Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

Optimal Planning of Community Integrated Energy Station Considering Frequency Regulation Service

With the extensive integration of high-penetration renewable energy resources,more fast-response frequency regulation(FR)providers are required to eliminate the impact of uncertainties from loads and distributed generators(DGs)on system security and stability.As a high-quality FR resource,community integrated energy station(CIES)can effectively respond to frequency deviation caused by renewable energy generation,helping to solve the frequency problem of power system.This paper proposes an optimal planning model of CIES considering FR service.First,the model of FR service is established to unify the time scale of FR service and economic operation.Then,an optimal planning model of CIES considering FR service is proposed,with which the revenue of participating in the FR service is obtained under market mechanism.The flexible electricity pricing model is introduced to flatten the peak tieline power of CIES.Case studies are conducted to analyze the annual cost and the revenue of CIES participating in FR service,which suggest that providing ancillary services can bring potential revenue.

Multi-energy Storage System Model Based on Electricity Heat and Hydrogen Coordinated Optimization for Power Grid Flexibility

Based on decreasing the flexibility of the power grid through the integration of large-scale renewable energy,a multi-energy storage system architectural model and its coor-dination operational strategy with the same flexibility as in the pumped storage power station and battery energy storage system(BESS)are studied.According to the new energy fluctuation characteristics and the different peak valley parameters in the power grid,this paper proposes a electricity heat hydrogen multi-energy storage system(EHH-MESS)and its coordination and optimization operational model to reduce the curtailment of wind power and photovoltaic(PV)to the power grid and improve the flexibility of the power grid.Finally,this paper studied the simulation model of an energy storage optimization control strategy after the multi-energy storage system is connected to the distribution networks,and analyzed three operational modes of the multi-energy storage system.The simulation results show that the EHH-MESS proposed in this paper has a better power grid regulation flexibility and economy,and can be used to replace the battery energy storage system based on MATLAB.

Availability estimation of wind power forecasting and optimization of day-ahead unit commitment

Due to the uncertainty of the accuracy of wind power forecasting,wind turbines cannot be accurately equated with dispatchable units in the preparation of a dayahead dispatching plan for power grid.A robust optimization model for the uncertainty of wind power forecasting with a given confidence level is established.Based on the forecasting value of wind power and the divergence function of forecasting error,a robust evaluation method for the availability of wind power forecasting during given load peaks is established.A simulation example is established based on a power system in Northeast China and an IEEE 39-node model.The availability estimation parameters are used to calculate the equivalent value of wind power of the conventional unit to participate in the dayahead dispatching plan.The simulation results show that the model can effectively handle the challenge of uncertainty of wind power forecasting,and enhance the consumption of wind power for the power system.

Impact Increment Based Hybrid Reliability Assessment Method for Transmission Systems

This paper proposes an impact-increment-based hybrid(IIHybrid)reliability assessment approach for power transmission systems.The proposed approach integrates the advantages of the impact-increment-based state enumeration method(IISE)and impact-increment-based Monte Carlo simulation(IIMC)to improve computational efficiency and accuracy.The IISE can efficiently assess the impacts of low-order contingencies.The accuracy is,however,sacrificed as highorder contingencies are usually neglected.The IIMC is more suitable for large-scale contingency spaces compared with IISE,although the calculation process is time-consuming.In this paper,the proposed IIHybrid takes advantage of its strengths while avoiding its shortcomings.The IISE and the IIMC are applied to lower and higher contingency spaces respectively.The high-order contingencies elimination technique proposed in our previous studies is still applicable to the IIHybrid.In addition,efficiency can be controlled by modifying the preset parameters to adapt to various scenarios.Case studies are performed on the IEEE 118-bus test system and PEGASE System.The results show that the proposed approach is more efficient and practicable than traditional methods.

Optimal Scheduling of Distribution Network Incorporating Topology Reconfiguration,Battery Energy System and Load Response

Distributed generation(DG)is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy.As the flexible resources in the active distribution network(ADN),battery energy system(BES)and responsive load(RL)are all able to assist renewable DG integration in day-ahead dispatch.In addition,the security and economic level can be significantly improved by adjusting network topology.Therefore,in this paper,a coordinated day-ahead scheduling method incorporating topology reconfiguration,BES optimization and load response is presented to minimize the total day-ahead operational costs in the ADN.Linearized current injection models are presented for renewable DG,RL and BES based on the linear power flow model,and an extensible linear switching operations calculation(ELSOC)method is proposed to address the network reconfiguration.Thus,a mixed integer linear programming(MILP)model is proposed for optimal coordinated operation of an ADN.The correctness and effectiveness of the proposed method are demonstrated by simulations on a modified test system.In addition,the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs,and the results of different uncertainties can further verify the feasibility of the proposed model.

An efficient identity-based signature protocol over lattices for the smart grid

As the promising next-generation power grid,the smart grid has developed rapidly in recent years.The smart grid enables energy to be stored and delivered more efficiently and safely,but user data’s integrity protection has been an important security issue in the smart grid.Although lots of digital signature protocols for the smart grid have been proposed to resolve this problem,they are vulnerable to quantum attacks.To deal with this problem,an efficient identity-based signature protocol on lattices is proposed in this paper.To improve our protocol’s efficiency,the tree of commitments is utilized.Moreover,a detailed performance evaluation of the proposed protocol is made.The performance analysis demonstrates that the potential utility of our protocol in the smart grid is huge.

Decentralizing access control system for data sharing in smart grid

Smart grid enhances the intelligence of the traditional power grid,which allows sharing varied data such as consumer,production,or energy with service consumers.Due to the untrustworthy networks,there exist potential security threats(e.g.,unauthorized access and modification,malicious data theft)hindering the development of smart grid.While several access control schemes have been proposed for smart grid to achieve sensitive data protection and fine-grained identity management,most of them cannot satisfy the requirements of decentralizing smart grid environment and suffer from key escrow problems.In addition,some existing solutions cannot achieve dynamic user management for lacking the privilege revocation mechanism.In this paper,we propose a decentralizing access control system with user revocation to relieve the above problems.We design a new multiple-authority attribute-based encryption(MABE)scheme to keep data confidentiality and adapt decentralizing smart grid applications.We also compare our proposal with the similar solution from both security and performance.The comparing results show that our access control system can achieve a trade-off among confidentiality,authentication,distribution and efficiency in smart grid.