Minimum Operating Cost Research Articles

A Novel Day Optimal Scheduling Strategy for Integrated Energy System including Electric Vehicle and Multisource Energy Storage

Aiming at problems such as high operating costs, the low utilization efficiency of renewable energy, and the increase in the peak-valley difference in user load caused by disordered charging of electric vehicles (EVs), the operation mode of “setting electricity by heat” or “setting heat by electricity” is adopted in the integrated energy system of intelligent residential areas on the distribution side. In this paper, a method is proposed to use multiple energy storage, demand response technology, and make EV orderly charge and discharge plan to consume renewable resources and realize optimal economic scheduling in the integrated energy system of combined heat and power (CHP). Firstly, the demand response model of electricity load adjusting according to renewable energy output and time-of-use electricity prices has been established. Secondly, considering the comfort of the space thermal load, the orderly charge and discharge plan of electric energy storage (ESS), thermal energy storage (HSS), and EV and aiming at the minimum operating cost of the integrated energy system, the optimal output operation of each equipment was scheduled. Finally, through simulation analysis and comparative experiments, it is verified that the proposed method can promote the local consumption of renewable energy, realize the decoupling of thermoelectric constraints, and reduce the operating cost of the integrated energy system.

Two-stage robust optimal operation of AC/DC distribution networks with power electronic transformers

Power electronic transformers (PET) are a new type of power electronic equipment with a multi-port flexible dispatch function, which can play the role of a power hub in a system composed of multiple AC-DC hybrid distribution grids for interactive sharing of power in multiple regions. In this study, a two-stage robust optimization operation model of a hybrid AC-DC distribution network with PET is proposed based on PET power transmission and transformation characteristics. The stochastic uncertainty of the distributed renewable energy output in the AC-DC grid is handled by a two-stage robust optimization method to determine the minimum total system operation cost under the worst case of distributed renewable energy output. Finally, a constrained column generation algorithm is used to solve the two-stage robust optimization model in the min-max-min form and verifies the validity of the model in this study.

Using an intelligent method for microgrid generation and operation planning while considering load uncertainty

The integration of distributed generation (DG), energy storage systems (ESS), and controllable loads near the place of consumption has led to the creation of microgrids. However, the uncertain nature of renewable energy sources (wind and photovoltaic), market prices, and loads have caused issues with guaranteeing power quality and balancing generation and consumption. To solve these issues, microgrids should be managed with an energy management system (EMS), which facilitates the minimization of operating (performance) costs, the emission of pollutants, and peak loads while meeting technical constraints. To this effect, this research attempts to adjust parameters by defining indicators related to the best possible conditions of the microgrid. Generation planning, the storage of generated power, and exchange with the main grid are carried out by defining a dual-purpose objective function, which includes reducing the operating cost of power generation, as well as the pollution caused by it in the microgrid, by means of the SALP optimization algorithm. Moreover, in order to make the process more realistic and practical for microgrid planning, some parameters are considered as indefinite values, as they do not have exact values in their natural state. The results show the effect of using the introduced intelligent optimization method on reducing the objective function value (cost and pollution).

Hierarchically coordinated optimization of power distribution systems with soft open points and electric vehicles

The integration of large-scale electric vehicles (EVs) into power distribution systems (PDSs) causes low voltage and line congestion problems. EV charger converters can improve the voltage profile by compensating the reactive power, whereas soft open points (SOPs) can transfer active power between branches and compensate reactive power, which can help balance the branch power. Therefore, this study proposes a coordinated day-ahead (DA) and real-time (RT) optimization model for PDSs with SOPs and high penetration of EVs. In addition, directly controlling large-scale EVs is difficult for the distribution systems operator. Thus, hierarchical management of EVs is performed by using an EV aggregator (EVA) model. First, on the basis of the probability distribution of the state of EVs of each EVA, a DA optimization model with the minimum operation cost is established. A dynamic SOC boundary model is proposed to optimize active and reactive power of EVAs and EVs in RT stage. The original nonlinear problems of the DA and RT models are linearized by convex relaxation and linearization, which significantly improves the solving efficiency. Finally, the case studies demonstrate that SOPs can promote the integration of EVs, and the hierarchical coordinated optimization strategy not only can reduce the operation cost of the PDS, but also control active and reactive power of EVs in real time.

Multi-objective optimization of integrated energy systems based on demand response

ABSTRACT The implementation of demand response (DR) in the operation optimization of the integrated energy system has become one of the research hotspots worldwide with the rapid development of the energy network. Integrated gas-electricity system (IGES) operators can allocate different energy carriers more rationally and decrease energy cost and exergy input considering the DR scheme. However, DR was mainly applied to electric power systems in the past, while natural gas systems were less considered. In this paper, based on the theory of price-elasticity, and simultaneously with the consideration of the DR for end-user (EDU) of electricity and natural gas, the optimization model of the IGES is established, which includes two objective functions, the minimum operating cost of the integrated system and the maximum EDU satisfaction. The constraints include compressors and gas storages in the natural gas system, coal and gas generator units in the electric power system, Pow to gas units, and EDU gas/electric load changes. Subsequently, the proposed model is employed for an integrated 7-node natural gas system and 6-node power system with the sensitivity analysis for operating cost. The results demonstrate that the implementation of DR brings specific economic benefits to the operation of the entire system, the total operating cost is reduced by 16.6%, and the natural gas load is reduced by 89.9%. The implementation of DR balances the 24-hour load, reduces the fluctuation of the EDU load, increases the efficiency of the load shifting, and alleviates the instability of upstream production and transportation.

Effects of various COD/NO ratios on NOx removal performance and microbial communities in a BTF−ABR integrated system

In this study, we investigated the removal performance of NOx and stability of the biotrickling filter−anaerobic baffled reactor (BTF−ABR) integrated system at various chemical oxygen demand (COD)/NO ratios (12.18, 6.71, and 4.63 in stages 1, 2, and 3, respectively) under 3.5% O2 and 50 ± 0.5 °C conditions for the first time. The results showed that the maximum elimination capacity of NOx was 4.46, 8.16, and 11.58 g/(m3·h) in stages 1, 2, and 3, respectively. The minimum operating cost in terms of glucose was 4.79 g of glucose/g of NO. However, a COD/NO ratio of 12.18 resulted in a wastage of carbon sources, while a COD/NO ratio of 4.63 led to about 20 mg/m3 N2O emission at the end of the study. Highly bacteria diversity and positive co-occurrence networks at the COD/NO ratio of 6.71 were the main reasons for no intermediate accumulation or N2O emission. Analysis of real-time polymerase chain reaction (PCR) indicated that nirS and norB were more sensitive to the changes in the COD/NO ratios than other denitrifying genes, and the denitrifiers with nirS filled more ecological niches as the NOx increased. Furthermore, although the decrease in COD/NO ratio significantly impacted the microbial community structure, the NOx RE was stabilized at over 90% because the micro-aerobic environment produced by ABR combined highly diverse microbes and functions in BTF, as well as the coordinated expression of denitrifying genes. Achieving efficient, stable, and low-cost denitrification is feasible in this BTF−ABR integrated system.

Resilience enhancement strategy of multi-energy coupling distribution network considering movable energy storage equipment

With the deepening of energy transformation, multi-energy coupling has become an effective method to alleviate the energy gap and improve the energy utilization rate. As a bridge connecting users and the power system, the distribution network is becoming multi-energy coupling as well. Therefore, to ensure a stable supply of power, heat, and other energy sources, it is of great significance to study the resilience improvement of multi-energy coupling distribution networks. This paper proposes a strategy for improving the resilience of multi-energy coupling distribution networks considering movable energy storage equipment (MESS) configuration. Firstly, the multi-energy coupling distribution network model is established, and the mathematical model of each piece of equipment is established considering the coupling of the power grid and heat grid. Secondly, based on the loss of load probability (LOLP), considering the importance of the load, it is expanded to the loss of important load probability as an index to evaluate the resilience of distribution networks. Thirdly, a distribution network resilience improvement model based on the configuration and operation of the MESS is established, and the optimal configuration capacity and operation strategy of the MESS is solved by taking the minimum operation cost as the objective function, i.e., the configuration cost of the MESS, the load shedding cost and the cost of wind and PV abandonment. Finally, based on the IEEE-33 node distribution network, a multi-energy coupling distribution network model is built, which verifies that the method proposed in this paper can effectively reduce the total cost and improve the resilience of the multi-energy coupling distribution network.

Multi-energy optimization under the water-energy-carbon nexus in a typical iron and steel plant

The multi-energy system in iron and steel plants has strong coupling characteristic, which makes the energy distribution complex and difficult in energy management. Additionally, the steel works is a manufacture process with high-carbon emissions, and it is vital to assess the environmental stress from the sights of nexus between energy, water and carbon emission. In this study, a multi-energy and multi-objective mixed integer non-linear programming (MINLP) model is established to optimize the energy system under the water-energy-carbon nexus in iron and steel plant. And the multi-energy is considered the couple of gas-steam-electricity. The multi-objective functions include the economic performance, the CO2 emissions, the water generation, and the global energy efficiency. Due to the contradictory of the objective functions, the utopia tracking approach is used to normalize and assess the functions. To fit the actual situation of the equipment, this paper made a fitting curve for the energy efficiency of the equipment. The minimum economic operation cost results show that the studied iron and steel plant has great potential in reducing economic cost (7.6 %), emission mitigation (4.7 %), system water production improvement (807 t) and global energy efficiency (54.48 %). Besides, the research will help the iron and steel plant to reach the carbon peak and carbon neutrality target.

A Multi-Period Model for Optimal Changi Airport Check-In Counter Operations

Growth in air passenger flow has caused severe congestion at the airport check-in counter, posing a significant problem for airport management. Particularly during the check-in process, the necessary authorities must coordinate sufficient facilities with adequate staffing levels. The airport check-in counter problem (ACCAP) is a field concerned with establishing the optimal number of check-in counters to balance operating expenses and passenger wait times in order to reduce airport congestion. Expanding the number of counters and staff to a minimum operating cost is able to prevent the congestion problem from escalating without incurring further operating expenses. This paper focused on proposing optimal scheduling of airport check-in counters operations, including staffing. A dynamic model with multi-period principles is adapted to address the aforementioned problem by balancing the trade-off between service performance and operational cost. As a case study, data from Singapore Changi International Airport was utilized. The findings are also discussed in terms of the flow of passengers throughout the airport check-in procedure and operations. As a result, the number of activated counters is minimized throughout all shifts by applying the dynamic model at the average service time. At the same time, there are fewer passengers in the queue.

Efficiency Improvement of Photovoltaic Panels: A Novel Integration Approach with Cooling Tower

Overheating of photovoltaic (PV) panels decreases their efficiency and lifetime, and subsequently increases the levelized cost of energy (LCOE). Passive PV cooling would enhance the PV operational stability and durability. The cooling tower (CT) technology offers an attractive approach for zero-cost capability. In this work, we developed and customized a CT specific for passive PV cooling. Since the dense downdrafted cooled air gained high velocity, a turbine was installed at the bottom of the CT for power production. At the height’s ambient temperature, the CT cooled the air from 50 °C down to 30 °C. The cooled air at 30 °C has enough capacity to cool the PV panels. This cooling capacity improved the average annual efficiency of the PV panels by 6.83%. The design specifications of the CT have the highest performance, with the maximum radius of PV area of panels that can be cooled at 50 m. Furthermore, the current design could operate during the night for power production with minimum operational cost.

Coordinated active and reactive power operation of multiple dispersed resources for flexibility improvement

The purpose of this paper is to reach the optimal active and reactive power operation of multiple dispersed resources consisting of mobile energy storage system (MESS), demand response (DR) and photovoltaic (PV), for flexibility improvement of distribution network with uncertain PV and DR, minimization of power loss and operation cost whilst satisfaction of both power factor and voltage variation requirement. Especially, the flexibility aspect of distribution network is focused due to its significance for supporting economic operation without voltage rise issue during high PVs integration. Firstly, the active and reactive power operation spaces of MESS and PV inverter are discussed under power factor constraint. Then, the stochastic characteristics of PV generation and DR of microgrids are investigated using probability distribution. After that, the optimization framework coordination with dispersed MESS, PV inverter and DR to ensure operational flexibility of distribution network is proposed. Finally, the total cost minimization based flexibility improvement approach is presented by optimizing power loss, uncertain risk, operation cost of distribution network and MESS, satisfying operation constraints of both distribution network and dispersed resources. Simulation results conducted on the IEEE 69-bus system demonstrate the effectiveness of the proposed approach for PV accommodation, voltage quality improvement as well as peak load shaving.

Ensuring the Reliability of Gas Supply Systems by Optimizing the Overhaul Planning

The aim of the article is the development of methods for optimal overhaul planning of compressor station equipment. Nowadays, due to uncertainties in the forecast of gas supply flow rates, increasing the reliability and energy efficiency of main gas pipelines is an urgent problem. The dependence of operating costs for major repairs on the maintenance periodicity is extreme. Reducing equipment’s maintenance period leads to an increase in repair costs. It also increases the reliability of equipment operation. Overall, all these facts reduce the probability of emergency failures and related expenses for emergency recovery, gas losses, and undersupply to consumers. Therefore, an optimal maintenance frequency exists, at which the total operating costs will be minimal. A procedure for optimizing the periodicity of repairs and equipment replacement is proposed. It was realized by constructing an objective function as a dependence of exploitation costs on the inter-repair period of major repairs. A probabilistic approach was applied to assess the aging process. The characteristics of the equipment’s state are described by distribution densities (i.e., pre-repair, inter-repair, and full-service life), which vary depending on product initialization time. The main characteristics of major repairs are their duration and intensity, which are evaluated by the quality factor related to repair costs. The extremum of the objective function is sought by the method of competing options. It was determined that the optimal management of the frequency of equipment replacement can be realized by choosing the optimal values of the average service life, average operation time of units until the first planned and preventive repair, and quality factor. As a result, the required technical condition for the technological equipment is ensured under minimum operating costs without reducing the system’s reliability.

A comprehensive life evaluation method for server hard disk

This paper introduces a comprehensive life evaluation method of server hard disk based on the material life, technical life and economic life evaluation model of hard disk. Firstly, based on the historical failure database, the Weibull distribution model is established to obtain the material life of the hard disk. Secondly, the concept of Gompertz change curve is introduced to establish the residual technical life model of hard disk. Then, considering the operation of each part of the operation of electronic products, the uncertain variables are determined by using D-S evidence theory, and the economic life evaluation model is established with the minimum operation cost as the optimization objective. Finally, considering the coupling relationship between the three, the comprehensive life evaluation model of hard disk is established. An example of electronic products such as server hard disk which are easy to change and easy to fail is analyzed, the comprehensive life of the product is evaluated, and the rationality and practicability of the proposed method are verified.

Mobile technology as a dependable alternative to language labs and to improve listening skills

There is an incessant gap between language acquisition and adoption of modern technologies, especially in semi-urban parts of economically growing countries in Asia. There is a need to establish infrastructure for Computer Assisted Language Learning (CALL), Information Communication Technology (ICT), Virtual Reality (VR), and Augmented Reality (AR) to enrich English Language Teaching. Though these types of establishments instil confidence and fluency among English learners, investment in procurement, running costs towards maintenance and service cost deter institutions from establishing Language Labs and Language studios. The objective of this study was to establish Mobile-Assisted Language Learning (MALL) in higher education institutions and enhance English fluency and accuracy with minimal operation and low maintenance cost. The study also methodologically shortlisted open-source mobile applications based on users' feedback on their user-friendliness, skill-levels, content, and options that promote Learners' Autonomy. A quantitative true-experimental study was conducted at the Vellore Institute of Technology, Vellore, India, to develop the listening skills of (n=121) students of first-year Bachelor of Technology in a varied branch. The pre-test and post-test results revealed improvements in the experimental group. The findings would immensely help the underprivileged to serve ESL learners from any part of the globe who intend to enhance their proficiency in English.

Optimal Operation of Integrated Energy Systems Based on Multi-Energy Coupling With Power-to-Gas Technology

Abstract Natural gas and renewable energy sources make up an increasingly large proportion of power generation due to their being environmentally friendly. Because of the intermittent and fluctuating output of renewable energy sources, the emerging technology of power-to-gas (P2G) units is of great significance to alleviate. This paper focuses on the coordinated optimization of the combined gas and electricity network (CGEN) system with bidirectional energy conversion. Gas-fired power generation (GPG) plants and P2G are considered as linkages between the two networks. A unified CGEN mathematical model is established with the minimum operation cost as the objective function. The injection and production process of underground natural gas storage (UNGS) is also taken into consideration that is not available in other literature. Variables such as the output of P2G and gas-fired power generation plants, the supply of UNGS, and the wind curtailment are optimized correspondingly. The improved 24-node power grid (PG) and Belgium 20-node natural gas pipeline network (NGPN) are adopted to test the validity and capability of the proposed model, and then we compare the results CGEN model with the single Belgium NGPN to analyze the impact on the NGPN. Moreover, we adopt a coupled operation system of a 6-node power system and a 7-node NGPN to further analyze the influence of P2G on the CGEN. The results show that compared with the system without P2G, the total operating cost of the system is reduced by 9.39%, the natural gas load shedding is reduced by 26.1%, and the wind curtailment is reduced from 326 MWh to zero.

Modelling and Energy Management of an Off-Grid Distributed Energy System: A Typical Community Scenario in South Africa

Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addition, mixed integer nonlinear programing (MINLP) optimization technique is exploited to maximize power system generation between interconnected energy sources and dynamic electrical load with highest reliability and minimum operational and emission costs. Due to frequent battery cycling operation in the DES, rainflow algorithm is applied to the optimization result to estimate the depth of discharge (DOD) and subsequently count the number of cycles. The validity and performance of the power management strategy is evaluated with an aggregate load demand scenario of sixty households as a benchmark in a MATLAB program. The simulation results indicate the capability and effectiveness of optimal DES model through an enhanced MINLP optimization program in terms of significant operational costs and emission reduction of the diesel generator (DG). Specifically, the deployment of DES minimizes the daily operational cost by 71.53%. The results further indicate a drastic reduction in CO2 emissions, with 22.76% reduction for the residential community load scenario in contrast to the exclusive DG system. This study provides a framework on the economic feasibility and effective planning of green energy systems (GESs) with efficient optimization techniques with capability for further development.

A reduced order model for triethylene glycol natural gas dehydration system

In natural gas processing plants, glycol dehydration is commonly used to remove water from the gas streams, to avoid pipeline blockage and equipment breakdown due to hydrates formation. This paper proposed a reduced order model developed based on integrated simulation-optimization approach for the glycol dehydration system, with the aim to minimize its operating cost while satisfying pipeline quality specifications. Steady-state process simulation software was used to identify important operating parameters for the glycol dehydration process; these include reboiler temperature and flow ratio of the regeneration column, and solvent flowrate. The identified parameters are built into a non-linear programming model, which was developed as a reduced order model for ease of implementation in the plant. The studied parameters are reboiler duties, hot oil, condenser, and pump, as well as TEG make-up flow rate and CO2 equivalent (CO2-eq) emissions. The Pareto Front is developed to identify the minimum operating cost at different levels of water dew point specification. The work has resulted in the annual savings of more than 34.6%.

Thermal efficiency appraisal of hybrid nanocomposite flow over an inclined rotating disk exposed to solar radiation with Arrhenius activation energy

The energy crisis forced the world to look for alternate energy solutions. Amongst these, solar energy is the first choice of scientists that convert solar radiation into heat or electricity to meet the energy deficit. Despite its marginally high installation cost, this renewable energy solution is long-lasting with minimum operating cost. Heading in the same direction, the attributes of the solar radiation towards the heat transfer rate of hybrid nanocomposite comprising paraffin wax amalgamated with copper-oxide, and cobalt-oxide (CuO-Co3O4) nanocomposites and thin-film spraying over an inclined rotating disk are discussed. The nanocomposites are considered in varied shapes. The model is supported by the Arrhenius activation energy in the concentration equation and the convective boundary condition at the surface. The model assumptions are translated into the system of partial differential equations which are then converted into differential equations with apposite similarity transformations. The numerical solution of the transformed system is obtained by the Keller box method. The obtained results are analyzed graphically. The maximum heat transfer rate is witnessed in the case of spherical-shaped particles. Furthermore, the hybrid nanocomposite fluid velocity is augmented when the width of the film is improved.

Decentralized Energy Management System in Microgrid Considering Uncertainty and Demand Response

Smart energy management and control systems can improve the efficient use of electricity and maintain the balance between supply and demand. This paper proposes the modeling of a decentralized energy management system (EMS) to reduce system operation costs under renewable generation and load uncertainties. There are three stages of the proposed strategy. First, this paper applies an autoregressive moving average (ARMA) model for forecasting PV and wind generations as well as power demand. Second, an optimal generation scheduling process is designed to minimize system operating costs. The well-known algorithm of particle swarm optimization (PSO) is applied to provide optimal generation scheduling among PV and WT generation systems, fuel-based generation units, and the required power from the main grid. Third, a demand response (DR) program is introduced to shift flexible load in the microgrid system to achieve an active management system. Simulation results demonstrate the performance of the proposed method using forecast data for hourly PV and WT generations and a load profile. The simulation results show that the optimal generation scheduling can minimize the operating cost under the worst-case uncertainty. The load-shifting demand response reduced peak load by 4.3% and filled the valley load by 5% in the microgrid system. The proposed optimal scheduling system provides the minimum total operation cost with a load-shifting demand response framework.

Optimal Dispatch of the Energy Demand in Electrical Distribution Grid with Reserve Scheduling

Abstract The operation of the electrical systems is a major problem for electrical companies’ subject to uncertainties threatening. In this study, the optimal management of the energy demand in the electrical distribution grid is done by interval optimization approach under electrical price uncertainty. The management of the energy demand is implemented via incentive-based modelling of the demand response programs (DRPs). The incentive-based modelling as reserve, and based on bid price for reduction of the electrical demand at peak hours is proposed. The interval optimization approach is used for the minimization of the electrical price uncertainty effects. The main objective in the proposed approach is minimizing operation cost; epsilon-constraint method is utilized to solve the problem. Finally, an electrical distribution grid has been used at various case studies to numerical simulation results and positive effects of the proposed modelling under uncertainties.