Load Scheduling Research Articles

A multi-stage balancing scheduling method for flexible loads of large-scale electric vehicles

To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.

Improving the Structure of the Electricity Demand Response Aggregator Based on Holonic Approach

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services.

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.

Optimization of Reclaiming–Loading Scheduling in Dry Bulk Terminals Based on Knowledge-Driven Memetic Algorithm

Reclaiming–loading operations in dry bulk terminals often face conflicts and delays due to limitations in equipment processing capacity and operational line accessibility, which significantly compromise the safety and efficiency of these operations. This paper aims to optimize the reclaiming–loading schedule for each incoming vessel by considering parallel equipment operations and potential conflicts, with the goal of enhancing both the safety and efficiency of the loading processes. Through a detailed analysis of bulk reclaiming and reclaiming–loading mechanisms, we formulate the dry bulk terminal loading scheduling problem to minimize the total operational time for all loading tasks, taking into account constraints such as parallel reclaiming, collaborative loading, operational conflicts, and line accessibility. In order to obtain a good solution, including task execution sequences and allocation of reclaimers and shiploaders, a knowledge-driven memetic algorithm is developed by integrating knowledge-driven mechanisms with problem-specific operators within a memetic computing framework. Finally, numerical experiments for various scales are conducted using the layout and operational data from the Huanghua Port’s coal port area. The experimental results demonstrate the effectiveness of the proposed optimization algorithm.

Robust integrated multi-mode scheduling of flexible loading and unloading operations with maintenance services in a port container terminal

This study proposes an unparalleled integrated scheduling model for simultaneous loading, unloading, and maintenance operations in a port container terminal, wherein several heterogeneous handling equipment serve inbound and outbound vessels. In this regard, a multi-mode loading approach and the possibility of multiple allocations are also considered, wherein outbound vessels can be loaded by containers belonging to inbound vessels or the storage area. In addition, the proposed model covers all cases of equality and inequality in the number of inbound and outbound containers. More importantly, a flexible loading approach based on a class-based stowage plan is considered to better utilize equipment and increase the efficiency of the loading process. Additionally, since the timing of maintenance operations undoubtedly affects scheduling and entails miscellaneous uncertainties, a two-phase data-driven method is presented to estimate robust maintenance operation times. The first phase involves a hybrid machine learning strategy that combines the Savitzky–Golay Filter (SGF), the Takagi–Sugeno–Kang (TSK) fuzzy system, and minibatch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) for the estimation of maintenance operation times. The second phase employs the distributionally robust optimization (DRO) technique that leverages φ-divergence to address the uncertainties associated with the estimated times. Finally, a case study is conducted to demonstrate the effectiveness and applicability of the proposed framework, accompanied by an examination of various simulation experiments. The results obtained indicate that the implementation of a flexible loading strategy can lead to a 24% reduction in the loading time of vessels.

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers.

Reducing Makespan and Enhancing Resource Usage in Cloud Computing With ESJFP Method: A New Dynamic Approach

ABSTRACTA well‐designed web‐based tool or application allows consumers to access on‐demand services on a pay‐per‐use basis via the internet in the cloud computing service paradigm. Cloud computing continues to be a leading technology trend, with a primary focus on optimizing and enhancing end‐user applications. The increasing challenge of meeting the diverse needs of clints for service providers is propelling the development of load scheduling algorithms. Some of the current scheduling algorithms used in cloud computing include First Come First Serve (FCFS), Shortest Job First (SJF) and Round Robin (RR). The response time and makespan—the interval between the start and end times of consecutive tasks on the same machine—are the two most crucial load balancing elements. Response time refers to the duration a server takes to respond to a client's request. Measured in milliseconds, this timer starts when a client sends a request and stops when the server sends its initial response. This study examines many load balancing methods and suggests improvements for cloud computing's Shortest Job First (SJF) methodology. To minimize makespan and maximize resource usage, we provide a solution that is Enhanced Shortest Job First with Priority (ESJFP) load scheduling algorithms. Under the ESJFP method, computers with more processing power are assigned the longest tasks with higher MIPS (million instructions per second) needs, while machines with lesser processing capacity are assigned the shortest jobs with lower MIPS requirements. By giving equal priority to all activities, this technique makes sure that neither high‐MIPS nor low‐MIPS jobs have to wait an extended period of time for resource allocation.

Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Demand response (DR) plays an essential role in power system management. To facilitate the implementation of these techniques, many aggregators have appeared in response as new mediating entities in the electricity market. These actors exploit the technologies to engage customers in DR programs, offering grid services like load scheduling. However, the growing number of aggregators has become a new challenge, making it difficult for utilities to manage the load scheduling problem. This paper presents a multi-agent reinforcement Learning (MARL) approach to a price-based DR program for multiple aggregators. A dynamic pricing scheme based on discounts is proposed to encourage residential customers to change their consumption patterns. This strategy is based on a cooperative framework for a set of DR Aggregators (DRAs). The DRAs take advantage of a reward offered by a Distribution System Operator (DSO) for performing a peak-shaving over the total system aggregated demand. Furthermore, a Shapley-Value-based reward sharing mechanism is implemented to fairly determine the individual contribution and calculate the individual reward for each DRA. Simulation results verify the merits of the proposed model for a multi-aggregator system, improving DRAs’ pricing strategies considering the overall objectives of the system. Consumption peaks were managed by reducing the Peak-to-Average Ratio (PAR) by 15%, and the MARL mechanism’s performance was improved in terms of reward function maximization and convergence time, the latter being reduced by 29%.

Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid.

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.

What can be observed in intervertebral cartilage endplate with aging? An animal model study of excessive axial mechanical loading.

The cartilage endplate (CEP) plays a crucial role as both a mechanical barrier and nutrient channel for the intervertebral disc, but it is vulnerable to excessive axial loading. We modified the Ilizarov external fixator and applied it to the CEP of the rat tail to impose diurnal, controllable excess axial loading. The objective was to measure morphological changes in the CEP when subjected to loading during the aging process. Two Kirschner wires were, respectively, inserted into the center of the eighth and ninth coccygeal vertebrae (Co8/9) of rat (n = 54) to apply axial loading to the CEP. A remote control device was used to establish the diurnal loading schedule. At the end of 4, 8, and 12-week periods, the Co8/9 CEPs in each group were analyzed using MRI, histological staining, and immunohistochemical staining techniques. The novel Ilizarov model that we modified successfully induced degeneration of the rat coccygeal CEP. MRI analysis revealed significant degenerative changes in the loaded Co8/9 CEP, including decreased signal intensity and the formation of Schmorl's nodes at 8 and 12 weeks. Histological examination showed progressive CEP degeneration (CEPD), characterized by decreased microporosity, thinning, and structural irregularities. Immunohistochemical analysis demonstrated a significant reduction in Aggrecan and Collagen II expression in the CEP and nucleus pulposus over time. Control and sham groups maintained normal CEP structure and composition throughout the study period. Excessive axial loading induced CEPD in the rat tail, primarily characterized by the formation of Schmorl's nodes and a reduction in CEP microporosity in this study. Our modified Ilizarov rat tail compression model, featuring stable and controllable axial loading capabilities, provided an alternative experimental paradigm for further investigation into CEPD.

Techno economic analysis of optimal load scheduling approach in a standalone hybrid microgrid system

To increase the profitability of a standalone hybrid microgrid system, an optimized load scheduling strategy is required. The aim of this paper is to establish a mavin load scheduling approach for a standalone hybrid microgrid system (SHMGS) that reduces power generation costs, load shedding and achieve energy efficiency. The SHMGS includes Photovoltaics, Wind Power, Battery Energy Storage System, Diesel Generator, and Methanol Generator. A techno-economic study is carried out using HOMER Pro. Two different modes of load scheduling approach (LSA) are considered on the basis of customer comfort violation, customer behavior uncertainty, and complexity. In addition, the study looks into the effects of two different modes of load scheduling, utilizing deferrable load sections and different dispatch strategies, on the economic efficiency of the SHMGS configuration. Optimized SHMGS with the LSA (mode-2)-dataset-2 has the cost of energy 0.1694 $/kWh, total net present cost 335 792 $, annualized cost of the system 29 017 $, excess electricity 45 503 kWh/yr, and carbon emission 30 786 kg/yr.

Incremental Efficiency of Electricity Generation Based on Thermal Consumption at CHPPs and its Importance in Reducing Carbon Emissions

The aim of the study is to develop a methodological approach to assessing the incremental efficiency of electricity generation at CHPPs (specific heat consumption for changing electric power of turbine) in operating modes, taking into account both energy and environmental indicators. To achieve this goal, the following tasks were solved: the methodology, based on mathematical models of cogeneration steam turbines, both integral and differential indicators of heat rate per unit of electricity generation increment, was justified; a comprehensive generalization of the calculation data on the tur-bine T-50-12.8 type was performed; a quantitative assessment of the integral electricity generation and greenhouse gas emissions parameters was made at various loads and its comparison with the corre-sponding indicators in condensation mode. The most significant results are the following: generalized dependences of the integral specific heat consumption per unit of increase in electricity generation and carbon footprint were calculated for a wide range of changes in operating conditions; boundary condi-tions were established under which obtaining these parameters by opening the sliding grid of low-pressure section turns out to be energetically and ecologically feasible; it was determined that when increasing the capacity without bypassing the delivery water, the carbon footprint increment with an increase in the capacity of cogeneration turbines by opening the sliding grid will be less than for con-densing turbines. The significance of the results obtained lies in their applicability for solving prob-lems of ensuring maximum efficiency during involving cogeneration turbines in regulating electric load schedules in different boundary conditions.

Evaluating the effectiveness of quantitative pupillometry in assessing dynamic aerobic training intensity

Sports injuries often arise from improper scheduling of exercise loads, and timely assessment of these loads is essential for minimizing injury risk. This article investigates the validity of using changes in pupillary light reflex (PLR) during dynamic aerobic training as a novel approach to evaluating exercise load. Dynamic aerobic training was conducted on a power bicycle for 15 min. With a 3-minute interval as the demarcation line, PLR measurement was performed before training and heart rate was recorded throughout the process. The Rating of Perceived Exertion (RPE) scale invented by Borg was used for scoring before training and after training. The normal distribution of data was confirmed through the Shapiro-Wilk test. Pearson correlation analysis was used to quantify the correlation between variables. The Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC) analysis were used to determine the indicators of exercise load. The optimal threshold of the indicators was calculated through the Youden index to evaluate sensitivity and specificity. Thirty male second-tier athletes with a mean age of 23.66 ± 2.21 years, a mean height of 175.3 ± 6.5 cm, and a mean weight of 68.99 ± 10.35 kg participated in this study. Based on the RPE scale results, it was confirmed that the 15-minute dynamic aerobic exercise successfully elicited varying levels of perceived exertion among the athletes. The findings of this study indicate significant changes in PLR and heart rate (HR) with increasing exercise duration and external load. There were strong correlations between RPE and maximum constriction velocity (MCV) (|r| = 0.8309, p < 0.001, negative correlation), maximum diameter (INIT) (r = 0.7641, p < 0.001, positive correlation), time to reach 75% recovery (T75) (|r| = 0.7289, p < 0.001, negative correlation), and HR (r = 0.8170, p < 0.001, positive correlation). Additionally, the results suggest that MCV is the most significant potential indicator for detecting internal load, exhibiting high specificity and sensitivity (AUC = 0.8509, p < 0.001). Further analysis using the Youden index identified 5.07 mm/s as the optimal cutoff value for MCV, indicating that when MCV ≤ 5.07 mm/s, the athletes’ internal load has reached an “Intense” state. PLR may be a potential indicator for assessing internal load Further investigation could involve developing a non-invasive exercise load detection system based on pupillary variable indicators, providing a valuable new approach for accurately measuring exercise load.

Optimizing Resource Utilization and Improving Performance in Cloud Computing Through PSO-Based Scheduling and ACO-Based Load Balancing

Optimizing Resource Utilization and Improving Performance in Cloud Computing Through PSO-Based Scheduling and ACO-Based Load Balancing

Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

A Novel Day-Ahead Optimization-Oriented Low-Carbon Economic Scheduling Scheme for Integrated Energy Systems

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system.

Real-world Use of Mogamulizumab Among Patients With Mycosis Fungoides and Sézary Syndrome Before and During COVID-19 in the United States

During the coronavirus disease 2019 (COVID-19) pandemic, professional organizations suggested extending dosing intervals for systemic cancer therapies to limit in-person visits. Mogamulizumab, indicated for adults with relapsed or refractory mycosis fungoides (MF) or Sézary syndrome (SS) after ≥1 prior systemic therapy, should be administered every 7 days of the first 28-day cycle (loading) and every 14 days of each subsequent cycle (maintenance) according to the approved prescribing information in the United States (US). This study examined the real-world use of mogamulizumab before and during the COVID-19 pandemic in the US. Using Symphony Health's Integrated Dataverse (IDV) database, adults with ≥1 diagnosis of MF or SS and ≥1 mogamulizumab claim between October 1, 2018 and December 22-, 2022 were identified. Patients in MF and SS cohorts were divided into 3 subgroups based on the date they initiated mogamulizumab treatment: pre-COVID-19 (October 1, 2018-March 31, 2020), COVID-19 Phase 1 (April 1, 2020-July 31, 2021), and COVID- 19 Phase 2 (August 1, 2021-December 22, 2022). During the study, 270 patients with MF and 337 patients with SS initiated mogamulizumab. The pre-COVID-19, COVID-19 Phase 1, and COVID-19 Phase 2 subgroups included 95, 81, and 94 patients with MF and 124, 119, and 94 patients with SS, respectively. In the MF cohort, mean loading dosing intervals were 13, 12, and 9 days for the pre-COVID-19, COVID-19 Phase 1, and COVID-19 Phase 2 subgroups, respectively, and mean maintenance dosing intervals were 16, 16, and 16 days, respectively. In the SS cohort, mean loading dosing intervals were 16, 11, and 11 days, and mean maintenance dosing intervals were 19, 18, and 16 days, respectively. For both cohorts, more patients in the COVID-19 Phase 1 and Phase 2 subgroups than in the pre-COVID-19 subgroup had gaps of ≤10 days between loading doses and ≤21 days between maintenance doses. In patients with MF and SS, loading dosing intervals in the pre-COVID-19 period were longer than the loading schedule per the approved prescribing information, but there was a trend towards closer concordance in the COVID-19 periods. Maintenance dosing intervals in patients with MF were consistently similar to the approved schedule across treatment periods, and in patients with SS became more closely aligned over time. Thus, dosing intervals for mogamulizumab in both loading and maintenance cycles do not appear to have been extended during the COVID-19 Phase 1 and Phase 2 periods compared with the pre-COVID-19 period, despite recommendations to extend dosing intervals for systemic cancer therapies during COVID-19.

Multi-User Optimal Load Scheduling of Different Objectives Combined with Multi-Criteria Decision Making for Smart Grid

Load balancing between required power demand and the available generation capacity is the main task of demand response for a smart grid. Matching between the objectives of users and utilities is the main gap that should be addressed in the demand response context. In this paper, a multi-user optimal load scheduling is proposed to benefit both utility companies and users. Different objectives are considered to form a multi-objective artificial hummingbird algorithm (MAHA). The cost of energy consumption, peak of load, and user inconvenience are the main objectives considered in this work. A hybrid multi-criteria decision making method is considered to select the dominance solutions. This approach is based on the removal effects of criteria (MERECs) and is utilized for deriving appropriate weights of various criteria. Next, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to find the best solution of load scheduling from a set of Pareto front solutions produced by MAHA. Multiple pricing schemes are applied in this work, namely the time of use (ToU) and adaptive consumption level pricing scheme (ACLPS), to test the proposed system with regards to different pricing rates. Furthermore, non-cooperative and cooperative users’ working schemes are considered to overcome the issue of making a new peak load time through shifting the user load from the peak to off-peak period to realize minimum energy cost. The results demonstrate 81% cost savings for the proposed method with the cooperative mode while using ACLPS and 40% savings regarding ToU. Furthermore, the peak saving for the same mode of operation provides about 68% and 64% for ACLPs and ToU, respectively. The finding of this work has been validated against other related contributions to examine the significance of the proposed technique. The analyses in this research have concluded that the presented approach has realized a remarkable saving for the peak power intervals and energy cost while maintaining an acceptable range of the customer inconvenience level.

Increasing photovoltaic self-consumption for objects using domestic hot water systems

The technique of estimating the expected decrease in electricity consumption from the grid and using PV energy for the taken load schedule based on archival data for 5 years is refined. With full self-consumption (SC), the reduction of consumption from the grid can be increased by 9.5%–30.7% for a year according to the rated PV power. Consumption should increase when PV generation exceeds a certain value. A discrete time control of the power of an electric storage boiler (ESB) is proposed based on the deviation of the storage battery (SB) state of charge from a given schedule with a heating concentration during hours of high PV generation. In the considered application, it is possible to increase SC by up to 21%. Reducing the load in the evening allows us to use SB energy to reduce consumption from the grid at night. The possibility of complete photovoltaic SC when the ESB is used with an air conditioner is substantiated. Limitations for air conditioner energy consumption according to PV generation are determined. The system’s 24h model of energy processes is supplemented with a thermal model. The standard use of ESB with water temperature maintenance was also considered for comparison. ESB power control allows you to reduce daily energy consumption from the grid by 1.7–2 times. When combining an adjustable ESB with an air conditioner, it is possible to reduce consumption from the grid by 1.466–1.558 times at minimum and increase consumption from the grid by 2–5% at maximum air conditioner consumption.