Global Energy Interconnection最新文献

Random and fluctuating wind speeds make it difficult to stabilize the wind-power output, which complicates the execution of wind-farm control systems and increases the response frequency. In this study, a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network, the Elman neural network, and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm. The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks. Although the complexity of the model is high, the accuracy of wind-speed prediction and stability are also high. The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system, the maximum power point cannot be easily determined in a timely manner. This study proposes a maximum power reference signal search method based on fuzzy control, which is an improvement to the climbing search method. A neural network-based parameter regulator is proposed to address external wind speed fluctuations, where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions. Finally, the effectiveness of this method is verified via Simulink simulation

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

The cooperative model of a multi-subject Regional Integrated Energy System (RIES) is no longer limited to the trading of traditional energy, but the trading of new energy derivatives such as Green Certificates (GC), Service Power (SP), and CO2 will be more involved in the energy allocation of the cooperative model. This study was conducted for the multi- entity RIES cooperative model considering the trading of electronics, GC, SP, and CO2. First, a cooperative framework including wind-photovoltaic generation system (WG), combined heat and power system (CHP), and power-carbon-hydrogen load (PCH) is proposed, and the mechanism of energy derivatives trading is also analyzed. Then, the sub-models of each agent in the cooperative model are established separately so that WG has the capability of GC generation, CHP has the capability of GC and CO2 absorption, and PCH can realize the effective utilization of CO2. Then, the WG–CHP–PCH cooperative model is established and equated into two sub-problems of cooperative benefit maximization and transaction payment negotiation, which are solved in a distributed manner by the alternating directed multiplier method (ADMM). Finally, the effectiveness of the proposed cooperative model and distributed solution is verified by simulation. The simulation results show that the WG–CHP–PCH cooperative model can substantially improve the operational efficiency of each agent and realize the efficient redistribution of energy and its derivatives. In addition, the dynamic parameter adjustment algorithm (DP) is further applied in the solving process to improve its convergence speed. By updating the step size during each iteration, the computational cost, the number of iterations, and the apparent oscillations are reduced, and the convergence performance of the algorithm is improved.

A larger number of uncertain factors in energy systems influence their evolution. Owing to the complexity of energy system modeling, incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation. This study focusses on long-term energy system optimization model. The important uncertain parameters in the model are analyzed and divided into policy, economic, and technical factors. This study specifically addresses the challenges related to carbon emission reduction and energy transition. It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems. Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review. Finally, important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed, and future research directions are proposed.

Owing to their stability, doubly-fed induction generator (DFIG) integrated systems have gained considerable interest and are the most widely implemented type of wind turbines and due to the increasing escalation of the wind generation penetration rate in power systems. In this study, we investigate a DFIG integrated system comprising four modules: (1) a wind turbine that considers the maximum power point tracking and pitch-angle control, (2) induction generator, (3) rotor/ grid-side converter with the corresponding control strategy, and (4) AC power grid. The detailed small-signal modeling of the entire system is performed by linearizing the dynamic characteristic equation at the steady-state value. Furthermore, a dichotomy method is proposed based on the maximum eigenvalue real part function to obtain the critical value of the parameters. Root-locus analysis is employed to analyze the impact of changes in the phase-locked loop, short-circuit ratio, and blade inertia on the system stability. Lastly, the accuracy of the small-signal model and the real and imaginary parts of the calculated dominant poles in the theoretical analysis are verified using PSCAD/EMTDC.

The premise and basis of load modeling are substation load composition inquiries and cluster analyses. However, the traditional kernel fuzzy C-means (KFCM) algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions. To overcome these limitations, an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper. This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm. The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio. Compared with the traditional KFCM algorithm, the enhanced KFCM algorithm has robust clustering and comprehensive abilities, enabling the efficient convergence to the global optimal solution

Cooperation in energy systems is no longer limited to the distribution of electricity, and more attention is paid to the trading of green certificates (GCs). This paper proposed a cooperative method for photovoltaic (PV) and electric-to- hydrogen (EH) trading, including GC trading under risk management. First, a novel PV and EH model is established and the cooperation mechanism is analyzed. Meanwhile, PV and EH models were risk-controlled using the conditional value at risk to reduce the impact of the uncertainty of PV electricity and EH loads. Then, the PV-EH cooperative model was established based on cooperative game theory; this was then divided into two subproblems of “cooperative benefit maximization” and “transaction payment negotiation,” and the above two subproblems were solved distributively by alternating direction multiplier method (ADMM). Only energy transactions and price negotiations were conducted between the PV and EH, which can protect the privacy and confidentiality of each entity. Finally, the effectiveness of the cooperation model was verified using a practical engineering case. The simulation results show that the cooperation of the PV-EH can significantly improve the operational efficiency of each entity and the overall efficiency of the cooperation and realize the efficient redistribution of electricity and GC.

With the construction of new power systems, the power grid has become extremely large, with an increasing proportion of new energy and AC/DC hybrid connections. The dynamic characteristics and fault patterns of the power grid are complex; additionally, power grid control is difficult, operation risks are high, and the task of fault handling is arduous. Traditional power-grid fault handling relies primarily on human experience. The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling. Therefore, this mode of operation is no longer suitable for the requirements of new systems. Based on the multi-source heterogeneous data of power grid dispatch, this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model, constructs a knowledge graph of power-grid dispatch fault processing and designs, and develops a fault-processing auxiliary decision-making system based on the knowledge graph. It was applied to study a provincial dispatch control center, and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets. The carbon emission cost (CEC) of coal-fired units becomes part of the power generation cost through market coupling. The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market. Study of carbon–electricity market interaction and CEC calculations is still in its initial stages. This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets. A long-period interactive operation simulation mechanism for the carbon–electricity market is established, and operation and trading models of the carbon emissions trading market and electric power market are established. A daily rolling estimation method for the CEC of coal- fired units is proposed, along with the CEC per unit electric quantity of the coal-fired units. The feasibility and effectiveness of the proposed method are verified through an example simulation, and the factors influencing the CEC are analyzed.