Load Scheduling Research Articles

A Robust Distributed Algorithm for Solving the Economic Dispatch Problem with the Penetration of Renewables and Battery Systems

In the field of energy networks, for their effective functioning, it is necessary to distribute the required load between all online generating units in a proper way to cover the demand. The load schedule is obtained by solving the so-called Economic Dispatch Problem (EDP). The EDP can be solved in many ways, resulting in a power distribution plan between online generating units in the network so that the resulting price per unit of energy is minimal. This article focuses on designing a distributed gradient algorithm for solving EDP, supplemented by models of renewable sources, Battery Energy Storage System (BESS), variable fuel prices, and consideration of multiple uncertainties at once. Specifically, these are: time-variable transport delays, noisy gradient calculation, line losses, and drop-off packet representations. The algorithm can thus be denoted as robust, which can work even in unfavorable conditions commonly found in real applications. The capabilities of the presented algorithm will be demonstrated and evaluated on six examples.

Load Scheduling of Smart Net-Zero Residential Buildings Based on Pandemic Situation

Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers’ preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings.

Online state estimation of Li-ion batteries using continuous-discrete nonlinear Kalman filters based on a nonlinear simplified electrochemical model

Online monitoring of Lithium-ion batteries (LIBs) during the charge and discharge periods is imperative to characterize their performance, assess their safety, and mitigate their degradation. One way to achieve this goal is to resort to model-based state estimations. Electrochemical based modelling has demonstrated significant potential to predict both the microscopic and the macroscopic battery dynamics. Existing electrochemical models are however continuous-time systems while measurements are performed at discrete time steps. Yet, most estimators are designed based on a fully discretized model. In this study, various types of continuous-discrete nonlinear Kalman filters are developed not only to address this challenge but also to compare their performance in utilizing highly nonlinear and meaningful physical variables. The filters rest on a simplified electrochemical model that accounts for the nonlinearities that stem from the dependence of the parameters on the electrolyte concentration. The performance of the estimators in adaptive and nonadaptive modes is tested for two different driving schedule loads (FUDS and Synthetic random higher C-rate). The results reveal that the estimators can track the real states' values if the filter model matches the real battery, even in the presence of 30 % error in the states' initial conditions. Furthermore, unlike the continuous-discrete extended Kalman filter (CD-EKF), the suggested continuous-discrete unscented Kalman filter (CD-UKF) effectively predicts the actual state values, even in instances where a significant structural mismatch exists between the embedded model and the synthetically generated measurement signal.

Optimizing energy consumption in smart homes: Load scheduling approaches

AbstractRising fuel prices, global warming, and environmental damage are leading to increased demand for rooftop solar energy systems connected to the power grid. The development of smart grids, modern metering systems, and energy management could promote energy conservation in households. In this study, two different meta‐heuristic optimization techniques were employed to schedule the shiftable load in suitable hours for decreasing electricity costs and minimizing peak to average ratio in a smart home while maintaining optimum user comfort. For power generation and storing energy, a grid‐connected residential load with rooftop solar panels, a battery, and an inverter is considered. First, the problem is theoretically described using a load model and an objective function, with the primary goal of reducing power costs by moving the time of usage for certain home appliances. Simulations validate the proposed strategies, which effectively reduce power costs by 4.5% by shifting the time of use, with both optimization algorithms showing similar output. The residential electricity cost before optimization was 507.12 BDT/day, which decreased to 484.33 BDT/day after optimization without compromising load turn‐off.

A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture

Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating scenarios. This paper proposes a deep learning-based energy optimization method for microgrid energy management in the new power system scenarios. This article constructs a microgrid cloud edge collaboration architecture, which collects interactive network status data through terminal devices and network edge sides. A microgrid energy management model is constructed based on Bi-LSTM attention in the network cloud. And the model is sunk to provide real-time and efficient comprehensive load and power generation prediction output optimal scheduling decisions at the edge of the network, achieving collaborative control of microgrid light load storage. The simulation based on the actual available microgrid data shows that the proposed Bi-LSTM attention energy management model can achieve rapid analysis and optimize decision-making within 7.3 seconds for complex microgrid operation scenarios.

Optimization of normal operation mode of an electric system with renewable energy sources in Mongolia

This article is aimed at developing an algorithm for optimizing the operation modes of the electric power system of Mongolia, particularly the central power system that include not only conventional thermal power plants, but also renewable sources (wind and solar power plants). This power system accounts for a large share of electricity consumption and generation in Mongolia. The method of linear programming was chosen to minimize financial costs and active power losses during power generation at thermal power plants, while Newton’s method was used to minimize power losses. In addition, the article uses load schedules of each node of the studied power system for its modeling based on the ranking model. Load graphs are predicted using ensemble machine learning algorithms. After the optimization by the criterion of power loss minimization in the grid, power losses were found to be 3.05% of the total power consumption (with power losses in the basic variant of 3.12% and the average selling price of thermal power plants of 0.51 units). Thus, the reduction in losses amounted to 0.07 percentage points, or 2.24%. In terms of the cost minimization criterion, the average selling price of electricity was 0.49 units, i.e., decreased by 3.92%. Average losses of electric power in the grid decreased by 0.6%. According to empirical data, the suggested algorithms can be applied to the optimization of power distribution between thermal power plants by given criteria. The suggested algorithms are implemented using pandapower, a Python-based tool for power system analysis, thus creating a unified system of predictive analytics of power system operation modes

Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

Cost-Centric Energy Management of a Test Grid System Considering Load Flexibility for DSM

Demand-Side Management (DSM) adjusts load demand by rebuilding the load demand model of a distribution system and thus reduces operating costs. One potential approach to achieving cost savings is the redistribution of flexible loads to time periods characterized by lower utility expenses per unit, or alternatively, the elimination of nonessential loads. The primary objective of this research is to examine the use of DSM approaches in the scheduling of domestic appliances. The aim is to effectively minimize operating expenditures, the peak-to-average demand ratio (PADR), and any associated problems. Domestic appliances may be categorized into three distinct types based on their ability to adjust to variations in time, temperature, and light: time-flexible, temperature-flexible, and light-flexible. The second step of the study focuses on modelling the power consumption scheduling issue for demand-side clients. This is done by including operating cost priorities and modes of operation while taking into account both demand and supply factors. This research optimizes an IEEE 33 bus test grid system using load scheduling, fossil fuel generators, and renewable energy. The primary objective of this optimization is to minimize operating costs. In order to optimize the load model, it was necessary to restructure it according to the DSM participation level. After considering the restructured load demand models, distributed load scheduling ideas are disseminated to reduce test grid system power distribution costs.

An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System

An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System

EFFICIENCY OF AUTONOMOUS HEATING SYSTEM IN INTERMITTENT MODE WITH HEAT ACCUMULATOR

Theeffectiveness of the autonomous heating system in intermittent mode with a heat accumulator of the building of the educational building of the Odessa National Polytechnic University was studied. The main requirements for solving the task of optimizing the room heating process are formulated. Ways to increase the efficiency of morning room heating are considered. On the basis of the developed mathematical model of the dynamics of thermal processes of the elements of the autonomous heating system, the regimes of the thermal load of the heating system when the temperature of the outside air changes are investigated. The main directions for improving the heat supply of buildings in the mode of intermittent-frequent heating are proposed and their practical application in modeling and research of the system is taken into account. The results of optimizing the daily load schedules of the main elements of the autonomous heating system under variable external conditions are presented. The optimal daily schedules of the dynamics of indoor air temperature and network water temperature of the heating system are given. The dependence of the filling factor of the load schedule of the heating system and the heat generator on the outside air temperature in the range from 5 to –15 °С was studied. The effect of daily heat accumulation on the reduction of the nominal thermal power of the heat generator was studied. Heat savings were evaluated based on the results of optimization of load modes of the proposed system in the intermittent heating mode compared to the 24-hour heating mode, which amounted to 25.2%, respectively. Based on the results of the study, it was concluded that daily heat accumulation for heating systems operating in intermittent mode is an effective measure for increasing the efficiency of morning room heating, achieving the maximum possible thermal power of the heating system during heating and reducing its duration to 1 hour. Keywords: intermittent heating mode, mathematical modeling, heat accumulation, morning heating of the room, load schedule filling factor, optimization of load modes

Enhanced PSO Optimized Leader in Cloud-Fog Task Scheduling for IoT and Mobile Crowdsensing Environments

The data generated by the IoT needs a powerful platform such as cloud computing for data processing. However, the cloud faces challenges when dealing with various types of resources, high delay, and cost, this represents a substantial challenge in scheduling tasks. Therefore, the need appeared to introduce the concept of fog. To address these limitations, optimization algorithms such as PSO were used. In traditional PSO, all particles in the swarm are influenced by a single global best particle (Gbest), if it becomes stuck in a local optimum, all the particles will move closer to it, thus, the PSO may easily get trapped in premature convergence. This paper proposed an adaptive cloud-fog integrated approach based on modified PSO called PSO Optimized Leader (PSO-OL). These modifications on four stages: Firstly, a method to ensure swarm diversity in the initialization phase is introduced. Secondly, to reduce the chance of the population getting trapped in a local optimum, the farthest-best particle is introduced. Third, in addition to the primary Gbest, a second Gbest represents a different good particle presented to explore multiple promising regions. Finally proposed a new crossover operator to get an optimized leader. The PSO-OL approach was evaluated and the results show the effectiveness of the enhanced leader by 40% with farthest-best, 45% with second-Gbest when compared to standard PSO, and when compared to scheduling algorithms where outperforms the other algorithms by minimizing makespan by 34%, cost by 14%, and increasing throughput by 75%, in comparison to existing load balancing and scheduling methods: RR, BLA, MPSO, ETS, and TCaS.

Study on graphs of electrical load and analysis of power consumption modes

The issues of planning and managing power consumption modes are considered, using electrical load schedules, comparisons of the declared maximum load with the actual one are presented, the main directions for increasing the efficiency of rational use of electricity and regulating electrical load schedules are given.

Spatio-temporal management of renewable energy consumption, carbon emissions, and cost in data centers

Under the background of "carbon neutrality ", data center enterprises are confronted with the challenges of high energy costs and the need to manage carbon emissions. Compared with traditional energy sources, renewable energy possesses the advantages of being low-carbon and cost-effective, making it an essential avenue for data centers to enhance their utilization of renewable energy. By employing a spatio-temporal scheduling method for computing power load, data center enterprises can maximize the benefits of renewable energy, achieve low-carbon and cost-effective operation, and enhance the consumption of renewable energy. This study developed a spatio-temporal scheduling model for computing load in data centers, with a specific focus on optimizing the utilization of renewable energy while considering the goals of low-carbon emissions and cost-effectiveness. A two-stage spatio-temporal scheduling algorithm (ESTS) was designed and implemented, and three sets of experiments were conducted to assess the effectiveness and applicability of offline load scheduling using offline load data from Alibaba's cluster-trace-v2018. The results demonstrate that the proposed scheduling method can achieve a significant reduction of carbon emissions by 70% and operating costs by 40% across various scenarios. Moreover, during the summer season when renewable energy is abundant, the application of this scheduling method in a single data center can effectively achieve the objectives of managing low-carbon emissions and minimizing costs.

Privacy-Preserving Load Scheduling in Residential Microgrids Using Multi-Agent Reinforcement Learning

Privacy-Preserving Load Scheduling in Residential Microgrids Using Multi-Agent Reinforcement Learning

Optimal Supply-Side and Demand-Side Management Strategies for Energy Efficiency in Residential Buildings using Particle Swarm Optimization

The global energy consumption of commercial and residential buildings, driven by economic and demographic growth, has become a critical concern. This surge in demand has resulted in elevated electricity prices, strain on the primary grid, and increased carbon emissions due to inefficient energy utilization. This paper introduces an innovative energy management system (EMS) integrating both supply-side (SSM) and demand-side management (DSM) strategies. The demand side management involves optimizing load scheduling based on predicted electricity pricing. In contrast, the supply side management determines the next 24 hours optimal power setpoints, incorporating a Photovoltaic System (PV), an Energy Storage System (ESS), and the Electrical Power Grid (EPG). The primary objectives of this proposed EMS are to concurrently decrease electricity costs and the peak-to-average ratio (PAR), all while ensuring user comfort in terms of appliance operating waiting times. The proposed multi-objective optimization problem is solved using Particle Swarm Optimization algorithm (PSO). Based on residential building energy demand and time-of-use pricing (TOU), simulations were conducted using MATLAB. The results demonstrate the effectiveness of the proposed approach, achieving a 28% reduction in both electricity costs and the PAR, while maintaining user comfort.

Comparison of application container orchestration platforms

This article presents a comparative analysis of three well-known container orchestration platforms: Docker Swarm, Kubernetes and Apache Mesos, focusing on the deployment of a test application and measuring parameters such as deployment time, memory, CPU and disk utilization, application response time and the time to restore a replica of the application using an auto-recovery mechanism. The aim of the research is to verify the performance and efficiency of the analyzed platforms, facilitating informed decisions while choosing an orchestrator for containerized applications. Two research hypotheses have been stated. The first one assumes that the time required to launch an application using the Docker Swarm tool is the shortest among the analyzed platforms. The second hypothesis is that Kubernetes provides the most efficient results in terms of load scheduling and application scaling. The analysis performed on the Jenkins application showed the superiority of the Docker Swarm platform over the other studied tools in terms of performance.

Power Bill Optimization for Grid-tie Solar PV-Battery House

This study focuses on the optimization of power bills for a house equipped with a grid-tie solar PV-battery system. Rather than adhering to conventional load scheduling practices or minimizing grid power usage at each time interval, a novel approach is adopted wherein the optimization is performed for the entire 24-hour period simultaneously. By directly incorporating time-of-use rates into the cost function, an absolute optimal solution is attained. The findings indicate that compared to single time step optimization, the proposed method results in a reduction of the power bill ranging from 6% to 10%, depending on load-generation variations. Furthermore, if the utility or government enforces the summer tariff consistently throughout the year, the savings escalate to a range of 15% to 22%. Introducing a more intelligent tariff structure can thus serve as an effective means to expedite the transition towards renewable energy by incentivizing individual investments in solar PV, battery systems, and smart home energy management.

Novel economic models for advancing urban energy management and transition: Simulation of urban energy system in digital twin

Globally, industrial sectors consume over 27 % of the overall power produced, half of it being utilized in equipment to process, produce, and assemble different products for the human society. Therefore, one can estimate that the planet would face a serious power shortfall which would affect the social welfare effectively. As a promising solution, utilizing current energy sources optimally will be one of the challenges to overcome the power shortages. The optimal scheduling of loads requires a useful method for achieving three technical and social goals: minimizing power consumption, optimizing peak-to-average power rate, and minimizing user wait time. In order to accomplish the above goals, this paper proposes a useful energy management system (EMS) to schedule the loads optimally. The proposed EMS is constructed based on automatic operation machines and day-ahead price methods, and it is integrated into a digital twin of the urban energy system. Moreover, a novel technical-social model based on gray wolf optimization algorithm is proposed to optimize power consumption for the industrial sector within the digital twin framework. The proposed model is applied and examined on woolen mill's various load units based on their regular operations in order to evaluate its performance and assess the potential benefits of this digital twin-based approach to urban energy management and transition. IGWOA's prowess in reducing daily power costs by 26.2 %, albeit with slightly longer wait times (1.7 h), and achieving a PAR of 7.4. Results demonstrate IGWOA's efficient computational performance with a running time of 8.702 s, enhancing its practical feasibility in real-world scenarios.

ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ НАКОПИТЕЛЕЙ ЭЛЕКТРОЭНЕРГИИ В ЭНЕРГЕТИЧЕСКИХ СИСТЕМАХ

This paper explores the possibility of using different types of electric power storage devices in energy systems. A comparative analysis of such types of storage devices as: electrochemical, electrical, electromagnetic, mechanical. The properties of lead-acid, nickel-cadmium, sodium-sulfur, lithium-ion batteries, supercapacitors, superconducting magnets, kinetic storage devices are analyzed with the identification of advantages and disadvantages of their use; the fields of their application are considered. The possibility of integrating storage devices with renewable energy sources is being considered. The analysis of the averaged load schedules for the winter and summer periods is performed; the feasibility of using the drive to smooth out the peaks of the consumption load schedules is justified. A schematic solution for connecting an electric power storage device with a control system for a medium-voltage power supply network is proposed. The connection of the drive in such a system is provided on the 0.4 kV side using converter devices. The using of an AC-DC converter with a control system in such a system regulates the flows of active power in order to perform a two-way energy conversion in the case of energy accumulation by the storage device and energy return to the network.

Optimization of power dispatching schedule of a charging station based on a micro grid with a photovoltaic module

The world is on a course toward total electrification of vehicles. In the near future, most vehicles will run on electric power. One of the main reasons for users' dissatisfaction with electric vehicles is the lack of public direct current charging stations. Since electric vehicles charging can cause an additional increase in peak load on the grid, the optimal solution is direct current charging stations with photovoltaic generation with a micro grid architecture. If the charging station has a connection to the public grid, then, provided that the solar energy and storage system are optimally utilized, the station aggregator's profit can be increased by selling excess energy to the grid. This paper analyzes the charging habits of customers at direct current charging stations. It was found that the peak demand for charging is observed around 9:00 and 14:00-17:00, the same time as the general peak load on the grid. Thus, the peak charging demand coincides with the peak grid load and increases the net peak of the system. However, this excess demand on the system in the form of charging load can be met by the installed solar photovoltaic system, as the output power of the photovoltaic system is sufficient to meet the charging demand during the peak hours of solar radiation. Thus, for the considered direct current charging station, the optimization problem of dynamic economic dispatch was formulated, since the generation and load schedules change over time. The goal of optimization is to minimize the cost of primary energy. This problem, formalized as a mixed integer linear programming problem, was solved using the interior-point solver of the GEKKO library in Python. Four scenarios for the operation of the station were worked out, in summer and winter, with a fixed and dynamic electricity tariff. According to the results of the study, it was found that in the conditions of a fixed tariff in the summer, the cost of primary energy can be reduced 2.5 times, in fact, increase profits, thanks to the sale of electricity to the public grid. In winter, the use of the optimization algorithm of the station will provide an insignificant cost savings due to low photovoltaic generation. Under the conditions of a dynamic tariff that corresponds to the prices on the day-ahead market, using the optimization algorithm, it was found that for this experimental variant of the station's operation, the maximum profit in summer will be 207.60 UAH, while in winter the cost of primary energy will be 177.47 UAH. The results obtained indicate that the operation of a charging station under dynamic tariffs in the day-ahead market in Ukraine is a promising direction for the development of charging infrastructure in the country and proves the possibility of efficient use of renewable energy sources. Thus, this paper analyzes the global experience of developing charging stations based on micro grids, the integration of renewable energy sources into them, and approaches to building electricity dispatch schedules. The financial feasibility of the station's operation in the context of the electricity market in Ukraine was also investigated