Abstract:Air conditioning loads,characterized by their large amount,ease of adjustment,and minimal impact on users,are ideal demandside flexible resources. It’s necessary to research progress and directions of air conditioning load participation in ancillary service technologies from various perspectives. Firstly,the modeling techniques for individual and aggregated air conditioning resources are introduced.Then,the main challenges and technical methods for air conditioning participation in various ancillary services such as primary frequency regulation,secondary frequency regulation,and reserve capacity are analyzed. Air conditioning control technologies considering information security and reliability are discussed,and evaluation techniques for air conditioning load from the perspectives of grid interaction and user comfort are summarized. Finally,suggestions for further exploring the regulation potential of air conditioning load are proposed to promote the transformation of air conditioning load control technologies from theoretical research to practical applications.

Abstract:Electric vehicles（EVs）are important new adjustable load resources,and predicting their flexibility is an essential prerequisite for implementing optimized dispatch. A method to infer the fleet charging feasibility domain based on normal charging session data of EVs is proposed,forming a historical dataset on the flexibility of EV fleets. Subsequently,considering the high stochasticity of charging load,a probability prediction method for the flexibility of EV fleets based on Gaussian process regression is proposed. The obtained probability prediction results can be used to establish chance constraints for the optimization problem of EV fleet and convert them into deterministic constraints under specific confidence levels. Lastly,simulation verification is conducted using actual charging data. The results show that the proposed method can accurately predict the charging flexibility of EV fleets from both energy and power aspects. By adjusting the confidence level,it is possible to balance the economy and the implementability when optimizing the EV fleets.

Abstract:In order to ensure the comprehensive integration of new energy sources and enhance the safety and reliability of the power system,it is imperative to involve more flexible resources in scheduling operations. Developing market-based mechanisms for entities such as load aggregators and fully leveraging their regulatory capabilities are crucial for advancing the construction and development of the new power system. The current situation of demand response participation in flexible loads is analyzed from the 4 aspects of participating entities,access conditions,response rules,and compensation rules. Based on the frequency and peak demand requirements in load control scenarios,an optimization dispatch model for flexible loads is constructed. The subsidy price and response quantity of each load subject are determined by the bidding principle of“price first,capacity first”,which is commonly adopted by most provinces and cities in their mode of unilateral quotation. A system test of 5 000 flexible loads,including independent loads and load aggregators,is conducted to evaluate the optimization speed. The test results indicate that the model meets the requirements of rapidly and efficiently adjusting loads in the demand response market.

Abstract:In order to improve the accuracy of large-scale air conditioning load aggregation and enhance the adjustable potential of air conditioning clusters,an optimization strategy for large-scale air conditioning load aggregation clusters under incentive conditions is proposed.Firstly,a large-scale air conditioning load aggregation architecture is established. Secondly,a second-order equivalent thermal parameter model for individual air conditioners is established,and the air conditioning loads in different regions are secondary aggregated based on the Monte Carlo method. Meanwhile,the relationship between user satisfaction and incentive levels is established. On this basis,optimization objective is to minimize the standard deviation between actual air conditioning aggregated power and the gap load,and to minimize the compensation cost for the air conditioning load aggregator. Particle swarm optimization algorithm is used to solve the problem. Finally,effectiveness of the proposed strategy is demonstrated through numerical examples.

Abstract:High proportion of new energy integration and slowing growth rate of thermal power installation will continuously compress the safe and economic space for reliable scheduling on the power generation side,and guide the coordinated development of the power system’s“source load”is an important way to promote energy transformation. Firstly,load resources are divided into three categories：real-time dispatchable load,guided elastic load,and emergency regulation load,and are defined and modeled separately. Secondly,based on the actual security network requirements for regulation,propose a regulation information exchange method and architecture for different load resources. Next,based on the requirements of regulatory business scenarios,market-oriented compensation methods and evaluation models are constructed for three types of load resources：grid balance reserve,real-time load regulation,and orderly electricity consumption with extreme supply-demand imbalance. Finally,quantitative analysis of compensation methods is conducted through numerical examples,providing reference for subsequent participation of load resources in the market.

Abstract:As the penetration rate of distributed renewable energy within microgrids increases,insufficient flexible resource scheduling leading to curtailment and loss of load will have a significant impact on the operation of microgrids. A microgrid day ahead risk scheduling strategy that takes into account source load correlation by fully scheduling various distributed flexible resources is proposed. Under the independent microgrid regulation architecture considering flexible resources,the Latin hypercube sampling method considering correlation is adopted to deal with the uncertainty of distributed photovoltaics and loads. The expected scheduling cost,loss cost of abandoned light and load,and conditional risk cost of microgrids under the influence of correlation are analyzed,and a day-ahead optimization scheduling model with the minimum expected total scheduling cost is constructed. The simulation results show that the proposed strategy can effectively balance the economic and operational risks of microgrid operation by reasonably scheduling flexible resources and reserving reserve capacity.

Abstract:In recent years,the demand for electricity continues to grow in China. In order to further deepen the management of electricity load and effectively ensure the supply of energy and electricity during peak summer（winter）,higher requirements have been put forward to deeply tap the potential of air conditioning load regulation and improve the mechanism of air conditioning load aggregation regulation.Firstly,the air conditioning load forecasting model and aggregation model from the perspectives of physical modeling and data modeling are summarized,the applicability of different models is analyzed,and a multi-level aggregation model suitable for large-scale air conditioning load is proposed. Then,the current progress in optimizing regulation around regulatory models and strategies is discussed. Finally,based on the current development situation in China,development suggestions are proposed from the aspects of algorithm fusion,dual carbon strategy requirements,and efficient operation of load aggregation systems.

Abstract:In response to the issue of operational costs generated when processing workflow loads in data centers,a workflow load allocation model considering bandwidth and power costs is first analyzed and established to optimize the economic costs paid by data center operators. Then,based on the characteristics of the optimization model,a heuristic algorithm is designed to solve the minimum operating cost and the allocation scheme of workflow subtasks in various data centers. Finally,a real workflow load topology and parameter settings are used for case analysis to compare three different types of workflow load topologies and two common workflow load allocation methods. The results verify the role of the comprehensive consideration of bandwidth traffic cost and power cost model in reducing the total operating cost of the data center.

Abstract:Non-intrusive load monitoring method is a critical technology for realizing power system intelligence,which helps to optimize energy management and promote efficient energy utilization. In order to cope with the existing problems of feature redundancy,limited recognition accuracy and computational inefficiency,a novel non-intrusive load identification method based on optimal signatures and improved random forest is proposed. Firstly,the optimal feature combination is autonomously determined by recursive feature elimination method to reduce the information redundancy. Then load identification is realized by constructing a weighted random forest model. The weights are established by utilizing out-of-bag data. Construction parameters of random forest are optimized using the improved whale algorithm. Ultimately,the experimental results prove the accuracy and superiority of the proposed load identification method.

Abstract:Decentralized electric heating load based on equivalent thermal parameter model faces difficulties in parameter identification and large simulation errors,which cannot meet the needs of power grid regulation. Therefore,a simulation model for decentralized electric heating load is proposed based on long short term memory（LSTM）network. Firstly,the model parameters are determined according to the heat transfer process between the distributed electric heating load and the building. Then the LSTM network parameters are determined by combining the model input variables and output variables to establish the load model. By dynamically updating the indoor temperature in the input data of the test set,the long-term temperature prediction is realized. In order to measure the accuracy of the model,model error evaluation indexes in both vertical and horizontal dimensions are proposed. Finally,the example analysis results show that compared with the second-order equivalent thermal parameter model of distributed electric heating load,the longitudinal error and lateral error of the prediction results of the distributed electric heating load model based on LSTM network are smaller and the model accuracy is higher.

Abstract:Under the background of“dual-carbon”,considering the demand response of electricity and heat and combine it with the market trading mechanism can help the integrated energy system to operate in a low-carbon economy,an integrated energy system model with demand response of electricity and heat together with the green certificate carbon trading mechanism and scheduling in a low-carbon economy is proposed. Firstly,the carbon capture power plant（CCPP）is modified by adding carbon dioxide storage tanks（CST）and establish a flexible operation model of the CCPP. Then,a mathematical model of the IES with the CCPP and the power to gas（P2G）equipment as the primary energy coupling equipment is established. Secondly,to minimize the peak-to-valley difference of the system’s electric and thermal loads and further reduce the output of high-carbon emission units,a mathematical model of the demand side’s electric and thermal demand response mechanism is established. Again,a mathematical model of the green certificate-carbon trading mechanism to improve system wind power consumption is established. Finally,the dispatch with an objective function to minimize the cost of operating the system is simulated. The simulation scheme verifies that the proposed model realizes the full consumption of wind power with an overall reduction of the total system cost,significantly improving the system’s low-carbon economic benefits.

Abstract:The comprehensive construction of energy to realize the high efficiency of terminal energy has become one of the important ways to support carbon neutralization and carbon peak. Aiming at the problems of energy efficiency improvement,planning and allocation of energy system in the park,a multi-objective optimal allocation model of economy and energy efficiency of comprehensive energy system in the park is put forward. By analyzing the basic structure of the park’s comprehensive energy system,taking the total annual cost and comprehensive energy efficiency as the optimization objectives,and considering various constraints of energy planning investment and energy operation safety,a multi-objective optimal allocation model of comprehensive energy system in the park is established. Then,based on the characteristics of NSGA-II algorithm and fusion simulated annealing algorithm,the search mechanism of fusion simulated annealing algorithm improves the solution performance of NSGA-II. Finally,a park in China is taken as an example to verify the effectiveness of the model and algorithm.

Abstract:Renewable energy,primarily wind power and photovoltaics,has become significant sources of energy supply in China. To address the issues of wind power output’s volatility and intermittency,the concept of integrated wind power and storage co-generation is introduced with wind-solar storage co-generation plants as the central focus,mechanism and model of participation in inter-provincial spot electricity energy market clearing is studied. In an effort to enhance wind energy consumption and reduce wind and solar power wastage,conducting secondary clearing in addition to the primary clearing is suggested. Additionally,improving the existing method of penalizing wind and solar power abandonment by introducing the complementary variable wind and light abandonment penalty approach is proposed. Furthermore,priority is recommended to be given to the output of wind-solar storage co-generation plants over that of conventional thermal power units. Lastly,the proposed mechanism is simulated using the interconnected IEEE-39 node system to validate its effectiveness.

Abstract:Distributed photovoltaic device in distribution network has the feature of wide distribution and strong intermittency. Its local consumption characteristic causes unbalanced distribution of carbon reduction contributions in distribution network. In order to measure the carbon reduction effect of distributed photovoltaic and energy storage devices in distribution network fairly,real lossy network is transformed into lossless virtual network through two-direction network loss allocation,and then the carbon emission flow distribution under a time section is obtained. Then,the carbon flow distribution corresponding to the planned power and actual power of distributed PV in longtime scale is calculated respectively to obtain the relative carbon emission intensity of distributed PV. Finally,the relative carbon emission intensity of distributed photovoltaic under different distribution network energy storage capacity scenarios is calculated,which is the basis of carbon reduction effect measurement of energy storage devices,so as to propose a carbon reduction contribution distribution method in distribution network including photovoltaic and energy storage devices that meets the principle of fair distribution,and the standard calculation example is verified.

Abstract:Focusing on the low-carbon development needs of the power industry and aiming at the problem that the current power market and carbon market can’t coordinate operation,an electricity carbon joint P2P trading mechanism-by introducing electricity P2P trading is designed,it gives users and power generators room to mobilize their flexibility,and provides carbon emission reduction services for thermal power enterprises. At the same time,it helps to promote the consumption of new energy and improve the benefits of all participants. The changes of the benefits of participants in the traditional power market and the trading mode after the introduction of P2P trading and carbon market mechanism is analyzed. According to the demand of the design mechanism,the clearing model of the electricity carbon joint market is established,and the feasibility and effectiveness of the trading mechanism are analyzed through the simulation of a design example.

Abstract:Under the background of the new power systems with a large number of distributed new energy sources connected,prosumers with autonomous decision-making ability have become a new type of subject participating in the peak shaving and load shifting ancillary service market. Firstly,considering the characteristics of various flexible resources within the control of the prosumers,a decision-making model is proposed for the prosumers to participate in the peak shaving ancillary service market,in order to form the prosumers’power generation plan for the next day. Secondly,based on the profit-seeking characteristics of prosumers and the psychological differences in economic benefits among different prosumers,an optimal bidding model for prosumers is proposed,which takes into account the sensitivity of the prosumers’winning probability to their bidding price. Finally,a market clearing model was proposed with the goal of minimizing the overall scheduling cost of the system. The study shows that proposed model not only facilitates the alleviation of system peak shaving pressure,but also has the effect of reducing line load pressure and improving node voltage.

Abstract:With the high proportion of household distributed photovoltaic access in low-voltage distribution system and the expansion of customer scale,the accuracy of the traditional transformer-customer relationship identification algorithm based on the principle of voltage correlation and the principle of energy supply-demand balance can no longer meet the requirement of low-voltage station. To tackle these obstacles,a two-stage household variable relationship identification algorithm integrating spatial-temporal information is proposed. Firstly,the spatial location information of customers and transformers is extracted for identifying the transformer-customer relationship based on the maximum power supply range of transformer,and the results were utilized in optimizing the initial input value of the next stage. Subsequently,according to the time series of energy consumption of transformers and customers,a time-sequential incidence convolution model based on the principle of energy supply-demand balance is established to realize transformer-customer relationship identification in low voltage station. Simulation results demonstrated that compared with the traditional identification algorithms,the proposed method shows significant advantages in improving accuracy in identifying transformer-household relationships and reducing computational complexity.

Abstract:Non-intrusive load identification technology can obtain the usage of various equipment at low cost,and can monitor and analyze power load on line,which is of great significance for load forecasting,demand response and other applications. In view of the diversity of general industrial and commercial users,the variety of loads and the complexity of equipment operation characteristics,anindustrial and commercial load identification scheme based on normalized three threshold event detection and LDA classifier is proposed,Firstly,a unified load event detection framework with adjustable parameters is designed for devices with different energy levels and different start-stop characteristics,which improves the detection accuracy of slow-moving,segmented and oscillating load events. Then an equipment type identification algorithm based on multivariate features and LDA linear discrimination is proposed,which achieves the same identification performance as nonlinear classifiers such as random forest while ensuring the computational efficiency of the edge.