Optimal Operation of Electric Vehicles and Distributed Generation Resources in Smart Grid Considering Load Management

Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply.Despite the complexity of their protection,as well as the operation of distributed generation resources in the distribution network,factors such as improving reliability,increasing production capacity of the distribution network,stabilizing the voltage of the distribution network,reducing peak clipping losses,as well as economic and environmental considerations,have expanded the influence of distributed generation(DG)resources in the distribution network.The location of DG sources and their capacity are the key factors in the effectiveness of distributed generation in the voltage stability of distribution systems.Nowadays,along with the scattered production sources of electric vehicles with the ability to connect to the network,due to having an energy storage system,they are known as valuable resources that can provide various services to the power system.These vehicles can empower the grid or be used as a storage supply source when parked and connected to the grid.This paper introduces and studies a two-stage planning framework for the concurrent management of many electric vehicles and distributed generation resources with private ownership.In the first stage,the aim is to increase the profit of electric vehicles and distributed generation sources;finally,the purpose is to reduce operating costs.The proposed scheduling framework is tested on a distribution network connected to bus 5 of the RBTS sample network.Besides distributed generation sources and electric vehicles,we integrate time-consistent load management into the system.Due to distributed generation sources such as photovoltaic systems and wind turbines and the studied design in the modeling,we use the Taguchi TOAT algorithm to generate and reduce the scenario to ensure the uncertainty in renewable energy.MATLAB software is used to solve the problem and select the optimal answer.

Optimal scheduling of flexible grid-side resources and auxiliary service cost allocation model considering renewable-energy penetration

Renewable energy has penetrated power grid enterprises on a large scale.Due to the intermittency and volatility of renewable energy generation,it is necessary to build new flexible grid-side resources to ensure the safe and stable operation of the power grid,which will cause great pressure on cost allocation for power grid enterprises.This article considers four types of flexible grid-side resources and constructs a dual-level configuration optimization model for flexible grid-side resources under the penetration of renewable energy.Based on the configuration results,the cost scale of flexible grid-side resources is estimated and an improved ancillary service cost allocation model based on the Shapley value method is proposed to smooth the allocation of ancillary service costs in the cost of flexible grid-side resources between the two main bodies of renewable energy and load.The calculation results show that,when the penetration rate of renewable-energy power is 30%and 35%,respectively,the cost of flexible grid-side resources is 9.606 billion yuan and 21.518 billion yuan,respectively.The proportion of ancillary service costs allocated to load is relatively high-about five times that of the ancillary service costs allocated to renewable energy-and the higher the penetration rate of renewable energy,the higher the proportion of ancillary service costs allocated to renewable energy.

Research on the risk-evaluation model of power transmission and transformation projects based on multilevel fuzzy-thought weighting for cloud-model improvement

The risk evaluation of power transmission and transformation projects is a complex and comprehensive evaluation process influenced by many factors and involves many indicators.In order to solve the uncertainty and fuzziness problems in the process of the multilevel fuzzy risk evaluation of power transmission and transformation projects,this paper introduces the cloud theory,which is specialized in the study of uncertainty problems and constructs the multilevel fuzzy comprehensive risk-evaluation model of power transmission and transformation projects based on the improved multilevel fuzzy-thought weighting based on the cloud model.Finally,the risk of the Beijing 220-kV Tangyu power transmission and transformation project is evaluated and the feasibility of the evaluation model is verified.The results of the evaluation and the evaluation layer cloud model are combined with MATLAB simulation to show that the risk level of the project is between large risk and general risk.

Investment risk evaluation of inland floating photovoltaic power plants in China using the HFLTS-TFN method

Inland floating photovoltaic power plants(IFPPPs)are the key to making full use of water advantages to develop solar resources in the future.Identifying the investment risk is an important prerequisite for promoting the projects on a large scale.This paper proposes a model to assess the investment risk of IFPPPs in China.First,this paper identifies the investment risk factors and establishes an evaluation indicator system from four aspects.Second,the indicator data are collected and described by adopting hesitant fuzzy linguistic term sets and triangular fuzzy numbers to ensure soundness and completeness.Third,a weighted method combining the best-worst method and the entropy method are utilized to determine the indicator weights under the consideration of the impact of subjective preferences and objective fairness.Fourth,the results show that the overall risk level of China’s IFPPPs is‘medium low’.Fifth,sensitivity analysis and comparative analysis are implemented to examine the stability of the evaluation results.Finally,this paper also provides some risk-response strategies for the development of China’s IFPPPs from economy,society,technology and environment.