Evolutionary game-based optimization of green certificate-carbon emission right-electricity joint market for thermal-wind-photovoltaic power system

With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-market trading process is proposed to study the market-based strategy for renewable energy.Considering the commodity characteristics of green certificates and carbon emission rights,the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models.Furthermore,considering the irrational bidding behavior of energy suppliers in the actual electricity market,an evolutionary game based multi-market bidding optimization model is presented.Subsequently,it is solved using a composite differential evolutionary algorithm.Finally,the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.

Evaluation index system of shared energy storage market towards renewable energy accommodation scenario:A China’s Qinghai province context

With the ever-increased installed capacity of renewable energy generation units in a power system,the so-called shared energy storage(SES),a novel business model under the umbrella of the shared economy principle,has the potential to play an essential role in the accommodation of renewable energy generation.However,unified evaluation standards and methods,which can help decision-makers analyze the performance of the SES market,are still not available.In this paper,an evaluation index system of the SES market is designed based on the trading rules of China’s Qinghai province and the structure-conduct-performance(SCP)analytical model.Moreover,the definition and characteristics of the indices,which can show the performance of the SES market from different perspectives,are given.Furthermore,the ideal cases are presented as the evaluation benchmark based on the development expectation of the SES market,and the analytic hierarchy process(AHP)and the technique for order preference by similarity to an ideal solution(TOPSIS)are applied to evaluate the SES market comprehensively.Finally,a case study based on actual data of the SES trading pilot project in Qinghai shows that the evaluation index system can reflect the operation status,existing problems and influencing factors of the SES market.

Investment optimization of grid-scale energy storage for supporting different wind power utilization levels

With the large-scale integration of renewable generation,energy storage system(ESS)is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty.This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load.A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS.Specifically,the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making.Then,an easy-to-implement variant of Benders decomposition(BD)algorithm is developed to solve the resulting mixed-integer nonlinear programming problem.Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power.In addition,the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.

Multi-point Layout Planning for Multi-energy Power System Based on Complex Adaptive System Theory

Aiming at the problem of multi-point layout planning of a multi-energy power system,the output characteristics of a multi-energy power system composed of wind power generation,photovoltaic power generation,hydropower generation,traditional thermal power generation and solar thermal power generation are comprehensively analyzed.Combining power optimization planning with complex adaptive system theory,a multi-point layout planning model of multi-energy sources based on complex adaptive system theory is proposed.The model takes the minimum construction step size of each new energy source as the agent.Through the interaction between the agents and the accumulation of experience,the behavior rules are constantly changed,the installed positions of various types of power sources are adjusted,and the optimal layout scheme of various power capacities of each node is obtained.Moreover,an agent modeling method based on a simple rule emerging complex phenomena is proposed,which reveals the core idea of complex adaptive system theory—adaptability makes complexity.Taking an actual power grid in a certain region of China as an example,it is verified that the proposed method has a significant effect on improving the consumption of new energy,and has certain guiding significance for the actual engineering construction.

Monitoring ground temperature and deformation of the cast-in-place footings in permafrost regions on the Qinghai-Tibet Plateau

The long-term stability of the cast-in-place footings in permafrost regions has received much attention due to its climate sensitivity.The current research lacks long-term data validation,especially in the context of climate change.Based on the 13-year(2011-2023)temperature and deformation monitoring data from the Qinghai-Tibet Power Transmission Line,this study investigates the characteristics of permafrost variation and its impact on the stability of tower footings under the cooling effect from thermosyphons.The results reveal that the thermosyphons effectively reduce the ground temperature around the footings.After the first freeze-thaw cycle,the soil around the tower footings completed refreezing and maintained a frozen state.In the following 13 years,the ground temperature continued to decrease due to the cooling effect of thermosyphons.The duration notably exceeded the previously predicted 5 years.The temperature reduction at the base of the footings cor-responded well with the frost jacking of the tower footings and could be divided into three distinct phases.In phase 1,the ground temperature around the footings rapidly reduced,approaching that of the natural field,while the footings experienced pronounced deformation.In phase 2,the ground temperature decreased at a faster rate,and the deformation rate of the footings slowed down.In phase 3,the frost jacking of the footings gradually retarded with the decrease in base temperature.Additionally,the ground temperature differences of over 1 C were observed among different tower footings,which may lead to the differential deformation among the tower footings.The ground temperature differen-tiation is attributed to the difference in solar radiation intensity,which is shaded by the tower structure from different directions.This study provides theoretical support and empirical accumulation for the construction and maintenance of tower footings in permafrost regions.

Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and Markov Chain

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.

Power Source Flexibility Margin Quantification Method for Multi-energy Power Systems Based on Blind Number Theory

The grid connection of a high proportion of re-newable energy generation increases the uncertainty in power systems.Therefore,the flexibility margin of different energy sources needs to be quantified to cope with the uncertainty change and maintain the dynamic balance of power system flexibility.In this paper,first,the flexibility characteristics of source,net,load and power and load community(PLC)are analyzed.The dynamic equilibrium relationship among them is briefly introduced.Secondly,taking into full consideration the complex output characteristics of different energy sources and combining their respective flexibility characteristics,a quantitative model of the power source flexibility margin for thermal power,hydro-power,gas power and concentrating solar power is established.A quantitative model for a power source flexibility margin in PV and wind power based on blind number theory is estab-lished.Furthermore,the calculation method of theoretical power generation capacity,which can reflect different characteristics of output power of various energy sources,is presented.The actual output power of each power source in each period is predicted.Finally,a case study shows that the model and method can consider the operating characteristics of different types of power sources,and quickly and accurately quantify the adjustable range of flexibility margins of each power source at different periods of time,which can provide an important basis for evaluating the capacity of renewable energy consumption and the optimal operation of multi-energy power systems(MEPSs).

Operational Reliability Model of Hybrid MMC Considering Multiple Time Scales and Multi-state Submodule

Environmental and electrical factors such as wind speed,air temperature and switching frequency have significant influences on the operational reliability of hybrid modular multilevel converter(MMC),which is commonly used for the wind power transmission.However,the existing reliability model of hybrid MMC based on statistics cannot accurately reflect the impact of these factors.In this paper,a new operational reliability model of hybrid MMC is presented.The reliability index of the hybrid MMC is coupled with its operation characteristics by calculating multi-term thermal cycling.In addition,an operation strategy of hybrid MMC is proposed to improve its reliability.The multi-state submodule(SM)is developed,which is capable of bypassing specific faulty power modules instead of the whole SM.Case studies show that the proposed operational reliability model could describe the impact of environmental and electrical factors.Also,the proposed operation strategy can improve the reliability of hybrid IMMC by extending the operation time of SMs.

Simultaneous Optimization of Renewable Energy and Energy Storage Capacity with the Hierarchical Control

To fully consider the complementary role of different energy sources and reduce the curtailment of renewable energy(RE)in high RE penetration systems,a hierarchical optimization algorithm is proposed to simultaneously optimize the capacity of RE generation and energy storage systems(ESS).Time sequence simulation(TSS)technology is adopted to fully consider the regional RE resource characteristics and make the model more reliable.An optimization model for evaluating ESS capacity is established at a lower level.To overcome the high dimensional complexity of time sequence data,this paper re-formulates this sub-model as a consensus problem,which can be solved by a distributed approach to minimize the system’s total investment costs.At the upper level,the model for assessing the proportion of wind and solar capacity is developed by maximizing the RE generation.The golden section Fibonacci tree optimization(GSFTO)algorithm is utilized to improve the efficiency and solution accuracy.The results show that the algorithm and model are feasible and applicable for the identified purposes,which can provide a useful guidance for the development of power generation and the energy storage capacity in high RE penetration systems.

Prediction of End-Use Energy Consumption in a Region of Northwest China

End-use energy consumption can reflect the industrial development of a country and the living standards of its residents. The study of end-use energy consumption can provide a solid basis for industrial restructuring, energy saving, and emission reduction. In this paper, we analyzed the end-use energy consumption of a region in Northwestern China, and applied the Markov prediction method to forecast the future demand of different types of end-use energy. This provides a reference for the energy structure optimization in the Northwestern China.