Abstract:

To further improve the operational flexibility and low-carbon emission capacity of integrated energy system (IES), a low-carbon optimal scheduling model of IES coupled with a solvent-storage carbon capture system (CCS) and an organic Rankine cycle (ORC) waste heat power generation device is proposed. Firstly, a combined heat and power (CHP) model for a gas turbine (GT) is constructed through waste heat utilization, and an ORC waste heat power generation unit is introduced to achieve thermoelectric decoupling of the unit and improve the operational flexibility of the system. Secondly, the carbon emissions of the system are constrained through the CCS, and the carbon reduction capability and flexible carbon control advantages of the solvent-storage CCS are further validated. Finally, a day-ahead optimal scheduling model is established with the objective of minimizing the total cost of the system, including energy procurement cost, carbon emission cost, and renewable energy curtailment cost. The results show that the ORC waste heat power generation unit achieves thermoelectric decoupling of the unit and enhances the operational flexibility of the system, while reducing operational costs and carbon emissions by 1.06% and by 1.03%, respectively. The solvent-storage CCS reduces the carbon emissions and total costs of the system by 77.91% and 11.84%, respectively, and the utilization rate of renewable energy reaches 75.48%. The simulation results demonstrate that the proposed model can effectively lower system operating costs, reduce carbon emissions, and promote renewable energy integration.

Abstract:

A low-carbon economic dispatch model for an integrated energy system (IES) is proposed under the "dual-carbon" policy framework to promote green and low-carbon energy transition. This model incorporates a green certificate-carbon trading joint mechanism and hybrid game theory. Firstly, a two-layer hybrid game model is constructed. In the upper-layer model, the system operator is treated as the leader, and energy purchase and sale pricing strategies are formulated to maximize its own profit. In the lower-layer model, the hydrogen multi-energy integrated energy system (HMIES) alliance is considered as the follower, and its output is optimized to achieve the best overall economic performance for the alliance. Secondly, a green certificate-carbon joint trading mechanism is introduced into the cooperative game model of the HMIES alliance to improve the overall low-carbon economic performance. Cooperative game theory is applied to analyze the conditions for cooperation, and the Shapley value method is used to allocate cooperative surplus fairly. This enables collaborative interaction between carbon capture power plants and renewable energy generators within the alliance. Lastly, the model is solved using a genetic algorithm combined with mixed integer linear programming (GA-MILP). Case simulations are conducted to verify the effectiveness of the proposed strategy. The results demonstrate that carbon emissions are effectively reduced, and system economic performance is improved.

Abstract:

Solar integrated energy systems are gradually becoming an important solution for residential district energy supply, yet spatial constraints pose significant challenges to the deployment of solar equipment. An integrated energy system for full-spectrum solar energy utilization coupled with hydrogen blending technology is proposed in this paper. Specifically, the system employs photovoltaic cells to convert high-energy spectrum of sunlight into electricity, while low-energy spectrum is directed to solar collectors for heat generation, thereby achieving multi-tiered and refined solar energy exploitation. Meanwhile, in order to extend the hydrogen utilization scenario, a hydrogen blending equipment operation strategy is introduced, offering hydrogen-mixed natural gas as a hybrid energy source for gas equipment. Finally, the optimal capacity configuration of the system is solved by the honey badger algorithm. The simulation results show that the solar spectrum beam splitting technology effectively improves the solar energy utilization of the system, thus reducing the footprint of the solar energy equipment. Furthermore, hydrogen blending technology and its operational strategy effectively reduce system operational carbon emissions, prevent over-allocation of hydrogen energy equipment, and strongly support the development of an efficient, clean, and flexible energy supply system.

Abstract:

A class of improved methods for optimal torque (OT) control enhances the tracking ability of wind turbines, and thus wind energy capture, by expanding unbalanced torque during maximum power point tracking (MPPT). However, these methods cause frequent fluctuations in electromagnetic torque, leading to a significant increase in drive-train loads of wind turbines. It is found that torque compensation under wind speed variation causes additional electromagnetic torque fluctuations, which is the main reason for the above problem. To address this, an accelerated OT control method for wind turbines considering load effects is proposed in this paper. It suppresses additional electromagnetic torque fluctuations by utilizing a constant torque transition phase during wind speed variation. At the same time, it improves tracking capability during MPPT. This enhances the wind energy capture efficiency of wind turbines while avoiding load increase as much as possible. Finally, the simulation results show that the proposed method significantly improves wind energy capture efficiency under various wind conditions and effectively suppresses the rise of drive-train loads.

Abstract:

Accurate power forecasting of distributed photovoltaic (PV) is crucial for the safe and stable operation of power systems. To enhance the ability of distributed PV forecasting models to accurately match and identify temporal and spatial information from historical data, a hybrid forecasting model for distributed PV based on similar time period matching and graph modeling theory is proposed. Initially, considering the temporal correlation of distributed PV power data, the method of similar time period matching is utilized to identify the most critical power periods for prediction, and an improved Transformer model is proposed to extract temporal features of PV power. Secondly, in response to the spatial correlation of distributed PV output, a graph structure model of distributed PV is constructed based on sub-area division results, and a graph attention mechanism-based multi-layer bidirectional long short-term memory (MBLSTM) neural network model is established to extract spatial features of PV power. Finally, a spatiotemporal feature fusion mechanism for distributed PV power is proposed, which enhances the model's understanding and utilization of spatiotemporal information, and a short-term power prediction ensemble model for distributed PV is established. Experimental results indicate that the proposed ensemble model can effectively extract both temporal and spatial information of distributed PV power, demonstrating higher prediction accuracy compared to other models.

Abstract:

A regional power grid reactive power and voltage optimization model based on a new energy output timing segmentation strategy is proposed to address the issues of relatively large active power loss and voltage fluctuation in regional power grids and high reactive power cost and environmental cost caused by high-proportion new energy integration. Firstly, a new energy output segmentation strategy is established according to the timing characteristics of new energy output. That is, the top-down algorithm is used for segmentation processing with the goal of maximizing the dispersion between average values of new energy output in each time period and minimizing the dispersion within each time period. Secondly, introducing a reactive power pricing model, an optimization model for regional power grids with multiple energy sources such as wind, solar, thermal, and storage is established with the goal of minimizing the sum of standardized active power loss, node voltage deviation, reactive power source cost, and environmental cost of thermal power units in each time period. The improved firefly algorithm (IFA) is used to calculate the new model. Finally, the IEEE 30-bus system is used to verify the validity of the model. The results show that the proposed optimization model can effectively reduce the active power loss, reactive power source output cost, and environmental cost of the system, reasonably allocate reactive power of the power grid, and improve the voltage quality of the system.

Abstract:

Energy conversion efficiency constitutes a critical parameter in the operation of charging facilities and serves as a pivotal focus of research in the domain of remote metering for these facilities. However, currently, the primary method for evaluating the energy efficiency of charging modules remains the direct method. This approach is challenging to measure indirectly due to the complex circuit structure and the characteristics of the components involved. An indirect measurement method for assessing the efficiency of charging modules based on grey box theory is proposed in this paper. Firstly, an accurate calculation model is established through the analysis of component losses. Next, the parameters of the complex model are processed using a parameter fusion method, resulting in an indirect measurement model of efficiency that utilizes the voltage and current outputs of the charging module as variables. Finally, the Levenberg-Marquardt (L-M) algorithm is employed to solve for the model parameters. The experimental verification of typical 15 kW and 20 kW charging modules demonstrates that the error of this method is less than 1.3% under low load conditions, and less than 0.5% under high load conditions. The indirect measurement method proposed in this paper exhibits high measurement accuracy and can be directly applied to most standard charging modules. It possesses good universality and can effectively meet the indirect measurement requirements for the conversion efficiency of charging modules.

Abstract:

When a pole-to-pole short-circuit fault occurs in DC distribution networks, the fault current rises to several times or even tens of times the rated current within milliseconds. This rapid increase results in instantaneous converter lockouts and may lead to system shutdowns. To ensure the continuous operation of DC distribution networks, a fault protection method based on differential current correlation is proposed. The transient characteristics of fault currents are investigated. For internal faults, the differential current is mainly formed by capacitor discharge currents from modular multilevel converters and DC transformers. For external faults, the differential current is primarily composed of discharge currents from line-distributed capacitors. Based on these characteristics, the Pearson correlation coefficient is applied to identify faulted lines by analyzing the correlation between line differential currents and their accumulated difference currents.Simulation results demonstrate that the proposed method can quickly and reliably identify faulted lines. Transient currents from line-distributed capacitors during external faults do not cause misoperation. The method is resistant to noise (30 dB) and synchronization errors (0.3 ms). It also accurately identifies high-resistance faults (100 Ω).

Abstract:

Three-phase pulse width modulation (PWM) rectifiers are essential power conversion devices in AC microgrids. However, DC load disturbances in the microgrid are unknown and filter capacitor parameters are imprecise, which will directly affect the control performance of the rectifiers. Therefore, a discrete model reference adaptive control (MRAC) method for three-phase PWM rectifiers is proposed to realize the high-performance operation of three-phase PWM rectifiers under the coexistence of DC load disturbances and unknown filter capacitance parameters. Firstly, a discrete model of the three-phase PWM rectifier is established. Secondly, based on the model reference adaptive control theory, a voltage-loop discrete adaptive controller and an adaptive law for load and filter capacitance parameters are designed respectively. The adaptive law is used to adjust the load and filter capacitance parameters in the adaptive controller in real time, which not only effectively improves the tracking accuracy of the output voltage of the three-phase PWM rectifier, but also significantly enhances its robustness against load disturbances and parameter variations of the filter capacitor. Meanwhile, the stability and convergence of the proposed controller are verified by combining the discrete Lyapunov stability theory. Finally, simulation and experimental results show that the designed adaptive law can accurately estimate the load and filter capacitance parameters. Compared with the double-loop proportion integral (PI) control method, the proposed control method exhibits shorter load regulation time, smaller voltage sags and lower grid-side current total harmonic distortion (THD), along with strong adaptability to load disturbances and high robustness against filter capacitance parameter uncertainties.

Abstract:

Cross-linked polyethylene (XLPE) cables generate cross-linking by-products such as acetophenone, cumyl alcohol, and α-methylstyrene during the manufacturing process. Under the influence of an electric field, these by-products dissociate, forming space charges that lead to the initiation and growth of electrical treeing, thereby reducing the insulation performance of the cables. Degassing treatment is considered one of the effective methods for reducing cross-linking by-products. However, the current selection of degassing time mainly relies on experience and lacks a unified standard. The effect of degassing time on the initiation and growth characteristics of electrical treeing in XLPE cables is experimentally investigated. Gas chromatography-mass spectrometry (GC-MS) is used to quantitatively analyze the changes in the volume fraction of cross-linking by-products. In conjunction with electrical treeing initiation and growth experiments, the mechanism by which cross-linking by-products influence the growth of DC electrical treeing is explored. The experimental results show that degassing treatment has a significant effect on reducing the volume fraction of all three cross-linking by-products, with α-methylstyrene exhibiting the most pronounced removal. The rate of decline in cross-linking by-product volume fraction varies at different stages of degassing. The reduction in cross-linking by-products makes the initiation and growth of electrical treeing more difficult. In practical industrial production, degassing should ensure that the volume fraction of cross-linking by-products is reduced to less than 20% of the initial level. A theoretical basis is provided for optimizing the degassing process of XLPE cables, and a reasonable degassing time is recommended for use in actual production to minimize cross-linking by-products, thereby enhancing the insulation performance and service life of the cables.

Abstract:

Heavy rainfall can bridge the sheds of insulators on transmission lines, substations, and converter stations with continuous water columns, leading to electric field distortion. At the same time, the porcelain insulator is easily deteriorated due to its own weight and wind load during long-term operation. The formation of zero-value insulators leads to the occurrence of rain flashover accidents. To investigate the lightning impulse discharge characteristics of porcelain insulator strings containing zero value insulators under heavy rainfall, lightning impulse tests are conducted on XWP2-160 porcelain insulator strings under various zero-value configurations. The results show that, both with and without zero-value insulators, the lightning rain flashover voltage of the porcelain insulator string decreases with the increase of rainfall intensity and rainwater conductivity. The flashover voltage of insulator strings without zero-value insulators is more significantly influenced by rainfall intensity and rainwater conductivity. As the number of zero-value insulators increases, the influence of rainfall intensity and rainwater conductivity on the lightning impulse flashover voltage becomes smaller. When zero-value insulators are located at different positions along the string, the lightning impulse flashover voltage gradually decreases with increasing rainfall intensity and rainwater conductivity, showing a tendency toward saturation. When the zero-value insulator is arranged at the high-voltage end, the rain flashover voltage is obviously greater than that at the middle and grounded end, and the degree of influence by the rainfall intensity and rainwater conductivity is also greater than that at the middle and grounded end. The research results can provide important guidance for the design and configuration of external insulation systems of transmission lines to cope with thunderstorms.

Abstract:

Transformers are susceptible to insulation aging and failure under high temperature and overload conditions, which affects the normal operation of the power system. The winding hot spot temperature is a key factor in evaluating transformer load capacity, but it is challenging to measure directly. To accurately calculate the hot spot temperature, a new thermal circuit model is developed based on the thermoelectric analogy principle, considering the effects of external environmental factors, internal nonlinear thermal resistance, and oil viscosity. This model integrates traditional mechanism analysis with a data-driven method and employs an improved differential evolution algorithm, which uses population initialization and adaptive mutation factor based on prior knowledge. The fourth-order Runge-Kutta method is used to solve the differential equation of the thermal circuit model, with the 180 MV·A/220 kV transformer from a substation in southern China as a case study. The calculated hot spot temperature is compared with the measured values, and the determination coefficient of the proposed model reaches 0.84, showing an improvement of 14.60% and 5.53% over the IEEE and Susa models, respectively. The comparison demonstrates that the proposed mechanism-data fusion thermal circuit model offers significant advantages and enhances the accuracy of hot spot temperature calculation for oil-immersed transformer windings.

Abstract:

A Bayesian optimized graph attention network (BO-GAT) based power flow calculation method is proposed for distribution networks. This method addresses the low computational speed and reliance on complete line parameters of conventional power flow methods. It also overcomes the limitations of existing data-driven approaches in handling frequent topology changes. The method utilizes the topology and node features of the distribution network to construct graph data, and calculates attention coefficients using the graph attention mechanism. By capturing correlations between nodes, the method enhances the adaptability of the power flow regression model to topology changes. The Bayesian optimization (BO) algorithm is introduced to optimize the hyperparameters, further enhancing the performance of the model. The model's regression accuracy and computational efficiency are evaluated on the improved IEEE 33-node system. The results demonstrate that the proposed method can achieve rapid power flow calculation without specific line parameters. It also exhibits strong robustness and topology generalization capability under measurement information loss and topology changes. Moreover, even with a significant increase in wind and solar energy penetration, the calculation accuracy remains high. Finally, the applicability of the proposed method to large-scale distribution networks is further validated on the IEEE 141-node system.

Abstract:

Aiming at the challenges of collaborative management, uncertainty in renewable energy output, coupled multi-market transactions of electricity, carbon and green certificate, and privacy protection faced in the optimization process of multi-energy microgrids system, a two-stage distributed robust optimization strategy for microgrid alliance considering electricity-carbon-green certificate trading is proposed. Firstly, in order to enhance the ability of microgrid individuals to cope with new energy output fluctuations, a two-stage distributed robust model of microgrid alliance is constructed. Secondly, in order to strengthen the microgrid decarbonization level, a new type of cogeneration unit coupled with power-to-gas and carbon capture systems (P2G-CCS) is constructed, and a stepwise carbon trading-green certificate trading coupling mechanism is accounted for. Thirdly, in view of address the interest demands of individual microgrids and the benefit distribution of microgrid alliances, the asymmetric Nash bargaining strategy for microgrid alliances is constructed based on the Nash negotiation theory on the framework of peer-to-peer (P2P) distributed energy trading. Finally, the adaptive alternating direction method of multiplier-column and constraint generation algorithm (AADMM-C&CG) is adopted to improve the solution rate. Simulation results show that compared with the distributional robust model without participating in the cooperative game and the common electricity-carbon-green certificate trading model, the total cost of the proposed model in this paper is reduced by 16.95% and 31.50%, and the carbon emission is reduced by 42.83% and 11.03%, which effectively protects the privacy of microgrids and realizes the reasonable distribution of benefits on the basis of the system's robustness and economy, and assists the energy structure to transform the energy structure to green and low-carbonization.

Abstract:

The frequency security of islanded microgrids is addressed through a distributionally robust scheduling method that optimizes heterogeneous frequency regulation resources, including generation sources, storage systems, and loads. A linear approximation model for frequency regulation power is developed by considering differences in response rates and dead zones. Frequency constraints under coordinated regulation are established and incorporated into the microgrid scheduling model. Wind power uncertainty is described using a moment-based ellipsoidal uncertainty set, and joint chance constraints are formulated to limit wind power output and frequency regulation reserves. Auxiliary variables are introduced to transform the joint chance constraints into individual chance constraints. Non-convex individual chance constraints are further converted by accounting for distribution unimodality. Bilinear terms are convexified using the McCormick envelope, reformulating the model into a mixed-integer second-order cone programming problem. Case studies demonstrate that the proposed method ensures the frequency of the islanded microgrid remains above 49.2 Hz following active power disturbances. The inclusion of frequency support from demand-side resources improves operational economics.