Advances in Power Science and Technology

In the context of achieving carbon neutrality, the substantial integration of high proportions of renewable energy sources has significantly impacted the dynamic characteristics of power systems, including frequency stability, voltage security, and synchronous stability, thereby posing formidable challenges to the secure and stable operation of power systems [...] Full article

The study maximizes the total profit of a hybrid power system with cascaded hydropower plants, thermal power plants, pumped storage hydropower plants, and wind and solar power plants over one operation day, considering the uncertainty of wind speed and solar radiation. Wind speed and solar radiation in a specific zone in Vietnam are collected using the wind and solar global atlases, and the maximum data are then supposed to be 120% of the collection for uncertainty consideration. The metaheuristic algorithms, including the original Slime mould algorithm (SMA), Equilibrium optimizer, and improved Slime mould algorithm (ISMA), are implemented for the system. ISMA is a developed version of SMA that cancels old methods and proposes new methods of updating new solutions. In the first stage, the cascaded system with four hydropower plants is optimally operated by simulating two cases: simultaneous optimization and individual optimization. ISMA is better than EO and SMA for the two cases, and the results of ISMA from the simultaneous optimization reach greater energy than individual optimization by 154.8 MW, equivalent to 4.11% of the individual optimization. For the whole system, ISMA can reach a greater total profit than EO and SMA over one operating day by USD 6007.5 and USD 650.5, equivalent to 0.12% and 0.013%. The results indicate that the optimization operation of cascaded hydropower plants and hybrid power systems can reach a huge benefit in electricity sales Full article

With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China’s 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable. Full article

Accurate and reliable wind speed prediction is conducive to improving the power generation efficiency of electrical systems. Due to the lack of adequate consideration of spatial feature extraction, the existing wind speed prediction models have certain limitations in capturing the rich neighborhood information of multiple sites. To address the previously mentioned constraints, our study introduces a graph isomorphism-based gated recurrent unit (GIN-GRU). Initially, the model utilizes a hybrid mechanism of random forest and principal component analysis (PCA-RF) to discuss the feature data from different sites. This process not only preserves the primary features but also extracts critical information by performing dimensionality reduction on the residual features. Subsequently, the model constructs graph networks by integrating graph embedding techniques with the Mahalanobis distance metric to synthesize the correlation information among features from multiple sites. This approach effectively consolidates the interrelated feature data and captures the complex interactions across multiple sites. Ultimately, the graph isomorphism network (GIN) delves into the intrinsic relationships within the graph networks and the gated recurrent unit (GRU) integrates these relationships with temporal correlations to address the challenges of wind speed prediction effectively. The experiments conducted on wind farm datasets for offshore California in 2019 have demonstrated that the proposed model has higher prediction accuracy compared to the comparative model such as CNN-LSTM and GAT-LSTM. Specifically, by modifying the network layers, we achieved higher precision, with the mean square error (MSE) and root mean square error (RMSE) of wind speed at a height of 10 m being 0.8457 m/s and 0.9196 m/s, respectively. Full article

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within a Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing the advanced capabilities of the dSPACE 1104 platform, this work establishes a dynamic data exchange mechanism between a variable voltage power supply and the SEPIC multiplier converter, enhancing the efficiency of solar energy harnessing. The proposed emulation model was crafted to simulate real-world solar energy capture, facilitating the evaluation of control strategies under laboratory conditions. By emulating realistic operational scenarios, this approach significantly accelerates the innovation cycle for PV system technologies, enabling faster validation and refinement of emerging solutions. The SEPIC multiplier converter is a new topology based on the traditional SEPIC with the capability of producing a larger output voltage in a scalable manner. This initiative sets a new benchmark for conducting PV system research, offering a blend of precision and flexibility in testing supervisory strategies, thereby streamlining the path toward technological advancements in solar energy utilization. Full article

In weak grid, feedforward of grid voltage control is widely used to effectively suppress grid-side current distortion of inverters caused by harmonics in point of common coupling (PCC) voltage. However, due to its introduction of a positive feedback loop related to the grid impedance, it results in a significant reduction in the system phase margin. In view of this, in this paper, the output impedance of a three-phase LCL grid-connected inverter under a quasi-proportional resonant (QPR) controller is first modeled. Instead of the traditional grid voltage feedforward control strategy, a band-pass filter is added to the grid voltage feedforward channel. Secondly, a multi-objective constraint method is proposed to make improvements to the feedforward function. Then, a multi-objective constraint function is established with the constraints of base-wave current tracking performance, system stability margin, and low-frequency amplitude, and the feasibility of its function optimization design method is verified. Theoretical analysis shows that the optimized grid voltage feedforward control strategy can effectively reshape the phase characteristics of the system output impedance, which greatly broadens the adaptation range of the system to the grid impedance. Finally, the effectiveness of the proposed control strategy is verified by building a semi-physical simulation experimental platform based on RT-LAB OP4510. Full article

In response to challenges such as voltage limit violations, excessive currents, and power imbalances caused by the integration of distributed photovoltaic (distributed PV) systems into the distribution network, this study proposes at two-level optimization configuration method. This method effectively balances the grid capacity and reduces the active power losses, thereby decreasing the operating costs. The upper-level optimization enhances the distribution network’s capacity by determining the siting and sizing of distributed PV devices. The lower-level aims to reduce the active power losses, improve the voltage stability margins, and minimize the voltage deviations. The upper-level planning results, which include the siting and sizing of the distributed PV, are used as initial conditions for the lower level. Subsequently, the lower level feeds back its optimization results to further refine the configuration. The model is solved using an improved second-order oscillating chaotic map particle swarm optimization algorithm (SCMPSO) combined with a second-order relaxation method. The simulation experiments on an improved IEEE 33-bus test system show that the SCMPSO algorithm can effectively reduce the voltage deviations, decrease the voltage fluctuations, lower the active power losses in the distribution network, and significantly enhance the power quality. Full article

The application of GaN power devices has significantly increased the power density of flyback power supplies but has also caused severe electromagnetic interference (EMI) issues. To address the challenge of conducted interference in flyback power supplies, a comprehensive analysis of the transmission mechanism of conducted common-mode noise is undertaken. This analysis involves simplifying the equivalent model of conducted interference and leveraging the circuit characteristics of conducted noise to propose a solution for attenuating common-mode noise. Considering the constraints of external compensation capacitors, a balanced winding is further introduced to mitigate the impact of noise. To enhance the efficacy of conducted interference suppression, it is suggested to change the winding structure of the transformer and incorporate a shielding winding. This configuration aims to minimize the generation and propagation of common-mode noise within the transformer. Finally, experimental verification is carried out using a 150 W GaN flyback power supply prototype. The experimental results demonstrate that the proposed method effectively suppresses common-mode noise in the circuit. Full article

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems. Full article

Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems. Full article

In the context of “dual carbon”, restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction. Full article

To combat global environmental deterioration and energy scarcities, it is crucial to implement energy-saving upgrades for urban road lighting. Comparatively, LEDs have emerged as an advanced and eco-friendly lighting option due to their low energy consumption, excellent performance, high color rendering index, and prolonged lifespan. By incorporating solar cell technology, a smart LED street light controller based on small-scale integrated circuits was developed to enable intelligent control for various lighting needs such as dimming, timing, automatic detection, and sound and light control. Through circuit simulations and experimental outcomes, it has been validated that the controller’s structure and performance parameters align with the design specifications. This design encompasses knowledge from diverse fields, including fundamentals of circuit and electronic technology, photovoltaic cell technology, power electronics, and sensor technology, showcasing robust engineering and practicality. Its utilization in the experimental course for second-year college students majoring in electrical engineering contributes to the grooming of professionals and expands the perspectives of future talents, enriching their application of knowledge and practical innovation capabilities. Full article

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs. Full article

This paper presents an innovative solution that is able to suppress the thrust ripple in a high-power asynchronous linear induction motor (LIM) used in a microgravity experiment facility electromagnetic launch (MEFEL) system. By addressing the crucial need for low levels of thrust ripple in MEFEL applications, we propose a dynamic model-based adaptive controller (MAC) and an enhanced quasi-proportional-resonant (PR) controller. The MAC is designed to compensate for the inherent impedance asymmetry of the linear motor. The PR controller minimizes thrust ripple by eliminating harmonics within the current loop. A comparative analysis indicates that both MAC and PR control are effective in reducing harmonics, suppressing the thrust ripple, and maintaining system stability. Computer simulations show a noteworthy 75% reduction in the thrust ripple and a decrease in the negative current. Partial tests on the MEFEL device validate the practical efficacy of the proposed control methods, emphasizing the method’s ability to enhance the quality of microgravity in real-world scenarios significantly. Full article

The popularization of electricity-gas systems leads to increasing demand for state management of systems. However, the existence of neglected measurement correlations brings uncertainties to the electricity-gas systems state estimation. In this paper, an interval state estimation method that considers measurement correlations existing in the electricity-gas systems is presented. We derive the linear measurement model for the electricity-gas systems through Taylor series expansion and estimate the measurement variance-covariance matrix with measurement correlations. The system parameter matrix and the measurement variance-covariance matrix containing measurement correlations are combined into an interval, and the interval state matrix considering measurement correlations is constructed. Then, the linear equations for the state estimation interval considering measurement correlations are established based on the measurement containing correlations and interval state matrix; as a result, the electricity-gas system state estimation model containing measurement correlations is established. In addition, a method for determining the range of state estimation intervals is proposed. Numerical tests on an integrated electricity-gas system comprising a 10-node natural gas network and IEEE 30-bus system indicate that the proposed approach has more advantages over the UT+KO approach in computation accuracy and computation efficiency. Full article

An effective equaliser is crucial for eliminating inconsistencies in the connected serial batteries and extending the life of the battery system. The current equalisers generally have the problems of low equalisation efficiency, slow equalisation speed, and complex switching control. A layered parallel equaliser based on a flyback transformer multiplexed for a lithium-ion battery system is proposed. The equaliser employs both hierarchical and parallel equalisation techniques, allowing for simultaneous processing of multiple objectives. This enhances both the efficiency and speed of the equalisation process. The efficiency of equalisation can be further improved by implementing PWM control with deadband complement. Additionally, the flyback transformer serves as an energy storage component for both layers of the equalisation module, resulting in a significant reduction in the size and cost of the equaliser. The circuit topology of the equaliser is presented, and its operational principle, switching control, and equalisation control strategy are analysed in detail. Finally, an experimental platform consisting of six lithium-ion batteries is constructed, and equalisation experiments are conducted to verify the advantages of the proposed equaliser in terms of equalisation speed, efficiency, and cost. Full article

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase. Full article

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. Full article

Friction parameters such as the angle of internal friction and the external friction of soils (bulk materials) show the possibilities of further material use. These are, for example, possibilities for soil processing, handling, and storage. The determination of friction parameters is usually carried out under laboratory conditions. For the possibility of determining the properties of soils outside the laboratory in terms of immediate material response, a laboratory prototype was developed. The main objective for its development was to determine the effect of the shape of the friction surface when “sliding” on the soil. This was achieved with the help of validation equipment designed to measure, test, and validate the processes of raking, material piling, material transfer and removal, and tool movement or sliding on or in a material. It was found that by using an appropriate speed and normal load, the Jenike method can be applied to determine the angle of external friction over a shorter distance with an error of about 6–7.5% from the values measured on a calibrated shear machine. The results also showed that the method can be applied to detect the shear stresses that arise when a tool is plunged into a material, and thus predict the possible increase in energy loss during the process. Full article

In recent years, rapid industrialization has driven higher energy demand, depleting fossil-fuel reserves and causing excessive emissions. China’s “dual carbon” strategy aims to balance development and sustainability. This study optimizes microgrid efficiency with a tiered carbon-priced economy. A Stackelberg game establishes microgrid-user equilibrium, solved iteratively with a multi-population algorithm (MPGA). Comparative analysis can be obtained without considering demand response scenarios, and the optimization cost of microgrid operation considering price-based demand response scenarios was reduced by 5%; that is 668.95 yuan. In addition, the cost of electricity purchase was decreased by 23.8%, or 778.6 yuan. The model promotes user-driven energy use, elevating economic and system benefits, and therefore, the scheduling expectation of “peak shaving and valley filling” is effectively realized. Full article

In light of the challenges posed by the widespread distribution of new energy sources in China and their distance from load centers, the power system must effectively integrate both new energy and thermal power transmission. To address this issue, we propose a dynamic coordinated scheduling model that combines wind, photovoltaic, and thermal power to optimize the profit of the energy complementary delivery system. Additionally, we present an improved ant lion optimization algorithm to investigate the coordinated scheduling and benefit distribution of these three power sources. This paper introduces a cooperative mode for benefit distribution and utilizes an enhanced Shapley value method to allocate the benefits of joint operation among the three parties. The distribution of benefits is based on the contribution of each party to the joint proceeds, considering the profit levels of joint outbound and independent outbound modes. Through our analysis, we demonstrate that the upgraded ant lion optimization algorithm facilitates finding the global optimal solution more effectively within the feasible zone. Furthermore, our suggested three-party combined scheduling model and profit-sharing approach are shown to be superior and feasible. Full article

An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%. Full article

A similarity criterion suitable for studying the vibration characteristics of converter transformers is proposed based on comprehensive consideration of geometric dimensions, electric field, magnetic field, force field, sound field, and coupling field interface interactions. By comparing the magnetic field, stress, displacement, sound field distribution, and vibration characteristics of the scale model of the converter transformer with the initial model, the reliability of the similarity criterion was determined. Based on the vibration similarity criterion of the converter transformer, a prototype of the proportional model was designed and manufactured, and vibration signals under no-load and load conditions were tested. These signals correspond to the vibration signals of the iron core and winding in the finite element model, respectively. Through comparative analysis, the reliability of the prototype and the vibration similarity model of the converter transformer has been proven, which can provide an accurate and effective laboratory research platform for in-depth research on the vibration and noise of the converter transformer and equipment protection. Full article

The control of the boiler-turbine unit is important for its sustainable and robust operation in power plants, which faces great challenges due to the control unit’s serious nonlinearity, unmeasurable states, variable constraints, and unknown time-varying lumped disturbances. To address the above issues, this paper proposes a receding Galerkin optimal controller with a high-order sliding mode disturbance observer in a composite scheme, in which a high-order sliding mode disturbance observer is first employed to estimate the lumped disturbances based on a deviation form of the mathematical model of the boiler-turbine unit. Subsequently, under the hypothesis of state constraint, a receding Galerkin optimal controller is designed to compensate the lumped disturbances by embedding their estimates into the mathematically based predictive model at each sampling time instant. With the help of an interpolation polynomial, Gauss integration, and nonlinear solvers, an optimal control law is then obtained based on a Galerkin optimization algorithm. Consequently, disturbance rejection, target tracking, and constraint handling performance of a controlled closed-loop system are improved. Some simulation cases are conducted on a mathematical boiler-turbine unit model to demonstrate the effectiveness of the proposed method, which is supported by the quantitative result analysis, such as tracking and disturbance rejection performance indexes. Full article

In today’s human society, diesel generators (DGs) are widely applied in the human energy and electricity supply system due to its technical, operational, and economic advantages. This paper proposes an intelligent nonlinear $H_2/H_{\infty }$ robust controller based on the chaos particle swarm gravity search optimization algorithm (CPSOGSA), which controls the speed and excitation of a DG. In this method, firstly, establish the nonlinear mathematical model of the DG, and then design the nonlinear $H_2/H_{\infty }$ robust controller based on this. The direct feedback linearization and the $H_2/H_{\infty }$ robust control theory are combined and applied. Based on the design of the integrated controller for DG speed and excitation, the system’s performance requirements are transformed into a standard robust $H_2/H_{\infty }$ control problem. The parameters of the proposed solution controller are optimized by using the proposed CPSOGSA. The introduction of CPSOGSA completes the design of an intelligent nonlinear $H_2/H_{\infty }$ robust controller for DG. The simulation is implemented in MATLAB/Simulink, and the results are compared with the PID control method. The obtained results prove that the proposed method can effectively improve the dynamic accuracy of the system and the ability to suppress disturbances and improve the stability of the system. Full article

In the pilot provinces of China’s electricity spot market, power generation companies usually adopt the separate bidding mode, which leads to a low willingness of demand-side response and poor flexibility in the interaction mechanism between supply and demand. Based on the analysis of the demand response mechanism of the power day-ahead market with the participation of power sales companies, this paper abstracted the game process of the “power grid-sales company-users” tripartite competition in the electricity market environment into a two-layer (purchase layer/sales layer) game model and proposed a master–slave game equilibrium optimization strategy for the day-ahead power market under the two-layer game. The multi-objective multi-universe optimization algorithm was used to find the Pareto optimal solution of the game model, a comprehensive evaluation was constructed, and the optimal strategy of the demand response was determined considering the peak cutting and valley filling quantity of the power grid, the profit of the electricity retailers, the cost of the consumers, and the comfort degree. Examples are given to simulate the day-ahead electricity market participated in by the electricity retailers, analyze and compare the benefits of each market entity participating in the demand response, and verify the effectiveness of the proposed model. Full article

Early detection of cascading failures phenomena is a vital process for the sustainable operation of power systems. Within the scope of this work, a preventive control approach implementing an algorithm for selecting critical contingencies by a dynamic vulnerability analysis and predictive stability evaluation is presented. The analysis was carried out using a decision tree with a multi-parameter knowledge base. After the occurrence of an initial contingency, probable future contingencies are foreseen according to several vulnerability perspectives created by an adaptive vulnerability search module. Then, for cases identified as critical, a secure operational system state is proposed through a vulnerability-based, security-constrained, optimal power flow algorithm. The modular structure of the proposed algorithm enables the evaluation of possible vulnerable scenarios and proposes a strategy to alleviate the technical and economic impacts due to prospective cascading failures. The presented optimization methodology was tested using the IEEE-39 bus test network and a benchmark was performed between the proposed approach and a time domain analysis software model (EMTP). The obtained results indicate the potential of analysis approach in evaluating low-risk but high-impact vulnerabilities in power systems. Full article