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Abstract The worldwide occurrence of wind droughts challenges the balance of power systems between energy production and consumption. Expanding inter‐day energy storage serves as a strategic solution, yet optimizing its capacity depends on accurately modeling future renewable energy uncertainties to avoid over‐ or under‐investment. Existing approaches that use the historical extreme scenario set (HESS) to represent future conditions are contentious due to potential inadequacies in forecasting future extreme scenarios (ESs), including those on a decadal or centennial scale. This study addresses the issue by proposing an advanced energy storage expansion framework that leverages Extreme Value Theory (EVT) and a novel Deep Generative Model, namely the Diffusion Model. To model the extremes in a principled way, this work leverages EVT to establish a severity‐probability mapping for wind droughts, guiding the training process of the Diffusion Model. This model excels in generating ESs that accurately reflect the distribution of real‐world extremes, thereby significantly enhancing the predictive capacity of HESS. Case studies on a real‐world power system confirm the method's capacity to generate high‐quality ESs, encompassing the most severe historical wind droughts not included in the training dataset, thereby facilitating resilient energy storage expansion against unforeseen extremes.

Given large-scale modern power systems with power electronic converters, the numerical simulation with subsynchronous oscillation (SSO) faces great challenges in engineering practice due to sharply enlarged modeling scale and high computational burden. To reduce the modeling scale, network partition and equivalent becomes a vital technique in numerical simulations. Although several methods have been developed for network equivalent, a generally accepted rule for network partition is still required. This paper proposes that the system can be partitioned into three parts, i. e., the internal, the middle, and the external subsystems, in which the internal subsystem consists of all power electronic components, the middle subsystem includes those selected AC dynamic components with detailed models, and the remaining components and buses constitute the external subsystem. The external subsystem is further represented by an equivalent RLC network determined by the frequency dependent network equivalent (FDNE) method. In the proposed method, the observability index and the electrical distance index are used to identify the interface between the middle and the external subsystems. Case studies based on a modified Hydro-Quebec system are used to verify the effectiveness of the proposed method.

Abstract Frequency security is the premise of realizing the net‐zero transition. A novel assessment scheme is proposed to quantify the frequency security levels of transmission systems under different inverter‐based resources (IBRs) and their control parameter combinations. A novel system frequency dynamic model is proposed as a parametric optimization method, where the saturation of generators, energy storage systems, and renewable energy sources is incorporated as differential algebra equations. This problem is further reformulated as a mixed‐integer linear programming problem to generate a sufficient amount of data under different IBR integration and control parameters. The frequency security assessment problem is formulated as a data‐driven multivariate classification problem, which is solved by the optimal classification tree (OCT) algorithm with better interptretionability. Simulations are conducted on a transmission system. Numerical results indicate that the proposed system frequency dynamic model can capture the frequency dynamic under different IBR reinforcement plans and the OCT can realize accurate classification of the synthetic data regarding frequency security.

Abstract Integrated generation, transmission, and storage expansion planning (IGT&SP) is the cornerstone to realize low‐carbon transition considering security constraints in the long run. A novel IGT&SP planning scheme is proposed to balance the planning cost, i.e. renewable energy sources (RESs) and energy storage systems, based on the distributionally ambiguity sets. A novel decision‐dependent ambiguity set is proposed to capture the relation between the uncertainties of RES output and long‐term planning. A two‐stage risk‐averse distributionally robust optimization is formulated, where the RESs, energy storage systems, and transmission line expansion are optimized in the first stage and a unit commitment problem is proposed in the second‐stage optimization to assess the performance of the expanded system. This problem is reformulated into a two‐stage optimization problem with complete mixed‐integer recourse, where the state variable is binary. A novel enhanced Benders decomposition algorithm is proposed to solve the IGT&SEP efficiently, where the cutting planes are generated by a primal‐dual relaxation of the recourse problem. Simulations are conducted on the modified IEEE‐30 test system and modified IEEE‐118 test system. Compared with adjustable robust optimization and L1‐norm Wasserstein distance‐based distributionally robust optimization, numerical results verify the effectiveness of the proposed IGT&SP, together with the solution algorithm.

Power systems faces significant uncertainty during operation owing to the increased integration of renewable energy into power grids and the expansion of the scale of power systems, these factors lead to higher load-loss risks; therefore, realization of a fast online load-loss risk assessment is crucial to ensuring the operational safety and reliability of power systems. This paper presents an online load-loss risk assessment method for power systems based on stacking ensemble learning. First, a traditional load-loss risk assessment method based on power flow analysis was constructed to generate risk samples. The label of the sample is load-loss risk assessment index and the features are multiple operational variables of the power system. And the recursive feature elimination using cross validation (RFECV) was adopted for feature selection. Second, four different machine learning models, including support vector regression (SVR), extremely randomized trees (ET), extreme gradient boosting (XGBoost) and elastic network (EN) were used to form a stacking ensemble learning model for sample training. Moreover, to further improve the model performance, the particle swarm optimization (PSO) algorithms was used for parameter optimization. Finally, based on this model, the online load-loss risk assessment of a power system was realized. The application of the proposed method on IEEE test systems demonstrated that the proposed method was more accurate than methods based on individual machine learning models, from which the stacking was designed, while still maintaining a significant advantage in terms of runtime compared to the traditional risk assessment method.

With the tightening restrictions on carbon emission globally, the carbon intensity of the economic is receiving more attention than ever, but the results obtained by conventional methods are not accurate enough for the responsibility division. To change this situation, a power flow tracking method aimed at the distribution networks and an electricity-consumption-based carbon intensity calculation framework are proposed in this paper. Firstly, to overcome the obstacles created by the three-phase asymmetries and the scale expansion of the networks while processing power flow tracing, a phase-split calculation regime and a high-efficiency power flow tracing method are proposed, respectively. On that basis, a whole-process carbon intensity calculation framework is reported. By adding simplified models about industrial production processes, this paper gives the calculation route from the energy consumption to the carbon intensity for each user in the distribution network. Finally, we emulated with the IEEE 34 bus and 123 bus test feeders and analyze the numerical results, to prove the efficiency and effectiveness of the framework proposed.

Abstract As the physical carrier of the Energy Internet, integrated energy system (IES) is a future development trend in the energy field, and the optimal scheduling of IES for improving energy utilisation efficiency has become a hot topic. An optimal day‐ahead scheduling model of multiple IESs considering integrated demand response (IDR), cooperative game and virtual energy storage (VES) is proposed innovatively in this study to maximise the overall benefits of the cooperative alliance. IDR and VES are considered together for the first time to optimise the internal scheduling of each IES, where IDR can enhance the response potential on the demand side and VES can improve the scheduling flexibility of IES. Cooperative game theory is utilised to process the energy trading mechanism among multiple IESs and the Nash bargaining method is utilised to solve the cooperative game problem and obtain a fair and Pareto‐optimal energy trading strategy. The case study shows that the proposed model effectively improves the operating benefits and the renewable energy penetration levels of each IES.

Due to the uncertainty and fluctuation of distributed generation (DG) and load, the operation of active distribution network (ADN) is affected by multi-dimension factors which are described by massive operation scenarios. Efficient and accurate screening of severely restricted scenarios (SRSs) has become a new challenge in ADN planning. In this paper, a novel bi-level coordinated planning model which combines the short-time-scale operation problem with the long-time-scale planning problem is proposed. At the upper level, the demand response (DR) resource, an effective non-component planning resource characterized by low capacity price, high energy price, and short contract term, is co-optimized with the configuration of lines and energy storage systems (ESSs) to achieve the economic trade-off between the investment cost and the operation cost under SRSs. At the lower level, with the planning scheme obtained from the upper level, massive operation problems are optimized to minimize the daily operation cost; and the SRSs are provided to the upper level through a shadow-price-based scenario screening method, which simulates the planning information (i.e., the restricted degrees of operation scenarios) feedback process from ADN operators to ADN planners. Case studies on a 62-node distribution system in Jianshan New District, Zhejiang Province, China, illustrate the effectiveness of the proposed bi-level coordinated planning model considering DR resources and SRSs.

Along with the rapid increase in the number of electric vehicles, more and more EV charging stations tend to have charging infrastructure, rooftop photovoltaic and energy storage all together for energy saving and emission reduction. Compared with individual design for each of the components in such kind of systems, an integrated design can result in higher efficiency, increased reliability, and lower total capital cost. This paper mainly focuses on the tertiary control strategy for dynamic state operation, such as PV generation fluctuation and random arrival/leave of EVs. The tertiary control aims to achieve stable operation under dynamic states, as well as to optimize the energy flow in the station to realize maximal operational benefits with constraints such as peak/valley price of electricity, state of discharge limitation of battery, etc. In this paper, four energy management functions in tertiary control level are proposed, and their performance is verified by simulations. By using prediction of PV power and EV load in the following 72 h, a novel tertiary control logic is proposed to optimize PVC and ESC power flow by changing their droop characteristics, so that minimum operational cost for the station can be achieved. Furthermore, a sensitivity analysis is conducted for three parameters, including ES battery capacity, weather influence, and PV and EV load prediction error. The results from sensitivity analysis indicate that ES battery capacity and weather condition lead to a great impact on the operational cost of the integrated charging station, while a typical prediction error of PV power and EV load will not influence the optimization result significantly.

In recent years, renewable energy has received extensive attention due to its advantages of sustainability, economy, and environmental protection. However, with the rapid development of renewable energy, the problem of curtailment is becoming increasingly serious. Studying the calculation method and establishing a quantitative evaluation system of renewable energy accommodation capacity are important means to solve this problem. This paper comprehensively considers the factors affecting the accommodation of renewable energy, establishes a accommodation calculation model with the maximum accommodation of renewable energy as the optimization target based on the time series production simulation method, and uses the hybrid particle swarm optimization (PSO) algorithm to solve it. The model is verified with historical data such as load, photovoltaic (PV), and wind power in a certain region throughout the year. The experimental results verify the rationality of the renewable-energy accommodation-capacity model proposed in this paper and the correctness of the theoretical analysis. The calculation results have important reference and guiding significance for the operation and control of power-grid planning and dispatching.

Switched-capacitor converters (SCCs) have been developed for decades and applied successfully in many applications thanks to their merits of high power density, high efficiency, and low cost. Existing SCC evaluation methods do not provide an efficient and effective way to estimate their performance. This paper proposes a new evaluation performance method for SCCs that transforms the topology optimization issue into a more typical non-linear regression problem. The SCC topologies are described by the adjacent matrix. A CNN model is then selected to solve the regression problem. Experimental results confirm that this CNN model greatly shortens the calculation time while performing better than other machine learning algorithms.

To guarantee the reliable and efficient development of wind power generation, oscillation problems in large-scale wind power bases with Type-IV generators are investigated from the view of resonance stability in this paper. Firstly, the transfer characteristics of disturbances in Type-IV wind generators are analyzed to establish their impedance model, based on the balance principle of frequency components. Subsequently, considering the dynamic characteristics of the transmission network and the interaction among several wind farms, the resonance structure of a practical wind power base is analyzed based on the s-domain nodal admittance matrix method. Furthermore, the unstable mechanism of the resonance mode is further illustrated by the negative-resistance effect theory. Finally, the established impedance model of the Type-IV wind generator and the resonance structure analysis results of the wind power bases are verified through the time-domain electro-magnetic transient simulation in PSCAD/EMTDC. Case studies indicate that there is a certain resonance instability risk in large-scale wind power bases in a frequency range of 1–100 Hz, and the unstable resonance mode is strongly related to the negative-resistance effect and the capacitive effect of Type-IV wind generators.

The uncertainty of generation and load increases in the transmission network in the power market. Considering the transmission congestion risk caused by various uncertainties of the transmission network, the optimal operation strategies of the transmission network under various operational scenarios are decided, aiming for the maximum of social benefit for the evaluation of the degree of scenario congestion. Then, a screening method for major congestion scenario is proposed based on the shadow price theory. With the goal of maximizing the difference between the social benefits and the investment and maintenance costs of transmission lines under major congestion scenarios, a multi-stage transmission network planning model based on major congestion scenarios is proposed to determine the configuration of transmission lines in each planning stage. In this paper, the multi-stage transmission network planning is a mixed integer linear programming problem. The DC power flow model and the commercial optimization software CPLEX are applied to solve the problem to obtain the planning scheme. The improved six-node Garver power system and the simplified 25-node power system of Zhejiang Province, China are used to verify the effectiveness of the proposed multi-stage planning model.