查看更多>>摘要：With the liberalization of the retail market,cus-tomers can sell their demand response(DR)resources to the distribution company(Disco)through the DR aggregator(DRA).In this paper,an intelligent DR resource trading framework between Disco and DRA is proposed by exploiting the benefits of deep reinforcement learning(DRL).The hierarchical decision process of the two players is modeled as a Stackelberg game.In the game,Disco is the leader who determines the retail price while DRA is the follower who responds to it.To protect their privacy,a dueling deep Q-network(dueling DQN)is then constructed to model the bi-level Stackelberg game,such that the lower-level problem doesn't need to reveal its detailed model to the upper-level.In the learning process,the uncertainties from the DRA's baseline load and wind power are considered.In order to boost the robustness against the estimation error,the baseline load is discretized into symbols before being used as the input states of the dueling DQN.And to mitigate the uncertainty of wind power,the scenario-based method is introduced when designing the reward.We demonstrate that the proposed dueling DQN-based method has good performance and is more robust against uncertainties.
查看更多>>摘要：Sustainable development of power and energy sys-tems(PES)can effectively handle challenges of fuel shortage,environmental pollution,climate change,energy security,etc.Data of PES presents distinctive characteristics including large collection,wide coverage,diverse temporal and spatial scales,inconsistent sparsity,multiple structures and low value density,putting forward higher requirements for real-time and accuracy of data analysis,and bringing great challenges to operation analysis and coordinated control of PES.In order to realize data quality improvement and further support flexible choice of operating mode,safe and efficient coordinated control,dynamic and orderly fault recovery of sustainable PES,this paper pro-poses an unscented particle filter algorithm,adopting unscented Kalman filter to construct importance density functions and KLD resampling to dynamically adjust the particle number.Simulation results obtained by taking an 85-node system as a benchmark for simulation verification show that compared with traditional PF algorithm and UKF algorithm,UPF algorithm has higher estimation accuracy.
查看更多>>摘要：Condition monitoring and maintenance are essential for ensuring stable and reliable operation of facility transformer,reducing costs,and increasing expected life.Foam cannot be avoided within the power transformer which illustrates at least one of the principal roots of insulation deterioration.Due to the intricacy of the dynamics within the insulation system,involving bubble creation,migration,and accumulation,challenges arise in understanding these mechanisms.Consequently,the problems caused by bubble dynamics require further investigation.This paper provides a scientific review of issues related to bubble formation,migration,and dispersion and their electrical effects on power transformers.Various descriptions are given to high-light impact of the dynamics of the bubble within high voltage insulation fields,which covers bubble generation mechanism,and therefore,the quintessential influencing factors,state-of-the-art advances in multi-physics coupled modeling of bubble dynamics,etc.Moreover,pending issues on bubble control and mitigation are addressed,as well as recommendations for future investigations.Nonetheless,this paper contains a useful quotation guide for researchers within an equivalent field of research,which can motivate readers to explore scientific solutions to prevailing bottleneck problems.
查看更多>>摘要：In order to effectively utilize the dielectric response characteristics of transformers to diagnose the insulation state,this paper proposes a two-level hybrid optimization method for analyzing time-domain dielectric response characteristics.The optimization algorithm is based on the combined statistical indicators(CSI)and random forest(RF)theory.The initial feature space set is formed with 23 time-domain characteristics.In the first-level stage,statistical indices correlation,distance,and information indicators are integrated to assess the synthesis score of the characteristics,while highly redundant and low-class discrimination characteristics are eliminated from the initial space set.In the second-level stage,the Random Forest based outside bagging data theory is introduced to evaluate the least important characteristics,and the characteristics with low importance indices are excluded to obtain the final optimal feature space set.The proposed method is carried out on 82 sets of data from actual dielectric response tests on oil-paper insulation transformers.Finally,the final optimal feature space set,along with several other data sets,is tested via different diagnosis methods.The results show that the optimal feature space set obtained via the proposed method outperforms other feature space sets in terms of better adaptability and diagnosis accuracy.
查看更多>>摘要：When a long distance HVDC transmission system discharges current into the earth through its grounding electrode,ground potential differences appear in a large area.And therefore part of the DC current may flow into nearby pipelines which may be dangerous to the equipment and personnel,and may aggravate corrosion.In this paper,an equivalent circuit based on the method of moments is introduced to calculate the current and potential distributions along a pipeline with damaged anticorro-sive coating.The current-dependent electrochemical polarization potential between soil and the metal pipe,due to the damage of the anticorrosive coating,is taken into account by using the Newton-Raphson scheme.The circuit is verified through a reduced scale experiment.By examining the circuit,the effect of the damaged anticorrosive coating on the leakage current and the pipe potential with respect to soil along the pipeline is analyzed.
查看更多>>摘要：Increasing penetration of renewable energy into power systems is the development trend of future energy systems.One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power.In this letter,we propose a novel extreme scenarios(ESs)based data-adaptive probability uncertainty set for the transmission expansion planning problem.First,available his-torical data are utilized to identify data-adaptive ESs through the convex hull technology,and the probability uncertainty set with respect to the obtained ESs is then established,from which we draw the final expansion decision based on the worst-case distribution.The proposed distributionally robust transmission expansion planning(DRTEP)model can guarantee optimality of expected cost under the worst-case distribution,while ensuring feasibility of all possible wind power generation.Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.
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