Global energy transition revolution and the connotation and pathway of the green and intelligent energy system

The essence of energy system transition is the"energy revolution':The development of the"resource-dominated"energy system with fossil energy as the mainstay has promoted human progress,but it has also triggered energy crisis and ecological environment crisis,which is not compatible with the new demands of the new round of scientific and technological revolution,industrial transformation,and sustainable human development.It is in urgent need to research and develop a new-type energy system in the context of carbon neutrality.In the framework of"technique-dominated"new green and intelligent energy system with"three new"of new energy,new power and new energy storage as the mainstay,the"super energy basin"concepts with the Ordos Basin,Nw China as a representative will reshape the concept and model of future energy exploration and development.In view of the"six inequalities"in global energy and the resource conditions of"abundant coal,insufficient oil and gas and infinite new energy"in China,it is suggested to deeply boost"China energy revolution',sticking to the six principles of independent energy production,green energy supply,secure energy reserve,efficient energy consumption,intelligent energy management,economical energy cost;enhance"energy scientific and technological innovation"by implementing technique-dominated"four major science and technology innovation projects',namely,clean coal project,oil production stabilization and gas production increasing project,new energy acceleration project,and green-intelligent energy project;implement"energy transition"by accelerating the green-dominated"four-modernization development',namely,fossil energy cleaning,large-scale new energy,coordinated centralized energy distribution,intelligent multi-energy management,so as to promote the exchange of two 80%s"in China's energy structure and construct the new green and intelligent energy system.

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

Integrated energy systems(IESs)can improve energy efficiency and reduce carbon emissions,essential for achieving peak carbon emissions and carbon neutrality.This study investigated the characteristics of the CHP model considering P2G and carbon capture systems,and a two-stage robust optimization model of the electricity-heat-gascold integrated energy system was developed.First,a CHP model considering the P2G and carbon capture system was established,and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived,which proved that the model could weaken the electric-thermal coupling characteristics,increase the electric power regulation range,and reduce carbon emissions.Subsequently,a two-stage robust optimal scheduling model of an IES was constructed,in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs.The objective function in the real-time scheduling stage was to minimize the equipment operating costs,carbon emission costs,wind curtailment,and solar curtailment costs,considering multiple uncertainties.Finally,after the objective function is linearized with a ψ-piecewise method,the model is solved based on the C&CG algorithm.Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

A coordinated operation method of wind-PV-hydrogenstorage multi-agent energy system

Wind-photovoltaic(PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization.In this study,a coordinated operation method was proposed for a wind-PVhydrogen-storage multi-agent energy system.First,a coordinated operation model was formulated for each agent considering peer-to-peer power trading.Second,a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers.Third,a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies.Finally,the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures,and the operational revenues of the wind power,PV,hydrogen,and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%,233.28%,16.75%,and 145.56%,respectively,compared with the independent operation model.

Optimal scheduling of a township integrated-energy system using the adjustable heat-electricity ratio model

With the expansion and implementation of rural revitalization strategies,there is a constant need for new energy sources for the construction of new townships.Consequently,integrated energy systems with the interconnection and interaction of multiple energy sources are developing rapidly.Biomass energy,a renewable green energy source with low pollution and wide distribution,has significant application potential in integrated energy systems.Considering the application of biomass energy in townships,this study established an integrated biomass energy system and proposed a model to optimize its operation.Lowest economic cost and highest clean energy utilization rate were considered as the objective functions.In addition,a plan was suggested to adjust the heat-electricity ratio based on the characteristics of the combined heat and power of the biomass.Finally,a simulation analysis conducted for a town in China was discussed,demonstrating that the construction of a township integrated-energy system and the use of biomass can significantly reduce operating costs and improve the energy utilization rate.Moreover,by adjusting the heat-electricity ratio,the economic cost was further reduced by 6.70%,whereas the clean energy utilization rate was increased by 5.14%.

Optimal Operation Strategy of Electricity-Hydrogen Regional Energy System under Carbon-Electricity Market Trading

Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

Coordinated restoration optimization of power-gas integrated energy system with mobile emergency sources

In an integrated energy system(IES) composed of multiple subsystems, energy coupling causes an energy supply blockage or shutdown in one subsystem, thereby affecting the energy flow distribution optimization of other subsystems.The energy supply should be globally optimized during the IES energy supply restoration process to produce the highest restoration net income. Mobile emergency sources can be quickly and flexibly connected to supply energy after an energy outage to ensure a reliable supply to the system, which adds complexity to the decision. This study focuses on a powergas IES with mobile emergency sources and analyzes the coupling relationship between the gas distribution system and the power distribution system in terms of sources, networks, and loads, and the influence of mobile emergency source transportation. The influence of the transient process caused by the restoration operation of the gas distribution system on the power distribution system is also discussed. An optimization model for power-gas IES restoration was established with the objective of maximizing the net income. The coordinated restoration optimization decision-making process was also built to realize the decoupling iteration of the power-gas IES, including system status recognition, mobile emergency source dispatching optimization, gas-to-power gas flow optimization, and parallel intra-partition restoration scheme optimization for both the power and gas distribution systems. A simulation test power-gas IES consisting of an 81-node medium-voltage power distribution network, an 89-node medium-pressure gas distribution network, and four mobile emergency sources was constructed. The simulation analysis verified the efficiency of the proposed coordinated restoration optimization method.

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

With increasing reforms related to integrated energy systems(IESs),each energy subsystem,as a participant based on bounded rationality,significantly influences the optimal scheduling of the entire IES through mutual learning and imitation.A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives.This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation.The studied IES includes cogeneration,power-to-gas,and carbon capture systems.Based on the Stackelberg master-slave game theory,sellers are used as leaders in the upper layer to set the prices of electricity and heat,while energy producers,energy storage providers,and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system.An IES bilayer optimization model based on the Stackelberg master-slave game was developed.Finally,the Karush-Kuhn-Tucker(KKT)condition and linear relaxation technology are used to convert the bilayer game model to a single layer.CPLEX,which is a mathematical program solver,is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system.As an experimental demonstration,we simulated an IES coupled with an IEEE 39-node electrical grid system,a six-node heat network system,and a six-node gas network system.The simulation results confirm the effectiveness and feasibility of the proposed model.

Sustainable Development of Energy Systems and Climate Systems:Key Issues and Perspectives

Climate change and energy security issues are prominent challenges in current energy system management,which should be governed synergistically due to the feedback relationships between them.The“Energy Systems Management and Climate Change”Special Collection Issue in the journal of Energy Engineering provide insights into the field of energy systems management and climate change.From an extended perspective,this study discusses the key issues,research methods and models for energy system management and climate change research.Comprehensive and accurate prediction of energy supply and demand,the evaluation on the energy system resilience to climate change and the coupling methodology application of both nature and social science field maybe the frontier topics around achieving sustainable development goals of energy systems.

An Investigation of Battery Energy Storage Aided Wind-Coal Integrated Energy System

This paper studies the feasibility of a supply-side wind-coal integrated energy system.Based on grid-side data,the load regulation model of coal-fired power and the wind-coal integrated energy system model are established.According to the simulation results,the reasons why the wind-coal combined power supply is difficult to meet the grid-side demand are revealedthrough scenario analysis.Basedon thewind-coal combinedoperation,a wind-coalstorage integrated energy system was proposed by adding lithium-iron phosphate battery energy storage system(LIPBESS)to adjust the load of the system.According to the four load adjustment scenarios of grid-side instructions of the wind-coal system,the difficulty of load adjustment in each scenario is analyzed.Based on the priority degree of LIPBESS charge/discharge in four scenarios at different time periods,the operation mode of two charges and two discharges per day was developed.Based on the independent operation level of coal-fired power,after the addition of LIPBESS(5.5 MWh),the average qualified rate of multi-power operation in March and June reached the level of independent operation of coal-fired power,while the average qualified rate of the remaining months was only 5.4%different from that of independent operation of coal-fired power.Compared with the wind storage mode,the energy storage capacity and investment cost of wind-coal-storage integrated energy system are reduced by 54.2%and 53.7%,respectively.

The Low-Carbon Transition of Energy Systems:A Bibliometric Review from an Engineering Management Perspective

As a major solution to climate change,the low-carbon transition of energy systems has received growing attention in the past decade.This paper presents a bibliometric review of the literature on the low-carbon transition of energy systems from an engineering management perspective.First,the definition and boundaries of the energy system transition are clarified,covering transformation of the energy structure,decarbonization of fossil fuel utilization,and improvement in energy efficiency.Second,a systematic search of the related literature and a bibliometric analysis are conducted to reveal the research trends.It is found that the number of related publications has been growing exponentially during the past decade,with researchers from China,the United Kingdom,the United States,Germany,and the Netherlands comprising the majority of authors.Related studies with interdisciplinary characteristics appear in journals focusing on energy engineering,environmental science,and social science related to energy issues.Four major research themes are identified by clustering the existing literature:(1)low-carbon transition pathways with different spatiotemporal scales and transition constraints;(2)low-carbon technology diffusion with a focus on renewable energy technologies,pollution control technologies,and other technologies facilitating the energy transition;(3)infrastructure network planning for energy systems covering various sectors and regions;and(4)transition-driving mechanisms from the political,economic,social,and natural perspectives.These four topics play distinct but mutually supportive roles in facilitating the low-carbon transition of energy systems,and require more in-depth research on designing resilient low-carbon transition pathways with coordinated goals,promoting low-carbon technologies with cost-effective and reliable infrastructure network deployment,and balancing multi-level risks in various systems.Finally,business models,nongovernment actors,energy justice,deep decarbonization,and zero-energy buildings are recognized as emerging hot topics.

Review of multi-objective optimization in long-term energy system models

Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues.The conventional optimization of long-term energy system models focuses on a single economic goal.However,the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives,such as environmental,social,and energy security.Therefore,a multi-objective optimization of long-term energy system models has been developed.Herein,studies pertaining to the multi-objective optimization of long-term energy system models are summarized;the optimization objectives of long-term energy system models are classified into economic,environmental,social,and energy security aspects;and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers.Finally,the key development direction of the multi-objective optimization of energy system models is discussed.

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.

A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.