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Virtual power plant (VPP) is a comprehensive demand response (DR) technology to aggregate various power supply and consumption resources to help stabilise power grid fluctuations and reduce CO2 emissions. Fast‐charging electric vehicles (EVs) with higher charging power and shorter charging time are the most potential high‐quality DR resources of VPP in the future. In this paper, a new 'spatial‐temporal' bi‐layer optimal control strategy is proposed to solve the daily real‐time DR dispatching problem of large‐scale EVs including fast‐charging ones in a multi‐area VPP oriented to China's dual carbon target. This proposed bi‐layer strategy has two main contributions in both spatial and temporal domains. One is that, compared with the limited geographical area in traditional VPP, the upper layer of the proposed strategy considers the DR problem of EVs in a VPP with multiple geographical areas and establishes a spatial optimisation model of EVs DR considering both energy and carbon transaction costs of the overall VPP. The other is that rather than traditional slow‐charging EVs DR control with 1‐h temporal interval, the lower layer of the proposed strategy adds fast‐charging EVs in the real‐time orderly charging and discharging DR control by a shorter temporal interval of 15 min and designs two different charging and discharging price stimulation scenarios. The simulation results in the last section of this paper show that the proposed 'spatial‐temporal' bi‐layer optimal control strategy can effectively guide the charging and discharging behaviours of EVs in both spatial and temporal domains of a multi‐area VPP, achieving lower energy and carbon transaction cost and smaller load fluctuations. [ABSTRACT FROM AUTHOR]

For the power generation system of wind, photovoltaic, hydro, thermal and out‐purchased electricity, taking the minimum economic cost of thermal power generation as the objective function, an optimal dispatching model including the complementary system of wind‐photovoltaic‐hydro‐thermal‐out purchased electricity is proposed. The power balance and renewable energy accommodation are prerequisites, thermal power unit maintenance plans and various constraints are considered. The improved multi‐objective cuckoo search algorithm was used for short‐term scheduling, and the proposed hybrid firefly and particle swarm optimization was used for mid‐long term scheduling. The results show that the two algorithms can be effectively applied to the short‐term and mid‐long term joint optimal scheduling problem of wind, photovoltaic, hydro, thermal and out‐purchased electricity. It makes the power grid arrange the unit output reasonably, ensure the security and stability operation. It can better absorb renewable energy, reduces coal consumption and pollution, and improves the overall benefit of the system. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fault point temperature reading and positioning, target device segmentation, and fault point matching fusion in infrared warning system of substation inspection robot, MUL‐PCNN and ASURF are applied to realizing accurate fusion between the fault area in infrared image and visual image. The experimental results show that this method has certain value in the aspect of infrared image segmentation and fusion of substation. [ABSTRACT FROM AUTHOR]

This paper proposes an effective method to estimate the state of health (SOH) of a lithium-ion battery based on the ohm internal resistance R0. Unlike other estimation methods, this work considers the variation of R0 with the state of charge (SOC). The improved unscented particle filter (IUPF) is presented to track and predict R0. That is, an unscented Kalman filter (UKF) is used to generate an importance probability density function in the particle filter, and a method to select the fittest particle in the resampling stage is proposed. Based on the experimental data, a second-order resistance-capacitance equivalent circuit model is set up and the parameters are identified. To verify the accuracy of the proposed method, UKF and IUPF are compared in the prediction of R0 at different SOC points under the same cycle and at the same SOC point of different cycles. The results show that IUPF has certain advantages, and the SOH estimation error is always less than 3% during the charge-discharge stage. [ABSTRACT FROM AUTHOR]

A novel integrated model predictive control (MPC) strategy for batch processes is proposed in this paper. Both batch-axis and time-axis information are integrated into a two-dimensional control frame. The control law is obtained through the solution of a MPC optimization with time-varying prediction horizon, which leads to superior tracking performance and robustness against disturbance and uncertainty. Moreover, both model identification and dynamic R-parameter are employed to compensate the model-plant mismatch and make zero-error tracking possible. Next, the convergence analysis and tracking performance of the proposed integrated model predictive learning control system are described and proved strictly. Lastly, the effectiveness of the proposed method is verified by an example. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is an important part in the energy management of micro-grids, and the forecasting error directly affects the economic efficiency of operation. Considering the strong randomness and high volatility of loads on the user side of micro-grids, a short-term load forecasting method based on Empirical Mode Decomposition (EMD), Extreme Learning Machine with different Kernels, and Switching Delayed Particle Swarm Optimization (SDPSO) is proposed. First, the history load dataset is decomposed into several independent Intrinsic Mode Functions (IMFs) by EMD, and the sample entropy values of the IMFs are calculated. According to the approximation degree of sample entropy values, the IMFs are divided into three categories. Then, ELM with different kernels is adopted to forecast the three categories. Finally, the prediction results are summed to obtain the final prediction result. SDPSO is used to optimize the relevant parameters in the forecasting model. Three micro-grids with different capacities are used to verify the model, and the experimental results demonstrate that the proposed forecasting model can provide better accuracy than the other two methods. The proposed model can provide practical reference for the efficient operation of micro-grids. [ABSTRACT FROM AUTHOR]

In this study, neuro‐fuzzy‐based identification procedure for Hammerstein model with coloured noise is presented. Separable signal is used to realise the decoupling of the identification of dynamic linear part from that of static non‐linear part, and then correlation analysis method is adopted to identify the parameters of the linear part. Furthermore, by combining multi‐innovation and gradient search theory, multi‐innovation‐based extended stochastic gradient approach is derived for improving the parameters estimation accuracy of the non‐linear part and the noise model. In addition, the convergence analysis in the martingale theory illustrates that the parameter estimation error will converge to zero under the persistent excitation condition. Finally, two simulation results demonstrate that the proposed approach has high identification accuracy and good robustness to the disturbance of coloured noise. [ABSTRACT FROM AUTHOR]

As China proposes to achieve carbon peak by 2030 and carbon neutrality by 2060, as well as the huge pressure on the power grid caused by the load demand of the energy supply stations of electric vehicles (EVs), there is an urgent need to carry out comprehensive energy management and coordinated control for EVs' energy supply stations. Therefore, this paper proposed a two-step intelligent control method known as ISOM-SAIA to solve the problem of the 24 h control and regulation of green/flexible EV energy supply stations, including four subsystems such as a photovoltaic subsystem, an energy storage subsystem, an EV charging subsystem and an EV battery changing subsystem. The proposed control method has two main innovations and contributions. One is that it reduces the computational burden by dividing the multi-dimensional mixed-integer programming problem of simultaneously optimizing the 24 h operation modes and outputs of four subsystems into two sequential tasks: the classification of data-driven operation modes and the rolling optimization of operational outputs. The other is that proper carbon transaction costs and carbon emission constraints are considered to help save costs and reduce carbon emissions. The simulation analysis conducted in this paper indicates that the proposed two-step intelligent control method can help green/flexible EV energy supply stations to optimally allocate energy flows between four subsystems, effectively respond to peak shaving and valley filling of power grid, save energy costs and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid. [ABSTRACT FROM AUTHOR]

In this paper, a neuro-fuzzy based identification procedure for Hammerstein output error model with colored noise is presented. Separable signal is used to realize the decoupling of the identification of dynamic linear part from that of static nonlinear part, and then correlation analysis method is adopted to identify the parameters of the linear part. Next, a filter is embedded to form extended Hammerstein model to calculate the noise correlation function by the information of zeros and poles of the extended model. The correlation functions which consist of the noise correlation function are applied to compensate the bias caused by colored noise. As a result, the parameters of the nonlinear part can be identified through recursive least square method. Examples results illustrate that the proposed approach has high identification accuracy and good robustness to the disturbance of colored noise. [ABSTRACT FROM AUTHOR]