High Proportion of Distributed PV Reliability Planning Method Based on Big Data

Abstract

The higher proportion of distributed photovoltaic and lower fossil energy integrated into the power network brings huge challenges in power supply reliability and planning. The distributed photovoltaic planning method based on big data is proposed. According to the impact of stochastic photovoltaics and loads on reliability planning, the probability model of distributed photovoltaic and load is analyzed, and the dynamic capacity–load ratios are presented based on big data. The multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source–load scenario matching model is proposed based on big data. According to the big data scenario of source–load, the reliability indexes and dynamic capacity–load ratio may be obtained. Finally, the IEEE 33-bus system is used as an example, and the results show that distributed photovoltaic planning methods based on big data can improve photovoltaic utilization and power supply reliability.

1. Introduction

With the development of carbon neutrality, various types of renewable new energy have grown rapidly, and the proportion of clean energy in the power grid has continued to increase. The renewable energy installed capacity has accounted for 47.3% of the total installed power generation capacity in China. In 2022, the photovoltaic installed capacity continues to maintain rapid growth and reaches 59.3% in new installed capacity, where distributed photovoltaics accounted for 58.5% of the newly installed capacity. In 2024, distributed photovoltaics will account for nearly half of the global photovoltaic market. However, it is very difficult to consume distributed photovoltaic for the existing power grid structure, power supply types, and market mechanisms. Distributed photovoltaic power generation is growing rapidly and costs are declining. Blindness in the development of new energy will inevitably lead to waste and other problems without a scientific plan. It needs to be carried out at the source of planning to ensure the sustainable development of distributed photovoltaic.

At present, there has been some research on the problems with integrating photovoltaic power generation into the distribution network. Aiming at the problem of the gradual increase in the penetration rate of photovoltaic power generation in the distribution network, the comprehensive coordination and optimization configuration method of photovoltaic power generation and the capacitors, which provides active and reactive power in the distribution network, is studied. The planning principle of merit and demerit applies under the circumstances [1,2,3]. The opportunity constrained programming model for the limit capacity calculation of grid-connected photovoltaic power stations is studied, and solar radiation stochastic time series models and photovoltaic system models with different tracking forms are introduced by maximizing the capacity of grid-connected photovoltaic power stations as the planning goal [4,5]. The allowable access capacity range of distributed photovoltaic power that meets voltage requirements under the same distribution of load and distributed photovoltaic power capacity along the feeder is studied [6,7,8]. A large number of photovoltaic connections are also studied. Post-entry permeability issues are reported. A method for optimizing the capacity allocation of photovoltaic power swap stations considering the cascade utilization of power batteries is proposed [9]. A comprehensive microgrid planning method that considers the efficient utilization of photovoltaics under complex envisioned scenarios is presented which involves the dynamic planning of multiple distributed energy sources connected to the power grid based on Monte Carlo simulation methods and the dynamic planning of multiple distributed energy sources, taking into account distributed multi-stage planning for energy access [10,11,12,13]. The reliability assessment framework of distribution systems with distributed energy sources is studied. Based on the existing reliability assessment theory, the reliability assessment models, indicators and calculation methods with distributed photovoltaics are studied [14,15]. On the combination of photovoltaic planning and control under various random conditions, the stochastic model of electric vehicle charging and its great impact on the technology and economics of expansion planning are studied. The effect of battery energy storage in the distribution network on distributed photovoltaic planning and control is presented. The optimal placement planning of distributed photovoltaics in the distribution network is studied from the aspect of photovoltaic grid-integrated operation control [16,17,18,19].

The above literature has some research on the determination and analysis methods of photovoltaic access capacity, but there is still little focus on the issue of reliable power supply after a high proportion of distributed photovoltaic is integrated into a distribution network. Small-scale distributed photovoltaics are connected to the distribution network and are generally consumed locally. For the planning of high-proportion distributed photovoltaic integration into the power grid, knowing how to determine the capacity-to-load ratio while ensuring power supply reliability is the key. The power generation of conventional power sources is determined and controllable, and the maximum capacity is higher than those of the load generally. Due to the random characteristics of photovoltaics, the maximum installed capacity may be greater than the load, but the actual power generation varies randomly, even to zero. It is difficult to determine the exact planning capacity-to-load ratio. The random fluctuations faced by the distribution network are more complex due to the dual randomness of the distribution network terminal load and power source, and the fuzzy boundaries. According to these characteristics, the new planning method needs to consider the coordination of the source–network–load in a wide range, especially the interaction between the source and the load.

The idea of “big data + distributed photovoltaic planning” is proposed in this paper, which can overcome the double-blind situation between source and load in traditional planning based on the big data interaction between source and load. Using the prediction and historical big data of distributed photovoltaics and loads combined with the random probability model, multiple scenario data of source and load can be obtained. Through the scenario data analysis with different probabilities, the reliability index and the weighted average capacity–load ratio can be calculated. Finally, simulation calculations are performed to verify the feasibility and effectiveness of the proposed method.

2. Planning Method Based on Big Data

2.1. Planning Architecture Based on Big Data

Conventional power grid planning is a deterministic plan in which the capacity–load ratio is deterministic between the power supply and the load capacity. There is no information interaction between the power supply and the load. This kind of planning is relatively conservative, so the capacity–load ratio is very big in distribution network with the high proportion of distributed photovoltaic. Big data technology has been widely used in power systems. In the planning stage, big data of distributed photovoltaics and loads can be obtained which can achieve accurate matching relation and interactive sensing between power source and load.

“Big data + distribution network planning” is not simply adding various types of energy and loads to the power grid, but deeply integrating the Internet into power grid planning based on big data technology and Internet platforms. Big data can optimize the configuration of distributed photovoltaics in the distribution network. Deeply integrating big data methods into distribution network planning may form a broad new planning platform.

Being based on big data means that a large amount of source and load data can be obtained which obtain the accurate spatiotemporal distribution and mutual relationships of source and load by interactively sensing. On the planning platform, the prior experience of power generation and user electricity consumption may be realized. According to the spatial and temporal distribution of photovoltaics and loads, the corresponding relationship between sources and loads as well as the appropriate capacity–load ratio can be obtained, which can ensure the reliability of power supply. With the high proportion of distributed photovoltaic integrating into power grid, some new loads such as electric vehicles are also growing rapidly, which brings the randomness of sources and loads into the distribution network. Without a way to deal with this randomness, traditional deterministic planning methods may lead to waste of new energy or imbalance of power grid. Based on the big data interaction of source and load on the Internet planning platform, the source determines the load, and the load determines the source. The interaction between source and load can realize an intuitive and specific planning model.

2.2. Source–Network–Load Big Data Correlation

In view of the randomness of source and load, the online dynamic interactive experience of source and load data is realized, and mutual perception is achieved. Based on reciprocal interaction between source and load, the spatiotemporal distribution of load can be viewed from the perspective of the power supply, or the spatiotemporal distribution of the power supply can also be viewed from the load perspective. The dynamic capacity–load ratio can be determined during operation. In order to improve photovoltaic utilization, controllable loads such as electric vehicles which have good flexibility in time and space can be used. The spatiotemporal translation of the load can be obtained through big data analysis, which can better realize the dynamic connection of source and load in the planning stage.

Based on the big data planning platform, prior experience of source–load interaction can be achieved. This planning model is derived from actual source–load big data, which may guide power grid planning and obtain more practice results. It can overcome the limitations of previous statistical experience-based and theoretical planning, such as passivity, blindness, and disorder, and can achieve sustainable dynamic planning. Big data planning can achieve the following advantages.

(1)

Accuracy: Various massive data from the Internet across regions, borders, and industries are fully used, the characteristics of photovoltaics and user are explored, and these promote source–load complementarity.

(2)

Interactivity: The big data model realizes deep interaction between sources and loads during planning and source–load simulation operation, which can avoid the blindness of energy planning due to information asymmetry.

(3)

Orderliness: The power generation and consumption behavior is intuitively evaluated by the source and load interaction. In a certain area, the source is determined by the load, and the load is determined by the source. These achieve orderliness in distributed photovoltaic planning.

(4)

Economical: Through source–load interaction, the operation distribution information of source–load can be obtained in time, and the precise correspondence relation between source–load capacity can be determined with less waste and more high-power-supply reliability.

The generation capacity of conventional power sources is deterministic and controllable. Generally, the maximum capacity of the power source is higher than the load, and the capacity–load ratio is easy to determine. Due to the random variation characteristics in PV, the maximum installed capacity is generally greater than the load, but the practical power generation changes randomly, and even to zero. For distribution networks with a high proportion of distributed photovoltaic, it is difficult to determine the planned capacity–load ratio. How to determine the capacity–load ratio relationship between photovoltaic capacity and load capacity? Using forecasts and historical big data of photovoltaic and load combined with random probability models, multiple scenario data can be obtained. Scenario data which intuitively reflects the randomness of source load changes can be directly used to calculate reliability indexes and the weighted average capacity–load ratio.

3. Analytical Models for Big Data Planning

Based on the existing distribution network, random big data of multi-point distributed photovoltaic access is obtained, and big data of flexible controllable loads are mined, which form a “big data + photovoltaic + controllable load + distribution network” planning model. The goal of distributed photovoltaic planning is to maximize utilization while ensuring a reliable power supply. The objective function is as follows:

$$\max {\displaystyle \sum _{i=1}^n{P}_{SO,i}}\hspace{1em}(i=1,2,\cdots n)$$

(1)

where P_SO,i is the capacity value of each photovoltaic access point, and n is the number of access points.

In the distribution network, the power flow constraints need to be met:

$$F(X,Y)=0$$

(2)

where X is the variable in the existing distribution network, Y = (P,V) is the photovoltaic and controllable load power and voltage variables connected to the distribution network, and the photovoltaic power and load power are

$$P=(P_{SO,1},P_{SO,2},\cdots \cdots P_{SO,n},P_{L,1},P_{L,2},\cdots \cdots ,P_{L,m})$$

where $P_{SO}^0$ is the total available photovoltaic capacity of the n initial access points, and $P_L^0$ is the total capacity of m controllable loads.

$$P_{SO}^0={\displaystyle \sum _{i=1}^n{P}_{SO,i}^0}$$

$$P_L^0={\displaystyle \sum _{j=1}^m{P}_{L,j}^0}$$

In addition, due to their natural characteristics, the photovoltaic output and load also need to meet the upper and lower bound inequality constraints:

$$\left\{\begin{array}{l}{P}_{SO,i}^{\min }\le P_{SO,i}\le P_{SO,i}^{\max }\\ P_{L,i}^{\min }\le P_{L,i}\le P_{L,i}^{\max }\end{array}\right.$$

(3)

The planning goal is to maximize the use of photovoltaics in the long term. Since photovoltaic output is uncontrollable, some loads are controllable. The load can be controlled according to the photovoltaic change curve to be consistent with the photovoltaic changes, that is, maximizing the use of the photovoltaics. In the short term, large differences between PV and load may occur, which can be balanced by energy storage or other power sources.

The ratio of photovoltaic dynamic power generation capacity to controllable load dynamic capacity at time t is called the dynamic capacity–load ratio K_D:

$$K_D=\frac{{\displaystyle \sum _{i=1}^n{P}_{SO,i}(t)}}{{\displaystyle \sum _{j=1}^m{P}_{L,j}(t)}}$$

(4)

where $P_{SO,i}(t)$ is the instantaneous power value of the ith photovoltaic at time t, and $P_{L,j}(t)$ is the instantaneous power value of the jth load at time t.

According to the random changes of the photovoltaic and load, the dynamic capacity-to-load ratio is optimized. The photovoltaic planning capacity is determined according to the practical situation of the distribution network and planning supporting measures. The average capacity-to-load ratio during planning can be obtained as ${\overline{K}}_D$:

$${\overline{K}}_D=\frac{{\displaystyle \sum _{i=1}^n{\overline{P}}_{SO,i}}}{{\displaystyle \sum _{j=1}^m{\overline{P}}_{L,j}}}$$

(5)

where ${\overline{P}}_{SO,i}$ is the average power value of the ith photovoltaic, and ${\overline{P}}_{L,j}$ is the average power value of the jth load.

(1)

Probabilistic analysis of photovoltaic output

The light intensity changes randomly, and its output power also fluctuates randomly. According to statistics, the light intensity within a certain period of time shows a Beta distribution [20], and its probability density is as follows:

$$f(E)=\frac{\mathrm{\Gamma }(\alpha +\beta )}{\mathrm{\Gamma }(\alpha )\mathrm{\Gamma }(\beta )}{(\frac{E}{E_{\max }})}^{\alpha -1}{(1-\frac{E}{E_{\max }})}^{\beta -1}$$

(6)

where $\mathrm{\Gamma }$ is the Gamma function, E and Emax are the actual light intensity and maximum value during this period, and α and β are the shape parameters of the Beta distribution.

The corresponding probability density function of photovoltaic output power is as follows:

$$f(P_{SO})=\frac{\mathrm{\Gamma }(\alpha +\beta )}{\mathrm{\Gamma }(\alpha )\mathrm{\Gamma }(\beta )}{(\frac{P_{SO}}{P_{SO,\max }})}^{\alpha -1}{(1-\frac{P_{SO}}{P_{SO,\max }})}^{\beta -1}$$

(7)

where $P_{SO}$ and $P_{SO,\max }$ are the actual PV power and maximum value during this period.

(2)

Probabilistic analysis of controllable loads

In order to make full use of randomly fluctuating photovoltaic power generation, controllable flexible loads are required. Distributed photovoltaics mainly come from homes or buildings. Controllable loads P_CL include electric vehicles P_EV, home loads P_home, and other loads P_L0.

$$P_{CL}=P_{EV}+P_{\mathrm{hom}e}+P_{L0}$$

(8)

Electric vehicles are mobile energy storage devices that can be used as loads for charging or power sources for discharging. The proportions of these three types of loads are as follows:

$$k_{ev}=\frac{P_{EV}}{P_{CL}}$$

$$k_h=\frac{P_{\mathrm{hom}e}}{P_{CL}}$$

$$k_0=\frac{P_{L0}}{P_{CL}}$$

Conventional load fluctuations have a certain regularity and can be simulated by existing probability distributions. The random changing characteristics of controllable loads, especially the random spatio-temporal changes of electric vehicles, are difficult to simulate with an analytical probability distribution, where the empirical probability distribution of big data is used to simulate. The larger k_ev and k_h are, the better the controllability is. Following the probability model of load combined with load shifting control, different load data series scenarios can be obtained.

The big data planning method is a kind of stochastic planning, which is different from conventional deterministic planning, and planning indexes are obtained mainly through data analysis. Based on the prediction and historical big data of source and load combined with the stochastic model, a series of randomly changing source and load data with different probabilities, that is, scenarios, are obtained. Through the source–load data series scenarios, the reliability level and capacity–load ratio under different scenarios can be intuitively analyzed and calculated.

4. Multi-Scenario Algorithm Based on Big Data

Probabilistic models of distributed photovoltaic and load stochastic fluctuations are generally difficult to use directly. Multiple scenarios can be generated based on the probability model, and each scenario is a possible planning solution. A large number of scenarios are generated through the probabilistic model. However, the huge number of scenarios results in a huge amount of optimization calculations which need to be reduced to a few most likely scenarios, that is, “scenario reduction” [21,22].

The “forecast bin” to count the prediction error distribution of point predictions is applied [23]. By sorting the predicted values from large to small and dividing the predicted values into some “numeric intervals”, the corresponding data group [predicted value, measured value] is put into the corresponding numerical interval according to the size of the predicted value. The length of the numerical interval is 0.02 p.u. in 50 obtained numerical intervals in total, and all data groups within each numerical interval are “prediction boxes”. The photovoltaic power output generates scenarios according to the probability distribution of Equation (7), and the load probability generates scenarios according to the empirical distribution probability.

4.1. Scenario Generation

The 100 dynamic scenarios of photovoltaics and loads were generated according to the following steps:

(1)

Using historical data and calling the ecdf function in MATLAB statistical toolbox, the empirical probability distribution of the 100 prediction boxes was estimated.

(2)

Based on the source and load power point prediction data, the range parameter ε was estimated, which was used to control the correlation strength of random variables with different ahead times [21,22]. The search range of parameter ε is [0, 400], and the scene scale of parameter estimation is 200.

(3)

Through calculating the 48th-order covariance matrix of the multivariate standard normal random variable Z and calling the mvnmd function of the MATLAB statistical toolbox, 100 random vector samples obeying Z~N(μ₀,Σ) were generated.

(4)

For each lead time t (t =1, 2, … 48), the prediction box the power point prediction pt of the lead time belonged to was determined. In this way, 100 multivariate normal random vectors were transformed into 100 dynamic scenes.

4.2. Scenario Reduction

Kantorovich distance [24] can be used to measure the approximation degree between the initial scene set S₀ and the reduced scene set S_r. For dynamic scenario problems of photovoltaics or loads, the Kantorovich distance form is as follows:

$$c\left(S_0,S_r\right)={\displaystyle \sum _{\omega \in S_0}\left\{p(\omega ){\displaystyle \sum _{{\omega }_r\in S_r}\left[p({\omega }_r)\cdot {‖\omega -{\omega }_r‖}_2\right]}\right\}}$$

(9)

where ω represents a dynamic scene in the initial scenario set S₀, ω_r represents a scene in the scenario set S_r, p(ω) is the probability of ω occurring, and ║ω-ω_r║₂ represents the Euclidean norm distance between scene ω and ω_r. A large number of initial scenes needed to be cut down to a few. The Kantorovich distance between the initial scene set S₀ and the corresponding reduced scene set S_r is shown in Equation (9). Based on the prediction and historical data of the photovoltaic and load combined with the stochastic probability model, initial multiple scenarios S₀ with different probabilities can be generated. In practical applications, only a few high-probability scenarios S_r need to be retained. The scenarios with similar distances in S₀ can be merged, representative scenarios are selected from them, and high-probability scenes are retained after reduction. A few high-probability scenarios can represent the several most likely states of the photovoltaic and load. It is very convenient to use only a few high-probability scenarios for reliability and planning analysis.

4.3. Scene Matching of Source–Load

After scene generation and reduction, the source and load scenes can be obtained, respectively. The matching between the source and load scenes is further analyzed, which can obtain an N × N combination. The largest and smallest source and load scenes are taken respectively to form four combinations: large source and large load, large source and small load, small source and large load, and small source and small load. These form boundary scenes, which can include all N × N scenarios. If the planning can meet these four boundary scenarios, all situations can be satisfied. But it can result in a conservative conclusion, which ensures power supply reliability.

Figure 1 shows the source–load scene, with 48 points in 12 h of daylight, or one point every 15 min. When the source–load dynamic scene interacts, the source–charge K_D at this time is calculated for the points on the source and load scene curves at the same time. The output of photovoltaic power generation is related to natural conditions, and its output power can reduce. However, the load can be controlled and be shifted. In Figure 1, Points O and C are the starting point and end point respectively, and the source power is equal to the load power at points A and B.

The K_D at different time points in the source–load scenario has the following situations:

(1)

K_D > 1: In this period between point A and point B, the photovoltaic power capacity is greater than the load capacity. If there is excess photovoltaic power, the photovoltaic power may be abandoned. The power supply planning can be appropriately reduced, or the load can be controlled to shift the subsequent electricity load to the present.

(2)

K_D < 1: In this period during OA and BC, the photovoltaic capacity is less than the load capacity, and the photovoltaic capacity planning can be increased, or the current electricity load can be shifted, or other power sources and energy storage can support it.

(3)

K_D = 1: In this period of point A and point B, the photovoltaic supply capacity is equal to the load capacity, which is the most ideal situation, but it rarely occurs in practice.

5. Reliability and Capacity-to-Load Ratio Calculation

The planning platform based on big data guides each source and load to actively participate. Through a scenario matching analysis, the source and load can interact with each other. After optimizing their K_D in a variety of scenarios, the final planned ${\overline{K}}_D$ can be determined. Through dynamic source–load interaction scene analysis, the power supply can guide the planning of the load, and the load can also determine the power supply planning.

The reliability indexes LOLP (Loss of Load Probability), LOLE (Loss of load expectation), and PSCE (Power supply capacity expectation) are introduced in this paper.

The LOLP mainly reflects the loss of load probability when the power supply cannot meet the load demand. It is as follows:

$$LOLP={\displaystyle \sum _{i=1}^N{\displaystyle \sum _{j=1}^N{p}_{SO}^{(i)}}}\cdot p_L^{(j)}\cdot \frac{\Delta T_{ij}}{T}$$

(10)

where $\Delta T_{ij}$ represents the length of the period when K_D is less than 1, and T represents the total time length. $p_{SO}^{(i)}$ and $p_L^{(j)}$ are the probability of the ith and jth scenario of the source and load, respectively.

The LOLE mainly reflects the loss of load expectation when the power supply cannot meet the load demand. It is as follows:

$$LOLE={\displaystyle \sum _{i=1}^N{\displaystyle \sum _{j=1}^N(\overline{P_L^{(j)}}(\Delta T_{ij})-\overline{P_{SO}^{(i)}}(\Delta T_{ij}))p_{SO}^{(i)}}}\cdot p_L^{(j)}\cdot \frac{\Delta T_{ij}}{T}$$

(11)

where $\overline{P_L^{(j)}}(\Delta T_{ij})\mathrm{and}\overline{P_{SO}^{(i)}}(\Delta T_{ij})$ represent the average power corresponding to the jth load and ith source scenario, respectively, in the $\Delta T_{ij}$ period when K_D is less than 1. $P_{SO}^{(i)}$ is the ith scenario of PV power, and $P_{SO}^{(i)}(t)$ is the power value of the ith scenario at time t. $P_L^{(j)}$ is the jth scenario of load power, and $P_L^{(j)}(t)$ is the power value of the jth scenario at time t.

The LOLP mainly reflects the power supply capacity expectation. It is as follows:

$$PSCE={\displaystyle \sum _{i=1}^N{\displaystyle \sum _{j=1}^N(\overline{P_{SO}^{(i)}}-\overline{P_L^{(j)}})p_{SO}^{(i)}}}\cdot p_L^{(j)}$$

(12)

where ${\overline{P}}_{L,j}$ and ${\overline{P}}_{SO,i}$ represent the average power of the source and load scenario, respectively.

On the problem of solar abandonment, it is assumed that, in general, the photovoltaic capacity is greater than the load capacity. The capacity–load ratio corresponding to the ordinate’s minimum difference between the power supply and load scenario is K_D,min. If the source curve can be fully covered by shifting the source curve up and down, then this most conservative planning capacity can be obtained.

The weighted average value $P_{SO}(t)$ and $P_L(t)$ of photovoltaic and load power under different probabilities is as follows:

$$P_{SO}(t)={\displaystyle \sum _{i=1}^N{P}_{SO}^{(i)}(t)\cdot p_{SO}^{(i)}}$$

(13)

$$P_L(t)={\displaystyle \sum _{j=1}^N{P}_L^{(j)}(t)\cdot }{p}_L^{(j)}$$

(14)

Based on the source–load scenario series, reliability can be easily and intuitively analyzed. Comparing the source–load scenario data curves gives the following weighted average planning capacity ${\overline{K}}_D$ under different probabilities:

$${\overline{K}}_D=\frac{{\displaystyle \sum _{i=1}^N\overline{P_{SO}^{(i)}}\cdot p_{SO}^{(i)}}}{{\displaystyle \sum _{j=1}^N\overline{P_L^{(j)}}\cdot }{p}_L^{(j)}}$$

(15)

A random dynamic matching analysis of source–load scenarios can determine the planning size of the photovoltaic capacity based on the above three methods. In addition, four boundary scenarios can be considered to further quantify the photovoltaic planning capacity and supporting measures. While determining other supporting measures in the plan, firstly, support by load shifting is considered; secondly, energy storage or other power supply support; and finally, abandon light.

6. Case Analysis

6.1. Case

As shown in Figure 2, the IEEE 33-node distribution network system [25,26] is taken as an example, which includes 33 nodes, 32 branches, and 5 tie-lines. It is assumed that the switches of 5 tie-lines are normally open to facilitate photovoltaic power transmission. The node 0 is the power supply node, which ensures the power supply of the base load. The rated voltage level is 12.66 kV. The base annual load of each node is expanded to 15,252.78 + j7641.96 kVA. Distributed photovoltaic power generation is connected to nodes 2, 5, 11, 14, 20, 28, and 32, as shown in the blue mark in Figure 2. The available planning capacity of photovoltaics is shown in

Table 1. Solar capacity of node (kW).

Node	2	5	11	14	20	28	32
Capacity	500	500	600	600	500	1000	1000

.

Based on source–load big data interaction analysis, the survey statistics of photovoltaic and controllable loads in a certain area are applied. The scenarios can be generated based on these data. Since photovoltaics can only generate electricity during the day, the scenario period is 48 points in 12 h, with one point every 15 min.

6.2. Simulation Results

The new controllable load is joined to each load node, and the proportion is 20% of the base load capacity. The load nodes 1, 3, 6, 7, 13, 23, 24, 28, 29, 30, and 31 include charging piles, where k_ev = 0.7, k_h = 0.2, k₀ = 0.1, k_ev = 0, k_h = 0.6, and k₀ = 0.4 in other nodes.

(1)

Source load scene generation and reduction

For the photovoltaics in Figure 3, 100 dynamic scenarios are generated based on the prediction data and probability density function. After the scenarios’ reduction, 10 high-probability scenarios are obtained, as shown in Figure 4.

For the load in Figure 5, 100 dynamic scenarios are generated based on the empirical distribution probability, and 10 high-probability scenarios are obtained, as shown in Figure 6.

After scenario reduction, a few high-probability scenarios are retained, which can be used to intuitively evaluate reliability and planned capacity–load ratio under different circumstances. The photovoltaic and load vary greatly in different regions, and different probability scenario data can be selected for practical planning.

(2)

The weighted average scenario

According to Equations (13) and (14), the average power value distribution of the photovoltaic and load at different times can be calculated, as shown in Figure 7.

In Figure 7, due to the random changes in photovoltaic power, the photovoltaic power is less than the load power in some periods, which cannot meet the reliable power supply demand. In order to solve this problem, load translation can be used to shift the part of the controllable load from the low photovoltaic power period to the high-power period so that the source and load can match. In addition, it can also be solved by installing energy storage. When the photovoltaic power is low, the energy storage supplies power to the load by discharging. When the photovoltaic power is high, the energy storage stores excess photovoltaic power by charging. By shifting load, the scenario in Figure 8 can be obtained. Further, by installing energy storage equipment of 300 kW, the scenario in Figure 9 can be obtained. In a practical distributed network, other power supplies may work together to solve this problem.

The LOLP, LOLE, PCSE, and ${\overline{K}}_D$ may be calculated according to Equations (10)–(12) and (15), respectively, in Figure 7, Figure 8 and Figure 9. The calculation results are shown in

Table 2. Reliability result.

	LOLP	LOLE	PCSE	${\overline{\mathit{K}}}_{\mathit{D}}$
1	0.327	289.23	573.86	1.436
2	0.296	127.15	589.26	1.467
3	0.238	58.34	482.43	1.276

.

In Figure 7, the natural, uncontrolled photovoltaic power supply has low reliability, and the LOLP and LOLE are lower. In Figure 8, the LOLP and LOLE increase by shifting the part of the controllable load. In Figure 9, the same result is obtained by installing energy storage equipment. In Figure 7, Figure 8 and Figure 9, the LOLP does not decrease much, but the LOLE decreases more, and the reliability level can be effectively improved together with other power supplies in the distribution network. In Figure 9, although ${\overline{K}}_D$ is smaller, the reliability is higher because the energy storage can be flexibly charged and discharged.

(3)

Border scenes

By combining a few high-probability scenarios in Figure 4 and Figure 6, some typical boundary scenarios are obtained, as shown in Figure 10 and Figure 11. In Figure 10, photovoltaic power generation is large and the load is small, and there is excess power generation at this time, resulting in waste. This is a conservative plan. However, the amount of solar abandon is also large, reaching as high as 2437 kW. The capacity–load ratio can be decreased, and the planned photovoltaic capacity can be reduced. However, energy storage may be required to meet the power supply needs, and it can also guide the more controllable loads.

In Figure 11, photovoltaic power generation is small, and the load is large. At this time, the amount of abandoned light is small and the capacity–load ratio is lower, but the photovoltaic capacity cannot meet the load demand. When the photovoltaic power is less than the load power, the load is first shifted. Then, energy storage or other power sources may be required to support it. Big data analysis can guide more photovoltaics to join the planning.

Based on source–load big data analysis, source–load scenario interaction can guide the planning sequence. For the scenarios in Figure 10, the available photovoltaic capacity in nodes 28 and 32 is relatively large. According to the node and the surrounding load, node 32 can currently reduce the maximum planned photovoltaic capacity, followed by node 28. Nodes 32 and 28 can guide more electric vehicle load planning. For the scenarios in Figure 11, the photovoltaic capacity is insufficient, and energy storage can be installed at 5, 11, and 28. According to the source–load scenario interaction, the planning sequence and the scalability of the current and future source–load can be determined or guided.

From the overall planning economics, if the probability of high-power photovoltaic scenarios is high, such as the high-light-intensity time exceeding 80% in a year, planning can be carried out according to the high-power scenario mode. For low-light-intensity times with a high probability, planning can be carried out according to low-power scenarios. As for the low-probability low-power photovoltaic scenario, it may occur for a few days in a year. At this time, it can be supported by other power sources in the distribution network or the load shedding, with no large energy storage required.

7. Conclusions

Regarding the power supply reliability problem caused by the current rapid development of high-proportion distributed photovoltaic power generation, a planning idea is proposed based on big data to solve this problem from the initial planning source.

The idea of distributed photovoltaic planning based on big data is proposed, which can realize the peer-to-peer data deep interaction between source and load and guide controllable loads to consume photovoltaics. According to the impact of stochastic photovoltaics and loads on reliability planning, the probability model of distributed photovoltaic and load is analyzed, and the dynamic capacity–load ratios are presented based on big data. The multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source–load scenario matching model is proposed based on big data. According to the big data scenario of source–load, the reliability indexes and dynamic capacity–load ratio may be obtained. Based on the source–load scenario matching analysis, the load shifting capacity, required energy storage capacity, and solar abandonment capacity are obtained, and the load is determined by the source, and vice versa. The orderliness of distributed photovoltaic planning is guided, and the power supply reliability is improved.

In future work, the planning coordination of photovoltaic, energy storage, and other power sources will be further studied. High-proportion renewable energy planning requires the accurate average weight capacity-to-load ratio of multiple complementary energy sources, which can achieve planning reliability and be more economical.