Energy hub-based optimal planning for integrated energy systems considering part-load characteristics and synergistic effect of equipment

Integrated energy systems(lESs)represent a promising energy supply model within the energy internet.However,multi-energy flow coupling in the optimal configuration of IES results in a series of simplifications in the preliminary planning,affecting the cost,efficiency,and environmental performance of IES.A novel optimal planning method that considers the part-load characteristics and spatio-temporal synergistic effects of IES components is proposed to enable a rational design of the structure and size of IES.An extended energy hub model is introduced based on the“node of energy hub”concept by decomposing the IES into different types of energy equipment.Subsequently,a planning method is applied as a two-level optimization framework-the upper level is used to identify the type and size of the component,while the bottom level is used to optimize the operation strategy based on a typical day analysis method.The planning problem is solved using a two-stage evolutionary algorithm,combing the multiple-mutations adaptive genetic algorithm with an interior point optimization solver,to minimize the lifetime cost of the IES.Finally,the feasibility of the proposed planning method is demonstrated using a case study.The life cycle costs of the IES with and without consideration of the part-load characteristics of the components were$4.26 million and$4.15 million,respectively,in the case study.Moreover,ignoring the variation in component characteristics in the design stage resulted in an additional 11.57%expenditure due to an energy efficiency reduction under the off-design conditions.

Optimal Configuration Planning of Multi-energy Microgrid Based on Source-load-temperature Scenarios Deep Joint Generation

An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme scenarios.First,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are obtained.Then,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather module.At the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are expanded.Then,the scenarios are reduced by clustering analysis.The normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by temperature.Finally,the optimal configuration of a multi-energy microgrid system is carried out.Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system.The proposed method can accurately capture the complementary potential of energy sources.And the economy of the system configuration is improved by 14.56%.

Energy storage resources management:Planning,operation,and business model

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring investigation.One of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize it.However,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient management.This study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business model.First of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined planning.This process is generally the first step in deploying ESS.Then,it explores operation management of ESS from the perspectives of state assessment and operation optimization.The so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation optimization.Finally,it discusses the business models of ESS.Traditional business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.