Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Probabilistic small signal stability analysis of power system with wind power and photovoltaic power based on probability collocation method

Recently, with increasing improvements in the penetration of wind power and photovoltaic power in the world, probabilistic small signal stability analysis(PSSSA) of a power system consisting of multiple types of renewable energy has become a key problem. To address this problem, this study proposes a probabilistic collocation method(PCM)-based PSSSA for a power system consisting of wind farms and photovoltaic farms. Compared with the conventional Monte Carlo method, the proposed method meets the accuracy and precision requirements and greatly reduces the computation; therefore, it is suitable for the PSSSA of this power system. Case studies are conducted based on a 4-machine 2-area and New England systems, respectively. The simulation results show that, by reducing synchronous generator output to improve the penetration of renewable energy, the probabilistic small signal stability(PSSS) of the system is enhanced. Conversely, by removing part of the synchronous generators to improve the penetration of renewable energy, the PSSS of the system may be either enhanced or deteriorated.

Distributed Stochastic Scheduling of Massive Backup Batteries in Cellular Networks for Operational Reserve and Frequency Support Ancillary Services

Base station(BS)backup batteries(BSBBs),with their dispatchable capacity,are potential demand-side resources for future power systems.To enhance the power supply reliability and post-contingency frequency security of power systems,we propose a two-stage stochastic unit commitment(UC)model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks,in which the minimum backup energy demand is considered to ensure BS power supply reliability.The energy,operational reserve,and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy(VRE)scenarios.Furthermore,we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center(DC)and BSBBs.The BS model parameters are stored and processed locally,while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework,which safeguards BS privacy effectively.Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation,ensuring post-contingency frequency security,enhancing operational economics,and fully utilizing BSBBs'energy and power capacity.Besides,the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations.Additionally,numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.

Robustness Assessment of Wind Power Generation Considering Rigorous Security Constraints for Power System: A Hybrid RLO-IGDT Approach

Fossil fuel depletion and environmental pollution problems promote development of renewable energy(RE)glob-ally.With increasing penetration of RE,operation security and economy of power systems(PS)are greatly impacted by fluctuation and intermittence of renewable power.In this paper,information gap decision theory(IGDT)is adapted to handle uncertainty of wind power generation.Based on conventional IGDT method,linear regulation strategy(LRS)and robust linear optimization(RLO)method are integrated to reformulate the model for rigorously considering security constraints.Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS.Moreover,a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming(MILP)problem for convenient optimization without robustness loss.Finally,results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.Index Terms-Hybrid RLO-IGDT approach,information gap decision theory(IGDT),operation security,robustness assessment,robustness security region(RSR).

Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system.To improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of GTS.First,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability events.Then,linear action space is developed to reduce dimensionality of action space for multiple controllable generators.Especially,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process.This can enhance the efficiency and learning process.Moreover,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning ability.Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

Power-to-Hydrogen by Electrolysis in Carbon Neutrality: Technology Overview and Future Development

Power-to-hydrogen by electrolysis(PtHE)is a promising technology in the carbon-neutral evolution of energy.PtHE not only contributes to renewable energy integration but also accelerates decarbonization in industrial sectors through green hydrogen production.This paper presents a comprehensive review of PtHE technology.First,technical solutions in PtHE technology are introduced to clarify pros and cons of one another.Besides,the multiphysics coupling and the multi-energy flow are investigated to reveal the insight mechanism during operation of compactly assembled industrial PtHE plants.Then,the development trends of core components in PtHE plants,including electrocatalysts,electrode plates and operation strategy,are reviewed,respectively.Research thrusts needed for PtHE in carbon-neutral transition are also summarized.Finally,three configurations of the PtHE plant in energy system integration are introduced,which can achieve renewable energy integration and efficient energy utilization.Index Terms-Carbon neutrality,power-to-hydrogen nby electrolysis(PtHE),multiphysics coupling,multidisciplinary.

Development and advances in modular multilevel converter based HVDC projects in China

1 Introduction To achieve the goal of carbon neutrality,promoting renewable energy integration has become an urgent priority for the green energy transition.Due to the geographical distribution of renewable energy bases,bulk electrical power is transmitted to load centers through long-distance transmission lines.The line-commutated converter(LCC)based high voltage direct current(HVDC)technology,emerging in the 1970s,is the most mature solution.

Grid Forming Converters in Renewable Energy Sources Dominated Power Grid:Control Strategy,Stability,Application,and Challenges

The renewable energy sources(RESs)dominated power grid is an envisaged infrastructure of the future power system,where the commonly used grid following(GFL)control for grid-tied converters suffers from lacking grid support capability,low stability,etc.Recently,emerging grid forming(GFM)control methods have been proposed to improve the dynamic performance and stability of grid-tied converters.This paper reviews existing GFM control methods for the grid-tied converters and compares them in terms of control structure,grid support capability,fault current limiting,and stability.Considering the impact of fault current limiting strategies,a comprehensive transient stability analysis is provided.In addition,this paper explores the typical applications of GFM converters,such as AC microgrid and offshore wind farm high-voltage direct current(OWF-HVDC)integration systems.Finally,the challenges to the GFM converters in future applications are discussed.

Low-carbon unit commitment with intensive wind power generation and carbon capture power plant

The paper proposes a stochastic unit commitment(UC)model to realize the low-carbon operation requirement and cope with wind power prediction errors for power systems with intensive wind power and carbon capture power plant(CCPP).A linear re-dispatch strategy is introduced to compensate the wind power deviation from the spot forecast.The robust optimization technique is employed to obtain a reliable commitment plan against all realizations of wind power within the uncertainty set given by probabilistic forecast.The proposed model is validated with IEEE 39-bus system.The advantages of flexible CCPPs are compared to the normal coal-fueled plants and the impacts of robustness controlling are discussed.

Transmission Network Expansion Planning Considering Uncertainties in Loads and Renewable Energy Resources

This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.

Optimal Trade-Off Between Regulation and Wind Curtailment in the Economic Dispatch Problem

Generator regulation and wind power curtailment are two conflicting ways to deal with the inaccurate prediction and volatility of wind power.This study focuses on planning the day-ahead schedule based on optimal trade-off between regulation of generators and wind curtailment.Compared to traditional economic dispatch methods,the proposed schedule is more robust and adaptive to multiple forecast scenarios instead of a single forecast scenario.The works of this paper are as follows:First,an economic dispatch problem based on multiple scenarios is formulated with the objective of minimizing both generator regulation and wind curtailment.Next,a forecast method for wind power scenarios is given.Finally,the proposed model is verified by comparing with other dispatch models that are based on single forecast scenario.The simulation results demonstrate the effectiveness of the proposed method with less wind curtailment,generator regulation,and operational cost.In addition,the penalty factors are set as parameters and the influences on the generators’regulation and wind curtailment are analyzed,providing the reference for system operators with different regulatory purposes.

Research on hybrid multi-terminal high-voltage DC technology for offshore wind farm integration

Multi-terminal high-voltage DC(MTDC)technology is a promising way to transmit large amounts of offshore wind power to the main grids.This paper proposes a hybrid MTDC scheme to integrate several offshore wind farms into the onshore power grids at different locations.A hybrid four-terminal HVDC system comprising two onshore line commutated converters(LCCs)and two voltage source converters(VSCs)connecting an offshore wind farm is constructed in PSCAD/EMTDC.A coordination control scheme based on the VSCs’AC voltage control and the LCCs’DC voltage droop control is designed to ensure smooth system operation and proper power sharing between onshore AC grids.The operational characteristics of the system are analyzed.In addition,a black start-up method without any auxiliary power supply for the VSCs is proposed.The transmission scheme is tested through simulations under various conditions,including start-up,wind speed variation,and the disconnection of one VSC or of one LCC.

Research on fast solid state DC breaker based on a natural current zero-crossing point

The DC fault characteristics of voltage source converter based high voltage direct current(VSC-HVDC)systems are analyzed in this paper.The phenomenon whereby the capacitor on DC side discharges quickly during a DC fault contributes to a large short-circuit fault current.Neither traditional DC breakers nor DC switches can cut off the fault current under this condition.A fast solid state DC breaker design method is proposed in this paper.This method is based on the fault current characteristics of the inverter in multi-terminal HVDC systems(MTDC),where a fault current appears at the natural zerocrossing point near the inverter.At this point,by coordinating the AC breakers near the rectifier,the DC breaker could reliably cut off the DC fault current and protect the system.A detailed model for this fast solid state DC breaker and its operation sequence are studied,based on this design method.Simulations modeling a five-terminal meshed DC grid and a fast DC breaker were carried out with PSCAD/EMTDC using this design method.The results from the simulations confirmed the validity of the design method.

Transient stability risk assessment of power systems incorporating wind farms

Large-scale wind farm integration has brought several aspects of challenges to the transient stability of power systems.This paper focuses on the research of the transient stability of power systems incorporating with wind farms by utilizing risk assessment methods.The detailed model of double fed induction generator has been established.Wind penetration variation and multiple stochastic factors of power systems have been considered.The process of transient stability risk assessment based on the Monte Carlo method has been described and a comprehensive risk indicator has been proposed.An investigation has been conducted into an improved 10-generator 39-bus system with a wind farm incorporated to verify the validity and feasibility of the risk assessment method proposed.

Fast valve power loss evaluation method for modular multi-level converter operating at high-frequency

There is no common accepted way for calculating the valve power loss of modular multilevel converter(MMC).Valve power loss estimation based on analytical calculation is inaccurate to address the switching power loss and valve power loss estimation based on detailed electro-magnetic simulation is of low speed.To solve this problem,a method of valve power loss estimation based on the detailed equivalent simulation model of MMC is proposed.Results of valve power loss analysis of 201-level 500MW MMC operating at 50Hz~1000Hz are presented.It is seen that the valve power loss of a MMC increased by 12,40 and 93%under 200Hz,500Hz and 1000Hz operating frequency.The article concludes that in a device with isolated inner AC system,MMC operating at higher frequency will be more competitive than typical 50Hz/60Hz MMC with moderate increase of operating power loss and significant reduction of the size of the AC components.

Energy Procurement and Retail Pricing for Electricity Retailers via Deep Reinforcement Learning with Long Short-term Memory

The joint optimization problem of energy procurement and retail pricing for an electricity retailer is converted into separately determining the optimal procurement strategy and optimal pricing strategy,under the“price-taker”assumption.The aggregate energy consumption of end use customers(EUCs)is predicted to solve for the optimal procurement strategy vis a long short-term memory(LSTM)-based supervised learning method.The optimal retail pricing problem is formulated as a Markov decision process(MDP),which can be solved by using deep reinforcement learning(DRL)algorithms.However,the performance of existing DRL approaches may deteriorate due to their insufficient ability to extract discriminative features from the time-series vectors in the environmental states.We propose a novel deep deterministic policy gradient(DDPG)network structure with a shared LSTM-based representation network that fully exploits the Actor’s and Critic’s losses.The designed shared representation network and the joint loss function can enhance the environment perception capability of the proposed approach and further improve the optimization performance,resulting in a more profitable pricing strategy.Numerical simulations demonstrate the effectiveness of the proposed approach.

Dynamic Modeling and Operations of a Heat-power Station System Based on Renewable Energy

The coexistence of pollution from coal-fired heating and inefficient utilization of renewable resources in northern China has raised great concern.Current studies on heat-power systems are primarily focused on traditional combined heat and power(CHP)systems and are limited to static energy flow analysis or the dynamic characteristics of a single system component.Hence,it is difficult to make full use of the complementary potential of electrical energy and heat energy.This paper proposes a heat-power station(HPS)system based on renewable energy for the purpose of utilizing the surplus renewable energy to generate heat and meet heating demands.The overall architecture of the HPS system is established and its operational principle and dynamic characteristics of both the electrical and heating components in the system are analyzed and dynamic mathematical models are also presented.In addition,a coordinated electro-thermal control method based on system characteristics is designed to ensure the normal operation of a HPS.Simulation results verify the effectiveness of the model and control in the case of renewables fuctuation and heat load variations.

Heat Pumps in Denmark:Current Situation in Providing Frequency Control Ancillary Services

-The Danish government set an ambitious goal to achieve a fully renewable-based energy system by 2050.In this context,the integrated electricity and heating system is undergoing rapid development in Denmark as a promising way to accommodate the ever-growing renewable energy sources(RESs).The electric heat pumps(HPs),coupled with the power and heat sectors,can propagate the flexibility on the heat consumer side to the power system operations,playing an important role of improving system flexibility and balancing the variability of the RES.In this paper,the current development situation of HPs in Denmark is analyzed,including both the large-scale HPs in the district heating system and individual HPs on the residential side.The possibility of using HPs to provide frequency control ancillary service(FCAS)is analyzed according to the market and technical requirements of the FCAS in the Danish transmission system and experimental results of representative demonstration projects of HPs.A comprehensive analysis of the advantages,barriers,future prospects,and challenges for using HPs to provide the FCAS are carried out from the perspectives of different entities.

Transmission Network Expansion Planning Considering the Generators’Contribution to Uncertainty Accommodation

This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach.While optimizing the TNEP solution,the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties,which is constrained by the extent to which uncertain parameters may change the operational range of generators,and network topology.This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization,and with the genetic algorithm,the optimal TNEP solution is derived iteratively.Compared with other robust TNEP results tested on IEEE 24-bus systems,the proposed method produces a least-cost expansion plan without losing robustness.In addition,the contribution that each generator can make to accommodate with every uncertainty is optimally quantified.Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.

Foreword for the Special Section on Sustainable Power Network Planning

OVER the past decade,renewable energy has seen rapid development across the world with a total installed capacity of wind generation reaching 435 GW and photovoltaic 223 GW by the end of 2015.This trend is likely to accelerate in the foreseeable future,especially in China where the government has committed to the goal of clean energy contributing 20% of all energy consumption by 2030.