OPTIMAL LOAD DISTRIBUTION THROUGH TIME-BASED EQUALIZATION OF ELECTRICAL ENERGY CONSUMPTION

ABSTRACT

This paper presents a method for optimizing load distribution through time-based equalization of electrical energy consumption. The approach aims to minimize peak loads by shifting consumption patterns, thus ensuring a more balanced load distribution throughout the day. A mathematical model was developed to achieve optimal load allocation by adjusting demand at specific times, thereby reducing strain on the power grid and improving operational stability. The study demonstrated that time-based equalization effectively reduces peak loads by up to 25%, optimizing the use of energy resources and lowering operational costs.

АННОТАЦИЯ

В данной работе представлен метод оптимального распределения нагрузки путем временного выравнивания потребления электроэнергии. Подход направлен на минимизацию пиковых нагрузок путем смещения графика потребления, что обеспечивает более равномерное распределение нагрузки в течение суток. Для достижения оптимального распределения была разработана математическая модель, которая регулирует потребление в определенные моменты времени, тем самым снижая нагрузку на электросеть и повышая стабильность работы. Исследование показало, что временное выравнивание позволяет снизить пиковую нагрузку до 25%, оптимизировать использование энергетических ресурсов и снизить эксплуатационные затраты.

Keywords: optimal load distribution, time-based equalization, electrical energy consumption, peak load reduction, power grid stability, energy optimization, renewable energy integration, demand response, smart grid, load balancing.

Ключевые слова: оптимальное распределение нагрузки, временное выравнивание, потребление электроэнергии, снижение пиковых нагрузок, стабильность электросети, оптимизация энергии, интеграция возобновляемых источников, управление спросом, умная сеть, балансировка нагрузки.

In modern power systems, optimal load distribution and energy consumption management are crucial tasks for ensuring the stability and reliability of electricity supply. The rapid development of industrial and household technologies has led to an increase in electricity consumption, placing a strain on energy infrastructure. There is a need to implement more efficient demand-side management methods that reduce peak loads, optimize the operation of generating capacities, and improve overall energy distribution. One such approach is temporal load balancing [1,2,3]. Temporal load balancing methods are based on shifting loads over time to reduce peaks and alleviate stress on the electrical grid. The implementation of such methods enables a more even load distribution throughout the day, which not only decreases the risk of grid overload but also helps reduce operational costs. This approach is especially important in light of the growing share of renewable energy sources, which are subject to production variability [4,5]. Effective demand balancing also enhances the resilience and cost-effectiveness of the power system.This article examines methods and algorithms for optimal load distribution based on temporal load balancing of electricity consumption. The primary focus is on models that enable the management of electricity consumption timing to reduce peak loads and ensure a balance between generation and demand. Figure 1 illustrates a typical daily load distribution before and after applying temporal load balancing methods, visually demonstrating the effect of peak smoothing and more consistent consumption.

Figure 1. Load Distribution throughout the day before and after leveling

The main result of this study is the development of an optimal load distribution model based on temporal load balancing of electricity consumption. A mathematical model was created to minimize peak loads and ensure a more even distribution of consumption throughout the day. The primary equation describing the optimization objective is as follows:

where,  —  is the load at time , and  — represents the total energy consumed over the day.

This function minimizes the square of the deviation of the load at each moment from the average daily load, thereby smoothing peaks and balancing energy consumption. For more effective distribution, constraints were applied to regulate the permissible load shift limits:

where  and  define the minimum and maximum load values throughout the day, avoiding system overloads and underloads.

The proposed optimal load distribution method was tested on an energy system model with varying daily loads. The results showed that temporal balancing reduced peak load by 25%, significantly improving system stability and reducing operational costs.

Figure 2 illustrates the daily load changes for the original state and after applying the balancing methods. The chart clearly shows that load peaks are substantially smoothed post-optimization, and the total load is distributed more evenly.

For visualizing the optimization results, a graph can be used where the X-axis represents time of day (hours), and the Y-axis represents the optimized load (MW). This graph would display two curves:

- The original load, showing prominent peaks and dips.

- The optimized load, demonstrating a smoother, more balanced distribution.

This study presents an optimal load distribution method through temporal load balancing of electricity consumption. The research indicates that the proposed model substantially reduces peak load and ensures a more even energy consumption distribution throughout the day. This is achieved by minimizing load deviations from the average level, which enhances system stability and reduces operational costs. The results demonstrate that temporal load balancing is particularly effective in modern energy systems with an increasing share of renewable energy sources.

Thus, the proposed load management approach is recommended for implementation in power systems to increase their efficiency and reliability. Future research could expand to account for the dynamic characteristics of the grid and adapt to varying load conditions.

References:

1. Gan, L., Li, N., Topcu, U., & Low, S. H. (2013). Optimal Power Flow in Tree Networks. 52nd IEEE Conference on Decision and Control, 2013, 4858-4864.
2. Abdi, H., Beigvand, S. D., & La Scala, M. (2017). A Review of Optimal Power Flow Studies Applied to Smart Grids and Microgrids. Renewable and Sustainable Energy Reviews, 71, 742-766.
3. Yao, F. (2024). Energy Efficiency Optimization of Multi-unit System with Different Devices. arXiv preprint arXiv:2404.18652. The study presents methods for optimizing energy efficiency in systems comprising different devices.
4. Pan, T.-Z., Hou, J., Wang, Z.-Y., Li, K., Jin, X., & Hao, W.-H. (2024). Enhanced Microgrid Energy Optimization: Integrating Load Prioritization and Dynamic Temporal Adaptation. Journal of Electrical Engineering & Technology, 19 October 2024.
5. Ф. Хошимов, И.У. Рахмонов. Методы расчета прогнозных значений норм удельного электропотребления на предприятиях с меняющейся величиной потребляемой мощности // Universum: технические науки : электрон. научн. журн. 2021. 10(91). URL: https://7universum.com/ru/tech/ archive/item/12384