This paper put forward an optimization model to study the cost changes caused by the allocation of additional costs when new energy stations participate in market transactions under the joint clearing of the electricity market and the auxiliary service market. First, the errors caused by different new energy stations during operation are calculated to allocate responsibilities for each station, ensuring fair cost allocation. Then, an upper-and-lower layer model is constructed, where the upper-level model aims to minimize the total cost of the power system under the joint clearing of the electricity market and the auxiliary service market, and the lower layer model optimizes the new energy bidding strategy. Through the collaborative optimization of the upper-and-lower layer model, the economic efficiency and reliability of the power system and new energy are improved. Case studies verify the effectiveness and feasibility of the model.

This paper develops an optimal scheduling model for a hydro-wind-solar integrated energy system considering the uncertainties in wind and solar power (WSP) generation. First, the Copula method is utilized to obtain probabilistic WSP output data for typical days. Then, the intra-day complementary characteristics of WSP are analyzed. Additionally, an optimal scheduling model is established to minimize residual load fluctuations and incorporate constraints such as water levels, flow rates, and wind-solar output. Furthermore, the model is solved by Gurobi solver, and the optimal scheduling schemes for the typical days are obtained. Finally, to validate the practicality and effectiveness of the proposed model, Xiaowan hydropower station is applied for case study.

With the increasing integration of renewable energy into power grids, integrated energy systems (IES) have gained global attention. However, the stochastic and uncertain characteristics of wind and solar challenge the stable grid operation significantly. To address these issues, this article proposes an optimal scheduling model for wind-solar-hydrogen IES, incorporating prediction uncertainty and demand response (DR). The equilibrium decision theory framework, combined with frequency enhanced decomposed transformer, interval prediction and decision, is proposed to quantify the uncertainties and determine the action plans. Then, an optimal scheduling model is designed to maximize economic revenue and minimize pollution emissions (PE), which is solved using multiobjective elephant clan optimization algorithm. Furthermore, DR strategy is integrated to enhance system flexibility and stability. Empirical results demonstrate that increased uncertainty leads to more conservative scheduling, improving system robustness. Additionally, DR boosts ER by over 3.50% and reduces PE by more than 3.20% across various scenarios.

In order to improve the reliability of the operation of the distribution network system, it is necessary to accurately establish a failure probability model for distribution network equipment. This article is based on the finite element software COMSOL to simulate typical operating scenarios of distribution network equipment, quantitatively analyze the impact of different fault factors on distribution network equipment, and construct a probability model of equipment failure under the current operating state. Studying the probability of equipment failure in the distribution network can not only provide support for the risk assessment of system operation, but also guide the online warning of equipment, avoid warning equipment, and arrange low-risk operation modes. These can effectively reduce the number of distribution network failures and improve the reliability of power supply in the distribution network.

This paper proposes a power system restoration method and system that takes social functions and load starting characteristics into account. Firstly, based on the absolute power outage duration, the method calculates the power supply restoration importance parameters for raw material preservation, production operation, and load starting characteristics. Secondly, it introduces social function correction coefficients such as the production function of equipment groups, the probability of electrical energy substitution, and the multi-energy coordinated restoration process. Furthermore, it dynamically corrects the power supply restoration importance of users and nodes, and generates a restoration strategy based on this. Through case verification, the proposed method can effectively improve the net benefit of the restoration process, with the benefit increasing by approximately 5.51% compared with the traditional fixed importance method, which demonstrates its advantages in refined energy management and optimal utilization of resources.

Frequent occurrences of extreme temperature events, such as heat waves and cold waves, can significantly reduce the output of renewable energy power sources while simultaneously increasing load demand. This exacerbates the imbalance between supply and demand within urban power grids. To address this issue, this paper investigates the influence mechanisms of heat waves and cold waves on the generation, and load sides of urban power grids. By comprehensively considering both conventional scenarios and extreme temperature events, a collaborative expansion planning model for distributed renewable energy sources and pumped storage power stations is proposed. The optimal planning capacity is determined through an iterative solution of a bi-level model, and the effectiveness and economic feasibility of the proposed planning scheme are validated through case studies.

The power grid, as the hub connecting the power supply and consumption sides, plays an important role in achieving carbon neutrality in China. In emerging carbon markets, assessing the investment benefits of power-grid enterprises is essential. Thus, studying the impact of the carbon market on the investment and operation of power- grid enterprises is key to ensuring their efficient operation. Notably, few studies have examined the interaction between the carbon and electricity markets using system dynamics models, highlighting a research gap in this area. This study investigates the impact of the carbon market on the investment of power-grid enterprises using a novel evaluation system based on a system dynamics model that considers carbon-emissions from an established carbon-emission accounting model. First, an index system for benefit evaluation was constructed from six aspects: financing ability, economic benefit, reliability, social responsibility, user satisfaction, and carbon-emissions. A system dynamics model was then developed to reflect the causal feedback relationship between the impact of the carbon market on the investment and operation of power-grid enterprises. The simulation results of a provincial power-grid enterprise analyze comprehensive investment evaluation benefits over a 10-year period and the impact of carbon emissions on the investment and operation of power-grid enterprises. This study provides guidelines for the benign development of power-grid enterprises within the context of the carbon market.

Biogas is a renewable and clean energy source that plays an important role in the current environment of low- carbon transition. If high-content CO2 in biogas can be separated, transformed, and utilized, it not only realizes high-value utilization of biogas but also promotes carbon reduction in the biogas field. To improve the combustion stability of biogas, an inhomogeneous, partially premixed stratified (IPPS) combustion model was adopted in this study. The thermal flame structure and stability were investigated for a wide range of mixture inhomogeneities, turbulence levels, CO2 concentrations, air-to-fuel velocity ratios, and combustion energies in a concentric flow slot burner (CFSB). A fine-wire thermocouple is used to resolve the thermal flame structure. The flame size was reduced by increasing the CO2 concentration and the flames became lighter blue. The flame temperature also decreased with increase in CO2 concentration. Flame stability was reduced by increasing the CO2 concentration. However, at a certain level of mixture inhomogeneity, the concentration of CO2 in the IPPS mode did not affect the stability. Accordingly, the IPPS mode of combustion should be suitable for the combustion and stabilization of biogas. This should support the design of highly stabilized biogas turbulent flames independent of CO2 concentration. The data show that the lower stability conditions are partially due to the change in fuel combustion energy, which is characterized by the Wobbe index (WI). In addition, at a certain level of mixture inhomogeneity, the effect of the WI on flame stability becomes dominant.

To expedite the restoration of high-penetration new energy power systems following outages， a partitioning method for parallel restoration is proposed. This method takes into account the characteristics of new energy output. Multi-time-step series scenarios are generated by comprehensively considering spatiotemporal correlation of the output of new energy power supply. An expected multi-time-step series scenario of new energy power supply， considering occurrence probability of each series scenario， is constructed. Based on the expected multi-time-step series scenario of the new energy power supply， the available power of each charged domain is determined， and each charged domain is partitioned accordingly. In cases where there is overlap in partitioning， adjustments are made for objects to be restored in the overlapping areas. Factors considered include time inertia constants， ramp rate， adjustment rate， expected additional generation capacity， and the restoration value of the de-energized branches. Finally， using the IEEE118-bus system as a basis， it is demonstrated that the proposed partitioning method for parallel restoration can accelerate the grid restoration and reduce the power outage losses.

In recent years, the penetration of solar and wind power has rapidly increased to construct renewable energy-dominated power systems (RPSs). On this basis, the forecasting errors of renewable generation power have negative effects on the operation of the power system. However, traditional scheduling methods are overly dependent on the generation-side dispatchable resources and lack uncertainty modeling strategies, so they are inadequate to tackle this problem. In this case, it is necessary to enhance the flexibility of the RPS by both mining the load-side dispatchable resources and improving the decision-making model under uncertainty during the energy and reserve co-dispatch. In this paper, due to the great potential in facilitating the RPS regulation, the demand response (DR) model of fused magnesium load (FML) is first established to enable the deeper interaction between the load side and the whole RPS. Then, based on the principal component analysis and clustering algorithm, an improved typical scenario set generation method is proposed to obtain a much less conservative model of the spatiotemporally correlated uncertainty. On this basis, a two-stage distributionally robust optimization model of the energy and reserve co-dispatch is developed for the RPS considering the DR of FML. Finally, the proposed method is validated by numerical tests. The results show that the costs of day-ahead dispatch and re-dispatch are significantly decreased by using the improved typical scenario set and considering the DR of FML in regulation, which enhances the operation economy while maintaining the high reliability and safety of the RPS.

With the advancement of new power system construction, many distributed resources have brought challenges to power grid dispatching. As a valid governance method of distributed resources, virtual power plant (VPP) has attracted much attention. Considering that the flexible adjustment of the external output characteristics of the polymerized distributed resources of VPP can alleviate system scheduling pressure to a certain extent, and the reasonable regulation of the internal distributed resources can reduce the operation cost of VPP, a VPP polymerization control strategy for system scheduling requirements considering the operation cost of VPP is established, the efficacy of the strategy is demonstrated by an arithmetic example analysis.

The continuous penetration of renewable energy resources has led to the proliferation of interconnected multienergy microgrids due to the economic benefits brought through energy sharing. Meanwhile, different types of energy storage are being integrated into the multi-energy systems to provide flexibility. Devices in the system are divided into two categories in terms of their ability in rescheduling. From the perspective of power and capacity, a two-stage model of hybrid energy storage participating in pre-and re-dispatch is proposed to describe various conversion modes under two-time scales. Besides, the rescheduling ability over multiple consecutive periods is also guaranteed. In this paper, a distributed roust optimization is built to minimize the system's day-ahead operation costs, in which the upper level-energy sharing network is regarded as a kind of virtual energy storage. The microgrid operator can choose to work in the regular mode or demand response mode at the lower level. The combined analytical target cascading and column-and-constraint generation method is implemented to achieve convergence within 14 times. A case study verifies the advantages and effectiveness of the proposed method, which improves the system economy by 2.8 %.

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 power- gas 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.

In case of fault of both cyber system and physical system of distribution network,the coordination between the two systems may achieve a better recovery effect. A kind of fault coordination recovery method of cyber physical system of distribution network is proposed by way of comprehensive considering such factors as gain,risk and control cost in the process of recovery. Firstly,adjacent matrix of cyber physical system is set up according in accordance with topology structure of distribution network in combination with related knowledge of graph theory. Secondly,by mapping the recovery of cyber system to the recovery of physical system,an optimization model of the coordinated recovery of cyber physical system based on the monetary dimension is set up with the goal of maximizing the net recovery gain. On this basis,a coordinated recovery strategy of cyber physical system is proposed. Finally,an example is given to compare different restoration schemes,and the rationality and effectiveness of the proposed recovery method are verified.

After a large number of new energy sources are connected, how to realize the rapid networking of various power sources in the recovery process to give full play to the load recovery capability of the network island is the key problem for the power system to quickly restore power supply and reduce power outage losses. Firstly, a pre-decision method for master-slave network of power supply for power grid restoration is proposed, and the necessary conditions and ranking indexes of power supply network operation are established. Then, an optimization model of power system restoration control with the goal of maximizing the net benefit of restoration is established, which comprehensively considers the load recovery benefit of the overall restoration scheme, the cost of network power supply and the risk of network restoration caused by factors such as the uncertainty of new energy power output and the time-varying fault probability of branches. Finally, based on the IEEE 69 bus system, the effectiveness of the proposed strategy is verified, and the networking scheme with economy, reliability and rapidity is effectively selected.

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets. The carbon emission cost (CEC) of coal-fired units becomes part of the power generation cost through market coupling. The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market. Study of carbon–electricity market interaction and CEC calculations is still in its initial stages. This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets. A long-period interactive operation simulation mechanism for the carbon–electricity market is established, and operation and trading models of the carbon emissions trading market and electric power market are established. A daily rolling estimation method for the CEC of coal- fired units is proposed, along with the CEC per unit electric quantity of the coal-fired units. The feasibility and effectiveness of the proposed method are verified through an example simulation, and the factors influencing the CEC are analyzed.

为解决含移动应急电源(mobile emergency generators,MEG)的主动配电网故障恢复问题,建立了配电网恢复效益函数,基于启发式算法,给出了一种配电网恢复效益最大化控制策略.为验证所提控制策略的可行性,以改进的IEEE 70 节点系统为例,开展了仿真对比分析与算法验证,仿真结果表明,采用提出的MEG接入优化策略,MEG恢复效益指标优化了每个MEG接入点接入的MEG台数;采用基于启发式算法的配电网恢复效益最大化控制策略,恢复负荷和恢复路径指标提高了配电网恢复效益.

With the rapid development of demand-side management, battery energy storage is considered to be an important way to promote the flexibility of the user-side system. In this paper, a Stackelberg game (SG) based robust optimization for user-side energy storage configuration and basic electricity price decisions is proposed. Firstly, this paper put forward a two-stage energy management framework considering the interactive relationship between the supplier-side system and the user-side system. Secondly, based on the two-part electricity price mechanism, a bi-level optimal sizing of user-side energy storage is established in which robust dispatching is considered to deal with the uncertainty of renewable energy. Thus, a three-layer optimization model of “pricing on the power supply side–basic scenario configuration on the user side–worst-case scenario scheduling on the user side” is formulated. Through relaxing the state variables of energy storage in the configuration and scheduling models and combining Karush-Kuhn-Tucher conditions, the user-side model is transformed into a single-layer problem. A distributed algorithm based on the method of bisection is used to solve the two-stage SG problem. The simulation results demonstrate the basic electricity price and energy storage configuration suggestions and prove the superiority of the proposed method.

Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3%, 18.1%, and 18.9% at 30%, 40%, and 50% PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.

A two-layer multi-time scale stochastic production simulation framework is constructed to account for the long-term contract electricity quantity of ultra-high voltage direct current (UHVDC) transmission. On the upper layer, based on the characteristics of load demand and renewable energy output extracted from the historical operating data, monthly and daily production simulation models are carried out considering the seasonal characteristics of hydropower during a high-water period and low-water period to optimize the distribution of contract electric quantity sending through UHVDC transmission in the target year or month. According to the DC transmission electric quantity optimized by the daily production simulation in the upper layer, together with the forecast scenario, the lower layer of the framework provides the optimization of day-ahead scheduling and intra-day rolling dispatch in the implementation process. The day-ahead dispatch optimization makes full use of the adjustment capability of transmission and optimizes the DC transmission electric quantity correction. Its compensation is based on the result of the daily production simulation, then the correction will be returned to the upper layer to restart the optimization of the remaining UHVDC contract electric quantity of the subsequent period and its distribution plan. Combined with the day-ahead DC transmission plan, the intra-day rolling optimization is carried out to adjust the output of the unit using more accurate forecasting scenarios. The distributionally robust optimization model is used in the lower layer to convert an uncertain problem into a deterministic quadratically constrained quadratic programming (QCQP) problem according to the form of an uncertain distribution set. Then the QCQP problem is further converted into a linear programming (LP) problem by using the reformulation linearization technique (RLT). A test system with the energy composition and distribution referring to a real provincial power grid in northwest China is established for verification. The results show that the proposed method can effectively improve the economics of system operation and the accommodation of renewable energy based on ensuring security.