查看更多>>摘要：Frequency stability and security have been a vital challenge as large-scale renewable energy is integrated into power systems.In contrast,the proportion of traditional thermal power units decreases during the decarbonization transformation process,resulting in poor frequency support.This paper aims to explore the potential of frequency regulation support,dynamic assessment,and capacity promotion of thermal power plants in the transition period.Considering the dynamic characteristics of the main steam working fluid under different working conditions,a nonlinear observer is constructed by extracting the main steam pressure and valve opening degree parameters.The real-time frequency modulation capacity of thermal power units can provide a dynamic state for the power grid.A dynamic adaptive modification for primary frequency control(PFC)of power systems,including wind power and thermal power,is proposed and improved.The power dynamic allocation factor is adaptively optimized by predicting the speed droop ratio,and the frequency modulation capability of the system is improved by more than 11％under extreme conditions.Finally,through the Monte Carlo simulation of unit states of the system under various working conditions,the promotion of the frequency regulation capacity with high wind power penetration(WPP)is verified.
查看更多>>摘要：The presence of renewable energy resources in LV distribution networks may lead to a distribution transformer,also known as a Smart Transformer(ST),experiencing the bidirec-tional power flow.Therefore,the ST must have the capability to operate in both directions.However,the reverse power is less as compared to the forward power,thus the design of ST with the same capacity in both directions increases the hardware cost and decreases the system efficiency.This paper proposes a Hybrid-modular-ST(H-ST),composed of a mixed use of single active bridge-based series resonant converter and dual active bridge instead of complete use of uni-or bi-directional converter adopted in the conventional solid-state-transformer.Based on the proposed H-ST,the impacts of power imbalance among cascaded modules in reverse operation mode are analyzed and then an effective solution based on reactive power compensation combined with the characteristics of the proposed architecture is adopted.The simulation and experimental results clearly validate the effectiveness and feasibility of the theoretical analyses.
查看更多>>摘要：This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind power.The proposed frequency control strategy is based on the novel nonlinear regulator theory,which takes advantage of nonlinearity of doubly fed induction generators(DFIGs)and generators to regulate the frequency of the power system.Frequency deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs,in various wind speed scenarios.The effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.
查看更多>>摘要：The outputs of renewable energy sources(RESs)are inherently variable and uncertain,such as wind power(WP)and photovoltaic(PV).However,the outputs of various types of RESs in different regions are complementary.If the capacity of RESs could be properly allocated during system planning,variability of the total output could be reduced.Consequently,system reliability and renewable energy(RE)consumption could be improved.This paper proposes an analytical model for optimal complementary capacity allocation of RESs to decrease variability of the total output.The model considers the capacity ratio of RESs as decision variables and the coefficient of variation(CV)of the total output as the objective function.The proposed approach transforms the single-level optimization model into a bilevel optimization model and derives an analytical equation that can directly calculate the optimal complementary capacity ratio(OCCR)of system RESs.Case studies on wind and solar farms in Xinjiang and Qinghai,China,are performed to verify the effectiveness of the proposed analytical allocation method.
查看更多>>摘要：Integrated power-gas systems(IPGS)have devel-oped critical infrastructure in integrated energy systems.More-over,various extreme weather events with low probability and high risk have seriously affected the stable operation of IPGSs.Due to close interconnectedness through coupling elements be-tween the power system(PS)and natural gas system(NGS)when a disturbance happens in one system,a series of complicated sequences of dependent events may follow in another system.Especially under extreme conditions,this coupling can lead to a dramatic degradation of system performance,resulting in catastrophic failures.Therefore,there is an urgent need to model and evaluate resilience of IPGSs under extreme weather.Following this development trend,an integrated model for resilience evaluation of IPGS is proposed under extreme weather events focusing on windstorms.First,a framework of IPGS is proposed to describe states of the system at different stages under disaster conditions.Furthermore,an evaluation model considering cascading effects is used to quantify the impact of windstorms on NGS and PS.Meanwhile,a Monte Carlo simulation(MCS)technique is utilized to characterize chaotic fault of components.Moreover,time-dependent nodal and system resilience indices for IPGS are proposed to display impacts of windstorms.Numerical results on the IPGS test system demonstrate the proposed methods.
查看更多>>摘要：In the event of a major power outage,critical park microgrids(PMGs)could be self-sustaining if mobile emergency generators(MEGs)are stationed to share energy.However,the need for privacy protection and the value of flexible power support on minute-time scales have not been given enough attention.To address the problem,this paper proposes a new self-sustaining strategy for critical PMGs integrating MEGs.First,to promote the cooperation between PMG and MEG,a bi-level benefit distribution mechanism is designed,where the participants'multiple roles and contributions are identified,and good behaviors are also awarded.Additionally,to increase the alliance benefits,three loss coordination modes are presented to guide the power exchange at the minute level between the MEG and PMG,considering the volatility of renewable generation and load.On this basis,a multi-time scale power-energy scheduling strategy is formulated via the alternating direction method of multipliers(ADMM)to coordinate the PMG and MEG.Finally,a dimensionality reduction technology is designed to equivalently simplify the optimization problem to facilitate the adaptive-step-based ADMM solution.Simulation studies indicate that the proposed strategy achieves the self-sustaining of PMGs integrating MEGs while increasing the economy by no less than 3.1％.
查看更多>>摘要：This paper proposes a residue based open-loop modal analysis method to detect low frequency modal reso-nance(LFMR),including asymmetric low frequency modal at-traction(ALFMA)and asymmetric low frequency modal repul-sion(ALFMR),of permanent magnetic synchronous generator based wind farms(PMSG-WFs)penetrated power systems.The formation of ALFMA and ALFMR caused by two open-loop low frequency oscillation(LFO)modes moving close and apart is analyzed in detail.Via predicting the trajectories of closed-loop LFO modes based on calculation of residue of open-loop LFO modes,both ALFMA and ALFMR can be detected.The proposed method can select LFO modes which move to the right half complex plane as control parameters vary.Simulation studies are carried out on a three-machine power system and a four-machine 11-bus power system to verify the properties of the proposed method.
查看更多>>摘要：Stochastic electricity markets have drawn attention due to fast increase of renewable penetrations.This results in two issues:one is to reduce uplift payments arising from non-convexity under renewable uncertainties,and the other one is to allocate reserve costs based on renewable uncertainties.To resolve the first issue,a convex hull pricing method for stochastic electricity markets is proposed.The dual variables of system-wide constraints in a chance-constrained unit commitment model are shown to reduce expected uplift payments,together with developing a linear program to efficiently calculate such prices.To resolve the second issue,an allocation method is proposed to allocate reserve costs to each renewable power plant by explicitly investigating how renewable uncertainties of each renewable power plant affect reserve costs.The proposed methods are validated in a 24-period 3-unit test example and a 24-period 48-unit utility example.
查看更多>>摘要：Extensive penetration of distribution energy re-sources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algo-rithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distri-bution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identi-fication scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topol-ogy identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.
查看更多>>摘要：Photovoltaic(PV)power forecasting is essential for secure operation of a power system.Effective prediction of PV power can improve new energy consumption capacity,help power system planning,promote development of smart grids,and ultimately support construction of smart energy cities.However,different from centralized PV power forecasts,three critical chal-lenges are encountered in distributed PV power forecasting:1)lack of on-site meteorological observation,2)leveraging extrane-ous data to enhance forecasting performance,3)spatial-temporal modelling methods of meteorological information around the distributed PV stations.To address these issues,we propose a Graph Spatial-Temporal Attention Neural Network(GSTANN)to predict the very short-term power of distributed PV.First,we use satellite remote sensing data covering a specific geographical area to supplement meteorological information for all PV stations.Then,we apply the graph convolution block to model the non-Euclidean local and global spatial dependence and design an attention mechanism to simultaneously derive temporal and spatial correlations.Subsequently,we propose a data fusion module to solve the time misalignment between satellite remote sensing data and surrounding measured on-site data and design a power approximation block to map the conversion from solar irradiance to PV power.Experiments conducted with real-world case study datasets demonstrate that the prediction performance of GSTANN outperforms five state-of-the-art baselines.
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