The Economic Influence of Energy Storage Construction in the Context of New Power Systems

Abstract

The increase in the proportion of renewable energy in a new power system requires supporting the construction of energy storage to provide support for a safe and stable power supply. In this paper, the computable general equilibrium (CGE) quantitative assessment model is used coupled with a carbon emission module to comprehensively analyze the benefits and costs of energy storage construction from a macro perspective. Taking Zhejiang Province as an example, the investment in and construction of energy storage under the new power system of the 14th Five-Year Plan will slow down the economic growth of Zhejiang Province to a slight extent, but this investment and construction can improve the income level of residents and further redistribute the income structure. At the same time, energy storage investment and construction are conducive to building a clean, low-carbon, and efficient power system. The construction of energy storage can smooth out changes in electricity demand, while enhancing the electricity consumption of the residential sector, making the core sector’s electricity consumption more efficient. In addition, the construction of energy storage in the new power system has a positive effect on carbon emission reduction in Zhejiang Province, with the CO₂ growth rate being significantly lower than the baseline scenario.

1. Introduction

The increase in the proportion of renewable energy in a new power system requires supporting the construction of energy storage to provide support for a safe and stable power supply [1]. This is a key point that is relevant for many countries and regions around the world, as the use of renewable energy sources is increasing in many places [2,3]. By highlighting the need for energy storage in order to support the growth of renewable energy, we address an important issue in the field of energy and environmental policy.

“New energy as the major body” is the main macro-performance strategy of the development of a new power system. In the long term, a new power system’s major difficulty is to significantly raise the proportion of new energy generation while maintaining a steady supply of electricity, or to attain a high energy–storage ratio [4]. The most common energy storage technique at the moment is pumped storage, but electrochemical energy storage has the potential to lower costs. Depending on the application situation and value, energy storage can be split into three categories: power-side energy storage, grid-side energy storage, and user-side energy storage [5,6]. Energy storage is a critical component in ensuring the steady operation of intermittent renewable energy sources. According to its technical form, energy storage technology can be divided into three groups: electrochemical energy storage, super-capacitor energy storage, and electromagnetic energy storage. Mechanical energy storage includes pumped storage, compressed air energy storage, and flywheel energy storage (including lithium batteries, sodium batteries, lead-acid batteries, etc.). Of course, hydrogen energy is also gaining interest due to its development prospects as a cutting-edge method of chemical energy storage [7,8].

Pumped storage is the most common technical type. However, due to the battery industry’s rapid development, the price of electrochemical energy storage is also steadily falling. The total installed capacity of energy storage projects that were put into operation by the end of 2020 was 191.1 GW, with pumped storage having the biggest cumulative installed capacity—172.5 GW, or 90.3%—and electrochemical storage coming in second with 14.2 GW. Pumped storage and electrochemical storage are also prevalent in China, with installed capacities of 3.27 GW and 31.79 GW, respectively [9]. The development of renewable energy has received unprecedented attention in 2020 as part of the “double carbon” goal, and due to the pressure from the high proportion of unstable renewable energy that is consumed, local governments and power grid companies in many provinces have proposed centralized “new energy + energy storage” concepts. In order to jointly promote the growth of the energy storage market and a new “scenery” power-generation energy market, the significance of energy storage technology to the large-scale popularization of new energy has frequently been acknowledged and has come to be accepted [10].

Additionally, it is anticipated that the first domestic application scenario for new energy generation alongside energy storage will serve as the primary impetus for the policy’s necessary requirements. According to a notice issued by the National Energy Board in July 2021 entitled “Notice on Encouraging Renewable Energy Power Generation Enterprises to Build or Purchase Peaking Capacity to Increase Grid Connection Scale,” the scale of new energy installations beyond the guaranteed grid connection of grid enterprises is allocated to build peak capacity in accordance with a 15% pegging ratio, and grid connection is prioritized in accordance with a 15% pegging ratio. Energy storage has essentially become the new energy project “standard,” with nearly 20 provinces issuing new specific energy-supporting storage quantitative requirements. The majority of the provinces require that the energy storage capacity should be 10–20% of the installed capacity of new energy power generation, and the length of energy storage is basically 1–2 h. In 2021, domestic provinces released wind power and photovoltaic project competitive allocation rules. The scale of supporting energy storage projects is presently close to 50 GWh, based on the outcomes of the competitive allocation of scenery projects and the scale evaluation of domestic new energy, generating side energy storage requirements that have been made public by each province.

Vigorous development of new power systems and increased investment and construction of energy storage would have two effects on the macro economy and society [11]. The positive effect is that the construction of energy storage directly drives investment and employment in related industries [12]. The drawback is that electricity generation development is now more expensive due to the existing high cost of energy storage, which also raises the cost of energy consumption. Considering the positive and negative effects, it is necessary to clarify the comprehensive impact of energy storage construction.

Our study offers some potential contributions. Firstly, we use a computable general equilibrium (CGE) quantitative assessment model coupled with a carbon emission module to analyze the benefits and costs of energy storage construction from a macro perspective. This is an approach that can provide valuable insights into the economic impact of energy storage construction [13]. Secondly, compared to existing articles that focus on the benefits of individual energy storage projects, we focus on the impact of the overall planning of energy storage in a region, considering the economic, social, and environmental impacts of GDP (gross domestic production), income structure, electricity consumption, and CO₂ emissions. Thirdly, by precise and detailed estimates of the costs and benefits of energy storage construction, this paper can help policymakers and other stakeholders understand the implications of different scenarios of energy storage.

The rest of this article is arranged as follows. Section 2 provides a literature review on energy storage. Section 3 constructs a CGE model and a SAM (social accounting matrix) table and gives data sources. Section 4 presents the empirical results and a discussion. Finally, Section 5 concludes the work.

2. Literature Review

The economic effect of energy storage construction has received increasing attention in recent years, as the use of renewable energy sources has grown, and the need for reliable and flexible power systems has become more pressing. Energy storage technologies, such as batteries and pumped hydro storage, can help to smooth out the fluctuations in electricity generation from renewable sources and provide valuable support for maintaining the stability of the power grid.

In the new power system of the 14th Five-Year Plan, the share of new energy generation increased. However, the location of new energy generation, which includes wind power and photovoltaic energy, is mostly in sparsely populated, economically underdeveloped areas, which is not consistent with the locations with the most electricity consumption, so there is a problem of consumption. In order to solve the problem of new energy consumption, one method is to establish extra-high voltage transmission lines to break through the spatial barrier between power generation and electricity consumption [14]. Another technique is to build energy storage systems to store generated electricity and discharge it during a peak in electricity consumption, breaking the time limitation of energy use [15]. Some studies have looked into the financial effects of building energy storage systems. Grid systems can benefit greatly from energy storage as well [4,16]. An energy storage system (ESS) can increase grid reliability and network efficiency [17], minimize peak–valley gaps and load balancing [18], and enhance power grid operation efficiency [19,20].

Recently, some studies have concentrated on the economic viability and ideal scale of energy storage. Research on the economics of energy storage can be grouped into two main categories. One category uses a bottom-up methodology, focusing on individual cases. Ref. [21] studied the satisfaction of the electrical energy demand of off-grid vacation homes through a hybrid PV/wind/fuel cell energy system from a techno-economic point of view. Considering different geographical and climatic conditions, it was concluded that the levelized cost of electricity (COE) of off-grid renewable energy systems is higher than the cost of grid electricity, but there was a significant downward trend compared to previous years, and the decreasing cost of energy storage batteries is an important reason. Despite significant uncertainty regarding the past, present, and future costs of lithium-ion technology, which dominates electrochemical energy storage batteries, there has been a significant decrease in costs. From 2007 to 2014, the industry-wide cost of lithium-ion battery packs was estimated to have declined by approximately 14% per year, with market leaders in particular experiencing faster cost declines, which has significant implications for the assumptions used in modeling future energy and transportation systems [22]. With the correct identification of application requirements and based on the investigation of the techno-economic and environmental impact of energy storage devices, a hybrid solution using a combination of various energy storage devices has been considered as a viable solution in this field [1,23].

There is also a top-down approach, in which the overall impact is simulated through models. Ref. [24] calculated the profit-maximizing scale and analyzed the economic viability of energy storage in the US electricity market. The findings demonstrated that for some places, energy storage might generate an alluring internal rate of return. Ref. [25] investigated the best method for choosing and scheduling battery storage in distribution systems with high photovoltaic (PV) penetration. Ref. [26] evaluated the financial viability of grid-connected storage systems with rising wind penetration levels. The economics of energy storage participating in arbitrage and regulation services across several markets were quantified in Ref. [27]. Ref. [4] estimated ideal storage scales for energy storage in China and assessed the economic viability of battery energy storage. A dynamic program was used to simulate the effect of power system outages on the operation and storage charging states in subsequent periods. Further, a probability distribution of the stored charging states for each period can be computed, which can be used as the stored forced outage rate in standard reliability-based capacity value estimation methods [28]. Considering manufacturing, construction and installation, and operations and maintenance, energy storage could create jobs in the power sector [29].

In studies of the electric power sector, computable general equilibrium (CGE) models are frequently employed. For instance, Ref. [30] used a CGE model to examine the macroeconomic effects of changes in coal and electricity prices in China. A CGE model was used by Ref. [31] to analyze the economic and environmental effects of the renewable energy quota system. Ref. [32] used the CGE model to simulate the effect of carbon trading on the Chinese power sector. In addition, the CGE model can provide the impacts of one policy for different groups of people in order to analyze the change in social welfare brought by the policy [33]. From the energy supply side, after a comparison of mitigation costs under different scenarios, storage with batteries was considered to be a measure to mitigate the curtailment of variable renewable energy [34].

Overall, the available literature suggests that energy storage construction can have significant economic benefits, including reduced costs of power generation, improved reliability of the power grid, and reduced carbon emissions. However, the existing research has mainly focused on the energy sector in a national or global region. Future research should continue to explore the economic effects of energy storage construction, as well as consider the potential impacts on different regions and sectors of the economy. The CGE model has been shown to be valid and generalizable in these studies; as a result, using the CGE model to evaluate the effects of an ESS in Zhejiang Province on various macroeconomic aspects can ensure results that are reasonably trustworthy and realistic.

3. Methodology

3.1. The CGE Model

The computable general equilibrium (CGE) model is a typical economic model. It can use mathematical expressions to describe the relationship among various agents in an economy and figure out the equilibrium state, so as to simulate and analyze the impact of economic policies in a country or region. We construct a CGE model to assess the influence of building an energy storage system. The CGE model used here incorporates a carbon emission module together with Zhejiang Province’s present economic development and electric power system characteristics. The electric power sector inputs in the input–output table have been updated to account for the effects of the new power system’s development. This includes a decrease in the industrial sector’s energy intensity, adjustments to household consumption patterns, and a balance of heavy industry output deviations. The model is also modified based on the characteristics of capital formation in the electric power industry of Zhejiang Province, assuming that when the demand for electricity increases, the industry responds by increasing the utilization rate of existing capital rather than adding new capital inputs right away. Increased demand will not result in more capital investment until all of the available capital has been used up. The construction of the new power system’s CGE macro green quantitative assessment model is then finished by combining a SAM (social accounting matrix) table of the new power system with the various functions on the supply and demand side, based on the understanding of the possible paths of action of the new power system’s construction on the macroeconomic impact.

The production function provides a description of the activities that make up the production sector. The power system will become increasingly important in the generation and use of energy in the future. In order to identify the production activities of the production sector, this study primarily uses the nested CES (constant elasticity of substitution) production function and the Leontief function [35]. In the following, Equation (1) illustrates the precise form of the CES production function using two factor inputs as an example:

$$Y(L,K)={\left(\alpha L^{-\rho }+(1-\alpha )K^{-\rho }\right)}^{1/-\rho }$$

(1)

where $\sigma =\frac{1}{1+\rho }$ is the elasticity of substitution between the two factors. The CES production function’s introduction can significantly lessen the restriction on the conditions of production function assumptions because of its superior flushness and consistent elasticity of replacement qualities.

According to its nested form, a three-factor CES nested production function with energy inputs can theoretically be expressed in the following three ways as Equations (2)–(4):

$$Y=A{\left[\beta {\left(\alpha K^{-{\rho }_1}+(1-\alpha )E^{{\rho }_1}\right)}^{-\frac{\rho }{{\rho }_1}}+(1-\beta )L^{\rho }\right]}^{-\frac{1}{\rho }}$$

(2)

$$Y=A{\left[\beta {\left(\alpha L^{-{\rho }_1}+(1-\alpha )E^{{\rho }_1}\right)}^{-\frac{\rho }{{\rho }_1}}+(1-\beta )K^{\rho }\right]}^{-\frac{1}{\rho }}$$

(3)

$$Y=A{\left[\beta {\left(\alpha L^{-{\rho }_1}+(1-\alpha )K^{{\rho }_1}\right)}^{-\frac{\rho }{{\rho }_1}}+(1-\beta )E^{\rho }\right]}^{-\frac{1}{\rho }}$$

(4)

The above three equations demonstrate different nesting structures, where ${\sigma }_1=\frac{1}{1+{\rho }_1}$ denotes the elasticity of substitution for the first level of nested relationships; $\sigma =\frac{1}{1+\rho }$ denotes the elasticity of substitution for the second level of nested relationships.

The nesting relationship between labor, capital, and energy in the production function should first be structurally described in the actual modeling process. On this foundation, the addition of other intermediate inputs enables the substitution relationships between all the inputs of production activities and various types of inputs in the economic system to be fully outlined.

As an illustration, consider the production function of total output, which is represented in the following by Equation (5) [16], where ${\alpha }_a$ is the scale factor or efficiency factor, whose larger value denotes a higher output capacity at particular inputs. The coefficient of the contribution share for various inputs is ${\delta }_a$. Parameters that can be determined using the formula $\sigma =1/\left(1-\rho \right)$ are related to the elasticity of substitution. QA stands for output quantity, QKELA for factor and energy composite input quantity, and QINTA for intermediate input quantity.

$$QA={\alpha }_a{[{\delta }_{a}QKEL{A}^{\rho }+(1-{\delta }_a)QINT{A}^{\rho }]}^{1/\rho }$$

(5)

The variables have the following relationships based on how the CES function works. PINTA stands for the price of other intermediate inputs, PA stands for the price of the product, and PKELA stands for the price of the composite of factor and energy inputs.

$$\frac{PKELA}{PINTA}=\frac{{\delta }_a}{1-{\delta }_a}{\left(\frac{QINTA}{QKELA}\right)}^{1-\rho }$$

(6)

$$PA·QA=PKELA·QKELA+PINTA·QINTA$$

(7)

Additionally, the function form is of the Leontief type for the intermediate inputs, and the following formula illustrates the structure of the function. The variables are: $QINT{A}_a$, which denotes the total amount of intermediate inputs in sector a; $ic{a}_{ca}$, which denotes the intermediate input structure share coefficient; and PINTA a, which denotes the price of intermediate inputs.

$$QIN{T}_{ca}=ic{a}_{ca}·QINT{A}_a$$

(8)

$$PINT{A}_a={\displaystyle \sum _{c\in C}ic{a}_{ca}·P{Q}_a}$$

(9)

With reference to the setting of the widely used CGE model, this study employs a five-level production function configuration. Energy inputs are separated from intermediate inputs in the output function, and the output function is also coupled with labor and capital input requirements. In other words, the inputs to the CES function in the first level of nesting include both “factor-energy” and “non-energy intermediate inputs”. Labor and capital energy are combined to form “factor-energy.” The fraction of various intermediate inputs is used to determine non-energy intermediate inputs according to the Leontief function. “Energy-capital” is a combination of the inputs “capital” and “energy”. The model also gives a more thorough overview of both the non-electricity sector and the sector that produces electricity. To reflect the energy use characteristics of various generation technologies, the electrical sector in the common database is expanded and divided into transmission and distribution, energy storage, coal, gas, hydropower, nuclear, wind, and photovoltaic.

The model employed in this study specifically divides energy inputs into electricity and non-electric energy inputs. Coal, coal processing, natural gas, oil, and the provision of heat are among the non-electric energy inputs. For instance, refined petroleum products cannot be quickly replaced by other energy sources, because they are primarily used in transportation or as raw materials for the creation of chemicals. Because refined oil is substantially more expensive than coal, alternative fossil fuels are typically not used to meet the industrial sector’s demand for coal (such as in power generation). As a result, the Leontief type is chosen for the production function of non-electric energy inputs. Both power-side and grid-side inputs are included in electricity inputs, and the energy storage side is nestled inside the power-side and grid-side as a subpart. Since power generation, transmission, and distribution generally cannot be substituted for one another, the production function of electric energy input also takes on the Leontief type. The function structure of the relevant links of the new power system that is finally proposed in this paper is shown in Figure 1.

Based on the standard CGE model, the provincial CGE model is constructed by combining the characteristics of provincial economy, and its structure is shown in Figure 2. The principal entity responsible for carrying out production operations is the production sector, which buys intermediate inputs from the commodity market and factor inputs such as labor and capital from the factor market. Whereas the factor rewards that the production sector obtains are distributed to the factor market, the output of the sector flows into the commodity market in the form of intra-provincial production.

In the commodity market, in addition to locally produced goods, there is also interprovincial trading with both domestic and international markets as well as import–out trade. Consumption, investment, net exports, and net interprovincial outflows are the four categories of demand that are eventually met by the output of the commodity market. Various agents of national accounts, such as resident accounts, government accounts, corporate accounts, international accounts, and domestic and extra-provincial accounts, are paid factors in factor markets. The accounts also exchange a variety of payments. Every account complies with the income and expense balance principle. The total demand is the amount of products demanded in each account. This makes up the end-use demand based on the overall demand and structure. The CGE model completes the macroscopic closure in this manner.

3.2. SAM Table and Account Setting

The computable general equilibrium model is based on general equilibrium theory, which can be widely used in the fields of resources and environment, finance and taxation, international trade, and energy and climate change, and it is now one of the mainstream analytical tools in the field of policy research. The model specifically presupposes that price changes in output in one market will, in the conventional sense, have an impact on other markets, which in turn will reverse the effect on the entire economic system and possibly even have some influence on the price–quantity equilibrium in the original market. A market’s price of output typically ripples through other markets, which in turn influences the economy as a whole and, to some extent, the price and quantity equilibrium in the original market. It is vital to construct a model that can take numerous markets at once into consideration in order to move beyond partial equilibrium analysis and to account for this intricate interaction in the economy. In essence, a general equilibrium model is an analytical framework for examining the interactions between various markets, industries, resource factors, and institutions. In order to evaluate the new electricity system in Zhejiang Province, the CGE model built in this paper uses the social accounting matrix (SAM) story as its data source. The following section introduces the framework, compilation principle, and balancing method of the SAM table.

The input–output table serves as the foundation for the social accounting matrix (SAM), which is then expanded by including institutional accounts. The SAM tracks the volume of transactions among economic agents as well as the primary transaction flows of the economy during a certain time period. It is possible to represent the economic relations between production and non-production sectors by expanding the input–output table and using the SAM, which is a unified accounting system for all sectors of the socio-economic system. This ensures the comprehensiveness and consistency of the data. Since SAM can quickly perform account decomposition and aggregation, the model can be more precisely targeted by decomposing or aggregating the pertinent production sectors, commodity sectors, and institutional sectors to meet the needs of the research challenge.

As a symmetrical square matrix, the SAM table primarily includes commodity accounts, activity accounts, factor accounts, institutional accounts, inter-provincial accounts, and international accounts. The Zhejiang Province’s SAM table is depicted as follows.

The domestic commodity market’s trading activity is portrayed in the commodity account. The commodity account’s spending is used to pay tariffs and purchase domestically produced goods and services (such as those from the trade sector); the account’s revenue comes from the consumption of intermediate demand, residential consumption, government consumption, and investment in production activities. The goods account’s balance suggests that the market for products has cleared.

The activity account displays how producers produce their goods. The activity account’s expenses go toward buying intermediate inputs, hiring labor to carry out production, and paying indirect taxes to the government. The activity account’s income comes from both domestic and international trade. A zero profit for the maker is implied by the activity account’s balance.

The flow of different production factors is reflected in the factor account. Labor, capital, and land are all considered factors [36]. Wages and rent from manufacturers’ production operations as well as factor export revenue from the international economy are generated by the factor account, and factor income is subsequently dispersed to people and businesses.

The changes in the capital market are reflected in the capital account. Its income is derived from overseas deposits as well as savings in other institutional accounts, and its outlays are reflected in investments and adjustments to inventories.

Residential accounts, business accounts, and government accounts make up institutional accounts, which collectively represent the exchanges between domestic social institutions. Residents’ income comes from factor income and various transfers from the government, businesses, or other countries. Residents’ expenses are primarily made up of consumption costs and income taxes, with the remaining amount going into their capital account to create savings. The primary sources of an enterprise’s income are factor income and various transfer payments, and the majority of their expenses are covered by direct taxes and international transfer payments, with any remaining funds going into the capital account to create corporate savings.

The economic interactions between the province and its surrounding areas are reflected in the interprovincial account. Interprovincial transfers in are recorded as income from the out-of-province account, whereas interprovincial transfers out, factors for the province, and various transfers to the province are recorded as expenditures from the out-of-province account. The remaining balance, which is reflected as out-of-province savings, is partially transferred to the capital account.

The province’s international economic links are reflected in the foreign account, which reflects income and expenses from a foreign perspective. Imports and foreign capital investment income are the sources of foreign account income from an income standpoint. From a spending perspective, exports make up the majority of the foreign account. The province’s economic connections to other domestic regions are shown in the other domestic regions account. When looking at income, the inter-provincial spending of the province, which includes both the inter-provincial foreign exchange transfer and the domestic inflow from outside the province, corresponds to the income of the domestic other regions account. From an expenditure standpoint, the rest of the country account contains both inter-provincial net savings as well as internal extra-provincial outflows that correspond to inter-provincial revenue within the province. Gross savings and gross investments are reflected in the investment account. Residential savings, corporate savings, government savings, and interprovincial net savings are some of the sources of the investment account’s revenue. From the standpoint of expenditure, net foreign savings and capital formation are both expenditures of the investment account. Net foreign savings are truly equal to the investment in foreign nations. They are the remaining income of the general account surplus after deducting various factor remuneration payments to foreign countries.

3.3. Data Sources

The input–output table (IO table) is the foundation of the SAM table. The 2017 Zhejiang Input–Output Table, which was finished in 2020 and includes 42 sectors, is the most recent input–output table for Zhejiang Province. Therefore, the SAM table chooses the same period as the base year for the CGE model calculations. In the input–output table, the production and supply of electricity and heat are coupled with the power generation, transmission, and distribution and heat supply sectors. On the other hand, the grid firms are most impacted by the policy of power price adjustment. This model divides the three sectors of the power generation side, transmission and distribution, and heat supply in order to facilitate model creation and more precise analysis.

A 50-sector input–output table is created by combining the 142 common sectors into 34 sectors and adding the six power generation sectors—coal, gas, hydro, nuclear, wind, and solar—that were separated from the power sector. The data sources for cash flow between different accounts in the SAM table are listed in

Table 1. Data source of cash flow between different accounts.

Cash Flow Items	Data Source	Cash Flow Items	Data Source
Total provincial output	IO table	Household tax	Finance Yearbook of China
Intermediate input	IO table		Finance Yearbook of Zhejiang
Household consumption	IO table	Firm tax	Balance item
Government consumption	IO table	Import	IO table
Export	IO table	Foreign capital reward	China Statistical Yearbook
Domestic and out-of-province outflow	IO table		Zhejiang Statistical Yearbook
Investment + stock	IO table	Net foreign savings	Balance item
Wages and salaries	IO table	Inflow of domestic and foreign provinces	IO table
Capital reward	IO table	Central government revenue	Finance Yearbook of China
Household capital reward	Zhejiang Statistical Yearbook		Finance Yearbook of Zhejiang
Transfer payment From government to household	Balance item	Household savings	Balance item
Transfer payment from government to firm	Balance item	Firm savings	Balance item
Firm capital reward	Balance item	Government savings	Balance item
Tax on activities	IO table	Net foreign investment	Finance Yearbook of Zhejiang
		Net interprovincial investment	Balance item

. By combining the rows and columns of related industries, the content pertaining to the aforementioned industry sector merger and splitting is substantially condensed. Sector splitting requires both the actual output from each sector as well as the input from other sectors into the splitting sector.

To incorporate CO₂ emissions into the CGE model, we calculate the CO₂ emission of each production sector by multiplying the consumption of fossil fuels and CO₂ emission factors based on carbon emission accounts and datasets (CEADs). Referring to Ref. [37], CEADs regularly publish updated CO₂ emission inventories for China and its 30 provinces and municipalities using the IPCC (Intergovernmental Panel on Climate Change) subsectoral emission accounting methodology.

3.4. Scenarios Setting

After completing the construction of the impact assessment model for the energy storage system in the new power system in Zhejiang Province, two scenarios are set to analyze the macroeconomic impact. Specifically, the baseline scenario is set as the current normative situation in Zhejiang Province; the ESS scenario of the new power system is set as the situation after increasing the investment in energy storage, in which a total of 3.4 million kW is added by setting pumped storage, and a total of 6 million kWh is added by new electrochemical energy storage. The capacity goal is set according to the 14th Five-Year Plan of Zhejiang Province. The simulation of other aspects of the new power system construction will be further analyzed and discussed in the future.

4. Results

In this section, we use the quantitative assessment model of CGE of the new power system coupled with a carbon emission module to measure the impact of ESS on the regional economic system of Zhejiang Province.

4.1. The Impact of Energy Storage Construction on Economic

Figure 3 first displays the annual change in the energy storage investment for the 14th Five-Year Plan’s investment and building scenario. The figure shows that starting in 2021, Zhejiang Province’s energy storage projects will be built more quickly as a result of policy planning and promotion. Pumped storage investment growth is larger than that of the new electrochemical energy storage system’s investment growth, and this difference is primarily due to the two technologies’ different cost structures [38,39]. By 2025, the investment in new electrochemical energy storage in Zhejiang Province will grow from RMB 1.2 billion to RMB 2.4 billion in one year, and the investment in pumped storage will grow from RMB 3.1 billion to RMB 5.7 billion, reflecting the importance and concern for the construction of a new electrochemical ESS in Zhejiang Province during the 14th Five-Year Plan.

During the 14th Five-Year Plan period, the new power system construction and investment in energy storage in Zhejiang Province will bring an economic change to the energy and power system of Zhejiang Province and also initiate the strategy of “pull one hair and the whole body moves”, which will have a profound impact on the economic development of the whole society. Figure 4 shows the changes in economic growth in Zhejiang Province during the 14th Five-Year Plan period under both the baseline scenario and the energy storage system construction scenario. As can be seen from the figure, the overall impact of the two scenarios on the overall economic growth of Zhejiang Province is not very different. Under both scenarios, the total GDP of Zhejiang Province is RMB 5.27 trillion in 2017 and RMB 7.17 trillion in 2020; after entering the construction period of the 14th Five-Year Plan, the change in GDP under the baseline scenario is slightly larger than that under the ESS construction scenario. By 2025, the GDP of Zhejiang Province under the baseline scenario will reach RMB 10.35 trillion, and the GDP of Zhejiang Province under the ESS construction scenario will be RMB 10.33 trillion, a difference of RMB 15 billion between the two scenarios. In other words, according to the energy storage investment plan of Zhejiang Province during the “14th Five-Year Plan” period, in order to promote the construction of new power systems, energy storage investment and construction will slow down the economic growth of Zhejiang Province to a slight extent, but compared with the investment and construction of energy storage, the loss with the change in GDP is not small. This initially shows that the active construction of new power systems in Zhejiang Province and the expansion of investment in energy storage systems will not produce significant harm to the economic development of Zhejiang Province, but society would suffer the negative impact of the transformation of the power system [33].

4.2. The Impact of Energy Storage Construction on Social Welfare

Based on the impact of the two scenarios on the GDP of Zhejiang Province, we further explore whether the investment in ESS construction under the new power system will have other impacts on the economic and social system from the perspective of income structure. According to the above assessment, the increase in the energy storage investment and construction in Zhejiang Province during the 14th Five-Year Plan period will not have a significant impact on the economic volume, but if the energy storage investment and construction will have a negative impact on the economic structure and income structure, this indicates that the increase in energy storage investment and construction will still have a significant negative effect on the economic development of Zhejiang Province.

Figure 5 shows the income structure of Zhejiang Province in 2017, in which household income accounted for 50% of total income, firms’ income accounted for 22%, and government income accounted for 28%. With the continuous promotion of high-quality economic development in Zhejiang Province, the income structure of the province will be more equally distributed, and the proportion of residential income will continue to rise. The measurement based on the new power system assessment model in this paper verifies this point, and Figure 5 shows the income structure of Zhejiang Province in 2025 under the baseline scenario and the energy storage construction scenario. It can be seen that the income structure of Zhejiang Province under the two scenarios is exactly the same. Among the different proportions of income, the income of residents rises from 50% to 60%, and the income of enterprises and government decreases by 3% and 7% to 19% and 21%, respectively, further rationalizing the income structure and increasing the income level of residents [40]. From the difference between the baseline scenario and the ESS construction scenario, we can see that only the firms’ income proportion increases by 0.02% due to ESS construction. Therefore, energy storage investment and construction likewise cannot produce significant negative effects on the economic and social development of Zhejiang Province by affecting the income structure.

4.3. The Impact of Energy Storage Construction on the Power System of Zhejiang Province

After analyzing the impact on the economic system generated by the investment and construction of energy storage in the construction of a new power system, this study further explores its impact on the power system of Zhejiang Province. In terms of electricity consumption, Figure 6 shows that the difference between the two scenarios of electricity consumption in Zhejiang Province is relatively small. Since a large proportion of renewable electricity access is not considered for the time being, the peak and frequency regulation role of energy storage is not well reflected. However, a closer look reveals that the change in electricity demand is also weakly smoothed by building energy storage.

In terms of the electricity consumption structure as shown in Figure 7, it can be found that the difference between the two scenarios is small. However, with the development of Zhejiang’s economy and the optimization of industrial structure, the top ten electricity consumption sectors in 2025 are different from those in 2017, and the core electricity consumption sectors in 2025 are more efficient. In particular, the electricity consumption of the residential sector achieves a significant increase [41].

4.4. The Impact of Energy Storage Construction on CO₂ Emissions of Zhejiang Province

In terms of CO₂ emissions, Figure 8 shows that the changes in CO₂ growth rates are similar for the baseline scenario and the ESS construction scenario, reflecting the fact that the CO₂ growth rate in Zhejiang Province is gradually slowing down [42]. At the same time, the CO₂ growth rate under the ESS scenario is significantly lower than that of the baseline scenario, reflecting the positive effect of energy storage construction on the reduction of carbon emissions in the new power system in Zhejiang Province [43].

5. Conclusions

Based on a macro perspective, this paper takes Zhejiang Province as an example to illustrate the impact of the 14th Five-Year Plan for energy storage construction on the macro economy, social welfare level, and power system, in order to have a more comprehensive understanding of the benefits and costs of energy storage and to provide a reference for the current investment and construction of energy storage around the world.

In summary, the impact of the ESS construction scenario on the total economy and income structure of Zhejiang Province is basically negligible, and the promotion of energy storage investment and construction will not have a large-scale negative impact on the economic system. This phenomenon is essentially determined by the relatively small scale of energy storage investment and construction in Zhejiang Province during the 14th Five-Year Plan period, and the impact of the transformation of the new power system on other aspects is not taken into account. Specifically, during the “14th Five-Year Plan” construction period, pumped storage increased by a total of 3 million kilowatts of installed power, and new electrochemical energy storage equipment increased by 6 million kilowatts of installed power. Compared to the entire power system in Zhejiang Province, the installed power increase is small, and the impact on the power system is relatively small. In the 14th Five-Year Plan for the Development of Electric Power in Zhejiang Province, the total installed capacity in 2020 is 101.42 million kilowatts, though another 33.64 million kilowatts of external power will be purchased, and it is expected that the total installed capacity will grow to 137.17 million kilowatts by 2025, and the external power will be expanded to 47.57 million kilowatts. Compared to the total installed capacity, the installed capacity growth of the two energy storage systems only accounts for about 6.5% of the total installed capacity expected in Zhejiang Province in 2025, which is relatively small in scale and naturally cannot significantly change the current power system. Ultimately, it cannot have a significant negative effect on the economic system of Zhejiang Province. At the same time, because the energy storage investment and construction scenario discussed in this part only considers the impact brought by energy storage investment, without further introducing the potential impact of the construction of other aspects of the new energy system, such as high proportional access to renewable power and large grid construction, which also makes it difficult for energy storage investment and construction to play a synergistic role with the power side and grid side of the new power system, limiting the impact of energy storage investment and construction.

There are several research limits to this paper. The first limit is that we only considered the direct economic impact of energy storage construction and did not calculate the indirect impact. According to the production cost model and the capacity expansion model, the construction of energy storage projects can reduce the cost of electricity for the power system, forming a positive feedback loop for the construction of new energy sources, thus indirectly promoting the development of new power system-related industries, which can also be reflected in the economic data. The second shortcoming is that, based on the government’s 14th Five-Year Plan, we have only considered the two main types of energy storage construction, i.e., electrochemical energy storage and pumped storage. In the future, there are other energy storage methods, such as hydrogen energy, that may play an important role, which will also directly or indirectly drive the development of other industries, so we need to pay attention to the planning and construction targets of other energy storage methods in the future.