This study presents a comprehensive impact analysis of the rotor angle stability of a proposed international connection between the Philippines and Sabah, Malaysia, as part of the Association of Southeast Asian Nations(ASEAN) Power Grid.This study focuses on modeling and evaluating the dynamic performance of the interconnected system, considering the high penetration of renewable sources.Power flow, small signal stability, and transient stability analyses were conducted to assess the ability of the proposed linked power system models to withstand small and large disturbances, utilizing the Power Systems Analysis Toolbox (PSAT) software in MATLAB.All components used in the model are documented in the PSAT library.Currently, there is a lack of publicly available studies regarding the implementation of this specific system.Additionally, the study investigates the behavior of a system with a high penetration of renewable energy sources.Based on the findings, this study concludes that a system is generally stable when interconnection is realized, given its appropriate location and dynamic component parameters.Furthermore, the critical eigenvalues of the system also exhibited improvement as the renewable energy sources were augmented.

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Current power systems face significant challenges in supporting large-scale access to new energy sources, and the potential of existing flexible resources needs to be fully explored from the power supply, grid, and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

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To fully exploit the rich characteristic variation laws of an integrated energy system (IES) and further improve the short-term load-forecasting accuracy, a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features.Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity, far and near time periods.The Gaussian mixture model (GMM) was used to divide the scene of the comprehensive load day, and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted.Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a “dynamic similar day”by weighting.The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM.Comparing the static features as input and the selection method of similar days based on non-extended single features, the effectiveness of the proposed prediction method was verified.

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The time-of-use (TOU) strategy can effectively improve the energy consumption mode of customers, reduce the peak-valley difference of load curve, and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First, this study proposes a period partitioning model, which is based on a moving boundary technique with constraint factors, and the Dunn Validity Index (DVI) is used as the objective to solve the period partitioning.Second, a control strategy for the curtailable flexible load is investigated, and a TOU strategy is utilized for further modifying load curve.Third, a price demand response strategy for adjusting transferable load is proposed in this paper.Finally, through the case study analysis of typical daily flexible load curve, the efficiency and correctness of the proposed method and model are validated and proved.

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Adding a reputation incentive system to peer-to-peer (P2P) energy transactions can encourage prosumers to regulate their trading behavior, which is important for ensuring the efficiency and reliability of P2P transactions.This study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value incentives.First, the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value, and a reputation incentive model of P2P transactions for prosumers was constructed.Then, the penalty coefficient was applied to the cost function of the prosumers, and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was established.Furthermore, the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function, and the Nash equilibrium solution of the game was obtained via a relaxation algorithm.Finally, the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation value.The results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P transactions.It can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.

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In this study, a novel non-intrusive temperature rise fault-identification method for a distribution cabinet based on tensor block-matching is proposed.Two-stage data repair is used to reconstruct the temperature-field information to support the demand for temperature rise fault-identification of non-intrusive distribution cabinets.In the coarse-repair stage, this method is based on the outside temperature information of the distribution cabinet, using tensor block-matching technology to search for an appropriate tensor block in the temperature-field tensor dictionary, filling the target space area from the outside to the inside, and realizing the reconstruction of the three-dimensional temperature field inside the distribution cabinet.In the fine-repair stage, tensor super-resolution technology is used to fill the temperature field obtained from coarse repair to realize the smoothing of the temperature-field information inside the distribution cabinet.Non-intrusive temperature rise fault-identification is realized by setting clustering rules and temperature thresholds to compare the location of the heat source with the location of the distribution cabinet components.The simulation results show that the temperaturefield reconstruction error is reduced by 82.42% compared with the traditional technology, and the temperature rise faultidentification accuracy is greater than 86%, verifying the feasibility and effectiveness of the temperature-field reconstruction and temperature rise fault-identification.

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A Beidou 3 (BD3) system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion.However,optimizing route selection to reduce both time synchronization error and delay is a challenging problem.In this paper, we establish a software-defined network-enabled power reference station time synchronization framework based on BD3.Then, we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization.A back propagation (BP) neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection, which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence.Simulation results show the superior performance of BP-RS in synchronization delay, synchronization error, and adaptability with changing routing topologies.

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Aiming at reducing the difficulty of cooling the interior of high-density motors, this study proposed the placement of a water cold plate cooling structure between the axial laminations of the motor stator.The effect of the cooling water flow,thickness of the plate, and motor loss density on the cooling effect of the water cold plate were studied.To compare the cooling performance of water cold plate and outer spiral water jacket cooling structures, a high-speed permanent magnet motor with a high loss density was used to establish two motor models with the two cooling structures.Consequently, the cooling effects of the two models were analyzed using the finite element method under the same loss density, coolant flow, and main dimensions.The results were as follows.(1) The maximum and average temperatures of the water cold plate structure were reduced by 25.5% and 30.5%, respectively, compared to that of the outer spiral water jacket motor;(2) Compared with the outer spiral water jacket structure, the water cold plate structure can reduce the overall mass and volume of the motor.Considering a 100 kW high-speed permanent magnet motor as an example, a water cold plate cooling system was designed, and the temperature distribution is analyzed, with the result indicating that the cooling structure satisfied the cooling requirements of the high loss density motor.

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This paper presents a finite-time economic model predictive control (MPC) algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability, real-time economic optimization, control of the system and optimal load dispatch from it.A generalized terminal penalty term was used, and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system, which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

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In this paper, the explicit state-space model for a multi-inverter system including grid-following inverter-based generators (IBGs) and grid-forming IBGs is developed by the two-level component connection method (CCM), which modularized inverter control blocks at the primary level and IBGs at the secondary level.Based on the comprehensive state-space model representing full order of system dynamics, eigenvalues of the overall system are thoroughly analyzed,identifying potential adverse impacts of not only grid-following inverters, but also grid forming inverters on the system smallsignal stability, with the underlying principle of oscillations also understood.Numerical and simulation results validate effectiveness of the proposed methodology on IEEE benchmarking 39-bus system.

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