1.1. Background and Motivation
To address the increasingly severe issue of climate change, countries around the globe are committed to reducing greenhouse gas emissions and proposing proactive and feasible net-zero emission goals [
1]. Considering the low-carbon and environmentally friendly characteristics of renewable energy, constructing a power system dominated by high-penetration renewable energy sources is an important measure for the power industry to achieve net-zero emission goals [
2]. However, renewable energy generation has the characteristics of randomness and volatility, which affect the safe and stable operation of power systems, thus bringing new challenges to the power industry [
3]. Therefore, it is of great significance to vigorously promote the development of flexible resources (FRs) [
4].
Figure 1 depicts the main components of FRs in a power system.
However, this requires significant financial outlays, as well as time, either on the generation side (transforming conventional power sources) or the storage side (building energy storage systems). In contrast, it is definitely more economical and sustainable to carry out demand response (DR) on the load side [
5]. According to the response method, DR can be further divided into incentive-based demand response (IDR) and price-based demand response (PDR). Of these, the former emphasizes that users adjust their electricity demand at appropriate times to obtain corresponding subsidies, and the latter reflects that users are guided by price signals and spontaneously adjust their electricity consumption [
6,
7].
Figure 2 depicts the main components of IDR and PDR.
As one of the PDRs, the time-of-use (TOU) tariff is formulated by the government and is more feasible and manageable compared with the real-time electricity prices (RTPs) determined by the market [
8]. Hence, TOU is still the main electricity pricing method implemented in most regions, especially in areas where the construction of the electricity market is in the early stages. The basic principle of the TOU tariff is to guide users to optimize their electricity consumption behavior and achieve “peak-cutting” and “valley-filling” by dividing the 24 h day into several time periods and assigning different tariff levels to different time periods [
9,
10]. However, there are three main issues in formulating a scientifically reasonable TOU tariff:
How could we divide different time periods?
How could we set different tariffs for each time period?
How could we set different tariffs for each season/month?
The answers to the aforementioned queries may aid in the more effective release of price signals that direct consumers to spontaneously modify their electricity use habits. On the one hand, power consumers have the option to select alternative ways to consume electricity during different time periods, which is more cost-effective than paying the same tariff all the time. On the other hand, by raising the rates during times of peak electricity consumption, the power grid can lessen the electrical congestion. As we progressively approach carbon neutrality, in the future, renewable energy will become increasingly prevalent. Simultaneously, the penetration rate of renewable energy will further increase. Expanding the FRs, like flexible power sources and energy storage systems, is not a feasible solution. In comparison, encouraging power customers to cut back on their unnecessary consumption through the guidance of the TOU tariff is more practically significant.
1.2. Implementation Status of the TOU Tariff
Globally, the TOU tariff is widely implemented. However, due to differences in users’ composition and load characteristics, the TOU settings vary greatly in different regions. Taking California (US) and New South Wales (Australia) as examples, the time period division and seasonal setting are shown in
Table 1.
As the world’s largest power entity, China’s electricity generation totaled 8534.3 terawatt-hours in 2021, accounting for 29.98% of the global total electricity generated (BP:
https://www.bp.com/ (accessed on 16 November 2023)). In an effort to conserve electricity resources and encourage reasonable electricity consumption, China’s central and southwestern areas started experimenting with the TOU tariff in 1984. With the steady improvement of the pricing mechanism, the TOU tariff has expanded and been applied in the majority of China’s regions. However, due to China’s power industry being new, the TOU tariff implemented in most regions still needs to be improved. In 2021, the National Development and Reform Commission (NDRC) announced the following goals for the optimization of the TOU tariff:
To improve the TOU tariff pricing mechanism via scientific time period division and establishing a reasonable tariff difference.
To establish a critical peak tariff mechanism.
To establish a sound seasonal electricity pricing mechanism.
As of November 2022, the implementation of the TOU tariff in 31 regions in China (excluding Tibet, Hong Kong, Macau, and Taiwan) is shown in
Table 2.
As shown in
Table 2, in various regions of China, there are the following issues:
As the regulation states, each region’s peak–valley tariff ratio should not be less than 3:1 in principle. In areas where there are notable peak–valley load differences, the peak–valley tariff ratio might potentially surpass 4:1. Currently, only half of the areas have a peak–valley pricing ratio greater than 3:1, and the majority of the regions continue to comply with the national requirements, without exploring more scientific and practical peak–valley tariff ratios with regional features.
- 2.
Widely implemented critical peak and unpopular deep valley tariffs
Most regions have established critical peak time periods and tariffs in order to strengthen the TOU tariff. Nonetheless, as renewable energy installations continue to rise, in some areas, the supply of electricity outpaces the demand. Therefore, investigating the creation of a deep valley tariff is important from a practical standpoint. At present, only Shandong and Jiangsu have implemented deep valley tariff policies.
- 3.
Seasonal tariff with lack of narrow time scales
The majority of regions, aside from Gansu, have implemented thorough seasonal TOU tariff mechanisms. Nevertheless, these tariffs are applied more often in the summer and winter, with little regard for the other seasons. Furthermore, variations persist in the characteristics of loads and the behavior of power consumption even within the same season. For instance, there is a noticeable change in the load for northern cities before and after heating on a day in winter. Hence, it is necessary to set a more refined TOU tariff, such as a monthly tariff.
1.3. Literature Review
The optimization of the TOU tariff is considered a key step in the green and low-carbon energy transformation. To address the aforementioned issues and form a rational TOU tariff, other scholars have also conducted various studies on the optimization of the TOU tariff, as follows:
The determination of a typical daily load curve is the first step in dividing the time periods. In general, clustering algorithms represented by K-means are the main methods used for determining the typical daily load curves [
11]. Considering the strict classification criterion of the K-means algorithm, some scholars also use the fuzzy C-means (FCM) clustering algorithm based on the membership degree to determine the typical daily load curve [
12]. However, the position determination of the initial cluster center is the main cause of the final clustering effect, and the above improvement has not solved this problem yet [
13]. From the perspective of time period division, in most regions, 24 h is simply divided into three time periods (peak–flat–valley) in a 1:1:1 ratio [
14]. Moreover, some existing studies have also followed the same approach to dividing time periods [
15].
- 2.
Price optimization
Power users spontaneously change their power consumption behavior based on the adjustment of electricity prices, thereby achieving the transfer or reduction in power loads. In general, consumers’ psychology analysis and PEED are the two most commonly used methods [
16,
17]. The former requires simulating the psychological state of the consumers, subjectively setting some parameters. The latter is mainly based on changes in users’ electricity demand and electricity prices, which is more objective [
18]. In terms of setting the objective function for price optimization, most existing studies have mainly focused on minimizing the peak–valley difference or maximizing the reduction in peak electricity load [
19]. However, with the proposal of carbon neutrality goals, the installed scale of renewable energy continues to expand, resulting in severe wind and light abandonment in some regions [
20]. Therefore, it is necessary to consider the absorption of renewable energy when optimizing the TOU tariff.
- 3.
Execution cycle setting
The setting of the execution cycle is crucial in optimizing the TOU tariff. As with the electricity pricing policies previously implemented in most regions, the execution cycle was set to 1 year in the existing studies [
21,
22]. In other words, one set of electricity prices is implemented throughout the year. However, due to the seasonal characteristics of climate and electricity consumption behaviors, there are significant differences in the power load among the different months/seasons [
23]. Hence, it is more reasonable to carry out differentiated TOU tariff optimization designed around different months/seasons. The existing studies on seasonal TOU tariff optimization mostly concentrate on the summer and winter [
24,
25], and only a few studies set different prices for the four seasons [
26].
Based on the existing literature, several issues exist, as follows:
The influence of initial clustering center selection bias is harder to mitigate using most applied clustering models of the typical daily load curve, which leads to inaccuracy in the subsequent procedures. Furthermore, the above research only looked at the load curve on the demand side, and insufficient consideration was given to renewable energy generation on the supply side. This is not sufficiently applicable in light of the increasing penetration of renewable energy.
The current literature does not give enough thought to more refined/high-frequency TOU tariff sets (such as monthly TOU tariffs), which may become increasingly crucial with the continuous development of the power industry. Furthermore, the typical three time periods—peak, flat, and valley—which are the foundation of most current research, need to be further extended for areas with significant renewable energy potential (such as the critical peak and deep valley).
1.4. Contribution and Article Organization
To fill the gap of the previous studies and existing practices, a three-stage monthly TOU tariff optimization model is constructed in this paper, and the framework is shown in
Figure 3. First, the monthly typical daily net load curve is clustered using the K-means++ algorithm, and moving boundary technology (MBT) is adopted to divide the time periods. Then, a TOU tariff optimization model is constructed based on the consumers’ price elasticity of electricity demand (PEED). Finally, a multi-dimensional evaluation index system is constructed to assess the effectiveness of TOU tariff implementation.
The following points make up the bulk of this paper’s contributions:
Rather than using a basic load curve, time period division and price optimization are based on the net load curve, which accomplishes joint consideration for the supply and demand.
The K-means++ algorithm is adopted to cluster the typical daily net load curve, which can enhance the clustering effect by 4.27–26.70% compared to that of the conventional K-means technique.
Compared to the earlier annual/seasonal models, the monthly TOU tariff optimization model designed in this paper has a finer particle size. Furthermore, critical peak and deep valley time periods have also been taken into account in order to adapt to the new power system with ever-higher penetration rates of renewable energy than those of the peak, flat, and valley time periods considered in the majority of the previous research.
The structure of this study is as follows: After the introduction,
Section 2 introduces the methodology applied in this paper.
Section 3 presents an empirical analysis, and
Section 4 presents a discussion of the results. The conclusions are given in
Section 5.