Load Scheduling Research Articles

Forecast-based stochastic optimization for a load powered by wave energy

Stand-alone renewable energy systems, which harness and use electricity without connection to a distribution grid, can benefit from using resource forecasts when making real-time decisions on how to manage power. An example of this is an ocean observation system that uses a wave energy converter (WEC) and battery bank to produce and store electricity in order to meet electrical load requirements for oceanographic sensors and instrumentation. By using wave forecasts to optimize the power consumption of this system, it is possible to reduce the size of the WEC and battery bank needed, thereby reducing the overall system’s cost and complexity. In this paper, we assess the benefits of forecasting for a generic wave-powered ocean observation system. We model an observation platform powered by a WEC and battery-storage system that can switch between four power states, full power: 600 W, medium power: 450 W, low power: 45 W, and survival mode: 1 W. To determine which power state to enter over time, we present a stochastic optimization method that interprets wave forecasts and system information to select a power state on an hourly basis and simulate over two months of consecutive decisions. Using this simulation framework, we compare 8 power management strategies across a range of WEC sizes (3–5 m) and battery capacities (2.5–35 kWh). We find that it is possible to maintain full (600 W) power consumption over the entire simulation window with a 5 m diameter WEC and 25 kWh battery bank, which is on the upper end of the ranges evaluated. To employ a smaller WEC or battery, load flexibility is required. We find that forecast-based methods for this power management handle tradeoffs in performance (e.g., cumulative power consumption, reducing intermittencies and meeting scheduled load targets) more effectively than power management strategies that do not use forecasting. Overall, our results indicate that forecasting for our stand-alone renewable energy system is most impactful when some loads must be met on or within a defined schedule.

Optimal household appliances scheduling for smart energy management considering inclining block rate tariff and net-metering system

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

Protection System of the Electric Power Distribution System of Notre Dame of Marbel University, Koronadal City, South Cotabato

A vital instrument for evaluating the effectiveness and safety of electrical systems is the power system analysis. Protecting people and equipment from errors is part of this. These assessments examine the load, the equipment, and the foreseeable needs. A lack of short-circuit current protection can harm equipment and endanger workers. The main goal of this study is to assess if the various protection devices utilized in the Notre Dame of Marbel University’s Main Campus electrical system comply with the established standards. Through a real survey (opening panel boxes and reviewing the original Electrical Plan), the researcher will ascertain the various protection devices in operation. Two approaches are used to test for short circuits: the MVA method and the per-unit method. When determining fault current, only the three-phase fault is considered in the calculation since it is the one having the largest fault value. Aside from the mentioned, the transformer rating calculations were also done. The resulting data will then be compared to the actual protective devices installed in the building. Short circuit currents must be less than the Kilo Ampere Interrupting Capacity rating of the installed protective devices. In conclusion, after conducting a thorough analysis of the current loading schedule and comparing the results obtained through the MVA and per-unit methods, it is evident that the various installed Overcurrent Protective Devices are operating safely. The calculations revealed a substantial disparity of 35.57% between the two methods. Furthermore, the t-test conducted on paired data confirmed the statistical significance of the differences between these two approaches. With this, the researcher recommends conducting a separate study to verify branch circuit calculations, implementing primary metering, and prioritizing the rehabilitation of the Pantua and Alunan lines to enhance the safety and reliability of the electrical system, particularly in tracing complex branch circuits. Keywords: power system analysis, MVA method, per unit method, comparative analysis, Notre Dame of Marbel University

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

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.

Increasing of the efficiency of solar batteries use

Ways of increasing the efficiency of using solar batteries are being considered. The current state and development trends of renewable energy in Ukraine and the world are analyzed. The share of renewable energy in the energy system has been constantly growing recently. For electricity generation, as well as for heating or desalination, solar energy is becoming increasingly popular around the world. It is one of the fastest growing renewable energy technologies and is playing an increasingly important role in the global energy transformation. Improvements in the technology of obtaining electrical energy from solar radiation lead to the fact that solar energy will eventually become the most available among other alternative sources. In Ukraine, the share of solar generation is the largest among renewable sources and makes up more than half of the installed capacity of all renewable energy. The generation of electrical energy by solar cells varies during the daylight hours. The power output graph is similar to a sine wave. The efficiency of using radiant energy by solar cells depends on their orientation in space. If the generated energy is used exclusively for own consumption, then the effective power and duration of generation do not always coincide with the load schedule. To increase the efficiency of using solar batteries, it is suggested to orient the photovoltaic modules of individual stations, or the photovoltaic modules of individual strings of one station in different azimuth directions. This will allow for a better match between the output schedules and the load, as well as increase the total duration of effective generation. Such a project was implemented during the construction of rooftop solar power plants (SPP) of Khmelnytskyi National University. It was established that the maximum displacement of the power output graphs of SES with an azimuthal angle of +250 and SES with an azimuthal angle of -600 is two hours and 15 minutes. The electricity produced during the daylight hours and the peak power of SES with different azimuthal orientation differ by up to 15 %. The conducted studies allow us to state that when the energy produced by solar batteries is not supplied to the network, but only provides its own consumption, the proposed technical solutions allow expanding the time limits of effective generation and agreeing power output schedules with load schedules.

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

Strategic load scheduling in smart home: Leveraging IOT and optimization algorithms for energy cost reduction

Strategic load scheduling in smart home: Leveraging IOT and optimization algorithms for energy cost reduction