Energy Purchase Research Articles

TECHNO-ECONOMIC ANALYSIS OF TRIANGULAR ROOFTOP SOLAR PV MODEL/PLN ON-GRID HOUSEHOLD SCALE IN INDONESIA

The use of solar PV as an alternative to fulfill household-scale electricity needs has begun to be widely developed. However, the problem of investment costs and the location of solar PV placement for household scale is still a challenge in its implementation. The construction model of rooftop solar PV can affect the investment cost and performance of solar PV. In this paper, the triangle model of rooftop solar PV on grid with PLN (PT. Perusahaan Listrik Negara) electricity network is studied in terms of technology and economics to determine the feasibility of implementing 900 VA household-scale power plants. Testing the application of solar PV technology under solar radiation conditions in the city of Surabaya, Indonesia as a case study. Calculation of electricity production, energy savings, energy sales, and energy purchases to determine technological feasibility as well Net Present Value (NPV), Benefit Cost Ratio (BCR), and Payback Period (PP) to determine the level of economic feasibility. The results of the research of 1,5 KWP (kilowatt peak) solar PV technology on a household scale are able to meet energy needs and reduce PLN electricity purchases to 0% and can sell electrical energy by 13.96% / year of the total electrical energy produced. In addition, the NPV, BCR with a value greater than zero, and PP of 8.6 is less than 15 years which is the service life of solar PV, so solar PV/ PLN on grid is feasible to be implemented for household scale power generation models.

Social innovation and global citizenship: Guiding principles for sustainable, just and democratic energy transition in cities

In this paper, we examine the relationship of global citizenship to social innovation and propose a set of guiding principles to facilitate sustainable, just and democratic energy transitions in cities. We applied a participatory research approach beginning with interviews with stakeholders with expertise in citizenship, participation, or energy research and policy to develop search criteria, which were used to create a shortlist of 20 European citizen-driven energy projects. The criteria were: 1) democratic governance; 2) civil empowerment and capacity building; 3) human rights approach; 4) diversity of actors; and 5) sustainability. Key informants with relevant expertise were asked to score the 20 cases based on the search criteria, leading to a final selection of six exemplar cases from across Europe. Highlights from these initiatives included: empowerment of young community members in making decisions about the city's climate change response (Manchester), including citizen board members as regulators of energy partnerships (Viladecans), democratising activities through open voting (Leuven), and enabling community members to decide how to spend the surplus from collective energy purchases (Brixton). We argue that the integrated application of the global citizenship and social innovation concepts reveals a “sweet spot” at the interface between them that aids practical application. Social innovation perspectives can help depoliticise ideological positions around energy by bringing together “communities of makers”, while global citizenship concepts help to orient innovative proposals more clearly around social justice and environmental objectives. The five criteria are proposed as guiding principles to aid city authorities in energy transition contexts.

Changes in household food grocery shopping patterns in Melbourne, Australia during COVID-19 restrictions in 2020

ObjectiveTo investigate the impact of COVID-19 restrictions in Melbourne, 2020, on food grocery purchases. MethodsGrocery purchase data for 2019 and 2020 were accessed for 1,413 Melbourne households (NielsenIQ Homescan Consumer Panel) and linked to a nutrition composition database (FoodSwitch). ResultsPer capita expenditure and dietary energy from groceries increased by 21.2% and 17.7%, respectively, during lockdowns, with marginally larger increases in expenditure and energy purchases from unhealthy products than healthy products (21.9% and 18.0% v 20.2% and 17.5%). The most socioeconomically disadvantaged households spent the least on but purchased the most energy from unhealthy products during lockdowns ($108 and 109MJ per capita per month), with the inverse found for the most advantaged households ($121 and 102MJ per capita per month). An increase in the overall proportion of total expenditure from unhealthy products during lockdowns was identified (+0.7%); however, there was no evidence of a difference in the proportion of energy purchased from unhealthy products. For most quintiles of household socioeconomic disadvantage/advantage, there were no statistically significant changes in the contribution of unhealthy products to total expenditure and energy purchases. ConclusionsThere was no substantial deterioration in the healthiness of grocery purchases during COVID-19 lockdowns in Melbourne. However, any additional purchases of unhealthy products are a concern. Further research on other sources of foods and drinks is also required to ascertain impacts on broader dietary patterns. Implications for public healthThe increase in energy purchased may have implications for overweight and obesity as a risk factor for COVID-19 and chronic diseases. Governments and retailers may need to consider measures to encourage improved diet quality during future crises.

Optimizing Power Market Clearing with Segmented Electricity Prices: A Bilevel Model

The energy sector plays a critical role in reducing emissions by transitioning to renewable energy. However, integrating renewable energy into the electricity market impacts market clearing. This article investigates market clearing decisions of renewable and traditional energy companies, considering electricity price segmentation. We propose an interprovincial bilevel market clearing decision-making model. The upper level optimizes the provincial market through a unilateral bidding model, minimizing the operating cost of the power market as the objective function. Meanwhile, the lower level optimizes power purchases by minimizing the cost of acquiring interprovincial renewable energy. A case study was conducted to determine the optimal interprovincial renewable energy purchasing power for multiple energy generation companies, considering the minimum market operating cost and segmented electricity prices. The proposed bilevel market clearing model captures the interplay between intraprovincial and interprovincial transactions, integrating electricity price and ecological attributes. By solving this model, we can attain optimal interprovincial electricity purchase of renewable energy, minimizing the overall market operating cost in the province. Through simulation, 18% of interprovincial transactions are needed to absorb 7.21 KWH of renewable energy from other provinces. When considering price segmentation, the cross-provincial two-level market clearing model can reduce the market cost by CNY 1,554,700.

Optimal energy management of an integrated energy system with multiple hydrogen sources

Hydrogen is considered a promising alternative to fossil fuels in an integrated energy system (IES). In order to reduce the cost of hydrogen energy utilization and the carbon emissions of the IES, this paper proposes a low-carbon dispatching strategy for a coordinated integrated energy system using green hydrogen and blue hydrogen. The strategy takes into account the economic and low-carbon complementarity between hydrogen production by water electrolysis and hydrogen production from natural gas. It introduces the green hydrogen production–storage–use module (GH-PSUM) and the blue hydrogen production–storage–use module (BH-PSUM) to facilitate the refined utilization of different types of hydrogen energy. Additionally, the flexibility in hydrogen load supply is analyzed, and the dynamic response mechanism of the hydrogen load supply structure (DRM-HLSS) is proposed to further reduce operating costs and carbon emissions. Furthermore, a carbon trading mechanism (CTM) is introduced to constrain the carbon emissions of the integrated energy system. By comprehensively considering the constraints of each equipment, the proposed model aims to minimize the total economic cost, which includes wind power operation and curtailment penalty costs, energy purchase costs, blue hydrogen purification costs, and carbon transaction costs. The rationality of the established scheduling model is verified through a comparative analysis of the scheduling results across multiple operating scenarios.

Multi-energy Complementary Power System Economic Dispatch Considering Carbon Emission and Regional User Coordination

The integration of multi-energy complementarity and source-grid-load-storage is an important initiative to promote energy transformation and the high-quality development of power systems. This paper proposes a two-tier day-ahead multi-energy complementary power system economic dispatch model from the perspective of clean and low-carbon, taking into account carbon emissions and multi-regional user synergy. The model optimises the production and purchase of electricity through different decisions between the upper generation side and the lower user side, which minimises the energy production cost in the upper tier and the energy purchase cost in the lower tier, and achieves mutual benefits between the upper and lower tiers. This paper validates the algorithm and model with an improved IEEE 39-node system.

Low-carbon Economic Scheduling of Combined Cooling, Heating and Power Microgrid Considering Demand Response and Shared Energy Storage

In order to further promote the new energy consumption of combined cooling, heating, and power (CCHP) microgrid, reduce carbon emissions, and optimize the total operating cost of the microgrid, a low-carbon optimal scheduling model of CCHP microgrid was proposed. It considered demand response (DR) and shared energy storage (SES). Firstly, the concept of SES and its operation mode are applied to the microgrid. Secondly, in order to take advantage of the flexible nature of multiple loads, an integrated demand response (IDR) model with a ladder subsidy mechanism was established. Then, the optimal scheduling model introduces a reward and punishment ladder carbon trading mechanism (RPL-CTM). Finally, taking energy purchase cost, load reduction subsidy cost, interaction cost with energy storage station, carbon trading cost, and wind abandoning penalty cost as optimization objectives, a multi-energy coupling, and multi-objective collaborative optimization scheduling model was established. Numerical analysis indicates that the model can efficiently lower the microgrid’s operating cost and encourage the use of clean energy and carbon emission reduction.

A decision-centric approach for techno-economic optimization and environmental assessment of standalone and grid-integrated renewable-powered electric vehicle charging stations under multiple planning horizons

The automotive sector is undergoing a transitional phase towards electrification with the goal of achieving climate sustainability. Renewable-powered electric vehicle (EV) charging infrastructure can play a vital role in this paradigm shift. To support and provide a roadmap to this transition, the current study aims to investigate a sustainable planning approach for optimal renewable-powered electric vehicle charging stations (EVCS) within institutional premises. A feasibility analysis for multiple renewable-based EV charging station configurations has been carried out based on multi-objective techno-economic and environmental indicators including Levelized cost of energy (LCOE), Net present cost (NPC), and carbon emissions. The proposed planning approach has been applied under Fast charging for private vehicles and on-board charging for Ez-bikes and shuttles (FCOBC) and Medium- charging for private vehicles and on-board charging for Ez-bikes and shuttles (MCOBC). Results reveal that a PV system backed with a utility grid enabling both energy purchase and sale during peak hours and off-peak hours respectively remained the most feasible configuration for EV charging infrastructure experiencing an LCOE of $0.1289/kWh to $0.1262/kWh and $0.1168/kWh to $0.1161/kWh for both FCOBC and MCOBC, respectively. Along with the environmental analysis, a forest absorbing carbon emissions analysis has been performed to provide a policy optimization for the future environmental outlook. Results validations have been undertaken for levelized average tariffs of single point supply (above 66 kV) of different government-owned distribution companies, which indicated the commercial efficacy of the proposed EV charging infrastructure, where the LCOE is lower than the real-time operational tariffs within the region. Finally, a sensitivity analysis is carried out under different uncertainties to check the optimization behavior of proposed systems.

Low-carbon economy dispatching of integrated energy system with P2G-HGT coupling wind power absorption based on stepped Carbon emission trading

To improve the renewable energy consumption capacity of integrated energy system (IES) and reduce the carbon emission level of the system, a low-carbon economic dispatch model of IES with coupled power-to-gas (P2G) and hydrogen-doped gas units (HGT) under the stepped carbon trading mechanism is proposed. On the premise of wind power output uncertainty, the operating characteristics of the coupled electricity-to-gas equipment in the system are used to improve the wind abandonment problem of IES and increase its renewable energy consumption capacity; HGT is introduced to replace the traditional combustion engine for energy supply, and on the basis of refined P2G, a part of the volume fraction of hydrogen obtained from the production is extracted and mixed with methane to form a gas mixture for HGT combustion, so as to improve the low-carbon economy of the system. The ladder type carbon trading mechanism is introduced into IES to guide the system to control carbon emission behavior and reduce the carbon emission level of IES. Based on this, an optimal dispatching strategy is constructed with the economic goal of minimizing the sum of system operation cost, wind abandonment cost, carbon trading cost and energy purchase cost. After linearization of the established model and comparison analysis by setting different scenarios, the wind power utilization rate of the proposed model is increased by 24.5%, and the wind abandonment cost and CO2 emission are reduced by 86.3% and 10.5%, respectively, compared with the traditional IES system, which achieves the improvement of renewable energy consumption level and low carbon economy.

Smart distribution grid management during a worst-case scenario of renewable energy outage

Nowadays, due to the high penetration of renewable energy sources such as wind generation and PV in the distribution network and the possibility of their outage, the optimal operation of the distribution grid has faced challenges. In this paper, a bi-level model based on mixed integer quadratic programming (MIQP) is proposed for the optimal operation of smart distribution networks under the worst case of the outage of renewable energy resources. At the upper-level problem, minimization of the energy losses, energy purchase, and load shedding in the demand side management program is formulated, and at the lower-level problem, the maximization of output and curtailment of renewable energy resources is modeled. At the lower level, the worst-case realization of the renewable outage is derived and at the upper level, optimal operation of the distribution network is done under the worst-case realization of renewable resources. The reformulation method based on Karush–Kuhn–Tucker (KKT) conditions is considered to solve the proposed bi-level model, which is faster and less complicated than similar algorithms. The IEEE 33-bus distribution grid is considered for the analysis of the proposed model and method, which proves the accuracy and optimal performance of the proposed model and method. The findings indicate that, with the implementation of the suggested model, the smart distribution network successfully avoided load-shedding even when various renewable resources were disconnected. Also, the proposed model in operation under normal conditions has caused a 56% reduction in losses and a 53% reduction in energy purchases compared to the outage of 6 renewable units in the distribution network.

Energy crisis and companies’ investment in renewable energy sources: case of Metal Processing Cluster

Motivation: Improving the energy efficiency of enterprises is now not only a postulate of inter-national and national energy transition strategies towards climate neutrality, but also a requirement for maintaining their profitability and competitive capacity. Rising energy fuel prices and the unstable geopolitical situation, and above all, the war in Ukraine, are causing many enterprises to take energy-saving actions (ESA’s). One of them is investing in renewable energy sources (RES) for their own consumption, understood as investing in future energy purchase savings.Aim: The article aims to identify the involvement of enterprises affiliated with the Metal Processing Cluster (MPC), which has the status of the Polish Key National Cluster, in RES investments in the context of the energy crisis. It also attempts to verify the hypothesis: greater interest in investing in RES is shown by enterprises that have seen a strong negative impact of energy price increases on their operations. The research was carried out on the basis of literature studies, analysis of Polish Central Statistical Office statistical data and analysis of reports of various institutions. Empirical verification of the hypothesis was possible on the basis of the results of surveys conducted in the enterprises of the MPC.Results: The research showed that in 2022, more than half of the surveyed companies affiliated with the MPC felt the negative (very strong and strong) effect of rising energy prices. It primarily had an impact on the decline in their profitability. In 73% of the surveyed companies, this situation influenced the decision to start investments in renewable energy sources, with the majority of these investments being in photovoltaic panels. The survey contributes to the knowledge of the impact of the energy crisis on RES investments in metal companies.

Robust optimal scheduling of integrated energy systems considering multiple uncertainties

AbstractMultiple uncertainties such as renewable energy output, energy purchase prices and integrated demand responses have brought about severe challenges to the safe and economic operation of integrated energy system (IES). To meet this challenge, this study takes the combined cooling heating and power as an example of IES and proposes an optimization model considering multiple uncertainties. First, the structure of IES is given, and these mathematical models and constraints are listed according to the energy supply characteristics. Second, considering uncertain factors such as the wind and solar output, comprehensive demand response and energy purchase price, the different characterization methods are selected to model the uncertainty sources by identifying the characteristics of multiple uncertainty sources. Then, combining the stochastic scenario method and robust optimization method, a day‐ahead optimal scheduling model of IES with multiple uncertainties is established, and the whale optimization algorithm is improved to obtain the optimal solution of the model. Finally, the actual data of an IES is selected to verify the rationality and effectiveness of the model. Meanwhile, the influence of uncertain factors on scheduling is introduced to verify the perfect coordination of economy and robustness of the model at runtime.

How smart are our companies really? a case study of the current rollout of smart meters in Germany

The digital transformation and the associated challenges for the energy transition and society remain a challenge due to the increasing number of e-charging stations and heat pumps. Nevertheless, the rollout of smart meters selected in Germany affects only a small proportion of the available consumers. Therefore, the aim of this work is to determine the current status of this technology in companies and to answer the question of which factors influence implementation. For this purpose, data from a case study with 386 companies were used. The focus was on the current status of the technology in companies and their knowledge about the technology. The data were recorded during on-site inspections with the help of an energy consultant. In addition, the frequencies of the answers and the implementation of efficiency measures of selected cross-cutting technologies were compared between companies with a smart meter and companies with an analog meter. Our evaluation revealed that 1) 82 percent of companies have a commitment to implementation. 2) Of these, 10 percent have installed a smart meter. 3) Many of the companies were either unaware of the commitment or did not even know what a smart meter was. 4) Companies with a smart meter have implemented more measures in lighting, energy monitoring, energy purchasing and peak load management than companies with an analog meter. To increase implementation and, thus, usage, companies need to be made much more aware. In addition, there is still a lack of value-added services.

Product-service system and possibilities of its application in the field of alternative energy

The aim of this article is to study and justify the possibilities of using a product-service system in the field of alternative energy, as well as to develop business-models for introducing relevant innovative developments in the field of renewable energy sources to the world market. Thus, CO₂ emissions are reduced, and the demand for green energy is met. There are few studies in the literature linking the product-service system and renewable energy sources. The originality of this work lies in the correlation of these two concepts. The authors conducted a preliminary analysis of the energy market and developed three business models based on the product-service system applicable in the field of renewable energy: On-Grid, Off-Grid and Hybrid systems. The results of the study show that the proposed business-models are ideal in an environment where traditional electricity tariffs are high or there is no power grid, as well as when it is possible to be a prosumer (consumer and producer of electricity at the same time) at the legislative level. The favorable impact of the product-service system application to renewable energy sources on the environment is substantiated. Also, the purchase of renewable energy by consumers will not require an initial investment. These models consider the demographic, legal, and cultural characteristics of countries and communities. Companies operating with renewable energy can apply these business-models regardless of which country they operate in or what type of renewable energy they provide, which shows the importance of applying alternative business-models for the development of renewable energy.

Supply-demand side management of a building energy system driven by solar and biomass in Stockholm: A smart integration with minimal cost and emission

As part of the transition to a sustainable future, energy-efficient buildings are needed to secure users' comfort and lower the built environment's energy footprint and associated emissions. This article presents a novel, realistic and affordable solution to minimize the footprint of smart building energy systems and enable higher renewable energy use in the building sector. For this, an intelligent system is being developed using a rule-based automation approach that considers thermal comfort, energy prices, meteorological data, and primary energy use. In order to lower the installation cost and part of the environmental footprint, batteries are not used, and the heat pump's size is decreased via component integration. Also, different renewable resources are effectively hybridized using photovoltaic thermal panels and an innovative biomass heater to increase the share of renewable energy, enhance reliability, and shave peak load. In order to secure feasibility, the suggested framework is assessed from the techno-economic and environmental standpoints for 100 residential apartments in Stockholm, Sweden. Our results show that 70.8 MWh of renewable electricity is transferred to the local grid, and the remaining 111.5 MWh is used to supply the building's needs and power the electrically-driven components. The biomass heater meets more than 65% of the space heating demand, mainly at low solar power and high electricity prices, illustrating the value of integration strategies to reduce the system's dependability on the local grid. The results further reveal that most energy purchases during the cloudy days and nights are repaid through the sale of excess renewable production during the warmer hours, with a bidirectional connection with the grid. The monthly energy cost is less than 140 $/MWh for most of the years. The cost can be held low due to the exclusion of batteries and minimizing the heat pump size. The proposed system has a low emission index of 11.9 kgCO2/MWh and can reduce carbon dioxide emissions by 70 TCO2/year compared to using the supply from the Swedish energy mix.

Efficient Day-Ahead Scheduling of PV-STATCOMs in Medium-Voltage Distribution Networks Using a Second-Order Cone Relaxation

This paper utilizes convex optimization to implement a day-ahead scheduling strategy for operating a photovoltaic distribution static compensator (PV-STATCOM) in medium-voltage distribution networks. The nonlinear non-convex programming model of the day-ahead scheduling strategy is transformed into a convex optimization model using the second-order cone programming approach in the complex domain. The main goal of efficiently operating PV-STATCOMs in distribution networks is to dynamically compensate for the active and reactive power generated by renewable energy resources such as photovoltaic plants. This is achieved by controlling power electronic converters, usually voltage source converters, to manage reactive power with lagging or leading power factors. Numerical simulations were conducted to analyze the effects of different power factors on the IEEE 33- and 69-bus systems. The simulations considered operations with a unity power factor (active power injection only), a zero power factor (reactive power injection only), and a variable power factor (active and reactive power injections). The results demonstrated the benefits of dynamic, active and reactive power compensation in reducing grid power losses, voltage profile deviations, and energy purchasing costs at the substation terminals. These simulations were conducted using the CVX tool and the Gurobi solver in the MATLAB programming environment.

Sustainable Green Energy Management: Optimizing Scheduling of Multi-Energy Systems Considered Energy Cost and Emission Using Attractive Repulsive Shuffled Frog-Leaping

As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables energy sharing between different sectors. The proposed model is based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm that optimizes the scheduling of energy resources, taking into account constraints such as capacity limitations and environmental regulations. The model considers different energy sources, including renewable energy and a power-to-gas (P2G) network with power grid, and incorporates a demand–response mechanism that allows consumers to adjust their energy consumption patterns in response to price signals and other incentives. The ARSFL algorithm demonstrates superior performance in managing and minimizing energy purchase uncertainty compared to the particle swarm optimization (PSO) and genetic algorithm (GA). It also exhibits significantly reduced execution time, saving approximately 1.59% compared to PSO and 2.7% compared to GA.

Multistep Multiagent Reinforcement Learning for Optimal Energy Schedule Strategy of Charging Stations in Smart Grid.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users' satisfaction ratio.

GHG Emission Accounting and Reduction Strategies in the Academic Sector: A Case Study in Mexico

The carbon footprint (CF) quantifies the greenhouse gas (GHG) emissions generated by human activities, expressed in carbon dioxide equivalent (CO2e) units. It is an instrument for monitoring and mitigating the effects of climate change, which particularly affects low- and middle-income countries such as Mexico. The Mexican government has established a goal of reducing GHG emissions by 22% from the levels in 2000 by 2030. Although most efforts to reduce GHG emissions have been focused on the energy and agriculture sectors, the academic sector is also important since it can advise changes in public policy. In this study, the 2019 CF of the Centro Roberto Garza Sada (CRGS), a design school at the Universidad de Monterrey, was estimated in an effort to develop measures for reducing GHG emissions. The GHG Protocol was employed to calculate the total CF of the CRGS and identify the greatest contributors, including commuting (50.2%), energy purchase (28.5%), business travel (19.6%), and energy generation, use of paper, refrigerants, and shipments (1.7%). Three progressive mitigation scenarios were developed to reduce the GHG emissions from commuting, energy consumption, collaborators and student mobility, and material resources. These strategies could reduce the GHG emissions of the CRGS by 63.5% of the baseline assessed.

Optimizing electricity demand scheduling in microgrids using deep reinforcement learning for cost‐efficiency

AbstractRenewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large‐scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small‐scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long‐distance transmission. This paper proposes a deep reinforcement learning (DRL)‐based energy management system (EMS) called DRL‐MG to process and schedule energy purchase requests from customers in real‐time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q‐learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real‐time scheduling methods significantly.