Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality.The power sector accounts for approximately 40% of China’s total CO2 emissions.Accordingly,collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic,environmental,and technological concerns as carbon emissions is necessary.This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP.This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies,flexible generation,and resource limitations constraints.This research further analyzes the advantages and disadvantages of current GEP techniques.Results show that the main determinants of new investment decisions are carbon emissions,reserve margins,resource availability,fuel consumption,and fuel price.The proposed optimization method is simulated and validated based on China’s power system data.Finally,this study provides policy recommendations on the flexible management of traditional power sources,the market-oriented mechanism of new energy sources,and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

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After suffering from a grid blackout,distributed energy resources (DERs),such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience.In this study,a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs).Here,the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations.Owing to the uncertainty,a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model.A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods.The algorithm is organized into a two-stage structure,in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem.In the second stage,an integer programming problem is calculated to acquire the outputs of both SOPs and power resources.A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

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This study proposes a combined hybrid energy storage system (HESS) and transmission grid (TG) model,and a corresponding time series operation simulation (TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy.First,a linear model for the optimal operation of the HESS is established,which considers the different power-efficiency characteristics of the pumped storage system,electrochemical storage system,and a new type of liquid compressed air energy storage.Second,a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS.Based on the proposed model,this study considers the transmission capacity of a TG.By adding the power-flow constraints of the TG,a TSOS-based HESS and TG combination model for peak shaving is established.Finally,the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

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Carrying out green energy transformation,implementing clean energy power replacement and supply,and developing a new power system are some primary driving forces needed to fulfill China’s carbon-peak and carbon-neutral strategic goals.The construction of new power systems in China’s provinces and cities is developing rapidly,and the lack of a typical model promotes the application.The new power system path design should be based on the actual development of the power grid in different regions,energy use characteristics,and other actual needs to carry out the differentiated path design.In this context,this study analyzes the characteristics of the new domestic power system based on the policy background of the new domestic power system,constructs a new model for power system development stage identification,and proposes the overall design of the new power system development path from the power supply,transmission and distribution,and load sides.It also uses the Hebei South Network as an example to explore the development stage of the Hebei South Grid based on actual development needs.Finally,this study designs a novel power system development path for the entire supply and demand chain for the Hebei South Grid to propose ideas for constructing a new power system in China and to help green energy transformation.

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This study proposes a wind farm active power dispatching (WFAPD) algorithm based on the grey incidence method,which does not rely on an accurate mathematical model of wind turbines.Based on the wind turbine start-stop data at different wind speeds,the weighting coefficients,which are the participation degrees of a variable speed system and a variable pitch system in power regulation,are obtained using the grey incidence method.The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds,and the power regulation capacity of all wind turbines is obtained.Finally,the WFAPD algorithm,which is based on the regulating capacity of each wind turbine,is compared with the wind speed weighting power dispatching (WSWPD) algorithm in MATLAB.The simulation results show that the active power fluctuation of the wind farm is smaller,the rotating speed of wind turbines is smoother,and the fatigue load of highspeed turbines is effectively reduced.

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Photovoltaic (PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently,large-scale PV power connections to the grid can threaten the stable operation of the power system.An effective method to resolve this problem is to accurately predict PV power.In this study,an innovative short-term hybrid prediction model (i.e.,HKSL) of PV power is established.The model combines K-means++,optimal similar day approach,and long short-term memory (LSTM) network.Historical power data and meteorological factors are utilized.This model searches for the best similar day based on the results of classifying weather types.Then,the data of similar day are inputted into the LSTM network to predict PV power.The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province,China.Four evaluation indices,mean absolute error,root mean square error (RMSE),normalized RMSE,and mean absolute deviation,are employed to assess the performance of the HKSL model.The RMSE of the proposed model compared with those of Elman,LSTM,HSE (hybrid model combining similar day approach and Elman),HSL (hybrid model combining similar day approach and LSTM),and HKSE (hybrid model combining K-means++,similar day approach,and LSTM) decreases by 66.73%,70.22%,65.59%,70.51%,and 18.40%,respectively.This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

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To realize the efficient transient simulation of a grid-connected power generation system based on multiple inverters,this paper proposes a hybrid simulation method integrating the models of electromagnetic transient and dynamic phasors.Based on a demonstration of the concepts and properties of dynamic phasors,the models of single-phase and three-phase inverters described by dynamic phasors are established first.Considering the numerical compatibility problem between dynamic phasors and instantaneous values,an interface scheme between dynamic phasors and instantaneous values is designed,and the efficiency and precision differences of various transformation methods are compared in detail.Finally,by utilizing MATLAB/Simulink,a hybrid simulation platform of a multi-inverter grid-connected system is built,and the efficiency and accuracy of the hybrid simulation are validated via comparison with the full electromagnetic transient simulation.

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In an integrated energy system (IES) composed of multiple subsystems,energy coupling causes an energy supply blockage or shutdown in one subsystem,thereby affecting the energy flow distribution optimization of other subsystems.The energy supply should be globally optimized during the IES energy supply restoration process to produce the highest restoration net income.Mobile emergency sources can be quickly and flexibly connected to supply energy after an energy outage to ensure a reliable supply to the system,which adds complexity to the decision.This study focuses on a powergas IES with mobile emergency sources and analyzes the coupling relationship between the gas distribution system and the power distribution system in terms of sources,networks,and loads,and the influence of mobile emergency source transportation.The influence of the transient process caused by the restoration operation of the gas distribution system on the power distribution system is also discussed.An optimization model for power-gas IES restoration was established with the objective of maximizing the net income.The coordinated restoration optimization decision-making process was also built to realize the decoupling iteration of the power-gas IES,including system status recognition,mobile emergency source dispatching optimization,gas-to-power gas flow optimization,and parallel intra-partition restoration scheme optimization for both the power and gas distribution systems.A simulation test power-gas IES consisting of an 81-node medium-voltage power distribution network,an 89-node medium-pressure gas distribution network,and four mobile emergency sources was constructed.The simulation analysis verified the efficiency of the proposed coordinated restoration optimization method.

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With the rapid development of new energy technologies,lithium batteries are widely used in the field of energy storage systems and electric vehicles.The accurate prediction for the state of health (SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries.To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications,this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm.A temperature characteristic,equal-time temperature variation (Dt_DT),is proposed by analyzing the temperature data of the battery charging section with the incremental capacity (IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model.Testing and analysis of the proposed prediction model are carried out using publicly available datasets.Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

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Accurate and seamless auxiliary services in the power market can guarantee smooth and continuous power system operation.China’s new round of power system reform has entered a critical period,and reform implementation requires comprehensive improvements in the maturity of the supporting auxiliary service market.This study reviews the development status and evolution path of the European unified power market and the US regional power market,provides experience for the development of China’s regional power market,then identifies the key influencing factors of auxiliary service trading mechanism design in regional power markets.To analyze the rationality of the auxiliary service trading evaluation index,this paper established an evaluation model for assessing regional power markets.Using combined weight optimization,the gray correlation TOPSIS method was applied to comprehensively evaluate auxiliary service trading in the regional power market.Finally,the application of the proposed evaluation method was briefly analyzed to examine four regional power markets in China and evaluate the effectiveness of current market construction in different regions and provide suggestions for future market construction.

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