A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.

Optimization of Chiller Loading Problem Using Improved Golden Jackal Optimization Algorithm Leads to Reduction in Energy Consumption

This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.

Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.

Optimization of energy consumption structure based on carbon emission reduction target:A case study in Shandong Province,China

It is urgent to significantly reduce greenhouse gas emissions to actively deal with global warming.This paper investigates Shandong Province,a typical province of energy consumption,as the research object,aiming to optimize total energy consumption and consumption structure in the future planning year.This paper constructs a methodological system to optimize energy consumption structure in Shandong Province,using a scenario combination of system dynamics(SD)prediction and analysis based on the coupling of key scenario elements affecting different energy consumption from different perspectives.Structural equation modeling and SD sensitivity analysis indicate an overlap between key factors restricting energy consumption.Pairing the key scenario factors can better reflect the internal mechanism of energy consumption development.Based on this,21 scenarios based on different combinations of the key elements are constructed.Through SD prediction and analysis,the most suitable scenario mode for optimizing energy consumption structure in Shandong Province is selected.This paper provides a suitable development range for the average gross domestic product growth rate,the proportion of secondary industry,energy consumption intensity of secondary industry,and the urbanization rate for Shandong Province.This paper can provide a reference for similar research and the government in formulating the optimization scheme of energy consumption structure.

Based on the water source heat pump system operation control and energy consumption optimization

The contradiction between the increasing material demand and resource,is the country has faced problems,to better solve the material demand and the contradiction between the environment and resources,is applied to the development of new energy,new energy,not only can alleviate people and resources,environment and resources,the contradiction between people and the environment,also can promote the sustainable development of world economy,HVAC technology has emerged a new generation of energysaving technology,HVAC has the characteristics of low consumption,low pollution,is a development of technology,to be promoted for environmentfriendly,resource-conserving society has an important role in promoting.This paper focuses on the HVAC technology,water source heat pump system operation control and energy consumption optimization,for the relevant personnel reference.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difficult areas of study.As every sensor node has afinite amount of energy.Battery power is the most significant source in the WSN.Clustering is a well-known technique for enhan-cing the power feature in WSN.In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing opti-mization.By using distributed data transmission modification,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area.To begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption.The Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efficient Cluster Head(CH).It also improves the network’s longevity and optimizes the routing.As a result of the study’sfindings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

Energy Proficient Reduced Coverage Set with Particle Swarm Optimization for Distributed Sensor Network

Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data.WSNs use sensor nodes(SNs)to collect and transmit data.However,the power supplied by the sensor network is restricted.Thus,SNs must store energy as often as to extend the lifespan of the network.In the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best ones.After the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to WSNs.Here each SN is considered a player in the game.The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest.The proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of networks.The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.

Optimized model-based control of main mine ventilation air flows with minimized energy consumption

In early 2018,the Boliden Garpenberg operation implemented an optimized control strategy as an addition to the existing ventilation on demand system.The purpose of the strategy is to further minimize energy use for main and booster fans,whilst also fulfilling airflow setpoints without violating constraints such as min/max differential pressure over fans and interaction of air between areas in mines.Using air flow measurements and a dynamical model of the ventilation system,a mine-wide coordination control of fans can be carried out.The numerical model is data driven and derived from historical operational data or step changes experiments.This makes both initial deployment and lifetime model maintenance,as the mine evolves,a comparably easy operation.The control has been proven to operate in a stable manner over long periods without having to re-calibrate the model.Results prove a 40%decrease in energy use for the fans involved and a greater controllability of air flow.Moreover,a 15%decrease of the total air flow into the mine will give additional proportional heating savings during winter periods.All in all,the multivariable controller shows a correlation between production in the mine and the ventilation system performance superior to all of its predecessors.

Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort:A Case Study of a Residential Building in Bushehr,Iran

In Iran,the intensity of energy consumption in the building sector is almost 3 times the world average,and due to the consumption of fossil fuels as the main source of energy in this sector,as well as the lack of optimal design of buildings,it has led to excessive release of toxic gases into the environment.This research develops an efficient approach for the simulation-oriented Pareto optimization(SOPO)of building energy efficiency to assist engineers in optimal building design in early design phases.To this end,EnergyPlus,as one of the most powerful and well-known whole-building simulation programs,is combined with the Multi-objective Ant Colony Optimization(MOACO)algorithm through the JAVA programming language.As a result,the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties.To evaluate the effectiveness of the developed method,it was performed on a residential building located in the hot and semi-arid region of Iran.To obtain the optimum configuration of the building under investigation,the building rotation,window-to-wall ratio,tilt angle of shading device,depth of shading device,color of the external walls,area of solar collector,tilt angle of solar collector,rotation of solar collector,cooling and heating setpoints of heating,ventilation,and air conditioning(HVAC)system are chosen as decision variables.Further,the building energy consumption(BEC),solar collector efficiency(SCE),and predicted percentage of dissatisfied(PPD)index as a measure of the occupants'thermal comfort level are chosen as the objective functions.The single-objective optimization(SO)and Pareto optimization(PO)are performed.The obtained results are compared to the initial values of the basic model.The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs,owing to the lower value of the deviation index.Moreover,the optimal solutions extracted through the PO are depicted in the form of Pareto fronts.Eventually,the Linear Programming Technique for Multidimensional Analysis of Preference(LINMAP)technique as one of the well-known multi-criteria decision-making(MCDM)methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions.Further,the results of PO show that although BEC increases from 136 GJ to 140 GJ,PPD significantly decreases from 26%to 8%and SCE significantly increases from 16%to 25%.The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making.In addition,the method presented in this study can be applied to different types of buildings in different climates.

Optimal Scheduling Strategy for Energy Consumption Minimization of Hydro-Thermal Power Systems

A comparison analysis based method for computing the water consumption volume needed for electric energy production of optimal scheduling in hydro-thermal power systems is presented in this paper. The electric energy produced by hydroelectric plants and coal-fired plants is divided into 4 components: potential energy, kinetic energy, water-deep pressure energy and reservoir energy. A new and important concept, reservoir energy, is proposed, based on which is divided into a number of water bodies, for example 3 water bodies, and a reservoir is analyzed in a new way. This paper presents an optimal scheduling solution of elec-tric energy production of hydro-thermal power systems based on multi-factors analytic method, in which some important factors, such as load demand, reservoir in-flow, water consumption volume increment rate of hydroelectric plants or converted from coal-fired plants, and so on are given to model the objective function and the constraints. A study example with three simulation cases is carried out to illustrate flexibility, adapta-bility, applicability of the proposed method.

Multi-Objective Optimization of External Louvers in Buildings

Because solar energy is among the renewable energies,it has traditionally been used to provide lighting in buildings.When solar energy is effectively utilized during the day,the environment is not only more comfortable for users,but it also utilizes energy more efficiently for both heating and cooling purposes.Because of this,increasing the building’s energy efficiency requires first controlling the amount of light that enters the space.Considering that the only parts of the building that come into direct contact with the sun are the windows,it is essential to make use of louvers in order to regulate the amount of sunlight that enters the building.Through the use of Ant Colony Optimization(ACO),the purpose of this study is to estimate the proportions and technical specifications of external louvers,as well as to propose a model for designing the southern openings of educational space in order to maximize energy efficiency and intelligent consumption,as well as to ensure that the appropriate amount of light is provided.According to the findings of this research,the design of external louvers is heavily influenced by a total of five distinct aspects:the number of louvers,the depth of the louvers,the angle of rotation of the louvers,the distance between the louvers and the window,and the reflection coefficient of the louvers.The results of the 2067 simulated case study show that the best reflection rates of the louvers are between 0 and 15 percent,and the most optimal distance between the louvers and the window is in the range of 0 to 18 centimeters.Additionally,the results show that the best distance between the louvers and the window is in the range of 0 to 18 centimeters.

Systematic investigation of SO_(2) adsorption and desorption by porous powdered activated coke:Interaction between adsorption temperature and desorption energy consumption

Porous carbon materials have been widely used for the removal of SO_(2) from flue gas.The main objective of this work is to clarify the effects of adsorption temperature on SO_(2) adsorption and desorption energy consumption.Coal-based porous powdered activated coke(PPAC)prepared in the drop-tube reactor was used in this study.The N_(2) adsorption measurements and Fourier transform infrared spectrometer analysis show that PPAC exhibits a developed pore structure and rich functional groups.The experimental results show that with a decrease in adsorption temperature in the range of 50–150℃,the adsorption capacity of SO_(2) increases linearly;meanwhile,the adsorption capacity of H_(2)O increases,resulting in the increase in desorption energy consumption per unit mass of adsorbent.The processes of SO_(2) and H_(2)O desorption were determined by the temperature-programmed desorption test,and the desorption energies for each species were calculated.Considering the energy consumption per unit of desorption and the total amount of adsorbent,the optimal adsorption temperature yielding the minimum total energy consumption of regeneration is calculated.This study systematically demonstrates the effect of adsorption temperature on the adsorption–desorption process,providing a basis for energy saving and emission reduction in desulfurization system design.

Energy-Constrained Duty-Cycle Optimization for Wireless Implanted Communication Devices

Energy minimization is an important goal in wireless implanted communication devices. In this context, a cross-layer method is used to optimize parameters in different layers of OSI model, but, there are still several challenges affecting the optimization algorithm. The first point is the accurate energy model, and the second point is the suitable channel model exclude traditional free space channel model. In this paper, we establish a system level accurate energy consumption model and build a suitable channel model for implanted communication devices;analysis the energy-constrained duty cycle optimization with a cross-layer method. Simulation results reveal that adaptive duty cycle to minimize the energy consumption of the wireless implanted communication system is implemented based on accurate energy consumption model and channel model. Simulation results show a good performance on energy saving.

LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.