Stepwise inertial control (SIC) provides a step-increase of power after load fluctuation, which can effectively prevent system frequency decline and ensure the safety of grid frequency. However, in the power recovery stage, secondary frequency drop (SFD) is easy to occur. Therefore, it is necessary to optimize SIC to obtain a better frequency regulation effect. The traditional method has the disadvantages of high calculation dimension and long consuming time, which is difficult to meet the requirements of providing the optimal control effect in different scenarios. In order to realize the optimal stepwise inertial fast control of wind power frequency regulation in load disturbance events, this paper introduces the deep learning algorithm and proposes a stepwise inertial intelligent control of wind power for frequency regulation based on stacked denoising autoencoder(SDAE) and deep neural network(DNN). First, sparrow search algorithm (SSA) is used to obtain the optimal parameters, and SDAE is used to extract the data features efficiently. Then, DNN is used to learn the data features, and the accelerated adaptive moment estimation is introduced to optimize the network parameters to improve the global optimal parameters of the network. Finally, the stepwise inertial online control of wind power frequency regulation after disturbance event is realized according to SDAE-DNN. The simulation analysis is conducted for a single wind turbine and a wind farm in the IEEE 30-bus test system. Compared with the results obtained by the traditional method, shallow BP neural network and original DNN network, it is found that the proposed network structure has a better prediction accuracy and generalization ability, and the proposed method can achieve a great effect of stepwise inertia frequency regulation.
The optimal Latin hypercube experimental design method is used to refine the vortex generator parameters, determine the test scheme, simulate and calculate the thrust and torque of the wind turbine, and obtain the experimental data. Based on the back propagation (BP) neural network, the aerodynamic performance model of the wind turbine vortex generator optimized by genetic algorithm is constructed. The reliability of the aerodynamic performance model is verified by calculating the error and root mean square of the predicted and simulated values of the aerodynamic performance model. Coupling the fish swarm algorithm and the aerodynamic performance model of the wind turbine vortex generator, an optimization method of the wind turbine vortex generator is established, and the height, length, and installation angle of the vortex generator are solved iteratively to realize the optimization of the vortex generator. The results show that compared with the original vortex generator scheme, the flow separation of the wind turbine blade section optimized by the vortex generator is effectively restrained and delayed, the surface fluid separation phenomenon is improved, the power of the wind turbine is increased by 1.711%, and the thrust of the wind turbine is decreased by 0.875%.
Aimed at the problem of uncertainties in the regional development stage and the difficulties in quantifying regional investment demand in different development stages, a robust optimization method for power grid investment decision-making considering regional development stage uncertainties is proposed to promise the matching degree between power grid investment decisions and development needs, and to improve the ability of decision-making results to deal with portfolio risks and uncertainties in regional development stage. First, investment risk constraints are constructed based on the modern portfolio theory. Then, a box uncertainty set is used to characterize uncertainties in regional development stage, and a robust optimization model for power grid investment decision-making considering uncertainties in development stage is established. In the optimization model, the outer minimization problem is used to solve the uncertain variables in regional development stage in the worst scenario, while inner maximization problem is used to obtain the decision-making plan that can maximize investment return in the worst scenario. Furthermore, according to the strong duality theory, the double-layer optimization model is transformed into a single-layer model that can be solved directly, and the big-M method is used to solve the model proposed. Finally, an actual example of 13 cities in an eastern coastal province verifies the applicability and effectiveness of the power grid investment decision-making model.
The modular multilevel converter (MMC) suffers from low output level and high harmonic distortion in medium-/low-voltage applications such as direct current (DC) distribution networks. In addition, the capacitor voltage of MMC is coupled with DC bus voltage in the traditional modulation method, leading to large fluctuations of capacitor voltages and deviation from the rated value under DC bus voltage margin. In order to solve the problems above, this paper proposes an improved nearest level control method, which can increase the output level of medium-/low-voltage MMCs by introducing a step wave correction. Based on the proposed modulation method, a capacitor voltage feedback control is thus proposed to limit the range of capacitor voltage fluctuations and improve equipment safety. The effectiveness of the proposed method is verified by MATLAB/Simulink simulation and real-time digital simulation system hardware-in-the-loop test.
Due to the considerable number and the characteristics of energy storage, it is possible for electric vehicles (EVs) to participate in the operation and regulation of power system to provide reserve service. In view of this, a multi-objective optimal scheduling model is established based on the wishes of electric vehicle users, with the objectives of the economic benefits of electricity collectors, microgrid power fluctuations and user satisfaction. Considering the uncertainty of load demand, the optimal scheduling analysis of multi-time scale scenes with the day-ahead time scale and the intra-day real-time correction time scale is conducted. The mainstream multi-objective intelligent optimization algorithm NSGA-III algorithm is adopted in the solution method, and the NSGA-II and MOEA/D algorithms are used for comparison. The optimal dispatching scheme is selected through comparative experiments and scenarios where EVs provide spare capacity are analyzed. The simulation results verify the feasibility and effectiveness of the proposed model.
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.
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.
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.
In order to reduce the forecast output error caused by the randomness and volatility of renewable energy in microgrid operation, a microgrid energy management strategy considering carbon quota guided demand response is proposed. A two-layer model predictive control (MPC) energy management model is constructed. The upper layer guides electric vehicles to participate in the demand response of microgrid by constructing a carbon emission quota mechanism to realize the economic operation of microgrid and reduce carbon emissions. The lower layer uses the model predictive control rolling optimization and the power fluctuation caused by the prediction error of renewable energy is suppressed by the short time scale model predictive control. The results of calculation analysis show that the proposed energy management strategy can effectively guide electric vehicles or other controllable loads to participate in demand response and realize low-carbon economic dispatch and stable operation of microgrid.
To promote the optimal allocation of resources across the country, China is actively developing inter-provincial electricity transactions, and will gradually form an inter-provincial and intra-provincial electricity market operation mode. In this context, a two-stage day-ahead, and intraday economic dispatch framework considering inter-provincial and intra-provincial bi-level market coordinated operation is proposed. In the day-ahead dispatch stage, an inter-provincial and intra-provincial bi-level economic dispatch model is constructed. In the intraday dispatch stage, an economic dispatch model considering the forecast error of source-load is constructed. To further deal with the influence of the uncertainty of source-load forecast on economic dispatch, a two-stage day-ahead and intraday distributionally robust economic dispatch model and its solution method are proposed, realizing the economic dispatch under random scene ambiguity set. Finally, a multi-sending ends and multi-receiving ends interconnected test system is constructed using IEEE 39-bus and 118-bus systems. The effectiveness of the proposed model and method is verified by simulation.
Aimed at the impact of extreme weather on the stable operation of microgrid, an optimal configuration strategy of microgrid based on fuzzy scene clustering is proposed. Using historical weather data, a fuzzy scene clustering method is used to deal with the problem of new energy output fluctuations caused by random weather on the source side, and a robust optimization model is established on the load side to deal with load fluctuations within a certain range. Using scenes of 8 760 hours in a year, typical scenes and extreme scenes are obtained by distinguishing the unique membership characteristics of fuzzy scene clusters. Considering the impact of extreme scenarios on the optimal configuration of the microgrid, a two-stage robust model with the smallest comprehensive cost is established, which is decomposed by the column and constraint method, and is finally solved iteratively by Cplex solver. The effectiveness and feasibility of the proposed optimal configuration strategy are verified by simulation analysis.
Aiming at the problem of weakly or negatively damped low-frequency oscillations caused by cross-zone transmission of electricity from large wind farms, this paper proposes a fast terminal sliding-mode additional damping controller based on the Lyapunov stability theory. By investigating the flexible power regulation characteristics and the capability of dynamic frequency response to damping regulation of doubly-fed wind turbines (DFIG), a rotor magnetic chain controller is designed according to the relationship between the applied voltage and magnetic chain of DFIG rotor and the sliding mode variable structure control method. When low-frequency oscillations occur in the system, the desired magnetic chain value will deviate from the actual magnetic chain value. The additional damping controller outputs an adaptive control signal for the rotor-side power control link to increase the active output of the wind farm and suppress low-frequency oscillations in the system. A simulation model of the wind power grid-connected system is established in MATLAB/Simulink for off-line simulations, and a real-time simulation experiment of a large wind farm cross-zone transmission model based on real time digital simulation system is conducted. The results of both off-line and real-time simulations show that when low-frequency oscillations occur in the system, the proposed control method can quickly regulate the active power emitted by the DFIG and enhance the damping level of the system, which is effective in suppressing low-frequency oscillations in the system.
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.
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 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.
The electricity cost of 5G base stations has become a factor hindering the development of the 5G communication technology. This paper revitalized the energy storage resources of 5G base stations to achieve the purpose of reducing the electricity cost of 5G base stations. First, it established a 5G base station load model considering the communication load and a 5G base station energy storage capacity schedulable model considering the energy storage backup power demand of the 5G base station and the power supply reliability of the distribution network. Then, it proposed a 5G energy storage charge and discharge scheduling strategy. It also established a model for 5G base station energy storage to participate in coordinated and optimized dispatching of the distribution network. Finally, it compared the economy of optimized dispatch of 5G base station energy storage of different schemes. The analysis results show that the participation of idle energy storage of 5G base stations in the unified optimized dispatch of the distribution network can reduce the electricity cost of 5G base stations, alleviate the pressure on the power supply of the distribution network, increase the rate of new energy consumption in the system, and realize a win-win situation between the communication operator and the grid.
To achieve the goal of carbon peaking and carbon neutrality, it is urgent to build a new power system with renewable energy as the main body, characterized by clean energy supply and electrification of energy consumption. Considering the intermittency and randomness of wind-solar power, as well as the energy storage and flexibility of pumped storage power stations and power-to-hydrogen, a short-term production simulation model of wind-PV-hydrogen-pumped storage zero carbon power system is established based on the stochastic programming theory. In the proposed short-term production simulation model, on the basis of meeting the total demand of flexible hydrogen load, the short-term production simulation is implemented, including electricity-hydrogen production schedule, reserve capacity, pumped storage-water discharge power output and wind-solar curtailment, with the goal of maximizing green on-grid electricity. Taking Zhangbei zero carbon power system of China as an example, many operation scenarios are established to simulate the proposed model. The simulation results show that the proposed model can effectively simulate the on-grid scheme situation of green power in which the system deals with the randomness of wind-solar output in any output scenarios of wind-solar power scene set. The flexible hydrogen load and the pumped storage power station can effectively promote wind-solar energy accommodation and increase the comprehensive benefit of the combined system.
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.
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.
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.
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.
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.
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.
Logistics center is the key node of the logistics network, which connects the regional logistics network and the external transportation system. Its operation and management have great impacts on the overall efficiency of the logistics system. Therefore, it has always been the fundamental issue of logistics management. In recent years, with the development of e-commerce and corresponding express services, the transportation tasks undertaken by logistics companies have continued to grow, which has posed great challenges to the operation of logistics centers. In order to fulfill the national “dual-carbon” target, logistics centers urgently need to control carbon emission intensity while ensuring the efficiency of transportation. In this context, this paper takes the logistics center as the focus, integrates the concept of microgrid, and summarizes its operation management and emission reduction measures, in order to promote the energy utilization of logistics enterprises while improving the quality and efficiency, and to ensure their sustainable development.
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.
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 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.
Wind power generation is different from traditional power generation in which wind power output is highly stochastic and spatio-temporally dependent. In the optimal scheduling problem of wind power grid-connected power system, ensuring the optimal execution of power scheduling in different wind power scenarios is the key of the decision-making problem. Therefore, high quality wind power scenario generation is of great importance. The spatiotemporal correlation of the output power of wind power plants are characterized based on Gaussian stochastic process and spatiotemporal covariance function, and the joint probability distribution is established by the Pair Copula model, and specific scenarios are implemented by the method of empirical probability inverse transformation. A variety of scene metrics of the generated scene are generated, which verifies the superiority of the generated scene. Finally, based on the modified IEEE 6-bus system, a mixed integer programming model for the unit output of the power system is established to solve the problems in different scenarios and verify the economic advantages of the scenario generation method in the dispatching problem of wind power grid connection.
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.
The passivity-based control (PBC) based on energy function has been studied for grid-connected converters to achieve a better performance. However, traditional PBC method relies on the accurate mathematical model of grid-connected inverter. In previous studies on PBC, the effect of digital control delay is rarely considered and the stability under grid impedance uncertainties is not discussed, especially in the capacitive grid or complex weak grid. To address these issues, this paper proposes an improved PBC method to reshape the output admittance for LCL-filtered grid-connected inverters. The system passive region is expanded up to the Nyquist frequency by adding a capacitor current feedback loop which can achieve active damping control of LCL resonant frequency under the wide range of grid impedance changes. The parameter design method is also presented for the proposed PBC control. To verify the correctness of the theoretical analysis, both simulation and experiments are conducted on a 3 kW grid-connected inverter prototype.
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.
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.
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.
Wind power generation has uncertainty due to the high fluctuation of wind speed. In traditional wind power prediction models, the uncertainty is measured by normal distribution with zero mean and constant variance. However, the variance may vary with time, which means the variance has heteroscedasticity. To improve the prediction accuracy, this paper proposes a new integrated probabilistic wind power prediction model for wind farm considering heteroscedasticity based on online least absolute shrinkage and selection operator and vector autoregression (LASSO VAR) and the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model. First, online LASSO VAR is used to forecast power output. Then, heteroscedasticity of residuals is validated by autoregressive conditional heteroskedasticity test. Considering heteroscedasticity, the news impact curve and dynamic significance line verify that positive and negative residuals affect future volatility asymmetrically. Thus, the EGARCH model is used to forecast the residuals to obtain the conditional variance of point prediction results. Finally, the probabilistic result of total power output is obtained by summing the power output of turbines in the wind farm considering the correlation of the active wind power of wind turbines. This method is applied to forecast the power output of a wind farm in East China and is proved effective in improving the prediction accuracy.
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.
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.
Hybrid three-level full bridge (H-TLFB) DC-DC converters increase the input voltage range by introducing a three-level bridge arm. Aimed at the problems of large power return and high current stress in the traditional dual phase shift control, a minimum return power control strategy is proposed. First, the power transmission characteristics of H-TLFB DC-DC converter are analyzed, the size of the return power value of the converter in two different operating modes is compared, and the optimal shift of the return power is calculated according to the mathematical relationship between the return power and the voltage ratio, the shift and the transmission power. The corresponding optimal shift of the return power is calculated, and the corresponding optimization control strategy is designed. Compared with the traditional dual phase shift control strategy, the return power in the minimum return power control strategy can reach the minimum value in the full power transmission range, and within a certain voltage ratio range, the return power and current stress can be optimized at the same time. Finally, the correctness and feasibility of the design control strategy are verified by experiments.
In order to evaluate the operation of energy consumption, environmental protection, and economy, a multi-index comprehensive evaluation model based on an improved technique for order preference by similarity to ideal solution (TOPSIS) is proposed. Based on the analysis of building operation, a multi-index evaluation system is constructed. Then, an improved TOPSIS evaluation method is introduced and a distance measure of the TOPSIS evaluation model by the gray correlation algorithm and analytic hierarchy process (AHP)-entropy weight method is determined. Next, a multi-attribute weighted evaluation model is established to analyze the building operation comprehensively. The multi-index evaluation of eight power office buildings indicates that the building comprehensive evaluation results vary with time and the energy consumption index score plays the main role in all indexes. A comparison of the evaluation results with those obtained by other evaluation methods verifies the effectiveness of the proposed building multi-index evaluation model.
With the continuous development of electricity market, electricity price mechanism has been continuously improved, but carbon emission factor has not been considered in the formulation of electricity price mechanism, and research on the demand side coupling of electricity price and carbon emission cost is still limited. Aiming at promoting carbon emission reduction, this paper fully considers the characteristics of prosumer differentiation in the process of load regulation and optimization of producers and consumers, and proposes a price demand response mechanism considering carbon emission and the edge cloud collaborative optimization strategy. By utilizing the proposed carbon electricity discount factor on the premise of fairness, prosumers who actively participate in the regulation of carbon emission reduction can obtain more electricity price discounts. The simulation results show that based on the proposed demand response mechanism with carbon electricity discount factor, the efficiency of electricity retailers can be increased by 2.4 and 0.9 times respectively when photovoltaic power generation is more or less than demand.
Aimed at the main power tube with problems of weak driving ability, poor soft switching performance, and large switching loss in a self-excited resonant wireless power transmission (WPT) system, an analysis of the working principle of resonant main circuit is conducted, which indicates that the root cause of the problem is that two uncontrollable diodes limit the driving resistance of main power tube. Thereofre, an improved self-excited resonant circuit is proposed by using a fully controlled low power switch instead of the diode. The circuit theoretical analysis of four working modes suggests that the main power tube of the improved circuit has a stronger driving ability. To further obtain the optimal parameters of the improved system, a nonlinear programming model of the system is established by considering the tolerance limit of each parameter, and a hybrid optimization algorithm is designed to obtain the system global optimal solution. A comparison of the results of system simulation and experimental prototype shows that the improved WPT system has an excellent soft switching performance. The temperature of the switch tube is reduced by about 7 ℃, and the efficiency is increased by about 4%.
