The declining level of synchronous inertia in renewable-dominated power systems has made frequency security a critical issue in day-ahead scheduling. This paper develops an adaptive frequency-constrained unit commitment framework in which wind power plants provide time-varying virtual inertia according to the commitment-driven shortage of synchronous support. The proposed strategy activates wind-based frequency support only during periods in which the synchronous commitment pattern is insufficient to satisfy prescribed frequency-security requirements. A scheduling model is formulated by jointly coordinating thermal commitment, wind reserve, equivalent inertia, and aggregate frequency-security metrics. Comparative studies based on cold-climate load scenario show that the adaptive strategy avoids over-conservative support allocation and achieves a more effective balance between economic operation and frequency security.

Carbon capture and storage (CCS) technology plays a critical role in reducing carbon emissions from coal-fired power plants (CFPPs) and offers significant potential for flexible operation to support power system regulation. This paper presents a decision-making model for retrofitting CFPPs with CCS, incorporating the operational flexibility of CCS equipment, which contrasts with previous studies that primarily view CCS as a carbon reduction technology. The model aims to unlock the full potential of CCS technology in the transformation of coal power. Simulation analyses across various scenarios validate the model’s feasibility and effectiveness. The results indicate that flexible carbon capture technology can help coal power save costs, enhance flexibility, and achieve decarbonization, significantly improving the low-carbon economic viability of the power system. When combined with other flexible resources, the economic benefits of flexible CCS technology become even more pronounced. Additionally, under fluctuations in carbon trading prices and fuel prices, as well as carbon emission constraints, flexible CCS retrofitting can reduce the risks associated with policy and market changes. The model developed in this paper provides theoretical support and practical basis for the low-carbon transformation of coal power, contributing to the rational planning and efficient operation of low-carbon electricity systems.

When quality-quantity regulation is adopted in heating systems, adding bypass pipes with electric valves in heating networks can increase the number of control variables, which can improve operation flexibility of integrated electricity and heat energy systems (IEHS). An optimal scheduling model of IEHS is established in this paper and the impact of bypass pipes regulation on renewable power consumption is studied. A sequential solution method combining linear optimization and nonlinear optimization methods is established to handle the non-convex and nonlinear characteristics of the scheduling model. Finally, the effectiveness of the method is verified in a case study. Results show that bypass pipes in heating systems can promote renewable power consumption.

With the increasing coupling between electric and gas systems, integrated electric-gas system dispatching has emerged as a critical research frontier. By harnessing operational flexibility across source, network, and load sectors of gas systems, additional accommodation space for wind power integration in power systems can be created. However, accurate forecasting of wind power output remains challenging due to its inherent stochasticity, and its probabilistic distribution is often unavailable a priori. Conventional robust dispatch methods tend to exhibit excessive conservatism when relying on predefined uncertainty sets. To address these limitations, this paper proposes a data-driven adaptive robust dispatching framework for integrated electric-gas systems. By synergistically integrating stochastic programming and robust optimization paradigms, the proposed method leverages historical data to construct worst-case probability distribution scenarios, achieving an optimal trade-off between economic efficiency and operational conservatism. Simulation experiments on a coupled 6-bus power network and 6-node gas network validate the efficacy of the proposed approach in enhancing wind power accommodation while maintaining system reliability.

The energy storage system (ESS) in the wind-storage co-generation system typically focuses on tracking planned output or smoothing the fluctuations of wind power grid-connected power. This approach often leads to low utilization rates and poor economic efficiency. To address this issue, we propose an economic schedule strategy for the wind-storage system considering multi-scenario synergy. In this strategy, ESS is prioritized for compensating prediction errors and smoothing fluctuations while also performing peak and valley arbitrage to enhance operational economics. Then, the strategy is applied in an economic scheduling model based on rolling optimization to optimize the allocation of storage power across multi-scenario and to obtain the pending power of storage. The model considers the inherent uncertainty of wind power and the constraints of the grid and ESS. Finally, we revise the pending power of storage by considering the fluctuations in wind farm output after compensation, resulting in the optimal time-series power of ESS. In simulations over two typical days, this strategy significantly outperforms other methods for wind power compensation, yielding net benefits from ESS of approximately 33,000 yuan and 62,000 yuan, respectively, resulting in substantial economic benefits.

This research proposes a robust dispatch technique that incorporates demand response within a carbon trading framework to attain environmental and financial optimization in integrated energy systems. First, this paper develops an efficient dispatch model utilizing CHP to enhance the adaptability of the integrated energy system. Second, a refined demand response method is constructed based on the energy delivery methods’ substitutability and the cost sensitivity matrix, taking into account two different types of loads: thermal and electric power loads. Subsequently, a hierarchical carbon trading mechanism is implemented to regulate the expenses and effects of emission trading of the system. Ultimately, the goal value aims to reduce the aggregate expense., including energy procurement, emission dealings, upkeep, and administration, and offsetting. After reforming the initial issue into a mixed-integer linear program solution, we use CPLEX to address it. (Abstract)

Recent years have witnessed significant scientific advancements and applications in the fields of Environmentally Extended Input-Output (EEIO) models and complex network analysis, often merging innovatively. Despite this progress, comprehensive studies on the research patterns, trends, and under-explored topics in each field individually, and at their intersection, remain sparse. Using the Word2vec model, this study addresses these gaps to analyze 172,509 papers published from 2000 to 2023. Our results show that the EEIO domain focuses primarily on life cycle assessment (LCA), carbon footprints, environmental impacts, sustainability, and greenhouse gas emissions. In contrast, complex network analysis is predominantly applied in biology, bioinformatics, medicine, and computer science. We identified 230 emerging and rising keywords in these fields, outnumbering the 103 keywords in decline, with 429 keywords remaining stable. Cross-integrated studies highlight the importance of LCA in the EEIO field and social network analysis in the complex network field. To enhance the cross-pollination of these disciplines, we recommend increasing the focus on water footprint analysis, renewable energy studies, and community detection techniques alongside other dynamic topics. This study aims to stimulate future research and innovation at the convergence of EEIO models and complex network analysis.

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

Abstract China encourages the development of user-side distributed new energy, and the rural user-side distributed “new energy + energy storage” system is an important measure to promote the “carbon peaking and carbon neutrality goals” and rural modernization construction. Based on the principle of “Maximum self-use” and “Surplus power is fed to the grid”, distributed new energy can participate in the electricity market, carbon market, and green certificate market to gain profits. This article takes the rural distributed wind power-photovoltaics-energy storage (WP-PV-ES) joint system as the research objective and proposes a two-layer optimization model for its participation in the electricity, carbon, and green certificate market. It solves the decision-making of energy storage unit charging and discharging during the scheduling cycle when the system participates in the electricity market and the distribution of grid electricity used by the system to verify green certificates and CCER. The energy loss caused by the charging and discharging of energy storage units is reflected in the total grid electricity calculation of new energy. The calculation results show that the revenue from participating in CCER transactions for wind power and photovoltaic units is lower than that in green certificate market units. New energy power stations can obtain the maximum revenue by using all the online electricity for green certificate verification.

After the high proportion of new energy access, the distribution network signal presents new characteristics of high noise, wide frequency band and obvious dynamic processes, which puts forward higher requirements for broadband measurement algorithm. Therefore, a dynamic broadband measurement algorithm based on all-phase Fourier transform (ApFFT) and improved Taylor Fourier transform (TFT) is proposed. Firstly, ApFFT analysis is applied to the sampled signal, and the phase spectrum is used to judge the existence of dense signals. For the part containing dense signals, the synthesized dynamic phasor is calculated by using the improved TFT with reduced calculation amount. The static and dynamic simulation results show that the algorithm can realize high-precision measurement of broadband signals under high noise.

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit’s charging and discharging processes during system operation.

In order to improve the utilization of energy storage, to explore the value of energy storage utilization, and to study the utilization of energy storage. In this paper, a shared energy storage optimization scheduling method based on wind power uncertainty is proposed. Firstly, the scenario generation method of Latin hypercubic sampling and Cholesky decomposition is used to quantify the wind power output, and the uncertainty output is converted into deterministic output; then the economic operation is taken as the optimal scheduling objective, and environmental benefits such as carbon capture and grid constraints are considered to carry out the optimized scheduling of shared energy storage; finally, the validity of the method is verified through the scheduling of the 6-node arithmetic example system, and the utilization of the energy storage is analyzed. Finally, the effectiveness of the method is verified by scheduling in a 6-node arithmetic system, and energy storage utilization is analyzed.

Virtual power plants (VPPs) integrate diverse energy resources using advanced communication technologies and intelligent control strategies. This integration enhances the utilization and efficiency of distributed generation. This paper explores the incorporation of VPPs into load frequency control (LFC) systems. It includes an analysis of VPP-aggregated resources’ frequency regulation characteristics and a VPP-inclusive LFC model. Additionally, a decentralized automatic generation control strategy is proposed to distribute power outputs effectively, enabling swift grid frequency adjustments. This study uses MATLAB simulations to demonstrate the benefits and efficacy of VPPs in LFC, underscoring their role in advancing grid management and stability.

With the continuous construction of high-voltage direct-current (HVDC) transmission projects, higher requirements are placed on the frequency security of the regional power systems. In order to ensure the frequency security of regional power systems, this paper proposes a transient frequency analytical method considering the emergency frequency control (EFC). Firstly, the aggregated system frequency response (SFR) model is constructed by reducing the order of the generator governor equation, the EFC equation, and the load model equation. Then, based on the aggregation model, the analytical solution of the transient frequency nadir of the regional power system is derived. The model can quickly and accurately calculate the transient frequency nadir of the regional power system considering EFC after the HVDC block fault. Finally, based on an actual regional power system model, the accuracy and applicability of the proposed method under HVDC block fault are verified.

This paper addresses the shared energy storage siting and sizing problem, considering grid constraints based on scenario generation techniques. In the context of high penetration of renewable energy, the power system faces practical challenges such as increased uncertainty and scarcity of flexible resources. To tackle these issues, a two-layer optimization model is proposed in this study. It utilizes scenario generation techniques to obtain possible scenarios for renewable energy and load outputs, and incorporates grid constraints for economic dispatch. Through iterative optimization, an optimal shared energy storage siting and sizing solution that meets the requirements of the power grid is obtained. Additionally, the paper discusses the impact on the accommodation capacity of renewable energy and proposes countermeasures in system economic dispatch. However, there are still limitations in uncertainty estimation, energy storage planning, and investment cost calculations in this paper. Furthermore, further enhancement is needed in terms of quantification theories supporting the economic evaluation of shared energy storage siting and sizing. Overall, this research aims to provide an optimization approach for the shared energy storage siting and sizing problem, incorporating scenario generation techniques and grid constraints, to promote stable operation and economic development of power systems with high penetration of renewable energy.

With the continuous increase in large-scale distributed photovoltaic (PV) integration, issues related to voltage stability and power quality in distribution networks have become increasingly prominent. To address this problem, this paper proposes a multi-time-scale reactive power optimization strategy for voltage regulation based on distributed photovoltaics. The strategy combines day-ahead and intra-day optimization: day-ahead optimization primarily focuses on coordinating the control of capacitors (CB), static var generators (SVG), and on-load tap changers (OLTC), while intra-day optimization dynamically adjusts the reactive power outputs of distributed PVs and SVGs. By constructing multi-objective functions that include network loss minimization, voltage deviation, and PV accommodation, and by balancing these objectives using contribution-based approaches, the overall operational efficiency of PV-integrated distribution networks is improved. Simulation results demonstrate that the proposed strategy effectively suppresses voltage fluctuations, reduces network losses, and enhances the accommodation of distributed PV resources. Meanwhile, it ensures voltage quality in the distribution network and facilitates efficient utilization of distributed PV resources, contributing to the stability and reliable operation of the distribution system.

Transformers hold great significance for power systems. Regarding the detection of transformer operating states, automatic defect recognition is of paramount importance to the safe operation of the power grid. This paper studies and proposes a transformer defect recognition method based on random forest and convolutional neural networks. The algorithm in this paper first detects the operating state of the transformer from the perspective of operating data through the state assessment method based on random forest and then identifies the appearance defects of the transformer through the convolutional neural network. Finally, the two algorithms jointly determine the operating state and defect types of the transformer. Through experimental analysis and verification, the accuracy of this method in identifying the operating state and defect type of the transformer exceeds 90%, which is helpful to improve the intelligence and automation level of transformer operation and maintenance service.

Abstract Environmental degradation and energy security issues require our country to accelerate the transition of its energy consumption structure towards decarbonization. Clarifying the impact of the pilot trading system of peak energy use rights products on the low-carbon transformation of energy consumption structure and the path of its effect is an important policy revelation for the construction of market-based environmental rights and interests regulation. In this paper, we study the implementation effect of the pilot provinces of peak energy use right product trading from the perspective of carbon emission reduction benefit and analyze the carbon emission reduction benefit generated by the peak energy use right product according to the pilot trading situation.

Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a renewable scenario generation method based on the hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) is used to divide the original data into linear and fluctuant parts according to the length of time scales. The HGAVCL is designed to optimally divide the linear part into different time sections. Additionally, each time section is described by the autoregressive integrated moving average (ARIMA) model. With the consideration of temporal correlation, the Copula joint probability density function is established to model the fluctuant part. Based on the attained ARIMA model and joint probability density function, a number of data are generated by the Monte Carlo method, and the time autocorrelation, average offset rate, and climbing similarity indexes are established to assess the data quality of generated scenarios. A case study is conducted to verify the effectiveness of the proposed approach. The calculated time autocorrelation, average offset rate, and climbing similarity are 0.0515, 0.0396, and 0.9035, respectively, which shows the superior performance of the proposed approach.

Safety assessment is of great significance in the development and promotion of integrated energy system. In order to make the traditional safety assessment can be better applied to large-scale systems, this paper optimizes the safety assessment process from the aspects of the generation of expected fault sets and the solution of multi-energy flow power flow. Implement improvements to the security assessment process.