Huichao Ji

During the winter heating period, the accommodation of wind and photovoltaic (PV) power is limited due to the prioritized scheduling of combined heat and power (CHP) systems to meet the heat load demand of users, which indirectly increases carbon emissions (CE). Due to the robust peak shaving and energy storage capabilities of pumped storage hydropower (PSH), PSH and tiered carbon trading can be employed to address these issues. However, there are scarce reports on research exploring the integration of PSH and carbon trading into CHP systems. To fill the technology gap, we propose a low-carbon optimal scheduling method that integrates hydropower-wind-PV-thermal-battery (HWPTB) with CHP. The scheduling model for coordinated multi-energy complementarity of HWPTB is developed to address the electric and heat demand of users. The proposed work is validated by numerical simulation, compared with the traditional optimal scheduling method without considering PSH and carbon trading, the wind and PV power curtailment rates are reduced by 7.17% and 6.77%, respectively, and the operation cost is also reduced by 43.6%, and the CE cost is -4163.3 ( ${\$}$ ). The results show that the PSH can shave the peak of the electric load and alleviate the reduction of renewable energy accommodation, with no increase in operation cost and profit by tiered carbon trading.

Electric heat storage (EHS) located on the grid side can improve the flexibility of combined heat and power (CHP) systems to reduce wind power curtailment (WPC), and its capacity is correlated with investment costs. However, a significant amount of distributed electric heat storage (DEHS) on the load side has already been funded and constructed by users and businesses. To effectively utilize DEHS for diverse user types in different geographic locations, a generalized scheduling method is required to address these issues. To fill the technology gap, an innovative optimal scheduling strategy for energy allocation in DEHS is proposed. Firstly, to consider the heat comfort of DEHS users and their willingness to serve grid scheduling, the equivalent energy allocation strategy (EEAS) and increased energy allocation strategy (IEAS) are developed, respectively. Secondly, a general model is proposed to schedule DEHS for different types of users. The electric heat time-shift characteristics of DEHS are used to reallocate the working time of energy storage. Thirdly, numerical simulations are employed to validate the proposed strategy. The proposed scheduling strategies of EEAS and IEAS contribute to the reduction of WPC. The WPC rates are reduced by 14.04% and 29.49%, respectively, when compared to the conventional scheduling strategy of time-of-use. Moreover, compared with EEAS, IEAS makes a significant contribution by providing 27.05% more scheduling space for the grid, thus greatly improving its flexibility in consuming wind power. The WPC can be reduced through the energy allocation of DEHS, while the flexibility of grid regulation is enhanced by increasing the deviation of energy allocation.

Image watermarking is a key technology for copyright protection, and how to better balance the invisibility and robustness of algorithms is a challenge. To tackle this challenge, a watermarking optimization method based on matrix decomposition and discrete wavelet transform (DWT) for multi-size images is proposed. The DWT, Hessenberg matrix decomposition (HMD), singular value decomposition (SVD), particle swarm optimization (PSO), Arnold transform and logistic mapping are combined for the first time to achieve an image watermarking optimization algorithm. The multi-level decomposition of DWT is used to be adapted to multi-size host images, the Arnold transform, logistic mapping, HMD and SVD are used to enhance the security and robustness, and the PSO optimized scaling factor to balance invisibility and robustness. The simulation results of the proposed method show that the PSNRs are higher than 44.9 dB without attacks and the NCs are higher than 0.98 under various attacks. Compared with the existing works, the proposed method shows high robustness against various attacks, such as noise, filtering and JPEG compression and in particular, the NC values are at least 0.44% higher than that in noise attacks.

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

The volatility and uncertainty of high-penetration renewable energy (RE) challenge the stability of the power system. To tackle this challenge, an optimal dispatch of high-penetration RE based on flexible resources (FRs) is proposed to enhance the ability of the power system to cope with uncertain disturbances. Firstly, the flexibility of a high-penetration RE integrated power system is analyzed. The flexibility margin of power supply and flexible adaptability of RE are then introduced as the evaluation indices for optimal operation. Finally, a multi-objective optimal dispatch model for power system flexibility enhancement based on FRs under the constraint of flexibility indices is proposed. The simulation results show that the proposed optimal dispatch can effectively enhance the flexibility of the power system and the penetration of RE and reduce pollutant emissions. Compared with the conventional method, the daily average emissions of CO₂, SO₂, and NO_x with the proposed method are reduced by about 83,600 kg, 870 kg, and 370 kg, respectively, the maximum allowable volatility of net load is increased by 7.63%, and the average volatility of net load is reduced by 2.67%.

This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.