Total System Cost Research Articles

A two-stage optimal mechanism for managing energy and ancillary services markets in renewable-based transmission and distribution networks by participating electric vehicle and demand response aggregators

The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids.

Comparing performance and complexity of TCHB and CHB multilevel inverters using NLC technique

This paper presents a modulation strategy applied to a 13-level three-phase transistor clamped H-bridge (TCHB) inverter, aimed at a renewable and electric vehicle drives application. A comparison is performed between the TCHB inverter and a traditional cascaded H-bridge (CHB) inverter, considering circuit complexity, switching losses, and total harmonic distortion (THD) attained from each multilevel inverter topologies. The TCHB inverter achieves a 13-level output with only 15 switches, whereas the conventional CHB inverter requires 24 switches. The modulation technique, employing a nearest level control, yields improved output quality for both the TCHB and CHB multilevel inverters. The results demonstrate that this strategy effectively minimizes the overall THD. Notably, previous modulation techniques mainly focused on carrier-based PWM or SVPWM, making this approach distinctive. The FFT analysis reveals a voltage THD of 5.49% for TCHB and 5.15% for CHB, indicating a marginal difference in THD content for each multilevel inverter. Despite the CHB inverter experiencing double the switching stress compared to TCHB, since less switches are required in the TCHB inverter, consequently, the system's total cost and complexity are reduced. The achieved results are verified through the use of simulations carried out in the MATLAB Simulink.

Optimal economical sizing of a PV-battery grid-connected system for fast charging station of electric vehicles using modified snake optimization algorithm

This paper presents a methodology for the optimal sizing of a proposed photovoltaic (PV)-battery grid-connected system for fast charging station of electric vehicles (FCSEVs) in Cairo, Egypt. The key objectives of the formulated optimization problem are to minimize the total system cost and to guarantee the validity of the energy balance principle within the considered system. The paper also develops a novel energy management strategy, which is designed based on two widely adopted pricing strategies, for optimally managing the power flow within the considered system with the purpose of reaching the optimum sizes of the considered system components. In addition, the paper suggests a modification to the Snake optimization (SO) algorithm, which is used to solve the formulated optimization problem using MATLAB software. Moreover, a comparison is conducted among the proposed modified Snake optimization (MSO) algorithm and four other common meta-heuristic optimization algorithms to validate the viability of the suggested MSO algorithm in achieving the desired eventual aims of the sizing process. Furthermore, a techno-economic study is carried out to assess the economic viability of the proposed system for each of the two adopted pricing strategies over the project lifetime. The yielded results indicated that the developed energy management strategy is capable of managing the energy flow within the considered system effectively. Also, they demonstrated that the suggested MSO algorithm can yield the best optimal solutions for the formulated optimization problem for each of the two adopted pricing strategies compared to the other considered optimization algorithms.

On the Efficiency Impacts of Berthing Priority Provision

Facing intensified interport competition in the global container shipping market, an increasing number of ports choose to offer berthing priority for carriers to increase their attractiveness. This study is the first to theoretically analyze the efficiency impacts of such prioritization. Specifically, this study models the steady-state dynamics for each terminal in a biterminal port as a prioritized queuing system. We explore the equilibrated shipping flow distribution and resulting total system cost (i.e., bunker consumption cost and waiting time cost) with and without priority provision, along with their major analytical properties. Then, we examine the “second-order” effects of these priority schemes on just-in-time (JIT) arrivals, an increasingly popular green port management tool. Specifically, we investigate how the equilibrium state associated with JIT arrivals could change with priority berthing. These analyses generate some interesting results, including (1) the total system cost increases or remains unchanged when a priority scheme is implemented under a symmetric port with equal service capacities for both terminals; (2) under the asymmetric biterminal case, however, it is also possible that berth prioritization could reduce the total system cost, and such phenomenon occurs only if the terminal which offers prioritization owns larger service capacity; (3) the results indicate that the “price of prioritization” could reach [Formula: see text] in port operation when the berth loading is heavy, implying that priority provision may significantly harm the operational efficiency; and (4) lastly, priority provision has a negative second-order effect on JIT strategies in a symmetric port, and such negative effect may neutralize the positive ones. Those theoretical results are validated by numerical experiments, and some of them are also supported by empirical data. The results provide important practical implications for the decision making of the port (or terminal) agencies. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371143 and 72188101]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0411 .

Multi-Objective Robust Optimization of Integrated Energy System with Hydrogen Energy Storage

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

This paper introduces a novel model for optimizing microgrid systems by integrating multi-purpose renewable energy (MEM) and cutting-edge technologies, including electric vehicles (EVs). The proposed MEM demand response programs encompass various energy sources such as wind energy, multi-carrier energy storage technologies, boilers, combined heat and cooling units, EVs, P2G (power-to-gas), and demand response capabilities. The primary objective is to minimize the total operational cost of the microgrid system. A distinctive aspect of the proposed method lies in considering the prices of all energy carriers as unknown variables. Market prices are integrated into the modeling process, incorporating scenarios with reasonable probabilities and taking into account demand-side management programs. Moreover, the model allows for customizable programming of different parts of the multi-energy microgrids, with a focus on maintaining convexity principles for the operating area of each CHP (combined heat and power) unit. To tackle the unique complexity of this optimization problem, a developed blue whale optimization algorithm is proposed. This algorithm builds upon the main whale optimization algorithm (WOA), a promising population-based optimization approach. However, the effectiveness of WOA heavily relies on the careful setting of exploration and exploitation parameters, which may lead to being trapped in local optima. To address this challenge, the paper introduces a self-adaptation modification based on wavelet theory to enhance the WOA's performance. The proposed model and optimization method are thoroughly evaluated through simulation studies for different scenarios, showcasing their efficacy in achieving cost-efficient and sustainable microgrid operation. The combination of MEM and emerging technologies like EVs, along with the improved optimization algorithm, marks a significant contribution to advancing the planning and operation of microgrid systems towards low-cost, reliable, and environmentally-friendly energy solutions.

The impacts of local wind power objection on the power system of the Midcontinent Independent System Operator area

Using the Regional Energy Deployment System (ReEDS) model, we estimate the deadweight loss imposed by county-level wind power development restrictions in the form of increased electricity costs due to suboptimal siting. This is accomplished by optimizing the power system of the United States' Midcontinent Independent System Operator (MISO) from 2020 to 2050. We perform the optimization with and without land-use constraints arising from simulated potential local ordinances restricting wind power development, and under multiple scenarios reflecting different renewable portfolio standards (RPS). We find that local restrictions on wind power increase the total system cost by 0.15%–0.3% and the wholesale electricity price by 1.8%–2.7%, depending on the RPS scenario. Changes in the generation and installed capacity mixes are more substantial and depend on both the level of county restrictions on wind power, and RPS requirements, thus indicating an interaction between RPS requirements and local wind power restrictions. We also find that plausible restrictions on wind development do not pose major barriers to meeting renewable energy targets in a cost-effective manner.

Optimize and analyze a large-scale grid-tied solar PV-powered SWRO system for sustainable water-energy nexus

The lack of clean water and access to energy are two major obstacles to global sustainable systems. The development of solar photovoltaic (PV) technology gained the attention of water desalination projects due to its cost decline, carbon-free emission, and the on-site energy production. However, studies on in-depth analysis of the performance of these systems remain rare. This study performs a comprehensive a design optimization and seven-factor analysis (energy, exergy, economic, environmental, energoeconomic, exergoeconomic, enviroeconomic - 7E) of a grid-tied PV system powering a large-scale seawater reverse osmosis (SWRO) plant. First, an optimal design model is developed based on real hourly measured data for the SWRO load demand and renewable resources, while considering net energy metering mechanism. The objective function is delineated to diminish the energy cost of the system, concomitant with the imposition of operational constraints. Second, an analytical model is established to perform exergetic calculations for the optimal design. Third, a sensitivity analysis is conducted to show how the model parameters affect the system design and costs. The results show that the optimal system relies on a 4.25 MW solar PV array and a 3.125 MW grid-tied inverter. This configuration reduces the levelized cost of energy by 48.3 % and carbon emissions by 50 % compared to a grid-only system. The implementation of net-metering has also maximized the designed system's revenues to 263,834 $/year. Further, the optimally designed system achieves a lower levelized cost of water (0.2217 $/m3), minimal capacity shortage, and negligible unmet load ratios (0.048 %) despite grid outages. From the exergetic assessment, the proposed system has a high exergy efficiency (9.3 %) which can be achieved despite the high value of the exergy loss. Moreover, the energoeconomic and exergoeconomic indicators have recorded tiny values (3.35E-5 kWh/$ and 0.0311 kWh/$, respectively), which are considered a desired outcome. Notably, a profit of 4285.4$ is achieved through the sale of carbon emissions reduction. The sensitivity analysis revealed an increase in the system's total cost of 4.91 % and 9.36 % and the system's energy cost of 4.91 % and 8.7 % if the energy purchase price raised by 10 % and 20 %, respectively. Also, it indicates that a fall in the electricity sellback rate of 10 % would remarkably raise the system's energy cost by 47.3 %. Finally, reducing the PV-Inverter costs by 15 % would drop the system's net present and energy costs by 51.4 % and 60 %, respectively. Overall, the insights from this study have the potential to expedite the realization of sustainable development goals 6, 7, and 13, particularly in developing nations abundant in solar energy resources.

Low carbon and economic optimal operation of electricity-gas integrated energy system considering demand response

Integrated Energy System is an important direction for the transformation of the power industry. IES can give full play to the complementary role of multiple energy sources and serve to achieve the industry's energy saving and emission reduction goals. Firstly, this paper establishes a P2G refined operation model considering two stages of hydrogen production from electrolytic water and hydrogen methanation. Secondly, based on the analysis of the IES load characteristics, the multi-energy loads are divided into longitudinal demand responses where the same energy type can be transferred and reduced in time, and horizontal demand responses where different energy types can be substituted for each other, and the corresponding mathematical models are established. Finally, the reward and penalty ladder carbon trading mechanism are used to quantify the carbon trading cost, and the IES low-carbon economic optimization model integrating the Power-to-gas refinement model and demand response is constructed. The model was subjected to second-order cone programming relaxation and incremental segmented linearization methods before being solved using the CPLEX commercial solver. Simulation analyses were performed in an IES consisting of an IEEE 33-node distribution network and a 24-node gas distribution network. The results show that considering demand response can fully exploit the flexible coordination performance of the load side, resulting in a 12.6% reduction in total system cost and a 38.8% reduction in carbon emissions.

Performance Assessment of Solar Ice Slurry Cold Storage System for Solar Refrigerated Vehicle

This article mainly analyzes the ice slurry cold storage system in solar refrigerated vehicles, calculates and studies the thermal properties of the ice slurry, the cold load of the refrigerated vehicle, and the solar photovoltaic panels, and calculates the fuel savings of using solar power generation. Study the demand for ice slurry in ice slurry storage systems under different energy utilization efficiencies, analyze the economy of solar ice slurry generation systems, and calculate the recovery period of the system in this paper. The daily power generation of solar photovoltaic panels can run the electric motor for 2.93 hours. The solar refrigerated truck can save 7.91 liters of oil per day and reduce carbon dioxide emissions by 1.25×107 liters per day. The average annual electricity cost savings are 4026.67 CNY. Based on the total cost of the photovoltaic energy storage hybrid quality system, it will take 3.89 years to recover the cost. Calculate the density and enthalpy of the ice slurry based on the thermophysical model of the ice slurry, and provide different utilization amounts for the ice slurry mass generated by the ice slurry generation system at different efficiencies. Calculate the total weight of the ice slurry system.

Simultaneous optimization of cost, active power loss and water quantity in irrigation: a techno-economic study incorporating PV panels and demand-side management

Currently, the whole world is facing energy shortage and water crisis. Management of water and energy usage in irrigation is essential to overcome the current crisis. This research introduces a techno-economic study to achieve simultaneous optimization of cost, active power loss and water quantity without violating the crop water requirement. Four scenarios are proposed incorporating energy demand-side management, PV panels integration and modification of the irrigation schedule and irrigation methodology for comparison against the existing scenario of pump-based irrigation system in Karot line, Assiut, Egypt. The grid-connected PV panels scenario achieves minimum system cost as well as minimum total system active-power loss and water quantity when compared with the other scenarios. Grid-connected PV panels with surface irrigation reduce the total system cost by 93% when compared with that of the Karot line. On the other hand, grid-connected PV panels with drip irrigation give a much more saving in water quantity and a further reduction in the total system active-power loss by 48.1 and 25.13%, respectively, when compared with those of the Karot line case.

Concentration versus diversification: A spatial deployment approach to improve the economics of wind power

Previous studies on the economics of onshore wind parks in Germany found that geographic diversification results in minor system costs savings (Pfluger et al., 2017a). Furthermore, such diversification does not necessarily result in higher market values (Eising et al., 2020) or better merchant profitability (Klie and Madlener, 2022). Therefore, the question arises whether an alternative allocation (i.e. concentration rather than diversification) of the German wind park fleet is more economical. In this paper, we compare the future (2030) market values, subsidy needs and total system costs (excl. intra-national grid costs due to congestion, redispatch and curtailment) of a more concentrated versus a more diversified allocation of onshore wind turbines in Germany. The results show that a concentration of turbines, in areas where the gap between market values and levelized costs of electricity is smallest (i.e. Northern Germany), is more beneficial in terms of subsidy need and system costs (ignoring congestion, redispatch and curtailment). The analysis further shows that these areas are also more beneficial in terms of market values, when using system-friendly turbine configurations selected based on the same approach. To incentivize such a selection of areas and turbine configurations based on minimal gaps between market values and levelized costs an alternative renewables support scheme is presented, which favors such minimal gaps in its auctioning process.

Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets

As the proportion of variable renewable generation capacity in our electricity systems rises, so too will the associated absolute forecast errors in their generation. This work quantifies the complex interactions between short-term renewable generation forecasts, short-term demand forecasts, generator unit commitment and economic dispatch in electricity systems with levels of variable renewable generation spanning 49–79% of annual electricity demand. When the short-term operational uncertainty caused by renewable generation becomes larger in absolute terms than that of demand, the accuracy of renewable generation forecasting has a critical influence on electricity market performance. That is, inaccurate short-term wind forecasts can increase hours of unserved energy and scarcity hours spent at market price cap, increase total system costs and prices, significantly increase returns to thermal generators and energy storage, reduce returns to wind generators, and increase greenhouse gas emissions slightly. For the generalised case study considered in this work, the value of improved short term wind generation forecasts looks to becomes significant once variable renewables generate more than roughly 60% of annual electricity demand. Reductions in average market prices of 5 $/MWh or more then appear plausible as acceptable levels of reliability are recovered. As such, improvements in short-term renewable generation forecasting are likely required in order to justify continued investment in variable renewables as the major means of achieving decarbonisation efficiently and reliably, and these could be achieved by any combination of improved forecasting, dispatch control or on-site firming.

Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems

Capacity expansion models provide the basis on which to decide where, when, how much and what technology type to deploy. In systems with large shares of variable renewable energy, the low temporal detail of these models has been shown to introduce biases, prompting much recent work to reduce them. This paper shows that this issue is fairly secondary compared to the impact of maximum investment rates. Through this parameter, typically not discussed in capacity expansion studies, many notions can collectively be expressed, such as the rate at which capacity is financed, institutions approve development, manufacturers roll-out equipment, civil engineers build infrastructure, network operators connect plants etc. This paper shows that considering even ambitious development rates significantly increases total system costs, and drastically changes the structure of an optimal generation mix. The presented sensitivity analysis is based on a multi-region representation of the European power system, modelled using the open-source tool OSeMOSYS, to which a novel power transmission module has been added. Results stress the extent to which hopes of meeting climate targets hinge on society’s collective ability to deploy new low-carbon infrastructure fast enough. Energy policy can enhance this ability by providing long-term visibility and stability, reducing investment risk.

Estimating the impacts of a new power system on electricity prices under dual carbon targets

The construction of a new power system dominated by renewables is crucial for achieving China's goals of carbon emission peaking and carbon neutrality. While numerous studies have investigated grid parity for large-scale renewables, some have overlooked the fact that the integration of variable renewable energy requires coordination with regulating resources. This coordination introduces additional costs, leading to subsequent increases in electricity prices. Our study comprehensively considers the total system costs and proposes a dynamic recursive linear optimization model. This model aims to simulate both short-term and long-term impacts of constructing a new power system on electricity prices in China. It evaluates the short-term effects of the Russia-Ukraine conflict and the COVID-19 pandemic on energy prices and demand, as well as the long-term effects of renewable energy penetration on ancillary service costs and technological progress on system costs. The results show that China's electricity price is projected to rise by 10.2% in 2030 for carbon peaking. By 2060, with an 83.3% renewable energy penetration for carbon neutrality, the electricity price will reach 0.859 CNY/kWh, representing a 27.2% increase. Ancillary service costs will experience the highest growth rate, accounting for 15.5% of system costs by 2060. Our study also proposes optimal technological pathways for the new power system and provides valuable data to support the development of effective electricity price mechanisms.

Investigation of a Fuzzy Production Inventory Model with Carbon Emission using Sign Distance Method

Reducing costs associated with inventory is the primary goal of conventional inventory models like the models of economic order quantity and economic production quantity. However, these models fall short of addressing defective goods or revising them. Imperfections in the manufacturing process result in flawed products alongside the final goods. To convert these flawed components into finished goods, rework is necessary. In manufacturing and reworking process produces carbon emissions which are harmful for the earth. To determine the optimal quantity for a single product manufactured in a single-stage manufacturing system that yields partially defective products that are reworked in the same cycle, is determined in real-life situations, where the inventory characteristics and objectives are not exact. Such a type of uncertainty may be characterized by fuzzy numbers. A pentagonal fuzzy number has been used to define the cost parameters. Due to fuzzy parameters, the model becomes a fuzzy quantity, and it is defuzzied by the sign distance method. this article formulates a model of manufacturing inventories with planned backorders. Furthermore, a closed form for the inventory’s system total cost is determined, and a range of actual values for defective products for which an appropriate method exists is also provided. A proper mathematical model is created to accomplish the goal, and the manufacturing lot size that reduces the overall cost is determined. The ideal amount of a production batch to reduce total cost is established in to attain this goal using an appropriate mathematical model. While formulating and solving the relevant model, the necessary and sufficient conditions for a single globally optimal solution have been determined. Examples used as visualizations are given and confirmed by data.

Optimization study of computational methods in coordinated integrated carrying capacity assessment of multi-voltage hierarchies

Abstract In this paper, we take a two-level distribution grid as the infrastructure of a multilevel distribution grid and study the comprehensive carrying capacity assessment model in depth. The minimum value of the maximum access capacity under each scenario is used to assess the maximum carrying capacity of the distribution network, and the maximum access volume of the multilevel distribution network is taken as the objective function, and the particle swarm algorithm is improved to establish the maximum carrying capacity assessment model. Using the alternating direction multiplier method, a two-level distribution grid carrying capacity distributed optimization model is established, and the multilevel distribution grid access volume problem is transformed into the maximum access volume subproblem at all levels of the distribution grid. Comparing the whale algorithm and particle swarm algorithm, the model in this paper converges at 7465 kW access capacity and about 12 iterations, respectively, and the weak degree is below 0.4 at different distribution network states, which is a good performance in terms of optimization accuracy and weak degree. We conduct simulation experiments with the modified IEEE33 nodes and find that using this paper’s model to participate in optimal scheduling reduces the total operating cost of the distribution network system by more than 6.24% compared to not using it. Finally, we use the model in this paper to evaluate the comprehensive carrying capacity of the distribution network in Ningxia, China.

Cooperative Scheduling for Directional Wireless Charging With Spatial Occupation

Wireless Power Transfer (WPT) technology has been developed rapidly in recent years. The cooperative charging model and corresponding scheduling methods have been proposed to save the charging cost in paid charging service. However, the state-of-the-art methods ignore the spatial occupation issue of rechargeable devices. Moreover, the cooperative charging scheduling in directional wireless charging has not been studied yet. This paper studies the cooperative scheduling for directional wireless charging with spatial occupation. We formulate the Cooperative Charging Scheduling with Spatial occupation (CCSS) problem of Mobile Rechargeable Sensor Devices (MRSDs) for optimizing the total cost of whole charging system. We first investigate the properties of optimal arrangement of MRSDs in charging group and calculate the tight intervals of charging angles of MRSDs. We show that it is sufficient to bound the error by conducting angle discretization for only two MRSDs in each charging group. Then, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\ln n+1)(1+\varepsilon )$</tex-math></inline-formula> -approximation algorithm of the CCSS problem is proposed based on greedy approach, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of MRSDs, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is the discretization error. The results of extensive simulations and field experiments demonstrate that our algorithm can reduce at most 42.5% total cost comparing with the benchmark algorithms.

Renewable Energy Sources constrained Generation Scheduling of Thermal Units in a Regulated Power Industry

Renewable energy production technologies use resources directly from the environment to generate electric power. These energy sources such as sunshine, wind, tides and biomass are some of the more popular gifts from the nature. The renewable energy resources emit nothing into atmosphere or never produce green house gas or pollute the environment at all. Even if the renewable energy sources are safe to the atmosphere, these resources meet out smaller percent of power requirement internationally This paper a population based optimization algorithm of Corona Virus Herd Immunity Optimizer (CVHIO) is proposed to solve the renewable energy constrained generation scheduling problem. The objective function of the problem is to minimize the total operating cost of hybrid system with satisfying standard operating constrains of thermal, wind and solar power plants. Initially the variables of the problem are effectively optimized using CVHIO algorithm. . Numerical example with IEE 39 bus test system (ten thermal twenty four hour) with an equivalent Solar and Wind generators are considered to prove the performance of CVHIO approach.

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

Fog-cloud computing is a promising platform for processing Mobile Crowdsensing (MCS) tasks that come with different requirements. A fog environment is more suitable for processing time-sensitive tasks due to its proximity to the MCS layer. On the other hand. the cloud environment provides powerful resources to handle large tasks. However, due to the heterogeneity of the computing nodes, scheduling MCS tasks in a fog-cloud environment is a challenging issue. This paper presents a non-cooperative game theoretical model for the task scheduling problem of MCS tasks in the fog-cloud environment. Then, the paper presents an improved genetic algorithm to efficiently solve the problem of task scheduling game model with main enhancements including a new strategy to generate a diverse initial population, incorporating the utility function of the game theoretical model with system fitness function, and finally, the paper introduces a new strategy for population sorting and grouping with applying adaptive crossover operator to meet the specific needs of each group. This improves the exploration of the unseen regions of the search space, as well as exploiting the already-found promising solutions, ultimately leading to a faster convergence toward the optimal solution. The experimental results demonstrate that the proposed approach has better performance in terms of reducing the makespan by 26%, decreasing the energy consumption by 32.4%, decreasing total system cost by 28%, and decreasing the degree of imbalance by 21.53% as compared with other scheduling approaches such as Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO, Time-Cost Aware Scheduling (TCaS), Moth Flame Optimization (MFO), and Bees Life Algorithm (BLA).