Abstract:With the advancement of multi-energy complementary policies, diverse energy sources with varying qualities are integrated into CCHP-type microgrids, but current dispatch strategies based on the first law of thermodynamics fail to distinguish energy quality differences and lack a quantitative index system, limiting the utilization of their useful work capacity. An exergy theory-based energy analysis method is introduced, a dispatch model considering new energy integration is constructed, an energy quality index system is established, and an exergy-driven dispatch strategy is proposed to address this issue. Finally, a two-layer optimization model for configuration and dispatch of multi-energy coupled microgrids is developed based on exergy theory to achieve collaborative optimization and overcome the limitations of independent optimization.

Abstract:Energy storage is a crucial component in building a new type of power system and a key support for achieving carbon peaking and carbon neutrality goals. Shared energy storage is an innovative model that combines energy storage technology with the concept of the sharing economy. First, the typical commercial operation models of shared energy storage is reviewed, which construc a full life cycle cost model and economic evaluation indicators for shared energy storage, and conduct technical and economic assessments of different commercial models to analyze their commercial and market value. Then, a sensitivity analysis is performed to examine the impact of changes in key boundary conditions on the internal rate of return of shared energy storage projects. Finally, suggestions are made for the current commercialization process of shared energy storage to provide references for improving the industrialization and scaling of shared energy storage.

Abstract:With the increase in the proportion of electricity in terminal energy consumption, the accounting of electricity carbon emissions is becoming more crucial for enterprises to save energy and reduce carbon emissions. At present, the calculation of the residual mixed factor（RMF）in China has not been realized at the grid-region and provincial levels, making it difficult to accurately reflect the value of green electricity. A calculation method for provincial RMF considering the deduction of green electricity is proposed. Green electricity is deducted from the inter-provincial transmission and provincial-level power generation in the calculation. At the same time, the calculation method of the provincial average emission factor（AEF）is improved, revealing a linear relationship between RMF and AEF, where the coefficient between the two is equal to the proportion of residual electricity in regional electricity consumption. The verification using the electricity data from 2021 to 2023 shows that this method can effectively reflect the carbon-reducing effect of green electricity on the receiving provinces. In the future, the impacts of grid losses and the deduction of all market-based electricity transactions on the calculation method will be considered to further improve the market-based enterprise electricity carbon emission accounting method.

Abstract:With the advancement of integrated energy systems, the interconnection technology for multi-park integrated energy systems has emerged as a prominent area of research. Drawing upon cooperative game theory, a cooperative framework for integrating industrial park and residential area energy systems is proposed, while enhancing the low-carbonization model of cogeneration units. Furthermore, considering electric vehicle batteries’potential as mobile energy storage devices, charging stations are incorporated as third-party participants in power storage and grid peak-valley regulation. Subsequently, Nash bargaining theory is employed to address the issue of benefit distribution within cooperation by formulating it as both a subproblem of maximizing benefits and distributing them equitably. To ensure information privacy among different entities involved, an iterative solution using alternate multiplier method is adopted. Finally, exemplary results demonstrate that compared to independent operation modes, overall costs can be reduc with respective cost reductions for residential operators, industrial park operators, and charging station operators, thus validating the effectiveness of the proposed strategy.

Abstract:To address the impact of the uncertainty from distributed energy resources and loads on the reliability of fault recovery strategies in active distribution networks, this paper explores the potential of collaborative participation of distributed generation and load in fault recovery. A robust fault recovery method based on scenario clustering is proposed. This method first generates a joint scenario set using historical operational data from the resources and loads, and employs a probabilistic distance reduction technique to obtain a typical scenario library. Subsequently, a two-layer robust fault restoration model for active distribution networks, considering island partitioning and source-load interaction., is established. The model is solved using a nested iterative solution method, which generates a robust fault restoration scheme. Finally, simulations are conducted using the IEEE 123-node test distribution network, and the results validate the effectiveness and superiority of the proposed robust fault recovery method.

Abstract:The electrolyzer is a key equipment in renewable energy electrolysis hydrogen technology, and its performance directly affects the efficiency, stability, and cost of hydrogen production. An optimization method for the capacity configuration of mixed electrolysis cells in wind solar hydrogen storage systems based on flexibility indicators is proposed. Firstly, a flexibility evaluation index system is constructed from four perspectives：fluctuation adaptability, hydrogen production efficiency, operating conditions, and economy, to comprehensively evaluate the comprehensive performance of electrolysis cells under various operating conditions. Secondly, combining the AHP-DEMATEL method to determine the weight allocation. Finally, the flexibility index is embedded into the optimization framework to conduct research on the configuration strategy of the hybrid hydrogen production system. The simulation results show that the hybrid electrolyzer capacity optimization configuration scheme proposed in this paper is significantly lower in hydrogen production cost than the single electrolyzer configuration scheme, correspondingly higher in hydrogen production volume than the single alkaline electrolyzer scheme, and provides a new method for the selection and evaluation of hybrid electrolyzers.

Abstract:The demand response potential of flexible adjustable load cluster has certain characteristics of randomness and spatio-temporal interaction. Therefore, a time-varying demand response potential evaluation method of load cluster based on consumer psychology model and adaptive graph attention network is proposed. Firstly, a demand response model of consumer psychology is established taking account of the randomness of response. Then, according to the power consumption characteristics and model parameters of pilot users in the load cluster, parameter migration and adaptive graph attention network are used to extract the spatio-temporal characteristics of demand response of load cluster. Finally, based on the temporal and spatial characteristics of the cluster and historical response data, the deterministic and uncertain time-varying parameters of the model are estimated respectively, and the time-varying demand response potential of the load cluster is evaluated.

Abstract:The integration of electric vehicles with the power grid, harnessing the flexibility of power batteries, is crucial for building new energy systems and power grids. However, a pivotal challenge lies in leveraging power market incentives to foster large-scale and routine vehicle-to-grid（V2G）. Firstly, the trials of large-scale vehicle-to-grid interaction are summarized and the challenges are proposed；Then,the cutting-edge research is summarized from 4 perspectives：the classification of V2G strategies, technical routes, business models of V2G and data source. The future research directions are prospected based on practical exploration and cutting-edge research, and a differentiated power service scheme is highlighted；Finally, the content is concluded considering the current development of China’s electricity market, and policy recommendations are provided.

Abstract:To address the impact of the uncertainty of renewable energy output on scheduling plans, a robust scheduling method considering stochastic scenarios of renewable energy is proposed. Firstly, for the generation of stochastic scenarios of renewable energy output, the interval and temporal characteristics of errors are taken into account. Kernel density estimation and Markov chain modeling are employed,followed by an improved K-means algorithm for scenario reduction, to generate day-ahead stochastic scenarios of renewable energy output for computation. Secondly, to tackle the issue of intraday deviations of renewable energy output from predicted values, a robust scheduling model incorporating stochastic scenario constraints is constructed, which considers both stochastic scenario constraints and scenario transition constraints. Furthermore, due to the large number of stochastic scenarios leading to an oversized robust scheduling model, a solution method based on stochastic scenario feasibility verification is proposed. Finally, the economic efficiency, safety, and effectiveness of the proposed robust scheduling model and solution method are validated through case studies based on a provincial and regional power grid framework. The results demonstrate that the proposed robust scheduling model can effectively solve the unit commitment problem in power markets with high penetration of renewable energy, and the running time and solution efficiency of the proposed model’s solution method are acceptable in practical market applications.

Abstract:To meet the low-carbon and economic demands of data centers, an energy scheduling optimization model for data centers with the objectives of minimizing carbon emissions and comprehensive energy costs is established. An improved non-dominated sorting genetic algorithm-II（NSGA-II）with enhanced speed and elite mechanism is proposed to solve the model. Firstly, the optimization scheduling framework for data center comprehensive energy systems is introduced, considering the load response characteristics of data centers and equipment energy consumption models, and an energy scheduling optimization model for data centers with economic and low-carbon objectives is established. Secondly, addressing the issue of uneven distribution of Pareto solution sets and poor diversity in traditional NSGA-II,an enhanced NSGA-II algorithm is proposed. It adopts dynamic distance comparison and elite retention selection of individuals to ensure both excellent solutions and improved diversity. Finally, through a case study of energy scheduling in a particular data center, the effectiveness of the model and method in reducing data center carbon emissions and comprehensive energy system costs is verified.

Abstract:Given the increasing proportion of renewable energy in the power system, there is an urgent need to explore the demand response potential of building air conditioning systems. Establishing a refined model of air conditioning load is of great significance for reflecting the characteristics of air conditioning load, assessing, and predicting the flexible adjustment potential of air conditioning load.First, classifying according to room type, room location, and indoor thermal gain activities, and combined with two types of air conditioning equipment, a refined air conditioning load simulation model library at the room level has been established, including 144 types of rooms.Then, based on the EnergyPlus platform, a batch simulation is carried out using the strategy of global temperature adjustment, generating an air conditioning load demand response dataset consisting of 1 070 000 days, 30 conditions and over 1 800 000 hours of demand response. The simulation results show that the refined air conditioning load model at the room level can effectively distinguish different rooms, and reception halls, general hotel rooms, high-end hotel rooms, gyms, and canteens have higher demand response potential, and hotel-type buildings have a larger proportion of such rooms, thus often having greater potential. The refined air conditioning load model library can quickly understand the demand response potential of large-scale air conditioning load in advance, providing insightful guidance for formulating demand response policies and systems.

Abstract:Despite the extensive research on the low carbon economic dispatch of the integrated energy system, the influence of the user side on the optimization of the system dispatch has been neglected, which makes it difficult to effectively regulate the source side. Therefore, a low-carbon economic dispatch method before RIES taking into account the integrated demand response and green certificate-carbon trading mechanism is proposed. First, the low-carbon complementary characteristics of source and load are considered. Second, an integrated demand response mechanism is introduced on the load side, and price-based demand response and incentive-based demand response models are established for different types of loads. Finally, the reward and punishment step-type carbon trading mechanism and green certificate trading mechanism based on the distributional decision-making carbon allowance system are adopted, and the carbon emission rights and green certificates of the system surplus are traded in the market.

Abstract:Considering the problems of wind curtailment and low efficiency of energy storage system in virtual power plant（VPP）under the operation mode of“thermal fixed power”, An optimal VPP scheduling method considering fine energy storage and conditional value-atrisk is proposed. Firstly, a fine energy storage model is established according to the characteristics of energy storage device in low temperature environment. Secondly, the conditional value at risk theory is used to quantify the uncertainty of wind power on the source side. The combined heat and power demand response mechanism is introduced in the load side to reduce the peak-valley difference of heat and electricity load, so as to promote the consumption of wind power. Finally, the aim is to minimize the total cost of VPP optimal scheduling. The examples are solved on the Matlab platform. The simulation results show that the proposed model is more economical and reliable than the traditional model. It can effectively improve the level of wind power consumption.

Abstract:To address the high operating costs of charging stations due to the uncertainty of charging times for new energy vehicles and the randomness of photovoltaic（PV）output, as well as to tackle the challenge of excessive action variables in large-scale electric vehicle （EV）charging processes, a two-layer sequential optimization scheduling model for a PV-hydrogen-storage charging stationis is proposed based on a deep reinforcement learning（DRL）algorithm. This model considers factors such as PV output, time-of-use pricing, load uncertainty, and the operational efficiency of each piece of equipment in the system, aiming to meet user demands while reducing the operating costs of the charging station. The twin delayed deep deterministic policy gradient（TD3）algorithm is employed to solve the two-layer sequential scheduling model. The simulation results show that the model can greatly reduce the operating cost of charging station under the premise of meeting the charging demand of users. In addition, when the number of charging piles increases, the real-time scheduling time of the model is not affected and solar curtailmentcan be effectively reduced.

Abstract:Coordinated energy supply from multiple complementary sources is an effective way to realize the“dual-carbon”goal in China.In order to increase the competitiveness of its application, an improved artificial hummingbird algorithm that introduces a probabilistic dynamic switching strategy for foraging mode selection is used to optimize the capacity allocation of a coupled wind-scenery-hydrogen-storage system that takes into account the interaction of purchasing electricity and selling hydrogen energy with a dual-objective optimization. The lowest total operating cost and the lowest carbon emission in the whole life cycle are selected as the optimization objectives, and the Pareto solution set obtained from the optimization is screened by entropy weight-improved topsis analysis to select the optimal solution. Case simulation is used to verify the excellent performance of the proposed electricity purchase and hydrogen sale energy interaction model in coupled system capacity allocation optimization. From the perspective of advancing the“dual-carbon”goals, the electricity-purchasing and hydrogenselling energy interaction model demonstrates superior applicability in optimizing capacity configuration for multi-source coupled systems.

Abstract:With the large-scale adoption of renewable energy generation such as wind and photovoltaic power, their inherent volatility and uncertainty caused by weather conditions pose significant threats to the safe and reliable operation of power grids. As an aggregator of distributed resources and a new market entity, virtual power plants can utilize advanced information communication technologies and software systems to achieve aggregation and coordinated optimization of distributed resources, thereby providing fast ramping services. To address the flexibility ramping requirements of power grids, this paper proposes a VPP fast ramping dispatch strategy based on a second-order consensus algorithm. According to the existing conditions of resource clusters within the VPP, the strategy enables the ramping rates of resource clusters to converge to a consensus under specific constraints, ensuring that the total ramping capacity of multiple resource clusters meets the ramping demand. A second-order consensus-based mathematical control model for VPPs is established, employing the ratio of current ramping rate to maximum ramping rate as the consensus operator. Case studies are conducted to verify the effectiveness of the pro?posed method.

Abstract:The spatial mismatch between China’s energy resources and demand, alongside rapid clean energy growth, has caused significant wind, solar, and hydropower curtailment, necessitating power market reforms for resource optimization. The inter-provincial power spot market enables renewable energy integration and power balancing via a“unified market, two-level operation”system. Yet, static price caps struggle to balance supply-demand dynamics and supply-price stability goals, with limited analysis of bidding behaviors of power plants and electricity purchasers. Using 2022 trial data, a big data-driven approach examines bidding characteristics and clearing outcomes of thermal, hydro, wind, solar power, and counterpart provinces’purchasers, uncovering temporal and seasonal patterns. The KMeans algorithm classifies bidding behaviors of various units, identifying differences in duration, bid volume, and price to analyze influencing factors. Findings support market mechanism optimization, enhancing renewable energy integration and dual-carbon goals.

Abstract:With the increasing installation capacity of distributed photovoltaic（PV）systems in distribution networks, grid operators frequently issue curtailment instructions. Traditional group control strategies mainly adopt complete curtailment approaches when receiving these instructions, which reduces PV consumption rates and compromises user fairness. A distributionally robust optimization-based group control strategy for distributed PV systems that considers both demand response uncertainty and PV output uncertainty is proposed. Recognizing that demand response loads can partially fulfill curtailment requirements, models for reducible and interruptible loads are developed. Then, firstly a distributionally robust optimization model for PV group control that aims to minimize operational costs while employing a combination paradigm to characterize PV output uncertainty is constructed. This approach reduces the impact of prediction errors and output fluctuations on distribution network stability. The proposed model is solved using a column and constraint generation algorithm.The effectiveness of our proposed strategy is validated using data from a distribution network in Henan Province. Results demonstrate that the strategy can develop reasonable group control strategies when curtailment instructions are issued, thereby improving the consumption capacity of distributed PV systems.