Minimum Operating Cost Research Articles

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid (MG), the Energy Management System (EMS) is a key decision-maker for integrating Distributed renewable Energy Resources (DERs) efficiently. The EMS is regarded as a strong enabler of providing the optimized scheduling control in operation and management of usage of disperse DERs and Renewable Energy reSources (RES) such as a small-size wind-turbine (WT) and photovoltaic (PV) energies. The main objective to be pursued by the EMS is the minimization of the overall operating cost of the MG integrated VPP network. However, the minimization of the power peaks is a new objective and open issue to a well-functional EMS, along with the maximization of profit in the energy market. Thus, both objectives have to be taken into account at the same time. Thus, this paper proposes the EMS application incorporating power offering strategy applying a nature-inspired algorithm such as Particle Swarm Optimization (PSO) algorithm, in order to find the optimal solution of the objective function in the context of the overall operating cost, the coordination of DERs, and the energy losses in a MG integrated VPP network. For a fair DERs coordination with minimized power fluctuations in the power flow, the power offering strategies with an active power control and re-distribution are proposed. Simulation results show that the proposed MG integrated VPP model with PSO-based EMS employing Egalitarian reDistribution (ED) power offering strategy is most feasible option for the overall operating cost of VPP revenue. The total operating cost of the proposed EMS with ED strategy is 40.98$ compared to 432.8$ of MGs only without EMS. It is concluded that each MGs in the proposed VPP model intelligently participates in energy trading market compliant with the objective function, to minimize the overall cost and the power fluctuation.

Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area. To improve the wind-power absorption capacity and operating economy of the system, the structure of the system is improved by adding a heat storage device and an electric boiler. First, aiming at the minimum operating cost of the system, the optimal scheduling model of the cogeneration system, including a heat storage device and electric boiler, is constructed. Second, according to the characteristics of the problem, a cultural gene algorithm program is compiled to simulate the calculation example. Finally, through the system improvement, the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme. The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation. The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem, which has a better optimization effect on the solution.

Technological proposal for the attention of the risk in the management of the work of a railway station with a port

The purpose of the article is to definean improved technology for managing the operation of a railway station with a port when carrying out container transportationwith the attention of the financial risk.To achieve this goal, the authors used methods of statistical data analysis of dynamically changing parameters, methods of mathematical statistics, probability theory, methods of optimization and combinatorial analysis.The article formalizes the technology of managing the work of a railway station with a port in the form of a mathematical model.The objective function of the model is based on the sum of operating costs and takes into account the risk of financial losses of the port station due to the occurrence of risky events that threaten the stable and safe operation of railway transport in the conditions of martial law. For representation of the level of financial risk, the use of the density function of the normal distribution law is proposed. The application of the proposed mathematical model will allow to determine the optimal sizeof a batch of large-tonnage containers intended for loading onto a shipwith minimal operating costs.

Joint Optimization and Learning Approach for Smart Operation of Hydrogen-Based Building Energy Systems

In recent years, hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, spatially and temporally coupled operational constraints, and nonlinear constraints, it is challenging to solve the formulated problem. Then, we propose an algorithm to solve the problem based on model-based optimization and data-driven based learning. The key idea of the proposed algorithm is summarized as follows: (1) transforming the long-term cost minimization problem into several single-slot subproblems using Lyapunov optimization techniques; (2) dividing each single-slot subproblem into two parts according to the availability of model information; (3) solving one part based on convex optimization and solving another part using multi-agent attention-based deep deterministic policy gradient. Simulation results based on real-world traces show the effectiveness of the proposed algorithm.

Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow

This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.

Study on the Optimization of Agricultural Production Waste Recycling Network under the Concept of Green Cycle Development

This study is based on the concept of converting agricultural waste into green new energy, we combine the concept of green cycle development and the relevant theories in modern system engineering to optimize the study of agricultural production waste recycling network. In this paper, the optimization of the agricultural production waste recycling network is divided into two aspects—facility site selection and vehicle path planning—with the objectives of agricultural production waste green recycling and the minimization of system construction and operational costs. In this study, the site selection and path planning problems were unified and an optimization model for the agricultural production waste recycling network site-path (LRP) problem was constructed. The optimization results of agricultural production waste recycling network facility location and recycling vehicle path planning were obtained by using the simulation data in the optimization model and designing the genetic algorithm design with the relevant characteristics of agricultural production waste recycling. The feasibility and operability of the model were verified through experiments. The research related to the optimization of agricultural production waste recycling networks can be used to both reduce production costs in agricultural areas and progress the practical theory of reverse logistics in agricultural areas. Agricultural waste resource utilization provides important support for the development of an ecological agriculture cycle and helps protect the environment.

Design, implementation and evaluation of a service for Medication compliance aids (MCAs) in an institutional Pharmacy in the city of Córdoba

The Pharmaceutical Service (PS) for the provision of Medication compliance aids (MCAs) consists of reconditioning and unifying, in a protocolized manner and in multi-compartment devices, the medications used by a patient, ordering them according to their dosage guideline. The objective was to design, implement and evaluate an PS of MCAs in an institutional pharmacy in the city of Córdoba. Observational, descriptive and longitudinal study. A Standard Operating Procedure (SOP) was developed from for PS of MCAs and applied to a sample of 20 patients. Adherence was analyzed by: tablet count and Morisky-Green test. A validated service satisfaction questionnaire was applied and a cost analysis was performed considering the initial investment and the times involved. A 98.0% adherence was obtained by counting the tablets and by the Morisky-Green test: 5.0% at the start of the service and 55.0% at 3 months. The patients expressed in the satisfaction questionnaire: 90.0% very satisfied and 10.0% satisfied. Regarding the cost analysis, the initial investment was USD 1,670.44. A value of US $ 4.29 was calculated for the first interview, and the monthly cost of preparation ranged from US $ 16.02, for 7-30 weekly takings, to US $ 28.73 for more than 121 takings. It was possible to develop and implement an SOP for the PS of SPD, with significant improvements in adherence. A high degree of patient satisfaction was observed and were obtained minimal operating costs to achieve sustainability.

An MINLP model for the minimization of installation and operational costs in water distribution networks

Water Distribution Networks (WDN) are important systems for industrial processes and urban centers. WDN can be formed by reservoirs, pipes, nodes, loops, and pumps and its complete design can be formulated as an optimization problem. The majority of published papers in the open literature use meta-heuristics for problem solution, as well as hydraulic simulators to calculate pressures and velocities. In the present study, a Mixed Integer Non-Linear Programming (MINLP) model was developed to the synthesis of WDN considering the minimization of the WDN total cost, given by the sum of installation and operational costs, which is the novelty in the paper. All the hydraulic calculations were included in the model (mass and energy balances and velocity and pressure upper and lower bounds), avoiding the use of additional software. Reformulation techniques are applied to the model considering the use of logarithms and disjunctive programming. Two case studies extracted from real WDN were used to test the model and global optimization techniques were employed to achieve the results. The results obtained show that the operational costs play an important role in the WDN system design.

A cooperative game approach for optimal resiliency-oriented scheduling of transactive multiple microgrids

The growth of distributed energy resources and microgrids (MGs) attracts attention to the transactive multiple MG (TMMG) concept. This paper proposes a novel framework that belongs to the competitive-based resilient scheduling of TMMGs. A cooperative game approach is investigated for optimal day-ahead scheduling of TMMGs, which concerns the possibility of long-duration disconnection from the main grid. The TMMG comprises multiple MGs that can have energy transactions through a local market. This local energy market lets the TMMG operator receive the MGs' 24-hour bid-offer amounts and set the hourly transaction prices. The proposed game fulfills the minimum operation cost for MGs in the normal operation mode (NOM) and ensures the serving of TMMG's load during the resiliency operation mode (ROM). Thus, the paper seeks to find an equilibrium point so that all the MGs reach acceptable strategies by meeting the constraints in the NOM and ROM. It is shown that the market tends to provide the NOM transaction at clearing prices lower than the wholesale price. Also, it tends to disperse a higher price in the ROM condition. Notwithstanding the profitable opportunity for renewable-penetrated MGs, they are exposed to high costs due to the inherent uncertainty and risk of generation deficiency.

Day-Ahead Operation Analysis of Wind and Solar Power Generation Coupled with Hydrogen Energy Storage System Based on Adaptive Simulated Annealing Particle Swarm Algorithm

As the low-carbon economy continues to evolve, the energy structure adjustment of using renewable energies to replace fossil fuel energies has become an inevitable trend. To increase the ratio of renewable energies in the electric power system and improve the economic efficiency of power generation systems based on renewables with hydrogen production, in this paper, an operation optimization model of a wind–solar hybrid hydrogen energy storage system is established based on electrochemical energy storage and hydrogen energy storage technology. The adaptive simulated annealing particle swarm algorithm is used to obtain the solution, and the results are compared with the standard particle swarm algorithm. The results show that the day-ahead operation scheme solved by the improved algorithm can save about 28% of the system operating cost throughout the day. The analytical results of the calculation example revealed that the established model had fully considered the actual operational features of devices in the system and could reduce the waste of wind and solar energy by adjusting the electricity purchased from the power grid and the charge and discharge powers of the storage batteries under the mechanism of time-of-use electricity price. The optimization of the day-ahead scheduling of the system achieved the minimization of daily system operation costs while ensuring that the hydrogen-producing power could meet the hydrogen demand.

Investigating economic replacement policy under uncertainty for managerial application based on grey-reliability approach using QCC

PurposeThe main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.Design/methodology/approachThe study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.FindingsThe findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.Originality/valueA conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO2 emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Abstract In order to increase sales, companies try their best to develop relevant offers that anticipate customer needs. One way to achieve this is by leveraging artificial intelligence algorithms that process data collected based on customer transactions, extract insights and patterns from them, and then present them in a user-friendly way to human or artificial intelligence decision makers. This study is based on a hybrid approach, it starts with an online marketplace dataset that contains many customers’ purchases and ends up with global personalized offers based on three different datasets. The first one, generated by a recommendation system, identifies for each customer a list of products they are most likely to buy. The second is generated with an Apriori algorithm. Apriori is used as an associate rule mining technique to identify and map frequent patterns based on support, confidence, and lift factors, and also to pull important rules between products. The third and last one describes, for each customer, their purchase probability in the next few weeks, based on the BG/NBD model and the average of transactions using the Gamma-Gamma model, as well as the satisfaction based on the CLV and RFMTS models. By combining all three datasets, specific and targeted promotion strategies can be developed. Thus, the company is able to anticipate customer needs and generate the most appropriate offers for them while respecting their budget, with minimum operational costs and a high probability of purchase transformation.

A bi-level optimal scheduling model for new-type power systems integrating large-scale renewable energy

Abstract The construction of a new type of power system is a key way to achieve the goal of ‘carbon peaking and carbon neutrality’. In the process of developing renewable energy, large-scale wind and photovoltaic power systems replace conventional units, which will have an impact on the stability of the power grid. To eliminate these concerns, this paper proposes a bi-level optimal scheduling model for new-type power systems. The upper level aims to minimize fluctuation of net load, with the consideration of demand response, to smooth the load curve. The lower level takes the minimum operating cost of the new-type power system as the objective function and the most economical scheduling strategy is established. To deal with the uncertainty of renewable energy integrated within the system, the lower model is transformed into a two-stage robust optimization model and solved by applying a column-and-constraint generation method. Finally, a regional power system is selected to conduct a case study comprising 760 MW thermal power, 50 MW·h energy storage, 200 MW wind turbine and 150 MW photovoltaic power. The results show that the constructed two-stage robust optimization model is conducive to establishing the most economical scheduling scheme of the system with an uncertain budget and the minimum operation cost is $45,9031. In addition, demand response and energy storage can reduce peak-to-valley differences in the power system.

Exploration of Vehicle Target Detection and Classification Method Based on Sea Lion Optimization with Deep Convolutional Neural Network

Abstract Presently, urban environments over the globe are highly employed to obtain solutions for enhancing the quality of the livers and enhance the usage of city infrastructure and resources with minimal operational cost. Urban remote sensing acts as a significant part in the ability of mapping, monitoring, and controlling infrastructure. High-resolution remote sensing data renders worldwide images faster than traditional data collection strategies. Hence, small objects such as cars are easily detected. Vehicle recognition on aerial remote sensing images (RSIs) in the complicated background of urban zones has always gained a lot of interest in the remote sensing field. The automatic vehicles enumeration research domain had a significant contribution in several applications, including traffic management and monitoring. Target detection technology will be a crucial part of computer vision (CV) technology, and target detection techniques were enforced in several domains. Therefore, this study develops a new Vehicle Recognition and Classification using Sea Lion Optimization with Deep Learning (VRC-SLODL) model on RSI. In the presented VRC-SLODL technique, the major intention lies in recognising and classifying vehicles present in the images. The bilateral filtering (BF) technique can initially improve the RSI quality. The VRC-SLODL technique employs a modified residual network (ResNet) model to produce a collection of feature vectors. Eventually, the SLO approach with long short-term memory (LSTM) technique was exploited for vehicle classification, where the SLO algorithm acts as a hyperparameter optimizer. The experiments were performed on a benchmark dataset to examine the better performance of the VRC-SLODL method. The obtained values reported the improved classification performance of the VRC-SLODL technique over other models.

Optimal energy management of nanogrid using battery storage system

This paper presents the optimal energy management approach with optimum sizing of the battery storage system (BSS) for the minimal possible total operating cost of the considered nanogrid. The five various operating modes are tested to achieve the optimal operating mode on an hourly basis. Linear programming (LP) is used to get optimal solutions for objective functions having non-integer variables whereas mixed-integer linear programming (MILP) is used to optimize the objective function having an integer variable. The proposed hourly-based optimal operating mode produces 17.8–94.5 % savings in the day operating cost compared to the best individual operating mode. The optimal capacity of the battery storage system is also derived by using the proposed operating modes. The achieved optimal capacity of BSS based on the day operating cost is further modified with the help of investment cost and its interest paid during the life span which suggests that the optimal capacity range will be 4–19 kWh for this nanogrid.

Simulation and design of a heat-integrated double-effect reactive distillation process for propylene glycol methyl ether production

A double-effect reactive distillation (DERD) process was proposed for the production of propylene glycol methyl ether from propylene oxide and methanol to overcome the shortcoming of low selectivity and high-energy consumption in the tubular plug-flow reactor. A single-column reactive distillation (RD) process was conducted under optimized operating conditions based on sensitivity analysis as a reference. The results demonstrated that the proposed DERD process is able to achieve more than 95% selectivity of the desired product. After that, a design approach of the DERD process with an objective of the minimum operating cost was proposed to achieve further energy savings in the RD process. The proposed DERD configuration can provide a large energy-savings by totally utilization of the overhead vapor steam in the high-pressure RD column. A comparison of the single-column RD process revealed that the proposed DERD process can reduce the operating cost and the total annual cost of 25.3% and 30.7%, respectively, even though the total capital cost of DERD process is larger than that of the RD process.

Rapid cooling effectiveness of Li-ion battery module with multiple phase change materials for plug-in hybrid electric vehicle

To curb the greenhouse gas (GHG) and carbon emission in line with the Paris climate agreement, the road transportation needs to be electrified. The predicted electrification of the road transport can substantially reduce the GHG emission. The fundamental component in electric vehicles (EVs) is the power battery, in which its performance is affected by temperature. To improve the cooling performance and enhance temperature uniformity in the battery stack, a battery thermal management system (BTMS) comprising heatsinks with fluid channels and multi-phase change materials (PCMs) was investigated in this paper. The module consists of eight prismatic cells with nine fluid passages and PCMs attached at each side of the cell. The heatsink containing PCMs is separated into three equal portions, each is filled with different PCMs. The cooling effectiveness and temperature uniformity of the BTMS were carefully explored based on compactness of the battery stack and cooling fluids. The results indicate that increasing the PCMs thickness supressed the temperature rise by more than 2.85 K and provide better temperature uniformity compared to increasing the fluid channel width for the same volume of heatsink to volume of the battery unit ratio. Liquid cooling is more effective than air cooling in mitigating temperature especially at high ambient temperature. Choosing an appropriate ratio of volume of cooling channel to battery unit is particularly essential and can minimize operation cost.

Multi-Objective Optimization for Peak Shaving with Demand Response under Renewable Generation Uncertainty

With high penetration of renewable energy sources (RESs), advanced microgrid distribution networks are considered to be promising for covering uncertainties from the generation side with demand response (DR). This paper analyzes the effectiveness of multi-objective optimization in the optimal resource scheduling with consumer fairness under renewable generation uncertainty. The concept of consumer fairness is considered to provide optimal conditions for power gaps and time gaps. At the same time, it is used to mitigate system peak conditions and prevent creating new peaks with the optimal solution. Multi-objective gray wolf optimization (MOGWO) is applied to solve the complexity of three objective functions. Moreover, the best compromise solution (BCS) approach is used to determine the best solution from the Pareto-optimal front. The simulation results show the effectiveness of renewable power uncertainty on the aggregate load profile and operation cost minimization. The results also provide the performance of the proposed optimal scheduling with a DR program in reducing the uncertainty effect of renewable generation and preventing new peaks due to over-demand response. The proposed DR is meant to adjust the peak-to-average ratio (PAR) and generation costs without compromising the end-user’s comfort.

Blockchain-based privacy preservation framework for healthcare data in cloud environment

ABSTRACTThe storage of Electronic Health Records (EHRs) on mobile cloud environments has undergone a paradigm shift in recent years, with mobile devices integrating with cloud computing to improve medical data transfers between patients and healthcare providers. This sophisticated paradigm allows for minimal operational costs, significant flexibility, and the use of electronic health records (EHRs). However, for e-health systems, this new paradigm poses concerns regarding data privacy and network security. It’s a challenging problem to exchange EHRs consistently among mobile users while maintaining high-security levels in the mobile cloud. It’s a challenging problem to exchange EHRs consistently among mobile users while maintaining high-security levels in the mobile cloud. Here, this paper intends to introduce a novel blockchain technology for secure health data in the cloud, which aids in ensuring authentication and offers integrity to medical records. Here, blockchain with optimal encryption is deployed via an improved blowfish model that also guarantees authentication features. Further, the optimal key generation is carried out by a new approach termed as Elephant Herding Optimization with Opposition-based Learning (EHO-OBL). Thus, the data integrity is maintained by the developed approach, and at last, the supremacy of the presented approach is proved concerning various measures. Accordingly, the key generation time of the proposed method has attained less value, and it was 51.04%, 91.48%, 92.64%, 91.48%, 89.99%, 91.06% and 91.48% better than traditional Blowfish, Rivest–Shamir–Adleman (RSA), Advanced Encryption Standard (AES), Elliptic-Curve Cryptography (ECC), Elephant Herding Optimization (EHO), Moth-Flame Optimization (MFO) and Whale Optimization (WOA) models, for file size of 10 kb.