1. Introduction
At present, China is building a new power system with a high level of renewable energy; hence, it faces the significant challenge of enhancing renewable energy consumption. The study in [
1] presented an overview of China’s renewable energy development status and analyzed the mechanism of accommodation of renewable energy and the key factors for the country’s curtailment of renewable energy. In the studies in [
2,
3]—based on experiences in advanced electricity markets in Europe and the United States—the consumption capacities of systems could be greatly improved by power delivery transactions and contract-transfer transactions between renewable energy and traditional power plants. With the deepening complexity of the power grid, as well as the complexity of its infrastructure and technical reform, the transmission lines require repair every year [
4]. Therefore, the influences of maintenance on the rate of consumption of renewable energy should be paid more attention, especially in the new power system.
Some previous studies analyzed the maintenance of a traditional transmission line [
5,
6,
7,
8,
9]. The study in [
10] investigated the maintenance of a local power grid. The study in [
11] proposed a new decision algorithm for solving the scale and configuration of a distributed power supply in a distribution network system. The study in [
12] optimized the expansion costs of a transmission system, network loss, old line replacement, maintenance costs, and so on. According to actual production engineering, an empirical formula has been summarized, but this formula is often only applicable to local production, and it lacks a mathematical and physical basis. The study in [
13] proposed a reliability-centered maintenance priority index and proved that it is more cost-effective than the traditional strategy. The study in [
14] focused on the co-optimization of maintenance scheduling and production cost minimization. The study in [
15] used the weighting–scoring method and the analytical hierarchy process to obtain the transmission line maintenance index. However, these studies of maintenance all focused on the traditional energy mode. In terms of development, few studies have addressed the influence of the maintenance strategy on renewable energy consumption. In order to understand this influence, an accurate predication of renewable energy generation is necessary. Thus, numerical simulation is a scientific way to reflect the effects on the transmission line.
There are three main numerical simulation methods: the typical daily analysis method, the random production simulation method, and the time-series production simulation method [
16]. The typical daily analysis method is an analysis of extreme cases, and the calculation results are conservative [
17,
18,
19,
20]; the random production simulation algorithm removes time constraints and converts a load curve into a continuous curve [
21,
22,
23,
24]; a time-series production simulation converts the renewable energy output and load into a time series and analyzes its changing trends over time [
25,
26,
27,
28]. Zhu et al. used a time-series production simulation model to select the power-flow constraints of critical sections of time in order to assess the annual consumption level of new energy in the system [
29]. Peng et al. used a time-series production simulation method to establish an optimal consumption model for renewable power sources in inter-provincial power grids and solved for the maximum renewable energy consumption [
30]. This paper also used a comparison with the typical daily analysis method to prove the superiority of their method. Ma et al. constructed a multi-point layout-planning model for a multi-energy power supply based on a time-series production simulation. An analysis of the simulation of the actual power system showed that this method was able to accurately and effectively determine the power quota line and provide a reference for power grid construction [
31].
Overall, in this paper, a time-series renewable energy production simulation (REPS) was developed and used to analyze the sensitivity of the different sections’ maintenance. The maintenance strategies for a sole transmission section or two transmission sections at the same time can be decided by comparing their consumption rates. Finally, a case study was carried out to prove the efficiency of the method and provide a reference for practical projects.
2. Time-Series Production Simulation
In this paper, a time-series RESP was used to simulate a real situation and determine the maintenance strategy for the transmission sections. In comparison with the traditional method, RESP is much closer to the reality of the grid; for example, an interval of 15 min was used for the real situation. RESP pays more attention to real structures and connections within the grid. The framework of this method is established in
Figure 1. First, the actual power grid was simplified through aggregation equivalence in order to adapt to the practicality of the simulation (
Section 2.1); then, the optimization model was constructed by maximizing the output of renewable energy power generation (
Section 2.2). Following this, a quick and useful solver (CPLEX equation solver) was used to calculate a mixed-integer linear program (
Section 2.3).
2.1. Renewable Energy Consumption Model
2.1.1. Power Grid Model
The time-series production simulation of renewable energy is based on the grid aggregation model. The grid aggregation model is based on the purpose and requirements of the calculation and analysis, and the aggregation is equivalent in order to make it more adaptable to the practical requirements of the simulation [
32]. The complex actual power grid is simplified into one or more aggregated power grids. The aggregation of the power system can not only better reflect the efficiency of power grid energy transmission, but can also provide a better solution for the decentralized layout and centralized scheduling of the power grid.
2.1.2. Unit Model
In the time-series production simulation, the renewable energy output is regarded as a time-varying sequence, and the characteristics of the renewable energy output of the guaranteed sequence are consistent with the actual characteristics.
2.2. Optimization Model
The time-series production simulation method is based on the consumption capacity of renewable energy.
2.2.1. Objective Function
The aim of the objective function is to maximize the power of renewable energy in a scheduling cycle.
In the formula, is the total number of aggregated power grids contained in the system, is an aggregated power grid, denotes the duration of the scheduling cycle, is the simulation time step, is the wind power output of aggregated grid in period , and is the photovoltaic power output of aggregated grid in period .
The renewable energy output equation is established based on the load balance constraint, line transmission constraint, and renewable energy output constraint [
33].
2.2.2. Constraints
(1) Regional Load Balance Constraint
In the formula, is the sum of the total power of all conventional units in period of the aggregated grid , and is the transmission power of the transmission line in period .
(2) Inter-Regional Line Transmission Capacity Constraint
where
and
are the upper and lower quotas of the transmission capacity of the transmission line
, respectively. The current reference direction is as follows: The inflow area is in the positive direction, and the outflow area is in the negative direction. So,
can take positive and negative values, and positive and negative values represent the direction of power transmission.
(3) Renewable Energy Output Constraints
In the formula, refers to the wind power time-series output when the installed capacity is constant at time , and refers to the photovoltaic time-series output when the installed capacity is constant at time .
2.3. Model-Solving Method
The mathematical essence of the renewable energy time-series production simulation model is mixed-integer linear programming.
where
is the set of variables to be optimized,
is the optimization objective function,
is the set of inequality constraints, and
is the set of equality constraints.
The CPLEX solver can be used to solve complex mixed-integer linear programming problems while ensuring optimal solution accuracy and robustness.
3. Case Study
An area in the northwest of China with abundant renewable energy resources was selected as a case study for this paper. This area contains an abundance of renewable energy that can be transferred to other locations by using transmission sections. Hence, the maintenance strategy for the transmission sections has a profound influence on the accommodation of renewable energy.
3.1. Initial Conditions
There were five renewable energy transmission sections in the chosen northwest region in 2019—section A, section B, section C, section D, and section E—and their topological configuration is shown in
Figure 2. The section quota refers to the power of transmission of electricity between two regions in 15 min. The section quota between the main network and section A was 1450 MW; the quota between the main network and section B was 1300 MW; the quota between the main network and section C was 780 MW; the quota between the main network and section D was 750 MW; the quota between the main network and section E was 1400 MW. Three maintenance scenarios were set from the perspectives of planning and operation based on the actual operation of the region in 2019.
The conditions for the REPS are the key elements.
Table 1 presents the real conditions of the renewable energy consumption capacity, and
Table 2 shows the initial consumption rates of the renewable energy in the main grids and transmission sections.
3.2. Sensitivity for Section Maintenance
This section analyzes the influences of maintenance factors on renewable energy consumption from a planning perspective. We changed the annual section quota of each section, obtained the corresponding data, fit the function of the section capacity and renewable energy consumption rate, and compared the impacts of the maintenance of the five sections on the accommodation of renewable energy. The maintenance time for zones with a significant impact on renewable energy consumption rates should be as short as possible, and the maintenance should be arranged when the renewable energy generation is lower.
The functional relationships between the section quota reductions and renewable energy consumption rate are as follows:
The results in
Figure 3 show that the renewable energy consumption rate of the whole network section decreased with the decrease in the section quota. The reduction in the section quota of section A ranged from 1000 to 100 MW, and the consumption rate ranged from 97.56% to 94.63%. The reduction in the section quota of section B ranged from 1000 to 100 MW, and the consumption rate ranged from 97.63% to 93.52%. The reduction in the section quota of section C ranged from 660 to 120 MW, and the consumption rate ranged from 97.22% to 91.43%. The reduction in the section quota of section D ranged from 500 to 100 MW, and the consumption rate ranged from 97.49% to 95.48%. The reduction in the section quota of section E ranged from 1000 to 100 MW, and the consumption rate ranged from 97.48% to 97.33%.
The maintenance of section C had the greatest impact on the renewable energy consumption of the whole network, while the maintenance of section E had the least impact. Section E should be chosen to overhaul the network, and section C showed the fastest rate of decline regarding the renewable energy consumption rate. Thus, as the section quota decreased, it must be chosen for maintenance carefully.
3.3. Strategy for Single-Section Maintenance
The analysis of the influences of maintenance factors on renewable energy consumption from the perspective of operation was as follows. In this section, we considered the impacts of the maintenance of a single transmission section in different months on renewable energy accommodation. According to the published literature and field research, transmission section maintenance is set to be carried out once a year, and its duration is 1 month. First, when the maintenance of a section was considered, the section quota became 0. The above consumption model was used to calculate the rate of consumption of renewable energy. The power and electricity were found to be unbalanced when the transmission section quota was 0, so the transmission section quota was set to the minimum value under the condition of power and electricity balance. The minimum value of each transmission section under the condition of ensuring power balance during maintenance is shown in
Table 3.
The renewable energy consumption rate could be calculated when the five sections were overhauled in different months. The renewable energy consumption rate in the whole network was calculated. For section A, the decrease in the value of renewable energy consumption ranged from 0.98 to 0.31; for section B, this was 0.85 to 0.45; for section C, this was 0.75 to 0.33; for section D, this was 0.53 to 0.21; for section E, this was 0.24 to 0.00.
The degrees of influence of transmission section maintenance on the consumption rate of renewable energy in the whole network were ranked from large to small as follows: section A, section B, section C, section D, and section E. The average decreases in the renewable energy consumption rate of the whole network under the maintenance of the transmission section over 12 months were 0.64%, 0.6%, 0.54%, 0.36%, and 0.05%, respectively, as shown in
Table 4 and
Figure 4.
3.4. Strategy for Two Sections’ Simultaneous Maintenance
In the actual maintenance process, in order to increase the maintenance efficiency, it is unnecessary to carry out single-section maintenance each time. It is possible to carry out the maintenance of multiple sections at the same time, but the impact of the simultaneous maintenance of more than two sections on the regional power grid is too large. We considered the impact of a typical scenario in which two transmission sections were simultaneously overhauled on the renewable energy consumption of the entire network. Under the condition of power balance, the double-section transmission section quota was set to the minimum value. We calculated the decline in the consumption rate of renewable energy when the two sections were simultaneously under maintenance for a month. The results are shown in
Figure 5.
For sections A and B, the decrease in the value of the renewable energy consumption rate ranged from 1.8 to 0.91. For sections A and C, the decrease ranged from 1.85 to 0.72. For sections A and D, the decrease was from 1.55 to 0.64, while for sections A and E, it was 1.26 to 0.32; for sections B and C, it was 1.56 to 0.85; for sections B and D, it was 1.39 to 0.68; for sections B and E, it was 1.15 to 0.41; for sections C and D, it was 1.28 to 0.58; for sections C and E, it was 0.90 to 0.37; for sections D and E, it was 0.68 to 0.22.
By analyzing the above three section maintenance methods, we concluded that the rate of consumption of renewable energy decreased with the increase in the section quota. In the case of reducing the transmission quota to the lowest value according to the power balance rule, the rate of consumption of renewable energy decreased less in summer and autumn, which are more suitable for maintenance. Considering the impacts of the three maintenance strategies on the renewable energy consumption rate, priority should be given to monthly reductions in the transmission quotas for a single section’s maintenance.