Research on Two-Stage Energy Storage Optimization Configurations of Rural Distributed Photovoltaic Clusters Considering the Local Consumption of New Energy

Abstract

As photovoltaic technologies are being promoted throughout the country, the widespread installation of distributed photovoltaic systems in rural areas in rural regions compromises the safety and stability of the distribution network. Distributed photovoltaic clusters can be configured with energy storage to increase photovoltaic local consumption and mitigate the impact of grid-connected photovoltaic modes. Against this background, this paper focuses on rural areas, combines typical operation modes of distributed photovoltaic clusters, and constructs the two-stage energy storage optimization configuration model for rural distributed photovoltaic clusters. Taking a Chinese village as an example, the proposed model is optimized with an improved particle swarm optimization algorithm. Given different combinations with and without energy storage and demand response, comparative analyses are conducted on photovoltaic local consumption and the economic benefits of independent operators in various scenarios. Simulations indicate that the photovoltaic local consumption proportion of distributed photovoltaic clusters with energy storage reaches 62.64%, which is 34.02% more than the scenario without energy storage. The results indicate that configuring energy storage for rural distributed photovoltaic clusters significantly improves the photovoltaic local consumption level. Meanwhile, implementing demand response can achieve the same photovoltaic local consumption effect while reducing the energy storage configuration, and the life-cycle economic benefits are appreciable. The simulation results show that participating in demand response can reduce the energy storage system cost by 7.15% at a photovoltaic local consumption proportion of 60%. This research expands application channels of rural distributed photovoltaic clusters and provides references for investment and operation decisions of distributed photovoltaic energy storage systems.

1. Introduction

Against the backdrop that traditional energy increases environmental pollution and ecological damage, energy transition has become a major strategy for promoting carbon-neutral and systemic change in China’s economy and society [1]. Energy transition not only effectively improves the problems of environmental pollution and climate change but also involves enhancing the energy utilization rate and optimizing the energy structure. In addition, it also helps to create a safe, effective, environmentally friendly, and sustainable energy system as well as to enhance the development and implementation of renewable energy technology [2]. Wind and photovoltaic (PV) power, with renewable and environmentally friendly characteristics, are the main path toward clean energy generation, which is the focus of the energy and power field [3]. China has implemented several regulations to hasten the promotion and application of renewable energy sources while encouraging innovation and industrial upgrading to enhance the economic feasibility and market competitiveness for sustainable energy. The rapid improvement of sustainable energy not only helps to lessen the reliance on fossil fuels but also promotes optimization and upgrading for industrial structures and the overall improvement in environmental quality.

One of the most important innovations for applying solar energy in new energy is the distributed PV power generation system, which features high flexibility, simple maintenance, self-generated self-consumption of residual power on-grid, and green environmental protection efficiency [4]. According to the most recent information from the National Energy Administration, China’s new grid-connected capacity exceeded 200 GW, of which the distributed PV power generation new grid-connected capacity was 96.29 GW, accounting for 44.52% of the annual PV power generation new grid-connected capacity. The PV accumulated installed capacity was 610 million kilowatts. The new installed capacity exceeded 200 million kilowatts for the first time, exceeding 50% of the world’s new installed capacity. In addition, the accumulated installed capacity of distributed PV reached 254 million kilowatts, accounting for 42%, and the structure has been further optimized [5]. With the further implementation of “Distributed PV County-wide Promotion”, distributed PV has gradually become the main part of rural clean energy, promoting energy transformation, effectively consolidating the results of PV poverty alleviation, and helping the country’s “dual-carbon” goal to be realized in various aspects [6]. China’s rural distributed PV accumulated number of installed households surpassed 5 million at the end of September 2023, leading to a successful investment in excess of CNY 500 billion [7]. Rural distributed PV has great potential for development and is a major pillar for promoting rural energy transformation.

With a substantial rise in the capacity of rural distributed PV and household PV, its share in the rural distribution network is similarly rapidly expanding [8]. As new energy generation depends on natural conditions such as sunshine and wind power, there are obvious fluctuations and uncertainties in its power generation [9,10,11]. With the continuous development and large-scale integration of distributed PV resources, the traditional regulation and balancing capacity of the power grid may struggle to accommodate the fluctuations caused by new energy generation, posing increasingly severe challenges. Therefore, local consumption of new energy has become an important solution. By directly utilizing or storing energy at or near the power generation site, it can successfully lessen the reliance on renewable power production of long-distance transmission networks and reach a balance between the power supply and demand in local areas, which reduces the influence on the stability of the power grid.

Equipping appropriate distributed PV systems with energy storage facilities is essential in enhancing PV local consumption as well as grid stability. The energy storage system could greatly address instability in PV power generation, balance power supply and demand, and optimize the electricity configuration [12,13]. The energy storage system may preserve excess electricity with enough sunlight and PV power. At night or on cloudy days when PV power generation is insufficient, or at times of peak power consumption, the energy storage system could release this stored power and meet electricity demand [14]. The energy storage system effectively alleviates peaks and valleys of residential electricity consumption, addresses problems in PV power abandonment and limitation, enhances the power utilization efficiency, and decreases dependence on an external power grid.

Some scholars have researched optimizing energy storage capacity and operational strategies that enhance local consumption of renewable energy. Previous literature [15] focused on the significant increase in investment scale in the distribution network and the difficulties in peak and voltage regulation brought about by the construction of distributed PV throughout the country. Based on the matching degree between the installed capacity of distributed PV and loads in the countryside, a comprehensive energy storage configuration strategy was proposed, taking into account the consumption capacity and grid regulation capability. This strategy promoted the consumption of distributed PV while alleviating the stress of grid peak and voltage regulation. In [16], an energy storage optimization method was developed using an improved particle swarm optimization algorithm for rural household PV systems. The study analyzed the operational performance of the system both before and after integrating energy storage in off-grid and grid-connected PV modes. The results showed that configuring energy storage could decrease the curtailment rate by 37.51% in off-grid mode, and in grid-connected mode, it could increase the PV local consumption rate by 29.09%. Another study [17] considered the interests of energy storage operators and multiple producers and consumers to establish an energy storage sharing framework. It showed that the proposed mechanism could increase the PV self-consumption proportion by 5.01%. According to master–slave game theory, reference [18] established a shared energy storage with PV community operation model. The upper layer of the model optimized the capacity configuration and operation by minimizing the shared energy storage cost, while the lower layer optimized the electricity consumption by minimizing the PV community electricity consumption cost. The simulation results showed that PV communities with shared energy storage had a significantly increased PV self-consumption rate and improved electricity consumption cost. To optimize the capacity configuration of electric–hydrogen hybrid energy storage in multi-microgrid systems, reference [19] took into account the volatility of new energy power and built a distributed robust model with the goal of operation economy with reliability. According to the findings, the power self-balancing capacity for multi-microgrid systems could be successfully enhanced by the addition of the energy storage proposed.

The precise configuration of energy storage capacity can better leverage the importance of distributed PV systems in promoting energy transition as well as sustainable development. A previous study [20] focused on minimizing the index in total cost and output power smoothing of PV systems, proposing a multi-objective optimization method for planning battery energy storage systems and enhancing the economy and dependability of PV systems. Reference [21] considered the costs in purchasing electricity, energy storage configuration, losses, and penalties for power fluctuations in interconnection lines and constructed a collaborative optimization model for planning and operating microgrid energy storage considering long-term uncertainty. In [22], an independent PV storage microgrid energy storage capacity optimization configuration model was established to maximize PV utilization and minimize the system annual cost. In terms of addressing the optimization configuration problem for energy storage, the authors of [23] constructed a two-phase energy storage configuration method that comprehensively considers both the reliability and operational economy of the distribution grid. The method built a power supply reliability model for energy storage systems under both single-point and multi-point access structures, building upon the reliability model of a single-main-power supply system. The algorithm is demonstrated using the modified IEEE33-node system as a case example. The validity of the method is verified across different scenarios, and the impact of varying outage risk prices on the planning outcomes is analyzed. Reference [24] proposed a hybrid electric and thermal energy storage optimization scheme that accounts for the probability of typical wind power output scenarios. By considering various output scenarios and incorporating scenario probabilities as weights in the objective function, the scheme established a capacity allocation optimization model that integrates the responses of both electric and thermal loads. The model was addressed by a particle swarm optimization algorithm. Another study [25] comprehensively considered factors such as power grid peak shaving, system reserve capacity adequacy, and the new energy utilization rate and proposed a two-stage storage optimization method for large-capacity energy storage with multi-zone coordination that met the requirements of new energy consumption. The method combined time series production simulation and the sequential sub-problem method to ascertain the appropriate energy storage capacity of each zone.

Overall, the existing literature has explored the configuration methodologies, economic performances, and optimization strategies for energy storage systems, contributing to broader utilization. However, as far as this paper is concerned, fewer studies have addressed the issue of combining the goals of increasing the economics and PV local consumption of distributed PV cluster energy storage systems over the full life cycle. Most studies have focused more on the optimal economic cost and configured capacity of energy storage systems.

Motivated by the works and gaps mentioned above, the main objective is to propose a two-stage energy storage optimization configuration for rural distributed PV clusters that considers the system economics and PV local consumption. The improved particle swarm algorithm is also proposed for the optimization solution, which supports application of distributed PV cluster energy storage systems by comparing the effects of the economic index and PV local consumption under multiple scenarios.

In conclusion, the results of this research could effectively promote the PV local consumption capacity of distributed PV clusters. The following are the main contributions:

• The two-stage energy storage optimization configuration model for rural distributed PV clusters is established. The optimization objectives and decision variables for the first stage are to maximize the annual revenue of independent operators and the energy storage capacity and power, and the optimization objectives and decision variables for the second stage are to maximize the PV local consumption proportion and typical daily output of energy storage for each season.
• Based on the multiple operation modes of distributed PV, this paper considers different combinations with and without demand response and energy storage configurations and designs a variety of scenarios to analyze the economic benefits and PV local consumption.
• The results of this paper reveal that PV local consumption can be greatly increased by setting up energy storage for distributed PV clusters in rural areas, which is environmentally friendly. Moreover, this paper demonstrates that implementing demand response can achieve similar PV local consumption while reducing investment costs of energy storage. The research results have certain practical value and can serve as a reference for promoting distributed PV cluster energy storage systems.

The remaining sections are organized as follows: The second section presents the typical operation modes of rural distributed PV clusters. The third section constructs a two-stage energy storage optimization configuration model for rural distributed PV clusters and proposes economic benefit indicators. The fourth section uses a village in China as a case to optimize the energy storage configuration for distributed PV clusters, which compares and analyzes the local consumption of renewable energy with cost–benefit indicators of independent operators in different scenarios. The fifth section is the conclusion.

2. Typical Operation Mode of Rural Distributed PV Clusters

Currently, rural distributed PV clusters operate under two typical modes: one where all power is fed into the grid, and another where power is self-generated and self-consumed, with any excess power being fed into the grid. We specifically analyze the transfer of energy flow of the two modes in Section 2.1 and Section 2.2.

2.1. Mode of All Power Connected to the Grid

In all power connected to the grid mode, all electricity generated by the distributed PV system is integrated into the grid and then purchased by the operators at the standard electricity price applicable to the resource area. In this mode, the primary source of revenue for distributed PV projects is generated from electricity sales. To ensure stable revenue, the operator needs to sign a grid connection agreement with the grid company, which specifies the connection methods, conditions, and power quality requests between the PV system and the grid. Moreover, it is necessary to install grid-connected equipment in accordance with the requirements of the grid company to ensure a stable connection between the distributed PV system and the power grid. Meanwhile, the independent operator needs to sign a lease agreement with the customer for the unused roof, specifying the annual lease cost to be paid to the customers who own the unused roof resource. Figure 1 reveals the schematic diagram of the mode above.

2.2. Mode of Self-Generation and Self-Consumption with Surplus Power Connected to the Grid

In the self-generation and self-consumption mode with surplus power connected to the grid mode, independent operators need to sign housing lease and PV power purchase agreements with users. The housing lease agreement is used to specify the annual idle roof rental cost paid to the user. The PV power purchase agreement should specify how to measure the self-consumption of electricity by users. Usually, this mode requires the installation of bidirectional electricity meters to accurately measure the amount of electricity transmitted from the distributed PV system to the grid and the amount purchased by users from the grid. In addition, the agreement needs to determine the price of self-consumed electricity by users according to national or local policies, market electricity prices, or pricing mechanisms agreed upon by both parties. Similar to the mode of all electricity connected to the grid, independent operators also need to sign a grid connection agreement with the power grid company and install grid connection equipment according to regulations.

2.2.1. Distributed PV Cluster System

The operation of the mode for self-generation and self-consumption with surplus power connected to the public electricity network for a distributed PV cluster works as follows: when the distributed PV output is oversupplied, power from the distributed PV first meets the electricity demand of users, and then the user load maximizes the consumption of PV. Any surplus is then fed into the grid. When PV power is inadequate, users will purchase electricity from the grid. A schematic of this operation mode is shown in Figure 2.

2.2.2. Distributed PV Cluster Energy Storage System

The operational mode of a distributed PV cluster with an energy storage system for self-generation and self-consumption, along with surplus power connected to the grid, is as follows: If the PV output exceeds demand, excess power will first be supplied to users. Any remaining surplus will be used to charge the energy storage system, while both the user load and energy storage system work to maximize PV power consumption. Surplus power will be fed into the grid once the battery is fully charged. The battery will offer electricity to the user load if the PV power is inadequate. When the need for power load is still impossible to meet, users would buy electricity from the grid. Figure 3 shows the schematic diagram.

3. Method

3.1. Cost–Benefit Analysis of Independent Operators

3.1.1. Cost Analysis of Independent Operators

There are a variety of subjects associated with distributed PV energy storage systems. This paper assumes that a third-party subject independent operator invests in and operates the system. We will analyze the investment and income situation of this subject.

• Construction cost of distributed PV power generation systems

Equipment investment and installation costs are included in the construction cost of a distributed PV power generation system. The following is the specific calculation formula:

$$C_{con}^{PV}=C_{inv}^{PV}+C_{ins}^{PV}$$

(1)

The distributed PV power system consists of hardware equipment such as the PV panels, inverters, brackets, and cables [26]. Therefore, the equipment investment of distributed PV power generation systems is shown below:

$$C_{inv}^{PV}=C_{pan}{P}_{rat}^{PV}+C_{u,inv}{P}_{rat}^{PV}+C_{u,sup}{B}_{num}+C_{u,cab}{K}_{len}$$

(2)

Installing PV panels, brackets, inverters, electrical connections, and other components is referred to as installing distributed PV power generation systems. The following represents the cost of installing a distributed PV power generation system:

$$C_{ins}^{PV}=C_{u,ins}^{PV}\times P_{rat}^{PV}$$

(3)

2.

Operation and maintenance costs of distributed PV power generation systems

The operation and maintenance costs of a distributed PV power generation system mostly involve the mechanical installation, daily monitoring of electrical connections, and cleaning of PV modules. The calculation formula [27] is shown below:

$$C_{mai}^{PV}=C_{inv}^{PV}\times \delta$$

(4)

3.

Construction cost of energy storage systems

The equipment investment and energy storage system installation costs are included in the system construction cost. The following is the calculation formula:

$$C_{con}^{ES}=C_{inv}^{ES}+C_{ins}^{ES}$$

(5)

The energy storage system equipment investment includes both the capacity cost and power cost. Therefore, the calculation formula is shown below:

$$C_{inv}^{ES}=C_{u,cap}\times Q_{cap}^{ES}+C_{u,pow}\times P_{pow}^{ES}$$

(6)

Multiplying the original energy storage investment by a particular percentage yields the installation cost of an energy storage system. The precise calculation formula is as follows:

$$C_{ins}^{ES}=C_{u,ins}^{ES}\times Q_{cap}^{ES}$$

(7)

4.

Operation and maintenance costs of energy storage systems

Generally, the operation and maintenance cost of an energy storage system can be calculated by multiplying the operation and maintenance rate by the initial investment [28], and the calculation formula is shown below:

$$C_{mai}^{ES}=C_{inv}^{ES}\times \omega$$

(8)

5.

The replacement cost of energy storage batteries and equipment

Batteries and equipment often have shorter service lives than the distributed PV and energy storage system installations. Therefore, during the project operational cycle, the cost of replacing the energy storage batteries and equipment must be taken into account [29]. The following is the specific calculation:

$$C_{rep}^{ES}={\displaystyle \sum _{i=1}^M\left(C_{i,u,rep}^{ES}\times B+C_{i,u,dis}^{ES}\times B\right)}$$

(9)

6.

Roof rental cost

The roof rental cost of distributed PV power generation systems is related to the roof rental area, rental price, and rental period. The calculation formula for the roof rental cost is shown as follows:

$$C_{ren}^{PV}=p_{ren}\times \alpha \times q$$

(10)

7.

Cost of demand response management

The demand response management cost of independent operators refers to a series of costs incurred by coordinating the participation of electricity users in demand response plans. These costs are positively correlated with the scale of adjustable load users within a certain range, and the calculation formula is shown below:

$$C_{dr,man,n}=p_{dr,man,u}\times K,\begin{array}{cc} & n=1,2,\cdot \cdot \cdot ,N\end{array}$$

(11)

8.

Compensation cost for demand response

The demand response compensation cost refers to the subsidy reward given by independent operators based on the response electricity quantity of power users. This cost is typically represented as the product of the response electricity quantity and the compensation unit price, as shown in the equation below.

$$C_{dr,sub,n}=q_{dr,n}\times p_{dr,sub},\begin{array}{cc} & n=1,2,\cdot \cdot \cdot ,N\end{array}$$

(12)

3.1.2. Analysis of Revenue for Independent Operators

• Grid-connected revenue

The PV grid-connected electricity and the grid-connected electricity price are multiplied to determine the grid-connected revenue of a distributed PV power system.

The PV grid-connected electricity is the overall power of the PV system in the mode of all power connected to the grid. The annual PV grid-connected revenue of independent operators is displayed in the calculation formula below.

$$R_{gri,n}={\displaystyle \sum _{k=1}^K{\displaystyle \sum _{d=1}^{365}{\displaystyle \sum _{t=1}^{24}{E}_{k,n,d,t}^{PV}\times p_{on,n,d,t}}}}\begin{array}{cc} & n=1,2,\cdot \cdot \cdot ,N\end{array}$$

(13)

The PV grid-connected electricity is determined by deducting the electricity sold to users with unused roofs from the total power generation of the PV system and then deducting the electricity charged to energy storage batteries. This is performed in the mode of self-generation and self-consumption with surplus power connected to the grid. The following is the precise formula used to determine the independent operators’ yearly PV grid-connected revenue:

$$R_{gri,n}={\displaystyle \sum _{k=1}^K{\displaystyle \sum _{d=1}^{365}{\displaystyle \sum _{t=1}^{24}\left(E_{k,n,d,t}^{PV}-{E^{\prime }}_{pur-pv,k,n,d,t}^{user}-E_{cha,n,d,t}^{ES}\right)\times p_{on,n,d,t}}}}\begin{array}{cc} & n=1,2,\cdot \cdot \cdot ,N\end{array}$$

(14)

2.

Subsidy income for PV systems

According to incomplete statistics, more than half of the regions in China that implement PV subsidy policies use initial installation subsidies [30]. Therefore, this paper also adopts the calculation method of initial installation subsidies. The specific calculation formula is shown as follows:

$$R_{sub,pv}=P_{rat}^{PV}\times p_{sub,pv}$$

(15)

3.

Subsidy income for energy storage systems

Local governments generally provide subsidies to energy storage systems for a certain period based on the actual discharge of energy storage. The specific calculation formula is shown as follows:

$$R_{sub,es}={\displaystyle \sum _{n=1}^Z{\displaystyle \sum _{d=1}^{365}{\displaystyle \sum _{t=1}^{24}{E}_{dis,n,d,t}^{ES}}}}\times p_{sub,es},\begin{array}{cc} & n=1,2,\cdot \cdot \cdot ,Z\end{array}$$

(16)

4.

Electricity sales revenue

The following is the formula used to calculate the electricity sales revenue of independent operators using self-generation and self-consumption modes with excess power connected to the grid [29]:

$$R_{sel,n}=\left(E_{sel,own,n}^{PV}+E_{sel,own,n}^{ES}\right)\times p_{sel,own}$$

(17)

5.

Compensation income for demand response

According to the following equation, the power grid firm gives independent operators a specific amount of demand response compensation based on the quantity of response electricity produced by demand response electricity users:

$$R_{dr,sub,n}=q_{dr,n}\times f_{dr,sub}$$

(18)

6.

Residual value of fixed assets

The product of the initial fixed asset value and the residual value rate can be used to determine the value of some residual materials that the distributed PV cluster energy storage system can recover during the fixed asset scrapping and cleaning process after the operation period concludes. The particular computation is displayed as follows:

$$R_{res}=\gamma \times \left(C_{con}^{PV}+C_{con}^{ES}\right)$$

(19)

3.2. Two-Stage Energy Storage Optimization Configuration Model for Distributed PV Clusters

The two-stage energy storage configuration model for distributed PV clusters is built based on the cost–benefit analysis of independent operators.

3.2.1. Distributed PV Output Model

• PV Output model

Significant volatility and randomness are present in the PV output, which is typically represented as the sum of the expected output and fluctuation deviation. The equation that follows shows this:

$$E_{real,s,t}^{PV}=E_{pre,s,t}^{PV}+{\epsilon }_{dev,s,t}$$

(20)

The PV output prediction deviation roughly follows a normal distribution under the optimal geographic and spatial distribution. Therefore, in this paper, it is assumed that ${\epsilon }_{dev,s,t}$ follows a normal distribution with a mean of 0 and a variance of ${\sigma }_{s,t}^2$. The standard deviation σ_s,t can be obtained by fitting the prediction deviations obtained from the historical forecasting process to a normal distribution. Additionally, this paper intends to employ a particle swarm optimization-based neural network (PSO-BP) for predicting the output of distributed PV systems.

2.

Latin Hypercube Sampling

Based on the standard deviation of the prediction deviations, the Latin Hypercube Sampling method is used to generate prediction deviations under L different scenarios as well as the likelihood that each prediction deviation will occur for each scenario. The PV system expected output can be obtained by combining it with the predicted output, according to the equation that follows:

$$E(E_{real,s,t}^{PV})={\displaystyle \sum _{l=1}^L(E_{pre,s,t}^{PV}+{\epsilon }_{dev,s,t}^l)}\times p_{s,t}^l$$

(21)

3.2.2. First Stage Capacity Planning Model

• Objective function

Using the energy storage system’s power and capacity as deciding factors, the first stage’s optimization goal is to maximize the independent operators’ yearly revenue. The objective function is as follows.

$$\begin{array}{l}\max J_1={\displaystyle \frac{1}{N}}\left[{\displaystyle \sum _{n=1}^N\left(R_{gri,n}+R_{dr,sub,n}+R_{sel,n}-C_{dr,man,n}-C_{dr,sub,n}\right)+R_{sub,es}}\right]\\ +{\displaystyle \frac{R_{res}\times i}{{\left(1+i\right)}^N-1}}+\left(R_{sub,pv}-C_{con}^{PV}-C_{mai}^{PV}-C_{con}^{ES}-C_{mai}^{ES}-C_{rep}^{ES}-C_{ren}^{PV}\right)\times {\displaystyle \frac{i\times {\left(1+i\right)}^N}{{\left(1+i\right)}^N-1}}\end{array}$$

(22)

2.

Constraint condition

The larger the scale is of the energy storage configuration, the higher the local utilization rate of PV, but the larger the scale is of the energy storage configuration, the higher the investment of independent operators. From the economic perspective, there exists a minimum energy storage configuration capacity for this problem that can precisely meet the constraints of the PV local consumption level. Therefore, the constraint condition for the first stage is the distributed PV utilization level constraint, as shown in the following equation:

$${\displaystyle \sum _{n=1}^N\left(E_{sel,own,n}^{PV}+E_{sel,own,n}^{ES}\right)}\ge \zeta {\displaystyle \sum _{n=1}^N{\displaystyle \sum _{k=1}^K{\displaystyle \sum _{d=1}^{365}{\displaystyle \sum _{t=1}^{24}{E}_{k,n,d,t}^{PV}}}}}$$

(23)

3.2.3. Second Stage System Operation Model

• Objective function

Maximizing the local consumption of distributed PV is the optimization goal in the second stage. The real-time charging and discharging power of the energy storage system is considered the decision variable, and the local consumption rate of PV during the project operating cycle is considered the optimization object. The following is how the objective function is displayed:

$$\max J_2={\displaystyle \frac{{\displaystyle \sum _{k=1}^K{\displaystyle \sum _{n=1}^N{\displaystyle \sum _{d=1}^{365}{\displaystyle \sum _{t=1}^{24}\left({E^{\prime }}_{pur-pv,k,n,d,t}^{user}+{E^{\prime }}_{pur-es,k,n,d,t}^{user}\right)}}}}}{{\displaystyle \sum _{k=1}^K{\displaystyle \sum _{n=1}^N{\displaystyle \sum _{d=1}^{365}{\displaystyle \sum _{t=1}^{24}{E}_{k,n,d,t}^{PV}}}}}}}$$

(24)

2.

Constraint conditions

$${\displaystyle \sum _{k=1}^K{E}_{k,n,d,t}^{PV}}+{\displaystyle \sum _{k=1}^K{E^{\prime }}_{pur-g,k,n,d,t}^{user}}+E_{dis,n,d,t}^{ES}={\displaystyle \sum _{k=1}^K{E}_{load,k,n,d,t}^{user}}+E_{cha,n,d,t}^{ES}+E_{on,n,d,t}^{PV}$$

(25)

(1)

Energy storage battery power constraints

The power of the energy storage batteries cannot surpass the maximum no matter whether they are being charged or discharged [31]. The equations are listed below:

$$0\le E_{cha,n,d,t}^{ES}\le P_{pow}^{ES}$$

(26)

$$0\le E_{dis,n,d,t}^{ES}\le P_{pow}^{ES}$$

(27)

(2)

Energy storage battery safety constraints

Charging and discharging should not occur simultaneously to guarantee the safety of the energy storage devices [32], as shown in the following equations:

$$0\le E_{cha,n,d,t}^{ES}\le {\alpha }_t{P}_{pow}^{ES}$$

(28)

$$0\le E_{dis,n,d,t}^{ES}\le \left(1-{\alpha }_t\right)P_{pow}^{ES}$$

(29)

$${\alpha }_t=\left\{\begin{array}{cc}1& {\displaystyle \sum _{k=1}^K{E}_{k,n,d,t}^{PV}}>{\displaystyle \sum _{k=1}^K{E}_{load,k,n,d,t}^{user}} and Q_{n,d,t}^{ES}\le Q_{cap}^{ES}\cdot SO{C}_{\max }\\ 0& others\end{array}\right.$$

(30)

(3)

Energy storage battery charge and discharge balance constraints

When charging and discharging the energy storage battery, the current and previous remaining power must satisfy Equations (29) and (30):

When the energy storage is in a charging state during $\Delta t$ period:

$$Q_{n,d,t}^{ES}=Q_{n,d,t-1}^{ES}+E_{cha,n,d,t}^{ES}\times \Delta t\times {\beta }_c$$

(31)

When the energy storage is in a discharging state during $\Delta t$ period:

$$Q_{n,d,t}^{ES}=Q_{n,d,t-1}^{ES}-{\displaystyle \raisebox{1ex}{$E_{dis,n,d,t}^{ES}$}\!\left/ \!\raisebox{-1ex}{${\beta }_d$}\right.}\times \Delta t$$

(32)

(4)

Upper and lower limit constraints on the state of charge of energy storage batteries

Both overcharging and over-discharging can cause damage to the energy storage batteries, affecting their performance and lifespan [33]. Therefore, the energy storage batteries should have upper and lower limits on their state of charge, as shown in the following equation:

$$Q_{cap}^{ES}\cdot SO{C}_{\min }\le Q_{n,d,t}^{ES}\le Q_{cap}^{ES}\cdot SO{C}_{\max }$$

(33)

(5)

Energy storage battery charge and discharge cycle number constraints

The frequency of energy storage device charging and discharging during operation will shorten the usable time of the battery [3]. The cycle count for charging and discharging of the battery must be limited. Equation (32) can be used to approximate that the battery undergoes one charge–discharge cycle in a day:

$${\displaystyle \sum _{t=1}^{24}\frac{E_{dis,n,d,t}^{ES}}{{\beta }_d}}\le Q_{cap}^{ES}\cdot SO{C}_{\max }$$

(34)

3.2.4. Model Solving

In order to balance the capabilities of both local and global searches, the adaptive inertia weight improved particle swarm optimization algorithm (APSO) dynamically modifies the inertia weight, which enhances algorithm performance [34]. Larger inertia weights aid particles in escaping local optima in the early stages of the algorithm. Particles are able to conduct more precise localized searches in the search space as the number of iterations rises because the inertia weight gradually drops. The likelihood of discovering the global optimal solution can be raised by using this algorithm, which can better balance local and global searches. The algorithm diagram and specific solution process are shown in Figure 4. Following several tests in which the initial population and iteration count were varied, the algorithm’s convergence under various conditions was seen, and the iteration count was ultimately set to 200. Figure 5 displays the adaptive curves of the two approaches with the same computational complexity and set of parameters. It is possible for the APSO algorithm and particle swarm optimization (PSO) algorithm to converge to identically high-quality solutions. The PSO algorithm takes 79 iterations to find a solution, which results in greater computational expenses, but the APSO algorithm has a faster convergence speed and finds a solution after 27 iterations. As a result, the APSO algorithm works better.

3.3. Economic Benefit Evaluation Indicators

We established three economic benefit evaluation indicators—net present value, internal rate of return, and dynamic investment payback period—that can be computed as explained below in order to compare the system’s economics under various scenarios.

3.3.1. Net Present Value

This paper evaluates the economy by calculating the net present value to assess the profitability of the investment plan [35], providing a comprehensive view of the economic status of the system throughout the lifecycle.

$$NPV={\displaystyle \sum _{n=1}^N\frac{{\left(CI-CO\right)}_t}{{\left(1+i_c\right)}^t}}$$

(35)

3.3.2. Internal Rate of Return

The discount rate at which the project’s annual net present value flow’s cumulative present value equals zero is known as the internal rate of return [36].

$${\displaystyle \sum _{n=1}^N\frac{{\left(CI-CO\right)}_n}{{\left(1+IRR\right)}^n}}=0$$

(36)

3.3.3. Dynamic Investment Payback Period

The point at which net income equals total investment represents the dynamic payback period, helping to reduce the investment risk.

$${\displaystyle \sum _{n=1}^N{\left(CI-CO\right)}_n{\left(1+i_c\right)}^{-n}}=0$$

(37)

4. Results and Discussions

4.1. Typical Scene Construction

This article proposes two operating modes for distributed PV clusters: all power connected to the grid, and self-generation and self-consumption with surplus electricity to the grid. Four operational scenarios are constructed, considering the presence or absence of energy storage configuration and demand response participation.

• Scenario 1: Mode of all power connected to the grid

Independent operators invest in the construction of distributed PV clusters in rural areas and adopt a mode of all power connected to the grid.

2.

Scenario 2: Mode of self-generation and self-use electricity by connecting surplus electricity to the grid

Independent operators invest in the construction of distributed PV clusters in rural areas, adopting a mode where the power is first generated and consumed on its own with surplus electricity connected to the power grid. Considering different combinations of energy storage configuration and requirement response participation, three scenarios from 1 to 3 in sequence are set, as shown in

Table 1. Design of different scenarios of self-generation and self-consumption with surplus electricity connected to the grid mode for distributed photovoltaic (PV) clusters.

Scenario	PV Power Generation First Generates and Consumes Power on Its Own, with Surplus Power Connected to the Grid	Configure Energy Storage	Rural Residents Participate in Demand Response
Scenario 2-1	✓	/	/
Scenario 2-2	✓	✓	/
Scenario 2-3	✓	✓	✓

.

4.2. Basic Parameter Settings

4.2.1. Simulation of the Load on Rural Residents

Referring to the per capita comprehensive electricity consumption index in the “Urban Electricity Planning Specification” (GB/T 50239-2014 [37]), combined with the actual electricity consumption of rural residents, residential electricity consumption can be divided into four categories, as shown in

Table 2. Classification of power consumption levels for rural residents’ daily lives.

Classification of Power Consumption Levels for Rural Residents’ Daily Lives	Monthly Average Household Power Consumption (kWh)
Classification I (High level)	Above 500
Classification II (Middle to high level)	350~500
Classification III (Middle level)	200~350
Classification IV (Low level)	Below 200

.

Assume a village in China has a total of 100 households, with 40 households belonging to Classification II and III power consumption levels and 10 households belonging to Classification I and IV power consumption levels. The load situation of the four types of users on typical days in different seasons can be divided according to the characteristics of different seasons, as shown in Figure 6, Figure 7 and Figure 8.

4.2.2. Simulation of Distributed PV Cluster Output Scenarios

The capacity installed of the distributed PV system constructed by the independent operators on the roofs of each household in a natural village in China is 4 kW. The village has 100 residents, and the total capacity of the distributed PV cluster system is 400 kW. Based on the historical PV output data from rural areas in North China across the four seasons, the particle swarm optimization-based neural network is employed to predict the output of the PV systems for typical days across all four seasons, obtaining PV output values for 24 one-hour time intervals. The parameter settings are as follows: the learning factors c₁ and c₂ of the APSO algorithm are set to 1.49, the iteration count is set to 200, the population size is set to 50, the training number of the BP neural network is set to 10,000, and the learning rate is set to 0.01. The PV output for the 24 time intervals is shown in Figure 9.

Based on historical prediction results, the prediction deviations obtained during the forecasting process are fitted to a normal distribution, following N(0, 3.062). Using these deviations as the basis, the Latin Hypercube Sampling method is applied to produce five random scenarios for the PV output prediction deviations and probability of every scenario. Considering the predicted PV unit output, the expected PV unit output can be calculated.

Table 3. The probability of the PV system output variation scenario set.

Output Scenarios	1	2	3	4	5
Occurrence probability	0.1875	0.2186	0.1793	0.2113	0.2021

shows the probabilities of the different scenarios.

The PV output and expected per-unit output under different scenarios are shown in Figure 10.

4.2.3. Other Data

According to the literature [16,38],

Table A1. The basic parameters of a PV system, energy storage, and demand response plan implemented by independent operator.

Parameter Type	Parameter Name	Parameter Value
Basic parameters of PV system	Unit capacity investment (CNY/W)	3.04
	Unit capacity installation cost (CNY/W)	0.45
	Grid-connected electricity price for PV (CNY/kWh)	0.35
	Service life of PV (Years)	25
	Proportion of annual operation and maintenance costs to initial investment (%)	1
	Fixed asset residual value rate (%)	5
	Initial subsidy for PV installation (CNY/W)	0.2
	Power of PV that can be installed per square meter of land (kW/m2)	0.2
	Roof rental fee for the PV system (CNY/m2)	5
	Discount on the grid sales price for rural residential users with unused roof resources (%)	10
Basic parameters of lithium-ion battery energy storage	Life-cycle of energy storage (Years)	10
	Unit capacity cost (CNY/kWh)	800
	Unit power cost (CNY/kW)	300
	Proportion of annual operation and maintenance costs to initial investment (%)	3
	Fixed asset residual value rate (%)	5
	Discharge depth of energy storage (%)	90
	Charge state of energy storage	0.05–0.95
	Charge–discharge efficiency (%)	88
	Subsidy provided for distributed PV energy storage systems from local government (CNY/kWh)	0.3
	Subsidy years (Years)	5
Basic parameters of the demand response plan implemented by the independent operator	Annual management fee for the independent operator to implement demand response for users (CNY/Year)	8000
	Demand response compensation unit price (CNY/kWh)	0.306
	Proportion of compensation income received by electricity users participating in the demand response plan (%)	40

lists the basic parameters of the PV system and lithium-ion energy storage battery, as well as the demand response plan implemented by independent operators in Appendix A. In addition,

Table A2. Peak/Valley/Flat periods and corresponding electricity prices.

Peak/Valley/Flat Periods	Time Frame	Electricity Price (CNY/kWh)
Peak periods	9:00–12:00 18:00–22:00	0.846
Flat periods	7:00–8:00 13:00–17:00	0.534
Valley periods	00:00–6:00 23:00–24:00	0.312

shows the peak, trough, and steady periods with corresponding prices of electricity.

4.3. Energy Storage Optimization Configuration

4.3.1. Energy Storage Configuration Results for Scenarios Without Demand Response in the Mode of Self-Generation and Self-Consumption with Remaining Power Connected to the Grid

Scenario 2-2 is a distributed PV cluster that first generates and consumes power on its own, with the remaining power connected to the grid and no demand response participation. After 140 iterations, the results of optimization tend to stabilize. Figure 11 shows the fitness curve.

The optimization configuration results of energy storage in Scenario 2-2 are shown in

Table 4. Energy storage optimization configuration results of Scenario 2-2.

Optimized Indicators	Capacity (kWh)	Power (kW)	Maximum Annual Net Profit (CNY)	Proportion of PV Local Consumption (%)
Optimization results	620	163	64,697	62.64

.

4.3.2. Energy Storage Configuration Results for Demand Response Scenarios in the Mode of Self-Generation and Self-Consumption with Remaining Power Connected to the Grid

Scenario 2-3 is a distributed PV that first generates and consumes power on its own, with the remaining power connected to the grid and participation in the demand response. After 110 iterations, the results of optimization tend to stabilize. Figure 12 shows the fitness curve.

The energy storage results of optimization configuration of Scenario 2-3 are shown in

Table 5. Energy storage optimization configuration results of Scenario 2-3.

Optimized Indicators	Capacity (kWh)	Power (kW)	Maximum Annual Net Profit (CNY)	Proportion of PV Local Consumption (%)
Optimization results	591	113	59430	64.11

.

4.4. Comparative Analysis of Local Consumption Capacity of New Energy

4.4.1. Analysis of Local Consumption Capacity of New Energy in Mode of All Power Connected to the Grid

Under the mode of all power connected to the grid, the capacity of local consumption of new energy is zero. The PV output is discontinuous and fluctuates owing to factors such as the weather, geographic location, and sunlight. Large-scale PV grid integration will lead to issues such as reverse power flow, voltage violations, harmonic distortion, and potential failure of protection devices, all of which impact the power quality of the system. Moreover, to accommodate widespread distributed PV integration, the power grid must invest in upgrading and transforming the distribution network, which increases the financial burden on grid companies. Therefore, the mode of all power connected to the grid for distributed PV clusters is not conducive to the safety, stability, and economy of the entire system.

4.4.2. Analysis of Local Consumption Capacity of New Energy in Mode of Self-Generation and Self-Consumption with Remaining Power Connected to the Grid

• Analysis of PV local consumption results in Scenario 2-1

In Scenario 2-1, the distributed PV system first generates and consumes power on its own, with surplus power connected to the grid. There is no demand response in this scenario, and energy storage is not configured. Simulation analysis is conducted on the distributed PV power production and load requirement situation in Scenario 2-1 to obtain detailed operating conditions of the distributed PV system on normal days throughout the four seasons, as shown in Figure 13.

Table 6. PV local consumption and the electricity supply situation of residents in the mode of self-generation and self-consumption with surplus power connected to the grid (Scenario 2-1).

Indicator	Spring and Autumn	Summer	Winter	Annual
Total power generation of distributed PV cluster (kWh)	289,213	216,384	92,671	598,268
Proportion of PV local consumption (%)	27.37	27.74	34.59	28.62
Proportion of PV power directly consumed by users with unused rooftop resources (%)	27.37	27.74	34.59	28.62
Proportion of PV power absorbed by energy storage system (%)	/	/	/	/
Proportion of PV grid connection (%)	72.63	72.26	65.41	71.38
User load of unused roof resources (kWh)	222,277	140,603	123,974	486,854
Proportion of load demand directly met by electricity generated by PV (%)	35.62	42.69	25.86	35.17
Proportion of load demand indirectly met by energy storage discharge (%)	/	/	/	/
Proportion of load demand met by grid electricity (%)	64.38	57.31	74.14	64.83

shows the PV local consumption and electricity supply situation of rural residents with unused roof resources in Scenario 2-1.

In Scenario 2-1, only 28.62% of the PV power is used for consumption by residents of rural areas in a year. The proportion of PV grid connection in spring and autumn is 72.63%; in summer, it is 72.26%; in winter, it is 65.41%; and the proportion of PV grid connection throughout the year is as high as 71.38%. It shows that although the distributed PV system adopts the operation mode of power first generation and consumption on its own with remaining power connected to the grid, there is still a large part of the PV power that needs to be connected to the grid for expenditure. Owing to the intermittency and volatility of PV power generation, large-scale PV power grid connections will have a significant impact on the safe and economic operation of the power grid. On the other hand, the proportion of residential users with unused roof resources whose annual load demand supplied by PV clean power is only 35.17%, and there is still 64.83% of the load demand that needs to be met through grid purchase. Most electricity in the grid is produced by traditional thermal power generation, and consuming too much electricity generated by traditional thermal power generation will increase carbon emissions and cause environmental pollution.

2.

Analysis of PV local consumption results in Scenario 2-2

In Scenario 2-2, the distributed PV cluster is equipped with energy storage, and the operation mode is self-generation and self-consumption with the remaining power connected to the grid. The minute operation of the distributed PV cluster energy storage system in Scenario 2-2 on normal days in each season is shown in Figure 14, Figure 15 and Figure 16.

Table 7. PV local consumption and the electricity supply situation of residents in the mode of remaining power connected to the grid (Scenario 2-2).

Indicator	Spring and Autumn	Summer	Winter	Annual
Total power generation of distributed PV cluster (kWh)	289,213	216,384	92,671	598,268
Proportion of PV local consumption (%)	62.66	51.45	88.75	62.64
Proportion of PV power directly consumed by users with unused rooftop resources (%)	27.37	27.74	34.59	28.62
Proportion of PV power absorbed by energy storage system (%)	35.29	23.71	54.16	34.02
Proportion of PV grid connection (%)	37.34	48.55	11.25	37.36
Proportion of PV electricity traded between surrounding users and PV power generators (%)	/	/	/	/
User load of unused roof resources (kWh)	222,277	140,603	123,974	486,854
Proportion of load demand directly met by electricity generated by PV (%)	35.62	42.69	25.86	35.17
Proportion of load demand indirectly met by energy storage discharge (%)	45.91	36.49	40.48	41.82
Proportion of load demand met by grid electricity (%)	18.47	20.82	33.66	23.01

shows the local consumption of PV power throughout the year and each season, as well as the electricity supply situation of rural residents with unused roof resources.

As shown in Table 7, under the mode of self-generation and self-consumption with remaining power connected to the grid, when the distributed PV cluster is not configured with energy storage, the proportion of PV grid connection for the whole year is 71.38%. Large-scale PV integration into the grid can impact both power quality and the safety of the grid. However, with the addition of energy storage systems, the share of distributed PV connected to the grid throughout the year drops to 37.36%, enabling greater local wastage of PV power and significantly mitigating the influence on grid operations. Simultaneously, the energy storage configuration decreased the perception of electricity purchased from the grid by rural residents, from 64.83% to 23.01%. On the one hand, it reduces the demand for power supply from the grid and eases the burden on the grid, especially during peak electricity consumption periods. On the other hand, it provides residential power supply with cleaner energy from local PV generation, resulting in significant environmental benefits.

3.

Analysis of PV local consumption results in Scenario 2-3

In Scenario 2-3, the distributed PV cluster is equipped with energy storage, the operation mode is self-generation and self-consumption with surplus power connected to the grid, and the independent operator implements a demand response plan based on subsidy incentives for rural residents. By using the CPLEX optimization solver, the load curves of typical days in four seasons before and after the participation in demand response of rural residents are shown in Figure 17, Figure 18 and Figure 19.

From Figure 17, Figure 18 and Figure 19, it can be observed that before participating in demand response, the use of summer air conditioning and winter heating increases the peak of user electricity load during peak periods, and the load curve fluctuates greatly. After implementing demand response, users change their electricity habits and shift some adjustable loads to nighttime, greatly reducing the peak load and making the load curve more stable. Compared with summer and winter, the peak electricity load in spring and autumn is relatively low, and users have a weaker ability to transfer or interrupt adjustable loads. On the other hand, Classification I users have the largest load volume, the most types of adjustable loads, the strongest ability to interrupt or transfer adjustable loads, and the best effect in participating in demand response. Compared with Classification I users, Classification IV users have a smaller load volume and the fewest types of adjustable loads, resulting in poorer participation in demand response.

According to the load dispatch results of rural resident users involved in demand response, and according to the optimized configuration of energy storage, the operation of the distributed PV cluster energy storage system in Scenario 2-3 on typical days in each season can be obtained as shown in Figure 20, Figure 21 and Figure 22.

The optimization results of Scenario 2-3 show the PV local consumption and the supply of electricity to rural residents throughout the year and each season, as shown in

Table 8. PV local consumption and the electricity supply situation of residents in the mode of surplus power connected to the grid (Scenario 2-3).

Indicator	Spring and Autumn	Summer	Winter	Annual
Total power generation of distributed PV cluster (kWh)	289,213	216,384	92,671	598,268
Proportion of PV local consumption (%)	64.33	52.1	91.46	64.11
Proportion of PV power directly consumed by users with unused rooftop resources (%)	29.19	28.49	39.84	30.59
Proportion of PV power absorbed by energy storage system (%)	35.14	23.61	51.62	33.52
Proportion of PV grid connection (%)	35.67	47.9	8.54	35.89
Proportion of PV electricity traded between surrounding users and PV power generators (%)	/	/	/	/
User load of unused roof resources (kWh)	222,277	140,603	123,974	486,854
Proportion of load demand directly met by electricity generated by PV (%)	37.98	43.84	29.78	37.59
Proportion of load demand indirectly met by energy storage discharge (%)	48.59	38.73	38.59	43.19
Proportion of load demand met by grid electricity (%)	13.43	17.43	31.63	19.22

.

The configuration of energy storage in Scenarios 2-2 and 2-3 is different. The capacity of the energy storage system in Scenario 2-2 is 620 kWh with a power of 163 kW, while the capacity of the energy storage system in Scenario 2-3 is 591 kWh with a power of 113 kW. However, the proportion of PV local consumption in Scenarios 2-2 and 2-3 is very similar, at 62.64% and 64.11%, respectively. The participation of rural residents in demand response can achieve the same PV local consumption effect while reducing the energy storage equipment, thereby reducing the initial investment in energy storage systems. For independent operators, implementing incentive measures to encourage rural residents to participate in demand response can achieve the flexible management of power resources and maximize economic benefits without significantly increasing upfront costs.

4.5. Comparative Analysis of Economic Benefits

The cost–benefit comparison of independent operators investing in the construction of distributed PV clusters between the mode of all power connected to the grid and the mode of self-generation and self-consumption with remaining power connected to the grid is shown in

Table 9. Cost–benefit analysis of independent operators in the mode of all power connected to the grid.

Indicator	Indicator List	Numerical Value
Income indicators	PV grid-connected revenue (CNY/Year)	209,394
	Subsidy income (CNY)	80,000
	Residual value of fixed assets (CNY)	69,800
Cost indicators	Initial investment of distributed PV cluster (CNY)	1396,000
	Operation and maintenance costs (CNY/Year)	13,960
	Rental cost (CNY/Year)	10,000
Revenue indicators	Equivalent annual return (CNY/Year)	69,946
Economic benefit indicators	Financial net present value (CNY)	943,900
	Internal rate of return (%)	15.96
	Dynamic investment payback period (Years)	9.23

and

Table 10. Cost–benefit analysis of independent operators in different scenarios under the mode of surplus power connected to the grid.

Indicator	Indicator List	Scenario 2-1	Scenario 2-2	Scenario 2-3
Income indicators	PV grid-connected revenue (CNY/Year)	149,460	78,212	75,146
	Subsidy income (CNY)	80,000	385,664	381,295
	Electricity sales revenue (CNY/Year)	99,199	246,429	229,436
	Compensation income for demand response (CNY/Year)	/	/	13,077
	Residual value of fixed assets (CNY)	69,800	138,006	133,129
Cost indicators	Initial investment of distributed PV cluster (CNY)	1,396,000	1,396,000	1,396,000
	One-time investment in the energy storage system (CNY)	/	1,364,111	1,266,582
	Operation and maintenance costs (CNY/Year)	13,960	30,329	29,159
	Rental cost (CNY/Year)	10,000	10,000	10,000
	Demand response management fee (CNY/Year)	/	/	8,000
Revenue indicators	Equivalent annual return (CNY/Year)	109,210	65,097	59,378
Economic benefit indicators	Financial net present value (CNY)	1,401,500	1,084,600	1,010,300
	Internal rate of return (%)	20.36	12.48	12.31
	Dynamic investment payback period (Years)	7.14	11.97	12.16

.

From Table 9, it can be observed that under the mode of all power connected to the grid, the financial net present value of the distributed PV cluster is CNY 943,900, the internal rate of return is 15.96%, the dynamic investment payback period is 9.23 years, and the economic benefits are good. This model ensures that the overall revenue of PV operators is stable and appreciable.

According to Table 10, in the scenario of self-generation and self-consumption with remaining power connected to the grid without energy storage and no demand response (Scenario 2-1), the financial net present value is CNY 1,401,500, the internal rate of return is 20.36%, and the dynamic investment payback period is 7.14 years. The economic benefit indicators of this operating scenario are also superior to those of the distributed PV cluster configuration energy storage operating scenario. This is mainly because there is no energy storage investment in the early phase of the project in this scenario, and the income produced by the PV grid connection is relatively high.

Although these two operating scenarios without energy storage have more economic advantages, the large-scale integration of PV power generation into the power grid affects the quality of electrical energy. To ensure the safety and reliability of the power system, power grid companies need to raise the expenses associated with upgrading and renovating the distribution network, which expands the outer cost of the system. The operation of distributed PV clusters in these scenarios is detrimental to the safe and economical operation of the entire power system.

The scenarios of self-generation and self-consumption with remaining power connected to the grid mode for distributed PV clusters set up with energy storage systems have not surpassed the scenarios for distributed PV systems without energy storage in terms of economic benefits, but the overall benefits of these scenarios are still appreciable. Specifically, the financial net present value of Scenario 2-2 is CNY 1,084,600, with an internal rate of return of 12.48% and a dynamic investment payback period of 11.97 years. The financial net present value of Scenario 2-3 is CNY 1,010,300, with an internal rate of return of 12.31% and a dynamic investment payback period of 12.16 years.

The advantage of these two scenarios is that they greatly improve the PV local consumption capacity, effectively alleviating the burden of PV power generation grid connection on the power grid system. At the same time, it significantly reduces the cost of upgrading and renovating the power grid, thus bringing positive impacts on both the economic and environmental aspects. Compared to Scenario 2-2, Scenario 2-3 has a lower initial investment in energy storage systems, but its equivalent annual returns are not as good as Scenario 2-2. This is mainly because in Scenario 2-3, rural residents participate in the demand response plans to transfer adjustable loads that were originally used during periods of high electricity price to periods of low electricity price at night, resulting in a decrease in annual electricity sales revenue for independent operators.

5. Conclusions

The widespread installation of dispersed PV systems in rural regions presents threats to the stability and safety of the electrical distribution system as a result of the widespread use of PV electricity. Distributed PV clusters that incorporate energy storage can increase local PV consumption and lessen the power grid’s impact from PV grid connections. The optimization of the storage of energy for distributed PV systems under common operating conditions is examined in this research with an emphasis on rural locations. In order to examine PV local consumption and the cost-effectiveness of independent operators in different scenarios, various combinations of energy storage and response to demand are taken into consideration. This serves as a guide for decisions on distributed PV system operations and investment across the country. The results indicate the following:

• Energy storage systems for distributed PV clusters significantly enhance the PV local consumption capacity. The energy storage system stores excess electricity during PV power generation and releases it during peak demand periods. After configuring the energy storage system, the proportion of distributed PV connected to the grid is reduced from 71.38% to 37.36%, which achieves smooth regulation of the output power of the PV and effectively alleviates the impact on grid operation when connected to the grid. Meanwhile, the proportion of PV local consumption of the distributed PV cluster with energy storage system reaches 62.64%, which is 34.02% more than the scenario without energy storage, proving that the local consumption level of PV power is greatly improved through energy storage discharge.
• Implementing a demand response mechanism for rural residents has a significant effect on reducing the configuration of energy storage systems. The calculation results show that participating in demand response can reduce the investment cost of the energy storage system by 7.15% at a PV local consumption proportion of 60%. This indicates that compared with rural residents not participating in demand response, the participation in demand response of rural residents can achieve the same PV local consumption effect while reducing the energy storage configuration, thereby reducing the initial investment of energy storage systems.
• According to calculations, the distributed PV energy storage system has a financial net present value of CNY 1,084,600. This indicates that the economic benefits of distributed PV energy storage systems throughout the entire life-cycle are appreciable, which is reflected in long-term operation. However, compared with distributed PV power generation systems without energy storage, distributed PV cluster energy storage systems still suffer from a long payback period, which will be a major challenge in promoting the application of the system.

Our work is important in increasing the stability of the power system, reducing the cost of electricity for locals, and enhancing resource use in rural areas. It offers insightful information that the government may use to encourage local PV energy use and improve the distributed PV energy storage systems’ cost compensation system. The involvement of distributed PV energy storage systems in demand response is also examined in this article, which lays the groundwork for the government to broaden the market-based PV energy trading model. Currently, the cost of PV systems is continuously decreasing, giving significant advantages in terms of initial investment and return on investment for projects configured with distributed PV power generation systems only. However, the high cost of energy storage systems still presents a challenge, causing distributed PV projects with energy storage to lag behind those without storage in terms of investment payback period. Measures to hasten the recovery of initial investment for distributed PV cluster energy storage systems must be put in place immediately. These include encouraging the construction of intelligent terminals, motivating owners of PV systems to engage in the carbon trading market, and bolstering the modernization of rural distribution networks. We will carry out further research on these topics in the future.