Prospects for Distributed Energy Systems in China Released in Beijing

On the afternoon of October 30,the International Energy Agency(IEA)and china5e.com held a joint press conference in Beijing for the release of an IEA report:Prospects for Distributed Energy Systems in China.At present,China is at a crucial stage of economic restructuring,seeking to transform its mode of economic growth.At the same time,it is also facing increasingly serious environmental challenges such as air pollution.To meet these objectives and promote energy transformation,

A comprehensive review of planning,modeling,optimization,and control of distributed energy systems

Distributed energy system,a decentralized low-carbon energy system arranged at the customer side,is characterized by multi-energy complementarity,multi-energy flow synergy,multi-process coupling,and multi-temporal scales(n-M characteristics).This review provides a systematic and comprehensive summary and presents the current research on distributed energy systems in three dimensions:system planning and evaluation,modeling and optimization,and operation and control.Under the regional environmental,resource,and policy constraints,planning distributed energy systems should fully integrate technical,economic,environmental,and social factors and consider device characteristics,system architecture,and source-load uncertainties.Further,this review presents four modeling perspectives for optimizing and analyzing distributed energy systems,including energy hub,thermodynamics,heat current,and data-driven.The system’s optimal operation and scheduling strategies,disturbance analysis,and related control methods are also discussed from the power system and thermal system,respectively.In all,more research is required for distributed energy systems based on an integrated energy perspective in optimal system structure,hybrid modeling approaches,data-driven system state estimation,cross-system disturbance spread,and multi-subject interaction control.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.