Total Operating Cost Research Articles

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

Treatment of tropical peat water in Sarawak peatlands nature reserve by utilising a batch electrocoagulation system

Tropical peat water is abundantly available in peatlands nature reserve, especially on Borneo Island. However, this water source contains humic substances that could be harmful to human health if consumed without undergoing water treatment process. Although tropical peat water could be treated with conventional treatment, the system possesses drawbacks in terms of debris build-up in pipeline networks. As such, this study aims to develop a batch electrocoagulation treatment system to treat tropical peat water for domestic consumption in Sarawak peatlands nature reserve. Correspondingly, this study also investigates the effect of electric voltage ranging from 5 V to 30 V and treatment time between 5 min and 30 min on the reduction of humic substances in peat water by utilising electrocoagulation treatment. Subsequent, this study has found that the batch electrocoagulation treatment system could effectively reduce 97.12% of colour, 95.56% of turbidity, 88.46% of total suspended solids (TSS), 90.00% of total dissolved solids (TDS), and 97.30% of chemical oxygen demand (COD) from peat water at 30 V of electric voltage and 25 min of treatment time. These operating conditions could generate an adequate production of aluminium hydroxide coagulants in order to remove humic substances from tropical peat water in the form of flocs. The study has noticed that the treated peat water could be utilised as a clean water source owing to the levels of 13 TCU of colour, 2 NTU of turbidity, 3 mg/L of total suspended solids, 2 mg/L of total dissolved solids, and 4 mg/L of chemical oxygen demand have met the Malaysia Class I standard in National Water Quality Standards (NWQS). From the total operating cost analysis conducted, the treatment of peat water by using batch electrocoagulation system cost only United States Dollars (USD) 0.65 per metre cubic. Overall, this study deduces that a batch electrocoagulation treatment system could be utilized to treat tropical peat water sources in Sarawak peatlands nature reserve.

Optimization Model and Application for Agricultural Machinery Systems Based on Timeliness Losses of Multiple Operations

Present agricultural practices confront issues such as mismatches between tractors and implements, imprecise machinery allocation, and excessive machinery investment. Optimization of agricultural machinery systems was a potent remedy for these concerns. To address inaccuracies in calculating objective functions and the incompleteness of constraints in existing models for agricultural machinery system optimization, a comprehensive mixed integer nonlinear programming (MINP) model for agricultural machinery system optimization was established. The model introduced timeliness loss costs for multiple key operations across various crops into the objective function, and constraints were enhanced by including operation sequence constraints and boundary constraints on initiation and completion dates of those key operations. Taking corn and soybeans as examples, timeliness loss functions of sowing and harvesting operations were derived through experiments. Solving the MINP model by Lingo (V.14.0) software, improvements in total power, workload per unit power, and total operational costs were shown when comparing the optimized machinery system through the MINP model against current systems. When the model omitted considerations for timeliness loss functions and operation sequence constraints, issues arose including an increase in total operational costs and an inversion of operation sequence. The model’s application in devising machinery allocation plans for production units of various operational scales revealed a gradual decrease in total power and costs per unit area with expanding scale, approaching stability when scale exceeded 1600 hm2. This study enriches theory and methodology for optimizing agricultural machinery systems, provides theoretical and technological underpinnings for rational machinery acquisition, and promotes the high-quality progression of comprehensive agricultural mechanization.

Co-optimisation of power-gas networks connected to industrial energy hubs with high penetration of storage systems

With proliferation of gas-fired generators and increase in the number of interconnected power-gas networks, co-optimisation of power-gas nexus has become a hot research area. Energy hubs (EHs) are efficient and reliable multi-carrier energy systems which typically take in electricity and natural gas and supply different forms of energy demands. As EHs typically import electricity and gas, their connection into a power-gas nexus may be an interesting option; their electricity input may be connected into an electric bus and their gas input may be connected into a gas node. This research focuses on the operation of power-gas nexus connected to industrial EHs. The Belgian network connected to a 24-bus power system has been used as case study and the studied EH includes boiler, combined heat and power unit, battery and thermal storage and supplies electricity and heat demands. The changes in shadow prices at different electric buses and gas nodes as well as congestion in both networks are investigated. The effect of connection points of EH on total operation cost, electric system cost and gas network cost is investigated and the optimal connection points are determined. According to the results, about 49.4 % of total cost belongs to electric system and the remaining 50.6 % belongs to gas system and a large portion of electric system cost belongs to demand shed cost. According to the results, the electric system is severely congested. At some buses and some hours, due to demand shed, the shadow price of electricity soars 10,000$/MWh which equals value of lost electric load.

Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Semi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.

Meeting User Needs through Building Automation and Control Systems: A Review of Impacts and Benefits in Office Environments

Smart building technologies and building automation and control systems (BACS) can offer a range of additional benefits beyond energy savings, in particular by improving building responsiveness to user needs. Although in recent years these technologies have gained popularity as a means of reducing energy consumption and improving building performance, a clear picture of the role of BACS in providing a wider range of benefits is still missing. This review identifies and collects BACS impacts in office buildings with a special emphasis on improving indoor environmental quality by adapting building operation to changing conditions and guaranteeing feedback and real-time interaction with occupants. The resulting benefits, such as increased employee productivity, fewer occurrences of sick leave, and lower rates of absenteeism are highlighted. Offices represent an interesting field of application, as small improvements in the built environment can have a significant impact on labour costs which are the predominant share of the total operating costs. Furthermore, quantitative relationships between physical factors of the indoor environment and benefits have been displayed where available. This literature review aims at establishing an approach that comprehensively evaluates BACS across their entire spectrum, leading to the promotion of novel business cases.

A line planning approach based on time-varying demand for high-speed rail under the combined operation of periodic and aperiodic services

Passenger demand for high-speed rail systems is highly fluctuating in temporal and spatial throughout the day. In order to effectively improve passenger satisfaction and service level for high-speed rail, the formulation of line plan must make the distribution of the trains’ capacities be meet with the time-varying demand of passengers. The aperiodic line plan can well meet this demand since it is more flexible with different stop patterns and various headways. However, the irregular arrival/departure times are not convenient for travel planning. We propose a new type of line plan that would combine both the regularity of the periodic line plan and the flexibility of the aperiodic ones. An optimization method of high-speed railway line plan based on a combination of periodic and aperiodic operation is structured, which can well satisfy the temporal-spatial distributions of passenger demands in different time periods. The main challenge is to coordinate the numbers of periodic and aperiodic trains, and their temporal-spatial distributions on the rail network considering the passengers’ train-choice decisions. Based on generating a set of candidate routes and constructing a passenger travel network, we propose a model to optimize the numbers of both periodic and aperiodic trains, and their paths, stops, approximate arrival and departure times at each visited stations for minimizing the total operation cost of trains and the total generalized travel cost of passengers. Then, a simulated annealing algorithm is designed to efficiently solve our proposed optimization model. Finally, numerical experiments based on the Shanghai-Nanjing intercity railway in China verify the effectiveness and practicability of the optimization method.

Economic Dispatch of Island Integrated Energy System Based on Ranking Group Learning Optimization Algorithm

In order to improve the flexibility of the Island Integrated Energy System (IIES) and reduce operating costs, the Cooling Thermal and Electric three-load Demand Response (DR) are introduced into the IIES. Under the constraints of the output of each unit, this paper establishes a comprehensive energy economic dispatch model with the lowest total operating cost of the system as the objective function, and a Ranking Group Learning Optimization (RGSO) is proposed to optimize the output of each unit. In order to verify the effectiveness of the proposed model and algorithm, simulation experiments are carried out on the typical operating scenarios of the IIES in summer and winter, and the results are compared with those of the other five optimization algorithms. Experimental results show that the proposed algorithm has a good global convergence performance, and the computational cost is lower than the other five optimization algorithms.

ANALISIS REALISASI BIAYA PADA KEGIATAN OVERBURDEN REMOVAL DI PT ABC KABUPATEN LAHAT PROVINSI SUMATERA SELATAN

Mining sector is very closely related to mechanical device utilization and is inseparable from cost so that production targets can be achieved. Operational costs can affect the company’s income, so an analysis of operational costs is necessary. This study analyzed the realization of operational costs in fleet 1 overburden removal activities to determine lost opportunities experienced by a company. The analysis process was conductued by identifying the target and actual production of overburden fleet 1. The production target of overburden removal was 15,000 BCM with the tools used 1 digging equipment and 3 hauling equipment and the operational cost consisted of solar cost, oil cost, filter cost, grease cost, tire cost, repair/maintenance cost and operator salary cost. The total operational cost for overburden removal activity was Rp237,579,500 with the overburden transportation cost of Rp29,500 for a distance of 1.2 Km, the gross income carned was Rp435,309,080. The factors that caused the lost opportunity were the unachieved production factor of overburden removal fleet 1 which was Rp7,190,920 and the mechanical equipment maintenance factor which was Rp103,261,000 with a lost opportunity of Rp110,451,920.

EVALUATION OF ENERGY-COST-EFFICIENT DESIGN ALTERNATIVES FOR RESIDENTIAL BUILDINGS IN KARNATAKA’S TROPICAL WET AND DRY CLIMATIC ZONES

Introduction: The existing building construction techniques in India account for increased operating energy consumption. Due to this, there is a need to apply sustainable construction methods by including passive design techniques. In accordance with the local climatic condition, a good passive design not only maintains a comfortable environment in the house, but also consumes less energy all the while performing the same basic functions as the traditional structures. Purpose of the study: The present research focuses on residential villas by analyzing parameters such as illuminance, energy, and cost consumption using Revit (Insight 360) software with two different cases. Methods: Considering Case1 as an existing villa of conventional design and Case2 presenting a proposed villa, a remodeling of the existing villa with an increased window wall ratio (WWR), different fenestration designs, and a new building plan. The authors followed the Indian Green Building Council (IGBC) guidelines relating to illuminance and generated an analytical energy model using Insight 360 to compare the energy consumption and cost efficiency of the cases. Results: The effectiveness of various fenestration types related to direct heat gain, window orientation that would provide more natural light, total operating energy consumption and costs of two different cases of villas are investigated in this paper.

URBAN DEVELOPMENT AND WASTE MANAGEMENT PLANNING IN KABUL NEW CITY, AFGHANISTAN: A CASE STUDY

The municipal solid waste collection system and services of existing Kabul City are not acceptable socially, economically, and environmentally. This study was carried out in 2020–2021 for the Phase-1 area of Kabul New City (KNC). The study aims to analyze different MSWCSS for the KNC area and choose the best option during the planning stage. The average incremental cost for different collection systems was determined, and the results show that the total annualized cost (AN) of the proposed waste collection system (PWCS) is 855.3699 AFN/tonne, while the total operation and maintenance cost (OP) is 577.1235 AFN/tonne. The AN and OP of the stand-alone underground waste collection system (SAUWCS), tailored collection point system, traditional (prevailed) system, and alley waste collection system (AWCS) are 911.3032 AFN/tonne and 499.9017AFN/tonne, 1,681.762 AFN/tonne and 1,267.28 AFN/tonne, 308.9254 AFN/tonne and 186.3363 AFN/tonne, and 716.7706 AFN/tonne and 410.5375 AFN/tonne, respectively. The social and environmental analysis for the PWCS, SAUWCS, existing system, and AWCS obtained scores of 19, 29, -6, 17, and -5, respectively. From the cost-benefit analysis, it has been proposed that for the PWCS of low-rise residential areas, three wheelie bins of different colors will be provided to each housing unit. The total costs per tonne of solid waste for the PWCS, SAUWCS, TCPS, and traditional system are 2,832.1952, 3,638.137205, 3,888.6272, 751.45984, and 1,975.8152, respectively. For the PWCS of residential areas, chute waste collection systems are planned for high-rise apartment buildings and SAUWCSs are planned in commercial areas.

Communication-efficient distributed electricity and carbon allowance management for community microgrids with hybrid energy supply stations

With the rapid development of plug-in electric vehicles and hydrogen fuel vehicles, the introduction of hybrid energy supply stations into modern energy systems has attracted much attention in recent years. The hybrid energy supply stations are promising energy facilities that can provide charging services for plug-in electric vehicles and refilling services for hydrogen fuel vehicles. This paper studies electricity and carbon allowance sharing among community microgrids integrated with hybrid energy supply stations from a new perspective. The main objective of this work is to reduce the communication overheads when implementing distributed energy and carbon allowance management for microgrids, rather than merely protecting the privacy of microgrids as other studies have done. Thus, a communication-censored alternating direction method of multipliers-based communication-efficient distributed algorithm is proposed for solving the electricity and carbon allowance sharing problem in a distributed manner. Moreover, an adaptive penalty coefficient selection method is introduced and integrated into the communication-censored alternating direction method of multipliers for improving the convergence performance of the distributed algorithm. Simulation results demonstrate that the proposed mechanisms for sharing electricity and carbon allowance among microgrids can result in a 50.18% reduction in the total operation cost and a 13.70% reduction in the total carbon emissions, compared to the scenario where such sharing is not implemented. In comparison to the alternating direction method of multipliers, the proposed distributed algorithm can reduce the total communication overhead by 46.47%. Furthermore, the adaptive penalty coefficient selection method can improve the convergence performance of the communication-censored alternating direction method of multipliers by 21.26%.

Modelled economic evaluation of a virtual emergency department in Victoria.

Virtual ED (VED) can potentially alleviate ED overcrowding which has been a public health challenge. The aim of the present study was to conduct a return-on-investment analysis of a VED programme developed in response to changing healthcare needs in Australia. An economic model was developed based on initial patient outcome data to assess the healthcare costs, potential costs saved and return on investment (ROI) from the VED. The VED programme operating as part of Alfred Health Emergency Services. The participants were the first 188 patients accessing the Alfred Health VED. VED is the delivery of emergency assessment and management of specific patients virtually via audio-visual teleconferencing. ROI ratios that compare cost savings with intervention costs. The mean total operational cost of VED for 79 days for 188 patients was A$344 117 (95% uncertainty interval [UI] $296 800-$392 088). The VED led to a potential A$286 779 (95% UI $241 688-$330 568) healthcare cost saving from reductions in emergency visits and A$97 569 (95% UI $74 233-$123 117) cost saving in ambulance services. The ROI ratio was estimated at 1.12 (95% UI 0.96-1.32). The VED was cost neutral in a conservatively modelled scenario but promising if any hospital admission could be saved. Ongoing research examining a larger cohort with community follow up is required to confirm this promising result.

A two-stage SCUC model for distribution networks considering uncertainty and demand response

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

Game-theoretic analysis of the impact of crew overnight hotel cost on airlines’ fleet assignment and crew pairing

Crew costs account for a high proportion of the total operational costs of an airline. The cost of overnight stay for the crew at a hotel is an important additional cost. To address this problem, this study establishes an integrated model to depict the interaction between the fleet assignment problem and the crew pairing problem. Because this cost is largely determined by the price agreed by the agreement hotel group, we further present a Stackelberg price game model to capture the interdependence between the integrated fleet assignment and crew pairing of airlines and the price game among hotel groups that provide accommodation services for airlines. We combine the idle time re-allocation with a spatio-temporal graph convolutional network to ensure the robustness of crew pairing. The solvability of the model is improved using the Reformulation-Linearization Technique. To obtain the overall Nash equilibrium of the Stackelberg price game model, we design a heuristic algorithm based on a decomposition algorithm and a proximity search algorithm. A case study is made to demonstrate that our robust model is cost-saving for the airline.

Multi‐stage stochastic dual dynamic programming to low‐carbon economic dispatch for power systems with flexible carbon capture and storage devices

AbstractThe carbon capture and storage (CCS) technique gains much attention due to its role in reducing CO2 emissions. By introducing flexible CCS devices into conventional power plants, the low‐carbon economic operation of the power system can be achieved. However, the uncertainty of renewable energy makes it hard to obtain an optimal operation policy. First, the detailed model of CCS consisting of bypass venting stacks, solvent storage tanks, absorber, and stripper is described. Then, a low‐carbon economic dispatch model of the power system with CCS is proposed to minimize the total operation and renewable energy abandonment penalty costs. Next, the problem is reformulated as multi‐stage stochastic programming, and the stochastic dual dynamic programming (SDDP) method is applied to relieve the computation burden. To control the probability of stopping the algorithm prematurely, a new stopping criterion based on a two‐sided hypothesis test is proposed. Finally, the effectiveness of the proposed model and the new stopping criterion is demonstrated by case studies on a practical power system.

Multi-Type Reserve Collaborative Optimization for Gas-Power System Constrained Unit Commitment to Enhance Operational Flexibility

With the wide application of the gas-power system, gas-power coupling equipment such as gas turbines are gradually becoming widely used, and the problem of insufficient system reserve capacity needs to be solved. In order to improve the operational flexibility of the gas-power system, this paper combines the source side, the load side, and the energy storage side to propose a multi-type backup system, and constructs the source-load-storage multiple reserve capacity system of the gas-power system. Through the participation of gas turbine, steam turbine, interruptible load, and energy storage battery to provide reserve capacity, it can fully cope with the output fluctuation of the load side and source side and realize the coordinated operation of multiple resources to provide reserve capacity. In addition, the coordination of gas turbines and steam turbines can further improve the operation flexibility of the gas-power system. Through the example analysis, it was found that the proposed method reduced the total operating cost of the system by 10.6%.

Optimization of BESS placement, technology selection, and operation in microgrids for minimizing energy losses and CO2 emissions: A hybrid approach

This research paper addresses the issue of placement, technology selection and operation of BESS energy storage systems (BESS) in microgrids under a variable distributed generation (DG) and energy demand scenario for an average year of operation. To address this issue, a mixed-integer nonlinear programming (MINLP) model was formulated. The model considers two different objective functions, which are optimized using a mono-objective approach: (i) total energy losses and (ii) total CO2 emissions. The MINLP model was solved using a master–slave methodology based on metaheuristic algorithms. In the master stage, the Chu & Beasley parallel genetic algorithm (PCBGA) was used to solve the BESS placement and technology selection problem, while in the slave stage, the vortex search algorithm (VSA) was used to solve the BESS operation problem. A matrix-based successive approximation power flow was also used in this stage to determine the objective functions and evaluate the technical and operational constraints defined in the mathematical model. To verify the results, the 33-node test system was adjusted to the generation and demand characteristics of the city of Medellín. In addition, the proposed methodology was compared with two optimization techniques used in the master stage, namely the Parallel Monte Carlo (PMC) method and the Parallel Particle Swarm Optimizer (PPSO). Simulation results obtained using MATLAB software for the test scenario showed that the CBGA-VSA methodology outperformed the other techniques in terms of solution quality, repeatability and processing times to minimize total annual operating costs.

Cost-Effectiveness of Introducing Autonomous Trucks: From the Perspective of the Total Cost of Operation in Logistics

This study explored the economic influence of introducing fully autonomous trucks (ATs) on the total cost of operation (TCO) from the viewpoint of freight transport operators. We utilized the cost–benefit analysis and scenario planning frameworks using a case study of a middle-mile route between a port and a large logistics distribution center in South Korea, where an autonomous truck pilot project has been running for several years. We employed a data source obtained from Hanjin Transportation Co., LTD., which enables us to reflect the actual expenses of operating freight transportation in the context of the study route. The findings of this study indicate that ATs would attain substantial operation cost savings for freight transport operators across all five scenarios, ranging from the most pessimistic to the most optimistic. Specifically, results of the base scenario reveal that monthly TCO decreases by 56.25, 47.04, 42.97, and 41.73 percent for 1-, 5-, 12-, and 25-ton trucks, respectively. The cost reduction per month was USD 2399 for a 1-ton truck, USD 2891 for a 5-ton truck, and USD 3438 for a 12-ton truck. Even in the most pessimistic scenario, the operation cost savings for operating with ATs in the route are USD 1415, USD 1642, USD 1985, and USD 2222 per month for 1-, 5-, 12-, and 25-ton trucks, respectively. When factoring in changes in cost, the wage reduction for vehicle drivers was substantial (USD 2430, USD 2835, USD 3240, and USD 3645 for 1-, 5-, 12-, and 25-ton trucks, respectively). However, remote operation was a new cost factor that increased TCO, which increased by USD 243,384,357 in the base case. This study’s findings have multiple implications, including (1) informing economic efficiency and productivity of operating autonomous vehicles (AVs), (2) providing insights on indicative cost estimates for AV operations, (3) providing valuable information for making informed investment decisions, and (4) supporting the notion that freight transport is an especially attractive market for AVs.

Receding horizon dispatch of multi-period look-ahead market for energy storage integration

Although noteworthy efforts towards energy storage participation in electricity markets have been witnessed worldwide during the last decades, market mechanisms for optimal storage integration are yet to be comprehensively studied. This paper presents a novel approach wherein a multi-period look-ahead market is proposed to progressively solve the optimisation problem over a receding horizon, incorporating intertemporal constraints to enable forward planning. The focus is specifically on modelling the bidding behaviour of energy storage, and to address this, we develop two distinct models for the participation of energy storage in the market. The integrated model aims to schedule the future dispatch of the storage to minimise total operational cost, taking into account the fixed costs of the storage, while the strategic model pursues the dual ambition to maximise storage profit by formulating a bilevel optimisation problem. We validate the viability of our proposed market mechanisms through case studies on the Australian National Electricity Market. We conduct case studies which provide evidence that both the strategic and integrated bidding models effectively address the requirement for forward planning, resulting in reduced market costs and volatility. Notably, the strategic model outperforms in terms of generating higher storage revenue, while the integrated model exhibits a slight advantage in terms of lower market costs compared to the strategic model.