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Abstract With the transition towards a low‐carbon energy system, renewable energy resources have been extensively developed. However, the limited ability of the power system to absorb renewable energy sources with high volatility, such as wind and solar power, has led to significant curtailment. Redundant electric energy can be converted into storable hydrogen energy through electrolysis and utilized for heating purposes. By leveraging the complementarity of diverse energy sources, optimal allocation of renewable energy can be achieved across a broader scope. However, in the current scheduling of multi‐energy systems, the efficiency of electrolyser is crudely assumed to be a constant, which results in scheduling solutions that deviate from the Pareto optimum. Therefore, a polymer electrolyte membrane electrolyser's model with non‐linear relationship between the load rate and conversion efficiency is proposed in this paper. To tackle the non‐convex optimal scheduling challenge, an adaptive chaos‐augmented particle swarm optimization algorithm is introduced, which effectively enhances computational efficiency while preventing entrapment in local optima. Case studies based on IEEE 14‐node system verified the effectiveness of the proposed method.

As digital technology continues to advance rapidly, the substantial operational electricity costs associated with the extensive deployment of globally distributed internet data centers have become a significant concern. Addressing this challenging issue is crucial for the future expansion of internet data centers. In this paper, a coordinated strategy is proposed that leverages the spatial–temporal flexibility of internet data centers and their interaction with the power system. Firstly, an energy-aware internet data center loading model is introduced that incorporates multiple coupled scheduling methods to maximize the utilization of internet data centers' flexibility potential. Secondly, the proposed internet data centers loading model is integrated into a coordinated optimization model aimed at achieving higher profits through cost reduction in response to varying electricity prices across multiple regions. To facilitate the coordinated operation of geo-graphically internet data centers, an incentive profit-sharing mechanism based on Nash Bargaining theory is designed to fairly distribute coordination profits based on the contribution ratio of each data center in coordination. Through comprehensive case studies utilizing real-world datasets, the results demonstrate that the internet data center loading model, considering multiple coupled scheduling methods, can enhance internet data centers’ flexibility potential, resulting in a significant 11.73% reduction in total cost, with the coupling relationship accounting for 5.05% of this reduction. Furthermore, the proposed profit-sharing mechanism effectively fosters coordination and enthusiasm among geographically distributed internet data centers.

Summary: Our research addresses the critical intersection of communication and power systems in the era of advanced information technologies. We highlight the strategic importance of communication base station placement, as its optimization is vital for minimizing operational disruptions in energy systems. Our study introduces a communications and power coordination planning (CPCP) model that encompasses both distributed energy resources and base stations to improve communication quality of service. This model facilitates optimal resource distribution, ensuring communication reliability over 96% and downlink transmission rates above 450 Mbps, enhancing network resilience and cost-effectiveness. Through case studies, we demonstrate CPCP’s potential to significantly reduce planning costs, particularly with increased renewable energy integration, supporting the transition to low-carbon energy systems. Our findings contribute to a comprehensive understanding of the symbiotic relationship between communication and power networks, emphasizing the need for coordinated planning in building future-proof energy infrastructures.

Summary: The increasing demand for chemical raw materials has provided opportunities for the ammonia (NH3) industry. However, little attention has been devoted to the economic feasibility of renewable-to-ammonia (RE2A). Therefore, this paper proposes a technoeconomic model to research the optimal capacity configuration and quantify the levelized cost of ammonia (LCOA) for RE2A, which is a retrofitted plant based on coal-to-ammonia (C2A). A cost model of C2A is established as a benchmark to evaluate the economic feasibility of RE2A. A case study in Inner Mongolia is adopted, which shows that the monthly NH3 output is 7–11×103t, which satisfies actual industrial production. The LCOA of RE2A is 469$/t, with investment in wind turbines accounting for 58%, which is lower than the NH3 market price (605$–650$/t). The LCOA of RE2A will equal that of C2A with a carbon tax of 47.1$/t CO2, which confirms the economic advantages of RE2A in the future.

With an increase in the electrification of end-use sectors, various resources on the demand side provide great flexibility potential for system operation, which also leads to problems such as the strong randomness of power consumption behavior, the low utilization rate of flexible resources, and difficulties in cost recovery. With the core idea of “access over ownership”, the concept of the sharing economy has gained substantial popularity in the local energy market in recent years. Thus, we provide an overview of the potential market design for the sharing economy in local energy markets (LEMs) and conduct a detailed review of research related to local energy sharing, enabling technologies, and potential practices. This paper can provide a useful reference and insights for the activation of demand-side flexibility potential. Hopefully, this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

Abstract With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM.

Abstract The increasing penetration of distributed energy resources (DERs) has led to increasing research interest in the cooperative control of multi‐prosumers in a transactive energy (TE) paradigm. While the existing literature shows that TE offers significant grid flexibility and economic benefits, few studies have addressed the incorporation of security constraints in TE. Herein, a market‐based control mechanism in real‐time markets is proposed to economically coordinate the TE among prosumers while ensuring secure system operation. Considering the dynamic characteristics of batteries and responsive demands, a model predictive control (MPC) method is used to handle the constraints between different time intervals and incorporate the following generation and consumption predictions. Owing to the computational burden and individual privacy issues, an efficient distributed algorithm is developed to solve the optimal power flow problem. The strong coupling between prosumers through power networks is removed by introducing auxiliary variables to acquire locational marginal prices (LMPs) covering energy, congestion, and loss components. Case studies based on the IEEE 33‐bus system demonstrated the efficiency and effectiveness of the proposed method and model.

Deep learning-based transient stability assessment has achieved big success in power system analyses. However, it is still unclear how much of the data is superfluous and which samples are important for training. In this work, we introduce the latest technique from the artificial intelligence community to evaluate the significance of the samples used in deep learning model for the transient stability assessment. From empirical experiments, it is found that nearly 50% of the low-significance samples can be pruned without affecting the testing performance at the early training stages, thus saving much computational time and effort. We also observe that the samples with the fault-clearing time close to the critical clearing time often have higher significance indexes, indicating that the decision boundary learned by the deep network is highly related to the transient stability boundary. This is intuitive, but to the best of our knowledge, this work is the first to analyze the connection from sample significance aspects. In addition, we combine the stability scores with the significance index to provide an auxiliary criterion for the degree of stability, indicating the distance between a sample and the stability boundary. The ultimate goal of the study is to create a tool to generate and evaluate some benchmark datasets for the power system transient stability assessment analysis, so that various algorithms can be tested in a unified and standard platform like computer vision or natural language-processing fields.

Distributed energy resources have been proven to be an effective and promising solution to enhance power system resilience and improve household-level reliability. In this paper, we propose a method to evaluate the reliability value of a photovoltaic (PV) energy system with a battery storage system (BSS) by considering the probability of grid outages causing household blackouts. Considering this reliability value, which is the economic profit and capital cost of PV+BSS, a simple formula is derived to calculate the optimal planning strategy. This strategy can provide household-level customers with a simple and straightforward expression for invested PV+BSS capacity. Case studies on 600 households located in eight zones of the US for the period of 2006 to 2015 demonstrate that adding the reliability value to economic profit allows households to invest in a larger PV+BSS and avoid loss of load caused by blackouts. Owing to the differences in blackout hours, households from the 8 zones express distinct willingness to install PV+BSS. The greater the probability of blackout, the greater revenue that household can get from the PV+BSS. The simulation example shows that the planning strategy obtained by proposed model has good economy in the actual operation and able to reduce the economic risk of power failure of the household users. This model can provide household with an easy and straightforward investment strategy of PV+BSS capacity.

In recent years, frequent occurrence of extreme weather events has caused great losses to the power system. Meanwhile, the high penetration of renewable energy has brought new challenges to the safe operation of the system. Coordination of various measures against different stages of disaster can effectively reduce the overall loss and enhance system's resilience. This paper proposed a methodology to coordinate preventive and emergency dispatch stages based on the defender–attacker–defender model, in which various dispatching measures are modelled as defenders, whereas the extreme weather event and wind power's uncertainty are modelled as attackers. Specifically, a two‐stage three‐layer robust optimal dispatching model is established, in which conventional generator's output, wind farm's grid‐connected planning, and load shedding are taken as the main dispatching measures. To solve this model, the column and constraint generation algorithm is used to transform the three‐layer optimization model into a mixed integer linear problem. The effectiveness of the proposed model is verified in the modified IEEE RTS‐79 test system, and the results show that the coordination of preventive and emergency dispatch can effectively reduce the load outage cost under extreme weather, thus enhancing the ability of power system to cope with extreme scenarios. [ABSTRACT FROM AUTHOR]

Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels, the proportion of renewable energy in power systems is gradually increasing across the world. Accordingly, the capacity of power systems to accommodate renewable energy must be improved. However, integration of a large amount of renewable energy into power grids may result in network congestion. Hence, in this study, optimal transmission switching (OTS) is considered as an important method of accommodating renewable energy. It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units, forming an interactive mode of coordinated operation of source and network. A stochastic unit commitment model considering deep peak regulation and OTS is established, and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively. The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

Abstract With the rapid development of vehicle‐to‐grid (V2G) technology, the use of the internet of things and advanced information technology to achieve the optimal dispatch of large‐scale plug‐in electric vehicles (PEVs) has become an important focus of scientific research and engineering practice worldwide. In order to achieve a balance between mass distributed energy resources and the secure and economic operation of the power grid, the authors propose an optimal market bidding strategy for a virtual power plant considering the feasible region of V2G. A detailed battery model considering the V2G mode of PEVs is established. A two‐stage stochastic optimisation model for the virtual power plant considering massive volumes of PEVs is built, taking into account the day‐ahead market revenue and the costs brought about by real‐time market deviations. Three bidding strategies—PEVs charging immediately after arrival, without V2G, and considering V2G—are compared using case studies to demonstrate the efficiency and effectiveness of the proposed strategy. Through the complete interaction between PEVs and the virtual power plant, this V2G‐based strategy can coordinate distributed energy resources, promote the accommodation of renewable energy, and significantly improve the economy of the virtual power plant operation.

China’s electricity market is undergoing a rapid development stage from plan to market and will exist in plan and market models for a long time. Based on the status quo of China’s power plan and market in-depth, this paper has studied and put forward a kind of market transaction mechanism adapted to the plan and market models of China. The mechanism arranges a priority generation plan first to match the priority electricity plan. Especially for renewable energy generation output greater than planned value, it carries out priority arrangement of renewable energy generation to guarantee its reliable consumption. Then medium- and long-term market transactions and incremental spot market transactions are carried out. This paper detailed design operational processes such as priority plan, medium- and long-term, spot (i.e., day-ahead, intraday, and real time), auxiliary services, and balance stages. Finally, based on actual data of the provincial electricity market, the calculation and analysis are carried out to verify that the method proposed in this paper can effectively reduce market imbalance and promote renewable energy consumption, which has a certain practical value for China’s electricity market development.

As renewable energy resources increasingly penetrate the electric grid, the inertia capability of power systems has become a developmental bottleneck. Nevertheless, the importance of primary frequency response (PFR) when making generation-expansion plans has been largely ignored. In this paper, we propose an optimal generation-expansion planning framework for wind and thermal power plants that takes PFR into account. The model is based on the frequency equivalent model. It includes investment, startup/shutdown, and typical operating costs for both thermal and renewable generators. The linearization constraints of PFR are derived theoretically. Case studies based on the modified IEEE 39-bus system demonstrate the efficiency and effectiveness of the proposed method. Compared with methods that ignore PFR, the method proposed in this paper can effectively reduce the cost of the entire planning and operation cycle, improving the accommodation rate of renewable energy.

Abstract This article explores and establishes comprehensive evaluation index system of wind power accommodation ability considering microscopic index and macroscopic index, and the index system includes conventional evaluation indexes such as forecast deviation, simultaneity factor and anti-peak rate, also newly introduced evaluation indexes such as installed capacity, power adequacy and accommodation space. Bayesian weight modified method is used for solving index weights of 8 wind power accommodation indexes. The paper puts forward a comprehensive evaluation model of wind power accommodation ability based on improved radar chart method, and this model changes traditional radar chart fan-shaped sector to quadrilateral evaluation region, and increasing angle bisector can solve the problem that evaluation results are not unique. It constructs new area and perimeter vectors of radar chart, which make the evaluation results give consideration to level of aggregation and balance degree of evaluation objectives, and case study results show that this model has a certain practical value.

Abstract At present, the problem of abandoning wind and PV power in “Three North” region of China is particularly significant, and how to alleviate this problem has become the focus of universal attention. Calculation of renewable energy accommodation capacity is the basis to solve the problem of abandoning wind and PV power. Main problems of Chinese renewable energy accommodation is analyzed from power supply, power grid and load side aspects, and it focuses on the effect of inter-provincial tie-line to renewable energy accommodation capacity. At present, the inter-provincial tie-line utilization level is limited, which affected renewable energy accommodation to a certain extent. Based on the sequential production simulation model, a new kind of renewable energy accommodation capacity model is put forward considering the utilization level of inter-provincial tie-line. According to different system stability constraints and different electricity constraints of inter-provincial tie-line, 4 schemes are designed for comparative analysis, and the evaluation model is used to calculate renewable energy accommodation capacity of “Three North” region of China in 2020. Example analysis results verify validity of the model that releasing curve constraints, electricity constraints and stability constraints in turn can significantly enhance renewable energy accommodation capacity through effective use of inter-provincial tie-line transmission capacity. Research work in this paper can provide strong support for the planning and scheduling control of power grid.

According to the demand of sustainable development and low-carbon electricity, it is important to develop clean resources and optimize scheduling generation mix. Firstly, a novel method for probabilistic production simulation for wind power integrated power systems is proposed based on universal generating function (UGF), which completes the production simulation with the chronological wind power and load demand. Secondly, multiple-period multiple-state wind power model and multiple-state thermal unit power model are adopted, and both thermal power and wind power are coordinately scheduled by the comprehensive cost including economic cost and environmental cost. Furthermore, the accommodation and curtailment of wind power is synergistically considered according to the available regulation capability of conventional generators in operation. Finally, the proposed method is verified and compared with conventional convolution method in the improved IEEE-RTS 79 system.

Wind power in China has experienced fast development in recent years. However, areas rich in wind power resources are often far away from loads centers, which leads to weak connection between wind turbines and power grid. When a grid fault occurs, new transient characteristics in weak grid integrated with doubly-fed induction generators (DFIGs) may present, such as voltage phase angle jump. Current control strategies for wind turbine with strong grid connection are hard to be adapted under weak gird connection. This paper explores the transient characteristics of DFIGs under voltage phase angle jump through analyzing the operation and control characteristics of DFIGs connected into weak grid when the voltage phase angle jumps. Fault ride through (FRT) control strategy of DFIGs based on adaptive phase-locked loop is proposed to adapt weak grid condition. The reference frame of the proposed strategy will be changed in real-time to track the operation condition of DFIGs according to the terminal voltage, and different phase tracking method is adopted during the grid fault. Field data analysis and time domain simulation are carried out. The results show that voltage phase angle jumps when a grid fault occurs, which weakens the FRT capability of DFIGs, and the proposed FRT control strategy can optimize transient characteristics of DFIGs, and improve the FRT capability of DFIGs.