A digital twin model-based approach to cost optimization of residential community microgrids

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.

Cost Optimization of Surface Grinding Process

This paper introduces a new study on cost optimization of surface grinding. In the study, the effects of grinding parameters including the dressing regime parameters, the wheel life and the initial grinding wheel diameter on the exchanged grinding wheel diameter which were investigated. In addition, the influence of cost parameters including the machine tool hourly rate and the grinding wheel cost were taken into account. In order to find the optimum exchanged grinding wheel diameter, a cost optimization problem was built. From the results of the optimization problem, a model for determination of the optimum exchanged grinding wheel diameter was found. By using the optimum diameter, both the grinding cost and grinding time can be reduced significantly.

Cost Optimization Modeling of Renewable Energy Sources in Smart-Grid Using SCADA

In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.

Feasibility of Scaling up the Cost-Competitive and Clean Electrolytic Hydrogen Supply in China

Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target.This study established an electrolytic hydrogen development mechanism considering the generation mix and operation optimization of power systems with access to hydrogen.Based on the incremental cost principle,we quantified the provincial and national clean hydrogen production cost performance levels in 2030.The results indicated that this mechanism could effectively reduce the production cost of clean hydrogen in most provinces,with a national average value of less than 2 USDkg1 at the 40-megaton hydrogen supply scale.Provincial cooperation via power transmission lines could further reduce the production cost to 1.72 USD·kg^(-1).However,performance is affected by the potential distribution of hydrogen demand.From the supply side,competitiveness of the mechanism is limited to clean hydrogen production,while from the demand side,it could help electrolytic hydrogen fulfil a more significant role.This study could provide a solution for the ambitious development of renewables and the hydrogen economy in China.

Cost Optimization Method of Large-scale Prestressed Wire Winded Framework on Multiple-island Genetic Algorithm

Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, because PWWF is a variable stiffness multi-agent structure, with non-linear loading and deformation coordination. In this paper, cost optimization method of large-scale PWWF by multiple-island genetic algorithm （MIGA） is presented. Optimization design flow and optimization model are proposed based on variable-tension wire winding theory. An example of the PWWF cost optimization of isostatic equipment with axial load 6 000 kN is given. The optimization cost is reduced by 21.6% compared with traditional design. It has also been verified by the finite-element analysis and successfully applied to an actual PWWF design of isostatic press. The results show that this method is efficient and reliable. This method can also provide a guide for optimal design for ultra-large dimension muti-frame structure of 546 MN and 907 MN isostatic press equipment.

Optimization of Aeroengine Shop Visit Cost in its Service Life Cycle

It is an important task for airlines to make a reasonable workscope for their engines, which has effects not only on engine performance and reliability, but also on airlines operating cost. Based on the recommendations given in the engine maintenance management manual, and taking the repair levels adopted in the previous shop visits into account, a series of module repair level optimization rules were set up, and a shop visit cost optimization model was also created for engine service life cycle. The particle swarm method was used to optimize the engine workscope and overhaul cost. The method proposed in this paper will be a reference for airlines to make engine workscope and to do engine maintenance management.

Black Widow Optimization for Multi Area Economic Emission Dispatch

The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.

Thinking About the Economic Management of Universities Based on Capital and Cost Management Optimization

The economic management of colleges and universities has always been a topic of great concern to China’s educational career,therefore,this paper will firstly make the necessary analysis of the current implementation of the economic management of colleges and universities in China,and then the reasons for the problems of economic management of colleges and universities in China is realized a detailed investigation,and finally,the economic management of colleges and universities based on capital and cost management optimization strategy is made a full discussion,looking forward to providing the necessary guidance for researchers in this field.

A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work,we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks.The work planned has two stages i)Selection of suitable routing protocol(RP)for given IoT applications using higher cognitive process and ii)Deployment of the corresponding routing protocol.We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability,path delay,energy utilization,and throughput.The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedinglyflexible manner during the second stage.Extensive simulation has been done and results are evaluated to point out the strength of the proposed work,while dynamically varying the specific requirements of IoT applications.We observe that the work proposed is path-breaking the prevailing methods,where a specific routing protocol is employed throughout the period of time.It’s clearly shown that the proposed,Low-cost Context-Aware Protocol Switching(LCAPS)scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.

OSS Effort Expense Optimization Based on Wiener Process Model and GA

Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.

Game theory approach to optimal capital cost allocation in pollution control

This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.

Optimal guaranteed cost control for fuzzy descriptor systems with time-varying delay

Based on the delay-independent rule, the problem of optimal guaranteed cost control for a class of Takagi-Sugeno （T-S） fuzzy descriptor systems with time-varying delay is studied. A linear quadratic cost function is considered as the performance index of the closed-loop system. Sufficient conditions for the existence of guaranteed cost controllers via state feedback are given in terms of linear matrix inequalities （LMIs）, and the design of an optimal guaranteed cost controller can be reduced to a convex optimization problem. It is shown that the designed controller not only guarantees the asymptotic stability of the closed-loop fuzzy descriptor delay system, but also provides an optimized upper bound of the guaranteed cost. At last, a numerical example is given to illustrate the effectiveness of the proposed method and the perfect performance of the optimal guaranteed cost controller.

Energy-Efficient Optimal Guaranteed Cost Intermittent-Switch Control of a Direct Expansion Air Conditioning System

To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.

Research on optimal guaranteed cost control of flexible spacecraft

This article is concerned with the modeling and control problems of the flexible spacecraft. First, the state observer is designed to estimate the vibration mode on the basis of free vibration models, Then, an optimal guaranteed cost controller is proposed to stabilize system attitude and damp the vibration of the flexible beam at the same time. Numerical simulation examples show the feasibility and validity of the proposed method.

The Optimizing Method of Equipment Renewal Opportunity Based on Dynamic Sunk Cost

This article brings forward a new conception of dynamic sunk cost, and then constructs a systematic model that could be used in analyzing equipment renewal opportunity. This model will do much help in solving problems refer to optimizing equipment renewal opportunity in right way.

A Stochastic Adaptive Radial Basis Function Algorithm for Costly Black-Box Optimization

In this paper,we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions.The exploration radii in local searches are generated adaptively.Each iteration point is selected from some randomly generated trial points according to certain criteria.A restarting strategy is adopted to build the restarting version of the algorithm.The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples.The numerical results suggest that the algorithm and its restarting version are very effective.

Droop Control for Micro-grid Operations Including Generation Cost and Demand Side Management

A new cost-based droop control method based upon generation cost and demand side cost management of the microgrid is proposed in this paper.At present,many droop control methods have been developed based on either the power rating or the generation cost of the distributed generation(DG)unit,without consideration of the demand side participation in the operation and control.This exclusion might not be appropriate,if different types of consumers are connected in the micro-grid systems.This study proposes a droop control method considering both DG and load operating cost characteristics in order to minimize the generation cost of the micro-grid.

Optimized design and integration of energy storage in Solar-Assisted Ground-Source Heat Pump systems

The integrated use of multiple renewable energy sources to increase the efficiency of heat pump systems,such as in Solar Assisted Geothermal Heat Pumps(SAGHP),may lead to significant benefits in terms of increased efficiency and overall system performance especially in extreme climate contexts,but requires careful integrated optimization of the different system components.In particular,thermal storages take a fundamental role in optimizing the integration of renewable energy sources and the system operation.This work investigates the potential design optimization of a SAGHP system in a mountain site by exploring many different alternatives to optimize the mutual relationship between the solar field,the geothermal field and the water thermal storages.This is done through an original simulation-based multi-objective optimization framework considering energy efficiency and economic feasibility,which allows appraising the impact of the different design alternatives on the overall system performance and on the dynamics of the different system components.Results identify a set of optimized system configurations that optimize the integrated exploitation of the different thermal sources showing a potential increase of the overall system performance leading to 34%lower global cost compared to the initial design.High robustness of the optimal design solutions is reported with respect to the current context of high economic uncertainty.

The Structure of Departure Process and Optimal Control Strategy N^＊ for Geo/G/1 Discrete-Time Queue with Multiple Server Vacations and Min(N, V)-Policy

This paper considers the departure process and the optimal control strategy for a discretetime Geo/G/1 queueing model in which the system operates under the control of multiple server vacations and Min(N, V)-policy. Using the law of total probability decomposition, the renewal theory and the probability generating function technique, the transient and the steady-state probabilities that the server is busy at any epoch n^+ are derived. The authors also obtain the explicit expression of the probability generating function for the expected number of departures occurring in the time interval (0^+, n^+] from any initial state. Meanwhile, the relationship among departure process, server's state process and service renewal process in server busy period is found, which shows the special structure of departure process. Especially, some corresponding results of departure process for special discrete-time queues are directly gained by our results. Furthermore, the approximate expansion for calculating the expected number of departures is presented. In addition, some other important performance measures,including the expected length of server busy period, server's actual vacation period and busy cycle period etc., are analyzed. Finally, some numerical results are provided to determine the optimum value N*for minimizing the system cost under a given cost structure.