”, it is of great significance to promote the electric and hydrogen complementary integrated energy system. However, with the gradual increase of the penetration rate of renewable energy, the inertia level of the system decreases, and the frequency security is threatened. Aimed at the problem that the traditional optimization scheduling method cannot guarantee the frequency stability of the system, a coordinated optimization scheduling method of electric-hydrogen complementary multiple buildings considering the frequency stability constraint is proposed. First, the architecture of the electric-hydrogen integrated energy system with the building as the ground floor unit is established, and the renewable energy generator sets in the system are controlled by the virtual synchronous generator technology to improve the inertia level of the system. Then, aimed at minimizing the total operating cost of the system in the scheduling period, and considering the inertia requirements of the system in different operation modes of grid-connected and islanding, an optimal scheduling model considering the system frequency stability constraint is established. Finally, an example is given to verify the effectiveness, economy, and environmental protection of the proposed method for frequency stability of the system.
The internal reaction state of air-cooled proton exchange membrane fuel cell (PEMFC) is the key factor affecting the output performance and stability of the cell. By developing an in-situ testing device for the reaction state of air-cooled fuel cell, the real-time measurement of cell temperature and current density is realized, and the influence mechanism of hydrogen outlet pulse interval, hydrogen inlet pressure and cathode wind speed on the performance of the cell is revealed. The results show that the distribution of temperature and current density in air-cooled cells is uneven. The temperature difference can reach 20 °C, and the current density difference reaches 400 mA/cm2 when the average current density is 500 mA/cm2. As the interval between pulses decreases and the inlet pressure increases, the performance of the hydrogen outlet area and the uniformity of the distribution increase, which can reduce the fluctuation of current density in the cells and improve output stability. If the cathode wind speed is too low, the temperature in central areas is high, and the temperature distribution uniformity is reduced. However, excessive wind speed causes the generating water to be blown away. The water content of the proton exchange membrane thus decreases, and the uniformity of the current density distribution deteriorates.
This paper proposes an interval prediction technology of photovoltaic (PV) power based on parameter optimization of extreme learning machine (ELM) model. First, the weighted Euclidean distance is proposed as the evaluation index of PV power prediction interval. The historical sample units are screened and the ELM training set is optimized. Then, a hybrid optimization algorithm for ELM parameters is proposed. The hidden layer input and output weights and biases parameters of the ELM model are optimized by using the elitist strategy genetic algorithm and quantile regression, and the trained model is used to predict the PV power range. Finally, an actual calculation example is constructed based on the historical data of PV power plants and weather stations. The PV power interval is predicted, and the results are compared with those obtained by other methods. The results of the calculation example show that the method proposed can greatly improve the accuracy of interval prediction while increasing the reliability of interval prediction.
Aimed at the problem of uneven supply and distributed renewable energy (DRE) in charging regions and electric vehicle (EV) loads, a multi-region optimal scheduling strategy for EVs considering compensation incentives is proposed to guide EVs to choose different charging regions, so as to promote local consumption of distributed energy. First, an EV charging response model is established based on the price elasticity of demand and users’ time anxiety. Then, based on the principle of remaining power availability and idle time redundancy, EVs are divided into responsive cluster and non-responsive cluster. A charging area decision model based on regret theory is used to further divide the responsive cluster by region. Finally, a multi-region optimal scheduling model for EVs is established, and the charging price is optimized in terms of maximizing the economic benefits of charging service providers and the consumption of DRE. Simulation cases show that the proposed optimization strategy can fully consider the impact of time anxiety and price elasticity on EV users, fully tap the users’ response potential, and has obvious effects in reducing the deviation of distributed new energy consumption and improving the economic benefits of charging service providers.
Hydrogen energy plays a crucial role in meeting the “carbon peaking and carbon neutrality” goals, and the carbon capture technology is a vital technique for emission reduction in the energy industry. Blending hydrogen with natural gas to produce hydrogen-enriched compressed natural gas (HCNG) facilitates the transportation and utilization of hydrogen energy. At the same time, applying the carbon capture technology to retrofit thermal power units can effectively promote the large-scale consumption of renewable energy and reduce carbon emissions. For this purpose, a detailed model of hydrogen production equipment and fuel cells is established. Then, aimed at the problem of system carbon emissions, a carbon emission and output model of carbon capture thermal power units and a mathematical model of hydrogen doped cogeneration are established, and a stepped carbon trading mechanism is introduced to control carbon emissions. Based on this, an optimal scheduling model for hydrogen-enriched compressed natural gas integrated energy system is established with the goal of minimizing the sum of energy purchase cost, carbon emission cost, wind abandonment cost, and carbon sequestration cost, and taking into account the constraints such as hydrogen blending ratio and carbon capture in the pipeline network, which is solved by using the particle swarm optimization algorithm in conjunction with CPLEX. The analysis of the models built in different scenarios verifies the advantages of the proposed scheduling model in low-carbon economy.
The doubly-fed induction generator (DFIG) is connected to the distribution network as a distributed power source, which turns a radial single power supply system into a dual power supply system and changes the topology of the distribution network. When a short-circuit fault occurs in the distribution network, the short-circuit current of the distribution network will be affected by the access to wind power. Aimed at the problem that the short-circuit current is affected by the access capacity of the wind turbine and the location of the access point when DFIG is connected to the distribution network by distributed power sources and a three-phase short-circuit fault occurs at different points in the distribution network, a theoretical derivation and a simulation analysis are conducted by combining the control strategy of DFIG. First, relational expression of short-circuit fault current of the distribution network including wind power is theoretically derived, and the short-circuit current provided by the wind turbine is analyzed. Then, model predictive control is introduced to be compared with classical vector control to analyze the impact of different control strategies on the short-circuit current. The changes in access capacity and location are analyzed when a three-phase short-circuit fault occurs at different points in the distribution network, and the impact of DFIG on the short-circuit current of the distribution network is summarized.
High penetrated renewable energy has brought great challenges to the flexibility of the power system due to its volatility and intermittency. To improve the capacity of renewable energy accommodation, the flexibility reformation of thermal power units, the construction of gas-fired units, and the electrical energy storage installation are considered as effective solutions. Thus, an optimization model for power system planning scheme considering multi-type flexible resources with their different output characteristics is established. The simulation results on a modified IEEE 24-bus power system and 12-node natural gas system demonstrate the effectiveness of the proposed model. In addition, the applicability of different flexible resource planning schemes is comprehensively evaluated from the perspectives of economy, accommodation capacity, and carbon reduction, so as to meet the different planning goals.
Accurately estimating the state of health (SOH) of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system. Addressing the issue where traditional SOH estimation methods fail under variable working conditions, an online SOH estimation method for lithium-ion batteries based on equivalent circuit model (ECM) and sparse Gaussian process regression (SGPR) is proposed. During the constant current charging process, the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters, based on which, a condition-insensitive health indicator is constructed. In combination with the SGPR, the indirect SOH estimation is achieved. This method uses the unified signal processing method and feature mapping model under various working conditions, and features strong robustness with low redundancy. The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94%, and the root mean square error stays below 1.12%. When benchmarked against existing methods, this method has significant advantages in comprehensive performance.
In power systems with a high proportion of renewable energy, to achieve coordinated optimal scheduling of source and load considering multiple uncertainties is an important issue in power system operation. Therefore, a probabilistic spinning reserve optimization model based on multiple scenarios is constructed. Multiple uncertain factors are considered in the model, such as wind power and solar power forecast errors, load forecast error and unscheduled generator outage. Renewable energy curtailment and load shedding are used as special reserve resources in the day-ahead security-constrained unit commitment (SCUC) to improve the economic operation efficiency. The calculations of reliability indexes, expected energy not served and expected energy curtailment, are simplified, and the inequality constraints related to these two indexes are reduced, which improves the computational performance of the model. The model optimizes the total expected cost considering multiple uncertainties. Case studies based on the IEEE-RTS demonstrate the effectiveness of the proposed model. The numerical results show that the improved calculation method of reliability indexes can effectively reduce the solution time of the SCUC model. The reserve optimization model can realize the dynamic allocation of the spinning reserve capacity of the system and improve economic operation of the system.
The problem of differential protection maloperation caused by inrush current during no-load closing of transformer has not been completely solved so far. To solve this problem, a method using long short-term memory (LSTM) neural network to identify inrush current and fault current is proposed. First, the simulation model of no-load closing and internal fault of transformer is built on the PSCAD software platform, and a large amount of three-phase current instantaneous sampling data is generated through simulation as the sample set to train the neural network. Then, the LSTM neural network model is built and trained by using the Keras platform. Finally, the new simulation data and fault recorder data is used to test the trained LSTM neural network. The results show that the LSTM neural network can quickly and accurately distinguish the inrush current and fault current under various conditions, which proves the effectiveness of the proposed method.
