Operating Cost Model Research Articles

Failure Consequence Cost Analysis of Wave Energy Converters—Component Failures, Site Impacts, and Maintenance Interval Scenarios

In the early stages of developing wave energy converter (WEC) projects, a quantitative assessment of component failure consequence costs is essential. The WEC types, deployment site features, and accessibility should all be carefully considered. This study introduces an operation and maintenance failure consequence cost (O&M-FC) model, distinct from conventional O&M models. The model is illustrated with case studies at three energetic Atlantic sites, each of which considers two types of generic floating WECs: a 300 kW point absorber (PA) with a hydraulic power-take-off (PTO) and a 1000 kW oscillating water column (OWC) with an air-wells-turbine PTO. This study compares 39 failure modes for PA and 27 for OWC in terms of direct repair costs and indirect lost production costs, examining the impact of location accessibility, capacity factors, and the mean annual energy production. The discussion revolves around the sensitive parameters. Recommendations for failure mitigations are presented, and the impact of planned maintenance (PM) during the operational phase is examined for 20 MW PA and OWC WEC projects. For a given WEC type, the method thoroughly evaluates how the location affects performance metrics. It offers a decision-making tool for determining optimal PM intervals to meet targets such as O&M costs, operating profit, or availability.

Design and evaluation of architectural framework for a secured local energy market model based on distributed ledger technologies

AbstractBlockchain‐based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper‐proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on‐chain features of blockchain with trusted execution environments to develop a transparent, tamper‐resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

A data-driven model for power system operating costs based on different types of wind power fluctuations

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method.

Sizing Optimization of a Photovoltaic Hybrid Energy Storage System Based on Long Time-Series Simulation Considering Battery Life

An energy storage system works in sync with a photovoltaic system to effectively alleviate the intermittency in the photovoltaic output. Owing to its high power density and long life, supercapacitors make the battery–supercapacitor hybrid energy storage system (HESS) a good solution. This study considers the particularity of annual illumination due to climate conditions in Harbin, China. A global optimal PV-HESS sizing method is proposed by constructing a PV-HESS operating cost model and taking the annual system operating cost as the objective function. To consider the effect of battery life degradation due to different charge and discharge rates and charge and discharge times, a semi-empirical model based on the Arrhenius model was used to quantify the battery life degradation. Based on the effects of different seasons and different photovoltaic panel sizes, batteries, and supercapacitors on the optimization results, four scenarios are proposed. The feasibility of the system configuration corresponding to the four scenarios is discussed, and an optimal sizing configuration of the system is obtained. The simulation results show that the proposed method can effectively balance the degradation of the ESS due to irregular charging and discharging and determine the minimum operating cost and a reasonable sizing configuration of the system.

Demand response based microgrid's economic dispatch

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

Two-stage stochastic decentralized low-carbon economic dispatch of integrated electricity-gas networks

To achieve low-carbon operation optimization of integrated electricity gas network (IEGN) system with uncertainty, a two-stage stochastic decentralized low-carbon economic scheduling model and its solution method for multi-region interconnection is proposed. Firstly, an IEGN operation cost model considering the stepped carbon emission cost and the benefits of P2G's participation in carbon market transactions is constructed, considering demand-side management (DSM) with user satisfaction and degree of regulation. Secondly, multi-region interconnection is served to overcome single-region space-time limitations with the multi-energy flow, regional decoupling is realized by copying multi-region IEGN boundary nodes, and the scenario method is used to quantify the output uncertainty of IEGN. Hence, a two-stage stochastic optimal scheduling model of multi-region IEGN is established. Then, an improved successive linearization method is proposed for the non-convex transformation. Considering the decentralized autonomy and information privacy of different decision-makers, a decentralized optimization model is established, and the synchronous alternating direction multiplier method (synchronous-ADMM) based on nested improved successive linearization is introduced for the iterative solution. Finally, the feasibility and superiority of the proposed model and algorithm are verified by comparing and analyzing the simulation results of multi-region IEGN under different scenarios and scheduling strategies.

Operation Cost Optimization Method of Regional Integrated Energy System in Electricity Market Environment Considering Uncertainty

In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

The Revolutionized Finance Sector with Reference to Fintech Industry in India

Fintech is the newest buzzword in the financial industry. The most recent evolution of Fintech, driven by startups, presents challenges for market participants and regulators alike, particularly in balancing the potential benefits of innovation with the potential risks of new financial sector approaches. Traditional financial institutions now use fintech. The expanding technology advancements in the financial sector are referred to as fintech. The banking sector is progressively being shaped by fintech. The adoption of fintech, the fintech news network, the structure of the Indian fintech industry, fintech startups in India, and fintech trends in India are all examined in this research study. In order to improve customer experience, stimulate acceptance and utilisation of the digital channel, and minimise operational friction, Indian FinTech companies might solve some of the crucial structural concerns plaguing Indian financial services. When banks try to catch up to these more agile and innovative start-ups, their legacy-prone processes and higher operating cost models will provide digital FinTech startups an advantage. The opportunity for FinTech is in market expansion, influencing consumer behaviour, and bringing about long-term reforms in the financial sector.

Transit Ridership Efficiency as a Function of Fares

The purpose of this study is to assist in the development of transit fare policies that exploit the benefits of public transit in the mix of transportation options for Washington State. The study relates fares to ridership performance, fare-box recovery, and system utilization.Ridership efficiency, a measure of transit performance, is estimated for 24 State transit systems. A mathematical model is developed that relates fares to ridership efficiency. The ridership efficiency function follows the complement of a cumulative normal probability distribution. The tail of the distribution is reached at $0.95. Higher fares have little impact on ridership efficiency. An operating cost model to estimate the fare-box recovery and operating cost subsidy is developed. Ridership, fare-box recovery, and system utilization estimates are made for selected transit systems.

Robust Bi-Level Planning Method for Multi-Source Systems Integrated With Offshore Wind Farms Considering Prediction Errors

Considering the economy, reliability, and output characteristics of multiple power sources (MPS) and energy storage (ES) comprehensively, a multi-source system integrated with offshore wind farms (OWFs) and its construction cost, and operating and maintenance cost model are established. The system is mainly composed of OWFs, thermal power plants, gas turbine power plants, and pumped hydro storage plants. Given the economy of the power system and offshore wind power accommodation, a bi-level optimal capacity configuration and operation scheduling method is proposed for the multi-source system integrated with OWF clusters with the objective function of optimal total cost. Then, a robust bi-level planning method for the multi-source system integrated with OWFs considering the dual uncertainty of load and offshore wind power prediction is proposed, in which the upper and lower models are solved by an improved particle swarm optimization (PSO) algorithm and CPLEX solver, respectively. Based on the method, the cost-optimal capacity configuration and operation scheduling scheme of an MPS and ES can be obtained. Finally, an OWF group in Shandong Province is taken as an example to check the validity and feasibility of the proposed method.

Mixed-Integer Linear Programming Model to Assess Lithium-Ion Battery Degradation Cost

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered.

Techno-economical aspects of electrocoagulation optimization in three acid azo dyes’ removal comparison

This study compares the optimization models for three acid azo dyes removal by electrocoagulation (EC). First, Response Surface Methodology (RSM) models were evaluated and compared via four independent parameters, including initial pH (4–10), initial dye concentration (0–600 mg/L), current density (41.67–291.66 A/m2), and reaction time (3–15 min). Then, second-order polynomial equations of dye removal and operating cost models were separately applied as benefit and cost functions in the two-objective Imperialist Competitive Algorithm (ICA), respectively. According to the statistical analysis, not all the parameters are essential in removal efficiency for the three selected dyes. For example, the initial dye concentration and current density were the effective independent parameters in the Acid Red 18 removal model. According to the Pareto solutions, by changing the operating cost function from US$/m3 to US$/Kg removed dye, the difference between the Pareto curve points of Acid Brown 14 and Acid Orange 7 decreased significantly. Therefore, the unit of operating cost and dye type can affect the treatment policy in practice. The two-objective optimization showed an RSM deficit in predicting some corner points as previously depicted in RSM main effect plots.

Study on economic operating mode for combined desulfurization system of circulating fluidized bed unit

With the stricter emission regulation taking effect, most of circulating fluidized bed (CFB) power generation units operates with conventional desulfurization in CFB furnace and flue gas desulfurization (FGD) technology to meet the requirements of ultralow SO2 emission. Therefore, it is urgent to analyse operating cost of the combined desulfurization system and explore the low-cost operating mode for CFB units. In this paper, the operating cost model of combined desulfurization system was established, in which the influence of desulfurization in furnace on thermal efficiency of the CFB boiler was considered. Then, the operating cost of combined desulfurization system under different load conditions for a 300MW subcritical CFB unit was analysed, combined with measured data. The operating cost optimization result shows that, under different loads, when Ca/S is in the range of 0.4 to 0.5, the operating cost of combined desulfurization system reaches the minimum. In addition, as the unit load increases, the higher proportion of SO2 removal in FGD is required to achieve low-cost operating mode for CFB units.

Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants

A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters.

Study on Operating Cost Economy of Battery-Swapping Heavy-Duty Truck in China

In recent years, battery-swapping heavy-duty trucks have seen rapid growth in China. Summarizing from the practical experiences gained in this development, and starting from market research and analysis of the most typical city of application case, Beijing, we aim to achieve the following: (ⅰ) Establish an operating cost model for battery-swapping heavy-duty trucks throughout a full operation cycle from the perspective of a heavy-duty truck freight transport capacity operator, based on four key cost dimensions, including transportation equipment, operation and maintenance, environmental protection compensation, and battery recycling compensation. (ⅱ) Calculate and compare the operating cost economy of battery-swapping heavy-duty trucks and other types of heavy-duty truck under different energy supplement modes, including charging, hydrogenation, and diesel. (ⅲ) Propose suggestions for faster and more successful heavy-duty truck electrification. The results indicate that battery-swapping heavy-duty trucks have good cost economy in a full operation cycle under specific scenario, and their economy will be improved with the popularization of battery-swapping stations.

Risk assessment of FPSO topside based on generalized Stochastic Petri Net

This paper presents a Generalized Stochastic Petri Net (GSPN) approach to assess risk of FPSO topsides, considering the weather window and imperfect maintenance effect. The FPSO topside is considered as a multi-component system consisting of several assemblies. A GSPN model is developed according to system identification and the correlation between different components. The reliability, availability, and maintenance information of the system are derived using the model, based on failures collected from existing literature and database. An operation and maintenance cost model is established, and an optimized maintenance strategy is finally determined based on the GSPN outcomes. The results indicate that the proposed approach is an effective tool to evaluate the system risk and save the maintenance expenditure of FPSO topsides.

Managing market risk caused by customer preference uncertainty in product family design with launch flexibility: Product option strategy

The uncertainty of customer preference is one of major sources of product family design risk. Since the prediction of customer preference entails forecasting and estimation error, the uncertainty is inevitable. The present study proposes the novel method for module-based product family design, product option strategy. Product option strategy provides the launch flexibility to companies. The firm has the right to release a maximum-utility product alternative among the product option set, corresponding to actual customer preference. Launch flexibility saves commercialization cost resulting from ineffective product launch and enhances market share by possessing diverse product alternatives. The strategy is implemented by the two-stage decision model with heuristic algorithm. The structure of decision model accommodates the request of consumer electronic industry on logical decision mechanism to persuade internal stakeholders. The decision model strikes the balance between development goals of main internal stakeholders, market effectiveness and operational efficiency. The proposed strategy is applied to product family design of LCD TV. In the case study, the proposed method achieves higher market share with the same or less operational complexity (cost) but the advantage might be limited under complex product architecture. In the future, operational cost model will be elaborated for the equivalent comparison between market share and operational cost.

Study on the Direct Operation Cost model of primary training aircraft

Which type of flight training aircraft the flight training school introduces depends largely on the direct operation cost (DOC) of the aircraft in the flight training. This paper divides DOC into two categories, namely fixed cost and variable cost. Then it constructed the calculating model for each cost item. The language of Java was used to achieve visual interface for the flight training aircraft. These above findings as well as the developed tool are expected to raise the evaluation accuracy in introducing the type of flight training aircraft for the flight training school.

Assessment of delivery models for semi-flexible transit operation in low-demand conditions

Semi-flexible transit (SFT) service pattern coupled with real-time dynamic scheduling of passenger requests and dynamic route assignment of vehicles is considered as a cost-effective alternative to serving public transportation users in low demand conditions. Transit agencies tend to provide SFT by delivering it in-house by public transit agencies or contracting-out to private operators like transportation network-, taxi- or microtransit companies. Past studies focussing on economic issues in implementing SFT delivery models mostly used rudimentary information provided by transit agencies to quantify the operating cost, including budgetary inputs such as average annual maintenance, fuel, and administrative costs. These aggregate costs imply a uniform cost for SFT operation per unit time; however, the cost is subjected to change through spatial and temporal variations in demand and service. This study developed analytical disaggregate operating cost models that could account for marginal variations in cost due to the change in demand and service characteristics. Such model can assist policymakers while evaluating the economic feasibility of SFT delivery models. Rigorous and approximate operating cost models are developed for two competing service patterns: commonly adopted fixed-route bus transit (FRBT) and proposed SFT. Assuming in-house delivery of FRBT, SFT operating cost Eq.s are derived for two service delivery models, namely Contract-Out Taxi, and In-House Transit. Subsequently, these cost models are used to identify the critical passenger demand, where service patterns or delivery models could replace one another to minimize the total cost of operation. Study models can be used as part of a decision support tool to establish contracting, planning, and operating policies to optimize SFT operation subjected to required service quality.

Estimation of Commercial Fishing Trip Costs Using Sea Sampling Data

When estimating commercial fishing costs, selection bias can impact any model derived from non-census sampling methodologies. In the northeastern United States, commercial fishing operating cost models may suffer from selection bias, as they are often estimated using data collected for biological, rather than economic, purposes. We investigate the effects of sampling bias on trip cost model estimations using weighted/unweighted least squares and Heckman sample selection models. Results suggest that (1) the propensity for a trip to carry an observer is not random with respect to costs and that (2) selection bias exists in the majority of cost models investigated. To gauge the magnitude of selection bias, we compare results of the unweighted least squares and Heckman models. The differences between models can lead to erroneous conclusions at the subfleet level and in estimating trip cost maxima. Results suggest that assessing and correcting for selection bias is necessary when using sampled fishing cost data.