Discussion on Distributed Energy Storage Design for Power Management in Industrial and Commercial Enterprises

Abstract: This article presents a new design for a distributed energy storage system aimed at managing power consumption in industrial and commercial enterprises. Given the high hardware replacement costs associated with storage systems, the approach taken optimizes control software based on the existing hardware framework of traditional storage systems. A distributed energy storage system model is established, analyzing the relationships between power generation, energy storage, and energy loss while addressing optimization needs. Based on a convergent optimization system schedule, the system’s modal switching strategy is determined to achieve efficient energy storage during power feed-in. Compared to traditional energy storage systems, the designed system effectively manages energy feed-in storage, reduces resource waste, and incurs lower energy dispatch costs, resulting in higher economic benefits.

Keywords: Power management in industrial and commercial enterprises; Distributed energy storage system

1. Introduction

In today’s society, energy consumption is substantial, and traditional energy sources are diminishing. Therefore, the exploration of new renewable energy sources has become a focal point in resource development research, leading to the emergence of distributed energy sources. To address the limitations of distributed energy sources and ensure power balance in microgrids, power companies have introduced distributed energy storage systems. These systems boast a wide range of applications and minimal pollution, enhancing the grid’s ability to absorb renewable energy. However, the development of distributed energy storage systems faces challenges. In power management for industrial and commercial sectors, energy losses during charging and discharging are significant, leading to resource waste and diminished economic benefits. To tackle this, a new distributed energy storage system for power management in industrial and commercial enterprises has been designed.

2. System Optimization Needs

During the control of the distributed energy storage system within the grid, should large-scale power consumption be introduced, the output of distributed energy sources will increase, exacerbating the load disparity between the distribution network and the main grid. This can result in voltage exceeding limits, leading to substantial energy losses in the distributed energy storage system and increased energy waste during control processes, which raises the overall economic costs of the system. Let us assume a certain time period where the load parameter of the distribution network is denoted as Ed and the output parameter of the distributed energy source is Et; when power feed-in occurs, the relationship can be expressed as:

Et < Ed (1)

Based on this relationship and a typical daily load curve (as shown in Figure 1), the system’s optimization requirements can be analyzed. When the input to the grid is excessive, the output of the energy storage system increases, leading to energy waste due to power feed-in situations. Therefore, it is necessary to control the storage system to manage energy feed-in during such occurrences, optimizing charging storage to reduce power resource waste and enhance the capacity to absorb wind and solar energy.

3. System Design

To improve the economic efficiency of the grid, it is essential to optimize the software design based on the traditional storage system’s hardware framework.

3.1 Establishing a Distributed Energy Storage System Model

The distributed energy storage system is an integrated energy system that combines generation, storage, and energy monitoring functions. It is vital to establish an appropriate mathematical model based on a thorough study of the system’s structure to analyze the relationships between power generation, energy storage, and energy loss, laying the groundwork for subsequent optimization. For predicting the system’s power generation, numerical simulation methods are typically employed to build mathematical models for the internal components of the storage system. Taking distributed photovoltaic sources as an example, the generated power depends on solar radiation and is also related to temperature. The instantaneous power output can be calculated using the following formula:

q = ∂ * ε * Y * G

Where ∂ represents the typical efficiency of the internal components, ε is the temperature coefficient, Y is the current actual temperature of the components, and G is the current solar radiation received. Based on this formula, a mathematical model for distributed photovoltaic sources can be established. However, the actual power generation is affected by factors such as the angle of sunlight, operating temperature of system equipment, and spectrum. All these factors need to be considered to optimize the aforementioned mathematical model. Assuming the effective radiation received by the photovoltaic source is E, the calculation can be expressed as follows:

E = s0 * αs * s * (tc – t0)

Here, s0 is the short-circuit current under normal conditions, αs is the temperature coefficient for short-circuit current, s is the short-circuit current within the system, and tc and t0 are the actual operating temperature and ideal operating temperature of the distributed energy source components, respectively.

3.2 Convergent Optimization System Scheduling

In line with the optimization needs of the distributed energy storage system, to reduce energy losses and enhance computational efficiency, storage scheduling within the system should be optimized based on time constraints to produce a convergent solution. Suppose there are n components capable of energy storage within the distributed energy storage system; to minimize charging losses during scheduling, the objective function can be expressed as:

Minimize: Σ (GiPi) for all i

This means eliminating feed-in power while maintaining grid stability. It is crucial to sustain power balance, which can be represented by the condition:

pg > 0

Where ph is the total load on the grid during a certain time period, pd is the power of the distributed energy source during the same period, and pc is the running power of the storage charging unit during that time.

3.3 Implementing Distributed Energy Storage through Modal Switching

Following the optimization of the system’s scheduling, to ensure the overall stability of the distributed energy storage system and reduce operational losses, it is necessary to determine an appropriate modal switching strategy to minimize voltage deviations within the system and achieve capacity optimization. In the context of industrial and commercial power management, the cost of line losses must also be considered. Assuming that the system incurs losses during operation, the cost loss D can be calculated as follows:

D = F * Δj

Where F is the loss cost of the energy storage unit components, j is a specific moment in the operation of the component, and Δj typically takes a value of 1. Based on this equation, one can analyze cost losses that occur during feed-in power situations. To enhance the voltage quality of the distributed energy storage system and reduce cost losses, it is necessary to minimize voltage deviations at the system nodes, calculated as:

H = Σ (Urt – UN) for all nodes

Where N is the number of nodes in the storage system, r is a specific node within that range, and Urt and UN are the voltages at time t for node r and the rated voltage at that time, respectively.

4. Experimental Verification

4.1 Experimental Preparation

To verify the effectiveness of the designed distributed energy storage system in maintaining voltage stability and optimizing energy scheduling, validation experiments were conducted. Testing involved a traditional energy storage system based on genetic algorithms and the distributed energy storage system designed for power management in industrial and commercial enterprises, with hardware parameters listed in Table 1.

4.2 Experimental Results

During the experiments, the load variations of both the traditional energy storage system and the newly designed system were monitored, with specific results illustrated in Figure 2. As shown, the distributed energy storage system designed in this article demonstrated superior control capabilities over grid loads compared to the traditional energy storage system. In the traditional system, the power fluctuation range was extensive, leading to multiple instances of power feed-in and insufficient stability during the 16-24 hour operation period. In contrast, the designed system maintained stable voltage control, effectively storing energy during charging with minimal occurrences of power feed-in, resulting in significantly reduced energy losses and higher economic benefits. To validate the cost advantages of the designed system during energy scheduling, the dispatch costs of the two systems were tested, as shown in Table 2.

5. Conclusion

To address the issue of resource waste in power management for industrial and commercial enterprises and to improve generation economic efficiency, this article has designed a distributed energy storage system specifically for this purpose and verified its application effectiveness. Future research will focus on further depth to promote the sustainable development of distributed energy storage systems.

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