In order to enhance the economic efficiency of coordinated operation between integrated photovoltaic-energy storage-charging (PESC) station and large-scale electric vehicle (EV) clusters, and to address the challenge of spatiotemporal distribution of charging demand, this study proposes a bilevel collaborative optimization approach based on a Stackelberg game framework. The upper-level model represents the dynamic pricing strategy of the PESC station operator, aiming to guide EV load distribution through time-varying price signals. The lower-level model captures the EV users’ charging and discharging behavior in response to price incentives. A hybrid solution model integrating genetic algorithm for dynamic price parameter updating and CPLEX-based precise solvers for EV behavior modeling is developed to achieve collaborative optimization equilibrium. Case studies across three operational scenarios confirm that the proposed method can improve operator profits while reducing user energy costs, and further validate the positive role of dynamic pricing in promoting source-load coordination.

Virtual Power Plant (VPP) technology enhances the accommodation potential of distributed energy resources and addresses blind spots in grid dispatch. To enable VPPs to guide large-scale integrated electric vehicle (EV) in orderly participation in distribution network optimization dispatch, this paper first establishes a control framework for VPPs in grid dispatch. Next, priority ranking indices for EV are selected, and the comprehensive weights of these indices are determined. Building on this, an improved TOPSIS model based on the Tanimoto coefficient is innovatively proposed to prioritize EV participation in dispatch. Finally, with the objective of maximizing VPP revenue, an optimized VPP dispatch strategy considering EV priority ranking is presented. Case study results demonstrate that the proposed strategy increases VPP profitability while effectively guiding EV in orderly participation in distribution network optimization dispatch.

To solve wind power volatility and hybrid energy storage optimization, this study proposes a two-layer optimal scheduling model integrating frequency decomposition. Firstly, Crown Porcupine Optimized Variational Mode Decomposition processes wind power signals into grid-connected direct output and hybrid energy storage references. These references are further decomposed into low and high frequency components managed by lithium batteries and supercapacitors. A two-layer optimization framework is established: the upper layer employs particle swarm optimization for cost-effective capacity and power configuration, while the lower layer provides parameter feedback through real-time scheduling. Case studies demonstrate the method's effectiveness in smoothing wind power fluctuations while maintaining economic benefits.

Aiming at the uncertainty of wind and storage power plant output and price volatility in the electricity market, this study proposes a collaborative bidding framework that integrates hybrid tariff prediction and dynamic risk management. First, a hybrid tariff prediction model combining independent component analysis and time series network is constructed to provide data support; second, a dynamic Conditional Value-at-Risk threshold model is designed to adaptively switch between aggressive and conservative bidding strategies based on the predicted value of tariff fluctuations; finally, a spread-driven dual-market synergistic mechanism is set up to dynamically adjust bidding ratios and storage scheduling schemes by comparing the day-ahead and real-time tariffs. The empirical analysis shows that this strategy can effectively improve the economic return and operational adaptability of the wind storage system, and verifies the core value of energy storage in multi-market synergy.

In this paper, a high-voltage active filter multiport DC/DC converter is proposed, which is composed of two energy buffer arms used to transfer energy and three active filter arms used to maintain the continuity of the current, and three switching valves composed of IGBTs connected in series with anti-parallel diodes. This structure has no AC link, does not require large magnetic components and transformers, and has the characteristics of low cost and small footprint. This paper describes the circuit structure, operation principle and control strategy. And simulation cases of 400MW, 500kV/400kV/200kV are given to verify the effectiveness of the proposed topology.

In recent years, with the development of DC grids, the interconnection of multiple voltage levels has become an inevitable demand for the future growth of DC grids. Based on the principle of capacitive energy transfer, this paper therefore proposes a novel multiport DC/DC converter topology and comprehensively designs its operational principles and control strategies. The proposed topology enables bidirectional power transmission and multi-voltage-level interconnection. To validate the approach, this paper conducted simulation experiments with a power rating of 200MW and voltage levels of 500kV/400kV/200kV, analyzing waveform characteristics of key parameters to demonstrate the topology’s effectiveness.

As a flexible load, electric vehicles (EVs) play an important role in achieving load peak shaving and valley filling. However, current methods for calculating the schedulable potential of EVs are difficult to balance practicality and accuracy. Therefore, A model-data hybrid driven method for calculating the schedulable potential of EV clusters is proposed. Firstly, refined modeling of the boundary of a single EV. Aggregating EVs into a generalized energy storage (GES) model based on Minkowski summation theory. Then, the parameters of the GES model were accurately predicted based on Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) Network. Finally, the three-way Decisions (T-WD) model was established according to the user intentions to participate in different charging behaviors. The self-scheduling model of EVs was established considering the user intentions to quantify the schedulable potential of EVs. The example analysis results show that the proposed method can quantify the schedulable potential of EV clusters on the basis of accurately predicting the parameters of EVs and judging the intentions of users.

To improve the accuracy of electric vehicle charging station fault prediction, a method based on improved Stacking with multiple model fusion is proposed. Firstly, the operational parameters of the charging station are preprocessed, and the processed data is used as input for the models. Subsequently, multiple algorithms, optimized using particle swarm optimization, are employed to build fault prediction models. The algorithm selection is based on the importance from random forest and Pearson correlation coefficient, considering the importance and predictive correlation of each model. Unnecessary models are eliminated, and the final ensemble of base learners is determined. The outputs generated by the base learner models through 5-fold cross-validation are used as inputs for training the meta-learner in the second layer, establishing the Stacking prediction model. Finally, a real dataset of charging station faults is used for simulation analysis. The simulation results demonstrate that the proposed method performs exceptionally well, achieving a fault prediction accuracy of 96.38%.

In order to solve the problem of excessive distribution grid load power caused by the superposition of disorderly charging of electric vehicles (EV) and user base load peaks, we propose to establish a cloud-side-end cooperative optimization scheduling framework based on the theory of cloud-side coordination and big data technology. Firstly, based on the cloud-side collaboration theory and big data technology, a cloud-side-end collaborative optimization scheduling framework is established with comprehensive interconnection and interoperability of distribution grids, charging station operators, smart charging piles and EV users' information; secondly, a charging scheduling mechanism for EV users that takes into account the minimum acceptable profit or maximum cost of the users is proposed; and then a two-layer multi-objective charging/discharging optimization and control model is established from the perspective of the grid-side and the user-side to propose Improved Coati Optimization Algorithm (ICOA) with multiple strategies; finally, the model is solved by applying MATLAB using the load data in the commercial area. The simulation results verify the validity and superiority of the proposed model and method.

With the large-scale connection of new energy sources such as wind power and photovoltaic (PV) to the power grid, strong correlation is presented between geographically close wind farms or between PV power plants. For this reason, we propose a dynamic probability power flow calculation method considering scenery correlation. Combining parametric and nonparametric probabilistic modelling theories, we establish the probability distribution model of input random variables and construct Copula function to portray the correlation between multidimensional random input variables, so as to obtain sample data more in line with the actual operation of the system. The combination of cumulant method and Gram-Charlier series expansion is introduced to compute the dynamic flow and tested on the 39-bus system with case. The accuracy and validity of the static performance of the proposed method is verified by comparing it with the Monte Carlo method and the traditional cumulative method, which take correlation into account. Finally, the effect of scenery correlation on the dynamic flow is analyzed by means of dynamic probability density comparison plots.

The development of the economy and society is inseparable from convenient transportation. In recent decades, the road transportation industry has developed rapidly, and the number of tunnels has also increased accordingly. As one of the important components of tunnels, the load of the lighting system cannot be underestimated. The lighting system that adopts the maximum illuminance principle has problems such as excessive energy consumption and high operating costs. To address this, it is necessary to promote the implementation of tunnel intelligent lighting systems (TILS). This paper focuses on the energy management of a tunnel DC power supply system containing photovoltaic power (PV) generation and energy storage equipment, taking into consideration the TILS. Firstly, a model of TILS is established to fully consider factors such as brightness outside the tunnel, vehicle speed, and traffic flow. Then, a multi-objective optimization scheduling model is constructed for the energy management of the tunnel DC power supply system. Finally, the improved non-dominated sorting genetic algorithm II (INSGA-II) is adopted to solve the multi-objective optimization model and obtain the optimal energy management solution.

The utilization of vehicle-to-grid (V2G) technology is an environmentally friendly approach to achieve the transition towards low-carbon energy systems. However, it is still unknown what impact the V2G policy will have on the charging market of electric vehicles (EVs) and whether it is competitive with other electricity sales strategies. Aiming at the above problems, the electricity price model of EVs in electricity market including V2G strategy is established. Firstly, a 2–3 unbalanced strategy evolutionary game (2–3 USEG) model integrating vehicle-network interaction is proposed. The asymptotically stable equilibrium points of asymmetric evolutionary games for two groups with unbalanced game strategies are obtained. Multiple game strategies between the power grid enterprises (PGE) and New power supply entities (NPSE) are studied Besides, the corresponding asymptotic stability conditions are given by the Lyapunov stability criterion. Finally, the simulation proves that the ample implemented V2G strategy not only has strong competitiveness, but also enables the charging electricity price market of electric vehicles to have the “virtual high” electricity price adjustment ability.

Traditional frequency droop control (TFDC) strategies can utilize frequency deviation and DC voltage to provide frequency support in voltage source converter-based multi-terminal direct current transmission (VSC-MTDC) systems. However, the DC voltage droop coefficient is fixed and real-time operation status of the grid and converter station capacity are often neglected when unbalanced power should be allocated. This can lead to converter station overload and significant frequency fluctuation in weak grid. Therefore, a novel frequency droop control strategy is proposed in this paper. Firstly, the power allocation relationship of TFDC strategy is analyzed. Secondly, frequency margin and power margin are introduced into the DC voltage droop coefficient. The control strategy proposed in this paper can ensure that converter station overload will not occur and the weak grid can remain stable when the system is disturbed. Finally, the simulation results based on MATLAB/SIMULINK show that the proposed strategy can improve the frequency adjustment capability and stable operation ability of the VSC-MTDC system.

Due to its unique uncertainty and volatility, wind power is increasingly penetrating various levels of power grids, and its impact on the stability of power grid operations is gradually increasing. In order to cope with the uncertainty of wind power output and to improve the stability of system operation, a robust optimization and scheduling model for the integrated electricity-gas-heat system (IEGHS), and heat considering the DC power flow security domain is proposed. Firstly, based on the traditional DC power flow security domain model, a DC power flow security domain model based on wind power uncertainty is established and the operating safety boundary of nodes is established. Secondly, considering the security domain and wind power uncertainty, a robust budget parameter is introduced, and a controllable robust optimization and scheduling model for IEGHS is proposed. Finally, the effectiveness and practicality of the proposed model are demonstrated through numerical simulation. In summary, the proposed model takes into account the uncertainty of wind power and the safety domain of the DC power flow, and introduces robust budget parameters to achieve a controllable robust optimization and scheduling for IEGHS. The simulation results demonstrate the effectiveness and practicality of the proposed model.

An improved control scheme of three-phase three-level Vienna rectifier is presented in this paper. A dual-closed-loop cascade control structure, consisting of linear active disturbance rejection control (LADRC) and super-twisting algorithm sliding mode control (STASMC), is proposed to suppress the load disturbance and adjust power factor. Voltage outer loop is made up of linear tracking differentiator (LTD), PD controller and linear extended state observer (LESO), regulating output voltage and providing reference current for inner loop, which employs STA using hyperbolic tangent function. Simulation and experimental results are given to prove the feasibility and effectiveness of proposed control scheme.

The Impedance-source inverters have been proposed for integrating voltage-boost, buck and inversion into a single stage with shared functionalities and hence lesser active switches. Recently, some of them even have coupled inductors included for raising gain, while retaining high dc-link utilization without extra components. However, leakage inductances of the coupled inductors have caused noticeable problems, like voltage spikes and resonances, during switching transients. These problems are not easy to analyze theoretically with traditional inverter models, because of tight topological integration within each impedance-source inverter. It is therefore the theme hereon to propose a novel model for analyzing transient and power losses of a coupled-inductor impedance-source inverter at each of its switching transitions. Readings from experiments have promptly verified the developed theoretical model for a coupled-inductor impedance-source inverter.

Power talk is a novel ultra narrow-band powerline communication (UNB-PLC) technique for communication among control units in MicroGrids (MGs). Unlike the existing UNB-PLC solutions, power talk does not require installation of additional dedicated communication hardware and, instead, uses only the power electronic converters through which the control units interface the common bus. This way the communication system has practically the same reliability as the power system. The information is transmitted by modulating the parameters of the primary control, incurring subtle power deviations that can be detected by other units. In this paper, we develop power talk communication strategies for direct-current (DC) MG systems with arbitrary number of control units that carry out all-to-all communication. We investigate two multiple access strategies: time-division multiple access, where only one unit transmits at a time, and full duplex, where all units transmit and receive simultaneously. We apply the concepts of signaling space, where the power talk symbol constellations are constructed, and detection space, where the demodulation of the symbols is performed. The proposed communication technique is challenged by the random changes of the bus parameters due to load variations. To this end, we investigate the performance of power talk when a solution based on training sequences that re-establishes detection spaces is employed. The presented evaluation shows that power talk has a potential to offer an effective and inexpensive solution for reliable communication among units in DC MGs.

We introduce a novel communication strategy for DC Micro Grids (MGs), termed power talk, in which the devices communicate by modulating the power levels in the DC bus. The information is transmitted by varying the parameters that the MG units use to control the level of the common bus voltage, while it is received by processing the bus measurements that units perform. This implies that the communication does not require a dedicated modem, but instead it is piggybacked on top of the power electronics. The communication is challenged by the random fluctuations of the voltage level due to the random load variations in the MG. We develop the corresponding communication model and address the random voltage fluctuations by using coding strategies that transform the MG into some well- known communication channels. The performance analysis shows that it is possible to mitigate the random voltage level variations and communicate reliably over the MG bus.

A frequency-timing synchronization scheme for the downlink of filtered multitone modulation (FMT) based Terrestrial Trunked Radio (TETRA) system is presented. To achieve high-rate data transmission and better transmission quality, TETRA2 has been proposed in recent years. However, there are serious issues with the new wide-band TETRA system in frequency-timing synchronization. TETRA mainly specifies 2 sequences for downlink synchronization, i.e. downlink sync sequence set (DSS) and frequency correction set (FrCS). Researching the cross-correlation properties of these sequences and the existing data-aided synchronization algorithm for FMT, we propose an efficient method to estimate the frequency-offset and timing-offset for TETRA2 and analyze its performance in AWGN.