RESEARCH ON OPTIMAL OPERATION OF WATER PRODUCING SYSTEM

Basing on a viewpoint of all sided water producing system,a comprehensive analysis of the operation status and the relationship between each working unit in water producing system,including coagulation,sedimentation and filtration etc.was made.By investigating the influence of backwashing consumption,the amount of sludge ejected from sedimentation tank and coagulant ratio upon the cost,the cost effective function of the system was presented.Afterwards,the solution was found out and the corresponding computer program was developed.When applied with real world data,it proved that this method significantly reduced the water producing cost.

Application of LINGO to the Solution of the Water Supply System′s Optimal Operation Model

In this research, LINGO is used successfully to solve the water supply system′s optimal operation model. Firstly, the language of LINGO and the using method were studied intensively, on the basis of which the model was transformed to LINGO form and solved successfully. Secondly, the research on the interface between LINGO and the popular office software was made. The optimization software was developed, which had Excel as the workspace and LINGO as the core of computation. Through practice, this software was found stable, easy to use and suitable for the application to the water supply corporations.

Optimal Operation for Baoying Pumping Station in East Route Project of South-to-North Water Transfer

Baoying pumping station is a part of source pumping stations in East Route Project of South-to-North Water Transfer in China. Aiming at the characteristics of head varying, and making use of the function of pump adjustable blade, mathematical models of pumping station optimal operation are established and solved with genetic algorithm. For different total pumping discharge and total pumping volume of water per day, in order to minimize pumping station operation cost, the number and operation duties of running pump units are respectively determined at different periods of time in a day. The results indicate that the saving of electrical cost is significantly effected by the schemes of adjusting blade angles and time-varying electrical price when pumping certain water volume of water per day, and compared with conventional operation schemes （namely, the schemes of pumping station operation at design blade angles based on certain pumping discharge）, the electrical cost is saved by 4.73%-31.27%. Also, compared with the electrical cost of conventional operation schemes, the electrical cost is saved by 2.03%-5.79% by the schemes of adjusting blade angles when pumping certain discharge.

Optimal operation of water supply systems with tanks based on genetic algorithm

In view of the poor water supply system’s network properties, the system’s complicated network hydraulic equations were replaced by macroscopic nodal pressure model and the model of relationship between supply flow and water source head. By using pump-station pressure head and initial tank water levels as decision variables, the model of optimal allocation of water supply between pump-sources was developed. Genetic algorithm was introduced to deal with the model of optimal allocation of water supply. Methods for handling each constraint condition were put forward, and overcome the shortcoming such as premature convergence of genetic algorithm; a solving method was brought forward in which genetic algorithm was combined with simulated annealing technology and self-adaptive crossover and mutation probabilities were adopted. An application example showed the feasibility of this algorithm.

Optimal operation of water distribution networks under local pipe failures

The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic model for a network under a local pipe failure was established by the statistical regression. After the operation objectives under a local pipe failure were determined, the optimal operation model was developed and solved by the genetic algorithm. The program was developed and examined by a city distribution network. The optimal operation alternative shows that the electricity cost is saved approximately 11%, the income of the water corporation is increased approximately 5%, and the pressure in the water distribution network is distributed evenly to ensure the network safe operation. Therefore, the proposed method for optimal operation under local pipe failure is feasible and cost-effective.

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.

Optimal operation of cold–heat–electricity multi-energy collaborative system based on price demand response

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

Output neighboring-extremal and NCO tracking control in the optimal operation of CSTR

A new optimizing framework of process operation is proposed to deal with optimizing op- eration of continuous stirred tank reactor （CSTR）. The optimization framework includes two layers： the first layer, necessary condition of optimally （NCO） tracking controller, calculates the optimal set-point of the process; and the second layer, output neighboring-extremal controller, calculates the input values of the controlled plant. The algorithm design and convergent analysis of output neighboring-extremal controller are discussed emphatically, and in the case of existing parametric uncertainty, the approach is shown to converge to the optimum atmost in two iterations. At last the approach is illustrated by simulation results for a dynamic CSTR.

Two-stage Planning Method for Energy Router Considering Its Optimal Operation in Distribution Networks

An energy router can effectively optimize the network loss and power quality of distribution networks. The optimal configuration of the energy router is relatively complex because issues dynamically influence each other, such as the location of the energy router, optimal number and capacity of its ports, and building new distribution lines for the ports. Presently, there has been very few research studies on the practical problems for the energy router. In this paper, a planning model of an energy router combined with the distribution network is established, which fully exploits the active and reactive power control abilities of the energy router to optimize the operation of the grid. The configuration problem is decoupled into two stages. The upper layer determines the location of the energy router and the parameters of the candidate new distribution lines for the ports. The lower layer calculates the optimal configuration of the energy router by minimizing the total annual cost. Because of the same rated bus voltage in the distribution network, the existing structure of the energy router is changed to be more applicable for the distribution network, and then the power flow model of the energy router is also modified. Due to the nonlinearity of the model, the planning model is converted into a mixed-integer second-order cone model to solve efficiently. In addition, some core factors influencing the optimal scheme of the energy router are also analyzed in this paper. Simulation results show that the optimal scheme of the energy router can significantly improve the economic deployment of the energy router, and optimize the network loss and power quality of the distribution network.

Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-scale Renewable Energy

As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system development.To effectively promote the efficiency and economics of energy storage,centralized shared energy storage(SES)station with multiple energy storage batteries is developed to enable energy trading among a group of entities.In this paper,we propose the optimal operation with dynamic partitioning strategy for the centralized SES station,considering the day-ahead demands of large-scale renewable energy power plants.We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory.This model is decomposed into two subproblems:the operation profit maximization problem with energy trading and the leasing payment bargaining problem.The distributed alternating direction multiplier method(ADMM)is employed to address the subproblems separately.Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities,enhances the actual utilization rate of energy storage,and increases the profits of each participating entity.The results confirm the practicality and effectiveness of the strategy.

Optimal Operation of Flexible Distribution Networks for Security Improvement Considering Active Management

This paper presents a stochastic optimal operation problem of gas turbine integrated distribution networks in the presence of active management schemes,which is formulated as a multi-objective chance-constrained mixed integer nonlinear programming problem.The control variables are the on-load tap-changer tap position,the power provided by the distributed generation(DG),the DG power factor angle,the load participating in demand side management and the switch status.The objectives defined in this paper are to simultaneously minimize the expectation cost and variation coefficient of security distance.Uncertainties related to DG output and load fluctuation and fault power restoration under contingencies are also considered in the optimization problem.The collaboration of normal boundary intersection and the dynamic niche differential evolution algorithm is proposed to handle the optimal operation mode.Simulation results are presented and demonstrate the effectiveness of the proposed model.Compared with the operation result without the consideration of security,the security-constrained operation can reduce the expectation cost.Therefore,the proposed optimization is reasonable and valuable.

Neural Network Based Feasible Region Approximation Model for Optimal Operation of Integrated Electricity and Heating System

This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)considering privacy protection.In this model,a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints.Based on the received approximation models of DHSs and detailed electricity system model,the electricity operator conducts centralized optimization,and then sends specific heating generation plans back to corresponding heating operators.Furthermore,subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan.In this scheme,optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters.Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.

Optimal Operation of Micro-energy Grids Considering Shared Energy Storage Systems and Balanced Profit Allocations

Following the unprecedented generation of renewable energy,Energy Storage Systems(ESSs)have become essential for facilitating renewable consumption and maintaining reliability in energy networks.However,providing an individual ESS to a single customer is still a luxury.Thus,this paper aims to investigate whether the Shared-ESS can assist energy savings for multiple users through Peer-to-Peer(P2P)trading.Moreover,with the increasing number of market participants in the integrated energy system(IES),a benefit allocation scheme is necessary,ensuring reasonable benefits for every user in the network.Using the multiplayer cooperative game model,the nucleolus and the Shapley value methods are adopted to evaluate the benefit allocation between multiple users.Numerical analyses based on multiple micro-energy grids are performed,so as to assess the performance of the Shared-ESS and the proposed benefit allocation scheme.The results show that the micro-energy grid cluster can save as much as 38.15%of the total energy cost with Shared-ESS being equipped.The following conclusions can be drawn:the Shared-ESS can significantly reduce the operating costs of the micro-energy grid operator,promote the consumption of renewable energy,and play the role of peak-shaving and valley-filling during different energy usage periods.In addition,it is reflected that the multiplayer cooperative game model is effective in revealing the interaction between the micro-energy grids,which makes the distribution results more reasonable.

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%.

Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

1 Introduction.With the continuous growth of the global population,the energy demand continues to increase.However,due to the dominance of fossil fuels in global energy and fossil fuels are non-renewable,it has led to the global energy crisis[1].Besides,the use of fossil fuels will generate a mass of air pollutants(e.g.,carbon dioxide,sulfur dioxide,etc.),which will cause serious environmental pollution,climate change[2],etc.To resolve the aforementioned issues,countries around the world have implemented a variety of measures hoping to fundamentally adjust the global energy structure and achieve sustainable development.Thereinto,“Paris Agreement”reached in 2015 under the framework of“United Nations Framework Convention on Climate Change”aims to control the increase in the average temperature of the globe to within 2°C below preindustrial levels,and thereafter to peak global greenhouse gas emissions as soon as possible,continuously decreasing thereafter[3].United Kingdom plans to reduce the average exhaust emissions of“new cars”to approximately 50–70 g/km by 20230,which is roughly half of what it is now[4].In addition,China proposed a plan at“United Nations General Assembly”in 2020 to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060.It is a fact that the whole world is committed to changing the current energy structure,protecting the Earth’s ecology,and achieving global sustainable development[5].

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Optimal Operation of Energy Hub Based Micro-energy Network with Integration of Renewables and Energy Storages

This study proposes an optimized model of a micro-energy network(MEN)that includes electricity and natural gas with integrated solar,wind,and energy storage systems(ESSs).The proposed model is based on energy hubs(EHs)and it aims to minimize operation costs and greenhouse emissions.The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects.A general algebraic modeling system(GAMS)is used to solve the optimal operation problem in the MEN.The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons.It also yields significant reductions in operation costs and emissions.The proposed model can contribute to future research by providing a more efficient network model(as compared with the traditional electricity supply system)to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.

Incorporating Optimal Operation Strategies into Investment Planning for Wind/Electrolyser System

As a conducive and prevalent technique for producing green hydrogen,hybrid wind-based electrolyzer system requires both effective planning and operation to realize its techno-economic value.Majority of the existing studies are focused on either of these two,but none of them sufficiently emphasize on their interrelationship.In this paper,we propose a two-stage multi-objective optimization framework to reveal optimal investment plans considering various operational strategies,such as economic revenue maximization and green hydrogen production maximization.The results reveal that:1)A trade-off exists between system investment and the capacity to accomplish optimal operational performance.For example,the system demands flexibility to boost operational profits,but this results in high investment costs.2)Differentiated operation objectives generate different component capacities during the planning phase.3)Regarding a wind-hydrogen system with gas storage,the Pareto optimal design manifesting the trade-off between system investment and prime operational performance can be actualized along the margins of a feasible solution.

Optimal Operation of Energy Internet Based on User Electricity Anxiety and Chaotic Spatial Variation Particle Swarm Optimization

Ignoring load characteristics and not considering user feeling with regard to the optimal operation of Energy Internet（EI） results in a large error in optimization. Thus, results are not consistent with the actual operating conditions. To solve these problems, this paper proposes an optimization method based on user Electricity Anxiety（EA） and Chaotic Space Variation Particle Swarm Optimization（CSVPSO）. First, the load is divided into critical load, translation load, shiftable load, and temperature load. Then, on the basis of the different load characteristics,the concept of the user EA degree is presented, and the optimization model of the EI is provided. This paper also presents a CSVPSO algorithm to solve the optimization problem because the traditional particle swarm optimization algorithm takes a long time and particles easily fall into the local optimum. In CSVPSO, the particles with lower fitness value are operated by using cross operation, and velocity variation is performed for particles with a speed lower than the setting threshold. The effectiveness of the proposed method is verified by simulation analysis.Simulation results show that the proposed method can be used to optimize the operation of EI on the basis of the full consideration of the load characteristics. Moreover, the optimization algorithm has high accuracy and computational efficiency.