Power distribution network energy storage planning method considering internal and external generation uncertainty and terminal

Abstract

The invention discloses a power distribution network energy storage planning method considering internal and external generation uncertainty and a terminal, and the method comprises the steps: constructing reference distribution about an initial load and reference distribution about an initial electricity price, taking the difference between the system investment cost and the Internet surfing income in a minimum planning period as a first objective function, constructing a planning model in a deterministic environment, and carrying out the optimization of the system investment cost and the Internet surfing income; according to an information gap decision theory, a planning model in an endogenous and exogenous uncertainty environment is established based on reference distribution about an initial load, reference distribution about an initial electricity price and a planning model in a deterministic environment, so that the exogenous uncertainty irrelevant to decision is considered; in addition, endogenous uncertainty which is caused by a decision and is internally related is considered, and a relatively reasonable planning decision can still be made under the condition that data acquisition is limited or the uncertainty is difficult to accurately quantify, so that distributed photovoltaic access to the power distribution network is safer and more stable, redundancy of investment planning of energy storage equipment is effectively avoided, and the reliability of the power distribution network is improved. And unnecessary cost burden is prevented from being increased.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to a distribution network energy storage planning method and terminal taking into account endogenous and exogenous uncertainties. Background Art

[0002] With the accelerated transformation of the global power system towards low-carbonization and the large-scale development and utilization of clean energy represented by solar energy, the penetration rate of photovoltaics has increased significantly. The large-scale access of distributed photovoltaics poses an important challenge to the safe and stable operation of the distribution network, especially exacerbating the volatility and unpredictability of the grid load, which becomes more complicated after the large-scale access of distributed photovoltaics, and thus puts forward higher requirements for the optimal configuration of energy storage equipment in the distribution network.

[0003] At present, the research on distribution network energy storage planning only focuses on external uncertainty factors, but fails to fully consider the complex impact of endogenous uncertainty. This can easily lead to overly conservative decision-making results and may cause redundancy in the investment planning of energy storage equipment, thereby increasing unnecessary cost burden.

[0004] Therefore, with the continuous deepening of the development and utilization of distributed photovoltaics in distribution networks, energy storage planning of distribution networks has become an important research topic in the power sector. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a distribution network energy storage planning method and terminal taking into account endogenous and exogenous uncertainties, which can effectively avoid redundancy in investment planning of energy storage equipment and avoid adding unnecessary cost burden.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A distribution network energy storage planning method considering endogenous and exogenous uncertainties comprises the following steps:

[0008] constructing a reference distribution about initial load and constructing a reference distribution about initial electricity price;

[0009] Taking minimizing the difference between system investment cost and online benefits within the planning period as the first objective function, a planning model in a deterministic environment is constructed;

[0010] Establishing a planning model under an environment of endogenous and exogenous uncertainty based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under the deterministic environment according to the information gap decision theory;

[0011] The planning model under the endogenous and exogenous uncertainty environment is solved to obtain a planning result.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A distribution network energy storage planning terminal considering endogenous and exogenous uncertainties comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0014] constructing a reference distribution about initial load and constructing a reference distribution about initial electricity price;

[0015] Taking minimizing the difference between system investment cost and online benefits within the planning period as the first objective function, a planning model in a deterministic environment is constructed;

[0016] Establishing a planning model under an environment of endogenous and exogenous uncertainty based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under the deterministic environment according to the information gap decision theory;

[0017] The planning model under the endogenous and exogenous uncertainty environment is solved to obtain a planning result.

[0018] The beneficial effects of the present invention are as follows: a reference distribution for initial load is constructed, and a reference distribution for initial electricity price is constructed, and the difference between system investment cost and grid-connected income within the planning period is minimized as the first objective function, and a planning model under a deterministic environment is constructed. According to the information gap decision theory, a planning model under an environment of endogenous and exogenous uncertainty is established based on the reference distribution for initial load, the reference distribution for initial electricity price and the planning model under a deterministic environment. The planning model under an environment of endogenous and exogenous uncertainty is solved, and the reference distribution for initial load is taken as exogenous uncertainty, and the reference distribution for initial electricity price is taken as endogenous uncertainty. In this way, not only exogenous uncertainty irrelevant to the decision is considered, but also endogenous uncertainty caused by the decision itself and inherently related is considered. When data acquisition is limited or uncertainty is difficult to accurately quantify, relatively reasonable planning decisions can still be made, making the access of distributed photovoltaic power generation to the distribution network safer and more stable, thereby effectively avoiding redundancy in the investment planning of energy storage equipment and avoiding unnecessary cost burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of the steps of a distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the structure of a distribution network energy storage planning terminal considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0021] Figure 3A schematic diagram of the IEEE 33-node distribution network structure in a distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of initial values ​​of photovoltaic power generation and load demand on each typical day in a distribution network energy storage planning method considering endogenous and exogenous uncertainties in an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of power supply by power grid in summer and winter days in a distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0024] Figure 5 (a) is a typical daily output in summer in a distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0025] Figure 5 (b) is the typical daily output in winter in the distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0026] Figure 6 A schematic diagram of charging and discharging power of nodes in the distribution network energy storage planning method considering endogenous and exogenous uncertainties in an embodiment of the present invention during summer daytime and winter;

[0027] Figure 6 (a) is the energy storage power of node 7 in the distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0028] Figure 6 (b) is the energy storage power of node 18 in the distribution network energy storage planning method considering endogenous and exogenous uncertainties according to an embodiment of the present invention;

[0029] Figure 6 (c) is the energy storage power of node 22 in the distribution network energy storage planning method considering endogenous and exogenous uncertainties in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0031] Please refer to Figure 1 , a distribution network energy storage planning method considering endogenous and exogenous uncertainties, comprising the steps of:

[0032] constructing a reference distribution about initial load and constructing a reference distribution about initial electricity price;

[0033] Taking minimizing the difference between system investment cost and online benefits within the planning period as the first objective function, a planning model in a deterministic environment is constructed;

[0034] Establishing a planning model under an environment of endogenous and exogenous uncertainty based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under the deterministic environment according to the information gap decision theory;

[0035] The planning model under the endogenous and exogenous uncertainty environment is solved to obtain a planning result.

[0036] From the above description, it can be seen that the beneficial effects of the present invention are: constructing a reference distribution about the initial load and constructing a reference distribution about the initial electricity price, taking the difference between the system investment cost and the grid-connected income within the planning period as the first objective function, constructing a planning model under a deterministic environment, and establishing a planning model under endogenous and exogenous uncertainty environments based on the information gap decision theory based on the reference distribution about the initial load, the reference distribution about the initial electricity price and the planning model under the deterministic environment, solving the planning model under endogenous and exogenous uncertainty environments, taking the reference distribution about the initial load as exogenous uncertainty, and taking the reference distribution about the initial electricity price as endogenous uncertainty, thereby not only considering the exogenous uncertainty that is irrelevant to the decision, but also considering the endogenous uncertainty that is inherently related and caused by the decision itself, so that when data acquisition is limited or uncertainty is difficult to accurately quantify, relatively reasonable planning decisions can still be made, making the access of distributed photovoltaics to the distribution network safer and more stable, thereby effectively avoiding redundancy in the investment planning of energy storage equipment and avoiding unnecessary cost burden.

[0037] Further, the constructing of a reference distribution about the initial load includes:

[0038] The prediction error of the load history data is obtained, and parameter estimation is performed on the prediction error of the load history data to obtain a reference distribution of the initial load.

[0039] From the above description, it can be seen that by performing parameter estimation on the prediction error of the historical load data and obtaining the reference distribution of the initial load, it is possible to accurately reflect the possible range and probability of load changes in the future.

[0040] Furthermore, the construction of a reference distribution of initial electricity prices includes:

[0041] Acquiring historical electricity price data, and establishing an initial reference distribution for an initial electricity price based on the historical electricity price data;

[0042] The initial reference distribution about the initial electricity price is corrected to obtain a reference distribution about the initial electricity price.

[0043] From the above description, it can be seen that establishing an initial reference distribution of initial electricity prices based on historical electricity price data can reflect the changing trends and distribution characteristics of past electricity prices. However, as an endogenous uncertainty variable, the specific performance of electricity prices in the future is not only affected by exogenous variables such as market supply and demand relations and macroeconomic factors, but also directly affected by various decision-making quantities in the power system. For example, photovoltaic capacity. With the continuous increase of photovoltaic capacity, photovoltaic penetration rate gradually increases. The system can provide a large amount of electricity to the power grid during the daytime when the sun is sufficient. Therefore, the supply and demand relationship may change. In this case, competition in the electricity market may intensify, prompting a downward trend in electricity prices. In particular, when photovoltaic power generation exceeds the load demand of the power grid, it may lead to a reduction in electricity prices to balance the supply and demand of electricity, which may lead to certain limitations in predicting future electricity prices. Its reference value may be limited. Therefore, correcting the initial reference distribution of initial electricity prices can more accurately reflect the uncertainty of future electricity prices.

[0044] Furthermore, the planning model in a deterministic environment is constructed by taking minimizing the difference between the system investment cost and the Internet access benefit within the planning period as the first objective function, and includes:

[0045] The first objective function is to minimize the difference between system investment cost and grid-connected benefits within the planning period, and establish constraints on the DC power flow model, system power balance, power transaction constraints with the grid, energy storage constraints, and energy storage charging and discharging constraints.

[0046] A planning model in a deterministic environment is generated according to the first objective function, the DC power flow model constraint, the power balance constraint of the system, the transaction power constraint with the power grid, the energy storage energy constraint, and the energy storage charging and discharging constraint.

[0047] From the above description, it can be seen that a planning model in a deterministic environment is generated according to the first objective function, the DC power flow model constraints, the system power balance constraints, the trading power constraints with the power grid, the energy storage constraints, and the energy storage charging and discharging constraints, so as to ensure the stable operation of the system while ensuring economy.

[0048] Furthermore, the first objective function of minimizing the difference between system investment cost and Internet access benefit within the planning period includes:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] In the formula, C0 represents the first objective function, v represents the discount rate, and M n represents the fixed investment cost of photovoltaic and energy storage in the system in year n, U n represents the operation and maintenance cost of photovoltaic and energy storage in the system in year n, T n represents the time-of-use electricity fee of the system in year n, O n represents the system's online revenue in the nth year, I represents the total number of nodes, and m Sun Represents the unit investment cost of photovoltaics, P i Sun represents the photovoltaic capacity installed at the i-th node, m ESS Represents the unit investment cost of energy storage, P i ESS represents the energy storage installed capacity of the i-th node, u Sun Represents the unit photovoltaic capacity, u ESS represents the maintenance cost of energy storage, τ t TOU represents the time-of-use electricity price at time t, P t buy represents the purchased power of the system at time t, τ t On-grid represents the on-grid electricity price at time t, P t sell Represents the power sold by the system at time t.

[0055] From the above description, it can be seen that taking minimizing the difference between system investment cost and online benefits within the planning period as the first objective function can effectively balance the relationship between system investment cost and expected operating benefits and improve economic efficiency.

[0056] Furthermore, the DC power flow model is constrained as follows:

[0057]

[0058]

[0059] Where P i,t represents the sum of active power of node i at time t, S i represents the set of nodes connected to node i, P ij,t represents the active power flow on line ij at time t, θ i,t represents the voltage phase angle of node i at time t, θ j,t represents the voltage phase angle of node j at time t, x ij Represents the reactance value of line ij;

[0060] The power balance constraint of the system is:

[0061]

[0062] In the formula, represents the photovoltaic output of node i at time t, represents the power purchased by the system at node i at time t, represents the discharge power of the energy storage device at node i at time t, represents the load demand of node i at time t, represents the electricity sales power of node i at time t, represents the charging power of the energy storage device at node i at time t;

[0063] The transaction power constraint with the power grid is:

[0064]

[0065] Where T represents the planning period Indicates the maximum time-sharing power, Indicates the maximum Internet access power;

[0066] The energy storage energy constraint is:

[0067]

[0068] In the formula, represents the maximum capacity of the energy storage device at node i at time t, g i represents the maximum energy multiplier of the energy storage device at node i, represents the maximum power of the energy storage device at node i at time t, E i,t represents the energy in the energy storage device at time t, Δt represents the time interval, E i,t-Δt represents the energy in the energy storage device at time t-Δt, r i ch represents the charging rate of the energy storage device at node i, r i dis represents the discharge rate of the energy storage device at node i, v i represents the lower limit of energy storage device energy at node i, represents the energy upper limit of the energy storage device at node i;

[0069] The energy storage charge and discharge constraints are:

[0070]

[0071] In the formula, Indicates the maximum charging power of the energy storage device, u i Indicates the charging and discharging status of the energy storage device at node i, Indicates the maximum discharge power of the energy storage device.

[0072] From the above description, it can be seen that by constructing DC power flow model constraints, system power balance constraints, transaction power constraints with the power grid, energy storage constraints, and energy storage charging and discharging constraints, the system can be guaranteed to operate safely and stably.

[0073] Furthermore, the planning model under the environment of endogenous and exogenous uncertainty is established based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under the deterministic environment according to the information gap decision theory, including:

[0074] Determining the actual load value based on the reference distribution regarding the initial load according to the information gap decision theory;

[0075] Determining the actual value of the electricity price based on the reference distribution of the initial electricity price according to the information gap decision theory;

[0076] Establishing a second objective function based on the reference distribution about the initial load, the actual value of the load, the reference distribution about the initial electricity price, the actual value of the electricity price, and the first objective function;

[0077] A planning model under endogenous and exogenous uncertainty environments is generated according to the second objective function, the DC power flow model constraints, the system power balance constraints, the trading power constraints with the power grid, the energy storage constraints, and the energy storage charging and discharging constraints.

[0078] From the above description, we can see that compared with traditional robust optimization, IGDT (Information Gap Decision Theory) is a non-probabilistic and non-fuzzy optimization method for dealing with uncertainty. It does not rely on the precise probability distribution of the uncertainty, but only needs to know its possible range of values ​​or degree of fluctuation, that is, the uncertainty radius. A planning model under endogenous and exogenous uncertainty environments is established based on IGDT, which can make relatively reasonable planning decisions when data acquisition is limited or uncertainty is difficult to quantify accurately.

[0079] Further, the determining the actual load value based on the reference distribution of the initial load according to the information gap decision theory includes:

[0080]

[0081] In the formula, represents the load actual value correlation function, α represents the load uncertainty radius, represents the reference distribution of the initial load at time t, represents the actual load value of node i at time t;

[0082] The determining of the actual value of the electricity price based on the reference distribution of the initial electricity price according to the information gap decision theory includes:

[0083]

[0084] In the formula, U(β,τ t ) represents the actual value correlation function of electricity price, β represents the uncertainty radius of electricity price, τ t represents the actual value of electricity price at time t, Represents the revised value of electricity price at time t.

[0085] From the above description, it can be seen that according to the information gap decision theory, the actual load value is determined based on the reference distribution of the initial load, and the actual electricity price is determined based on the reference distribution of the initial electricity price. This can ensure the effectiveness of energy storage planning and make the access of distributed photovoltaic power generation to the distribution network safer and more stable when data acquisition is limited or uncertainty is difficult to quantify accurately.

[0086] Further, the establishing of the second objective function based on the reference distribution about the initial load, the actual value of the load, the reference distribution about the initial electricity price, the actual value of the electricity price and the first objective function comprises:

[0087]

[0088] Where C represents the investment cost of the planning model under endogenous and exogenous uncertainty, h represents the cost aversion factor, and C0 represents the first objective function.

[0089] From the above description, it can be seen that the performance of the energy storage planning scheme under the influence of uncertain factors can be adjusted by the cost avoidance factor to select the appropriate scheme according to the actual situation. This helps to ensure the stable operation of distributed photovoltaics after they are connected to the distribution network, thereby increasing tolerance for uncertainty while maintaining the economy of the system and formulating a more flexible planning strategy.

[0090] Please refer to Figure 2 Another embodiment of the present invention provides a distribution network energy storage planning terminal that takes into account exogenous and exogenous uncertainties, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned distribution network energy storage planning method that takes into account exogenous and exogenous uncertainties is implemented.

[0091] The distribution network energy storage planning method and terminal considering endogenous and exogenous uncertainties of the present invention can be applied to distribution network energy storage planning scenarios, which are described below through specific implementation methods:

[0092] Please refer to Figure 1 , Figure 3-Figure 6 , Embodiment 1 of the present invention is:

[0093] A distribution network energy storage planning method considering endogenous and exogenous uncertainties comprises the following steps:

[0094] S1. Construct a reference distribution of initial load and a reference distribution of initial electricity price, including S11-S13:

[0095] S11. Obtain prediction errors of historical load data, and perform parameter estimation on the prediction errors of the historical load data to obtain a reference distribution of the initial load to reflect the possible range and probability of load changes in the future.

[0096] S12: Obtain historical electricity price data, and establish an initial reference distribution for initial electricity price based on the historical electricity price data.

[0097] S13. Correcting the initial reference distribution about the initial electricity price to obtain a reference distribution about the initial electricity price.

[0098] Assuming that the initial electricity price λ0 follows a normal distribution, the initial reference distribution of the initial electricity price is λ0~N(μ0,B0). The reference distribution of the initial electricity price obtained after correction is specifically:

[0099]

[0100] In the formula, τ P represents the electricity price correction value, μ0 represents the expectation, represents the photovoltaic unit capacity of node i, represents the maximum value of system load, Δθ represents the correction factor of the mean, B0 represents the variance, and ΔB represents the correction factor of the covariance.

[0101] In the planning of the power system, an initial reference distribution of the initial electricity price is constructed based on the historical data of electricity prices. This distribution is intended to reflect the changing trend and distribution characteristics of past electricity prices. However, as an endogenous uncertainty variable, the specific performance of electricity prices in the future is not only affected by exogenous variables such as market supply and demand relations and macroeconomic factors, but also directly affected by the intervention of various decision-making quantities in the power system, such as photovoltaic capacity. Specifically, with the continuous increase of photovoltaic capacity, the photovoltaic penetration rate gradually increases, and the system can provide a large amount of electricity to the power grid during the sunny period during the day, so the supply and demand relationship may change. In this case, competition in the electricity market may intensify, prompting a downward trend in electricity prices, especially when photovoltaic power generation exceeds the load demand of the power grid, which may lead to a reduction in electricity prices to balance the supply and demand of electricity. Therefore, the initial electricity price distribution generated based on historical data may have certain limitations in predicting future electricity prices, and its reference value may be limited. In order to more accurately reflect the uncertainty of future electricity prices, it is necessary to correct the initial reference distribution of the initial electricity price. Compared with the initial reference distribution, the corrected reference distribution of initial electricity prices has higher accuracy and practical significance. It can not only more accurately reflect the possible changes in actual electricity prices, but also provide more comprehensive, reliable and flexible information support for electricity market participants.

[0102] S2. Taking the minimization of the difference between system investment cost and online benefits within the planning period as the first objective function, a planning model in a deterministic environment is constructed, which specifically includes S21-S22:

[0103] S21. The first objective function is to minimize the difference between the system investment cost and the grid-connected income within the planning period, and establish the DC power flow model constraints, the system power balance constraints, the transaction power constraints with the power grid, the energy storage constraints, and the energy storage charging and discharging constraints.

[0104] The first objective function of minimizing the difference between system investment cost and Internet access benefit within the planning period includes:

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] In the formula, C0 represents the first objective function, v represents the discount rate, and M n represents the fixed investment cost of photovoltaic and energy storage in the system in year n, U nrepresents the operation and maintenance cost of photovoltaic and energy storage in the system in year n, T n represents the time-of-use electricity fee of the system in year n, O n represents the system's online revenue in the nth year, I represents the total number of nodes, and m Sun Represents the unit investment cost of photovoltaics, P i Sun represents the photovoltaic capacity installed at the i-th node, m ESS Represents the unit investment cost of energy storage, P i ESS represents the energy storage installed capacity of the i-th node, u Sun Represents the unit photovoltaic capacity, u ESS represents the maintenance cost of energy storage, τ t TOU represents the time-of-use electricity price at time t, P t buy represents the purchased power of the system at time t, τ t On-grid represents the on-grid electricity price at time t, P t sell Represents the power sold by the system at time t.

[0111] This objective function can effectively balance the relationship between system investment cost and expected operating income and improve economic efficiency.

[0112] The DC power flow model constraints are:

[0113]

[0114]

[0115] Where P i,t represents the sum of active power of node i at time t, S i represents the set of nodes connected to node i, P ij,t represents the active power flow on line ij at time t, θ i,t represents the voltage phase angle of node i at time t, θ j,t represents the voltage phase angle of node j at time t, x ij Represents the reactance value of line ij;

[0116] The power balance constraint of the system is:

[0117]

[0118] In the formula, represents the photovoltaic output of node i at time t, represents the power purchased by the system at node i at time t, represents the discharge power of the energy storage device at node i at time t, represents the load demand of node i at time t, represents the electricity sales power of node i at time t, represents the charging power of the energy storage device at node i at time t;

[0119] The transaction power constraint with the power grid is:

[0120]

[0121] Where T represents the planning period Indicates the maximum time-sharing power, Indicates the maximum Internet access power;

[0122] The energy storage energy constraint is:

[0123]

[0124] In the formula, represents the maximum capacity of the energy storage device at node i at time t, g i represents the maximum energy multiplier of the energy storage device at node i, represents the maximum power of the energy storage device at node i at time t, E i,t represents the energy in the energy storage device at time t, Δt represents the time interval, E i,t-Δt represents the energy in the energy storage device at time t-Δt, r i ch represents the charging rate of the energy storage device at node i, r i dis represents the discharge rate of the energy storage device at node i, v i represents the lower limit of energy storage device energy at node i, represents the energy upper limit of the energy storage device at node i;

[0125] The energy storage charge and discharge constraints are:

[0126]

[0127] In the formula, Indicates the maximum charging power of the energy storage device, u i Indicates the charging and discharging status of the energy storage device at node i, Indicates the maximum discharge power of the energy storage device.

[0128] S22. Generate a planning model in a deterministic environment according to the first objective function, the DC power flow model constraint, the power balance constraint of the system, the transaction power constraint with the power grid, the energy storage constraint, and the energy storage charging and discharging constraint.

[0129] S3. Establishing a planning model under an environment of endogenous and exogenous uncertainty based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under the deterministic environment according to the information gap decision theory, specifically including S31-S34:

[0130] S31. Determine the actual load value based on the reference distribution of the initial load according to the information gap decision theory, specifically:

[0131]

[0132] In the formula, represents the actual load correlation function, α represents the load uncertainty radius, represents the reference distribution of the initial load at time t, Represents the actual load value of node i at time t.

[0133] S32. Determine the actual value of the electricity price based on the reference distribution of the initial electricity price according to the information gap decision theory, specifically:

[0134]

[0135] In the formula, U(β,τ t ) represents the actual correlation function of electricity price, β represents the uncertainty radius of electricity price, τ t represents the actual value of electricity price at time t, Represents the revised value of electricity price at time t.

[0136] S33: establishing a second objective function based on the reference distribution of the initial load, the actual load value, the reference distribution of the initial electricity price, the actual electricity price value and the first objective function, specifically:

[0137]

[0138] Where C represents the investment cost of the planning model under endogenous and exogenous uncertainty, h represents the cost aversion factor, and C0 represents the first objective function.

[0139] S34. Generate a planning model under endogenous and exogenous uncertainty environments according to the second objective function, the DC power flow model constraints, the system power balance constraints, the trading power constraints with the power grid, the energy storage constraints, and the energy storage charging and discharging constraints.

[0140] S4. Solve the planning model under the endogenous and exogenous uncertainty environment to obtain a planning result.

[0141] The above method of the present invention is specifically described with an example:

[0142] When the time is from 0:00 to 8:00, the electricity price is 0.318 yuan / kW; when it is from 8:00 to 12:00 or 18:00 to 22:00, the electricity price is 0.916 yuan / kW; when it is from 12:00 to 18:00 or 22:00 to 24:00, the electricity price is 0.610 yuan / kW. Figure 3 As shown in Figure 1, integrated photovoltaic (PV) and energy storage (ESS) systems are installed on nodes 7, 18, and 22. The initial values ​​of PV power generation and load demand on each typical day are as follows: Figure 4 shown.

[0143] For the sake of comparison, typical summer days and typical winter days are selected for analysis. The output results of the planning model and the power grid transaction power under endogenous and exogenous uncertainty environments are shown in the figure below. Figure 5 As shown, Figure 5 (a) is a typical daily output in summer, Figure 5 (b) is the output of a typical winter day. The results show that the planning model under the environment of endogenous and exogenous uncertainty shows a high degree of economy and adaptability, especially when dealing with time-of-use electricity prices. The model actively purchases electricity during the low electricity price period and reduces purchases during the peak electricity price period to optimize the cost structure. Specifically, at 0-8 and 22-24 hours, the time-of-use electricity price is at its lowest point, and the power load of the system is relatively low. The planning model under the environment of endogenous and exogenous uncertainty purchases a large amount of electricity from the power grid; at 8-12 and 18-22 hours, the time-of-use electricity price reaches its peak. Faced with this situation, the planning model under the environment of endogenous and exogenous uncertainty will adjust its strategy accordingly and reduce the power purchased directly from the power grid.

[0144] like Figure 6 As shown, Figure 6 (a) is the energy storage power of node 7, Figure 6 (b) is the energy storage power of node 18, Figure 6 (c) is the energy storage power of node 22. The energy storage system of the planning model under endogenous and exogenous uncertainty environment can flexibly adjust its charging and discharging strategy according to the fluctuation of time-of-use electricity price and the change of real-time load demand to achieve efficient utilization of energy and cost saving. The specific performance is as follows:

[0145] At 0-8, the time-of-use electricity price is at a valley value, and the energy storage system chooses to operate in charging mode. Afterwards, at 8-12, because the time-of-use electricity price is at a peak, the energy storage system switches to discharge mode to meet the increased load demand in the system and reduce the purchase of electricity at high prices during peak hours. At 12-18, the time-of-use electricity price is at a flat value, and the photovoltaic power generation not only meets the load demand, but also has a surplus. Therefore, the energy storage system switches to the charging state again, and the system can also sell excess electricity to the grid. At 18-22, the time-of-use electricity price reaches a peak again, and the energy storage system continues to discharge to meet the power demand in the system. At 22-24, in order to optimize the service life and efficiency of the energy storage system, and the electricity price is relatively stable, there is no need for the energy storage system to adjust.

[0146] In order to better compare different models and highlight the advantages of planning models under endogenous and exogenous uncertainty, this paper compares the planning model under endogenous and exogenous uncertainty with the planning model under deterministic environment under the same background. Table 1 gives the photovoltaic and energy storage configuration results of the two models, presenting the photovoltaic capacity, energy storage capacity, investment cost and benefits during the planning period.

[0147] Table 1 PV and energy storage configuration results

[0148]

[0149]

[0150] From the data in Table 1, it can be observed that the differences in the photovoltaic installed capacity predicted by different models are not significant, but there are large differences in the planning of energy storage capacity. The total investment of the planning model under the deterministic environment is 10849670.694, while the total investment of the planning model under the environment of endogenous and exogenous uncertainty is 14140309.965, which is 30.329% higher than the total cost of the planning model under the deterministic environment. This is because the proposed model takes into account the relationship between electricity price uncertainty and decision capacity. Specifically, the planning model under the deterministic environment assumes that both load demand and on-grid electricity price are regarded as known and constant parameters, ignoring the inevitable volatility and uncertainty in actual operation. Although this approach helps to reduce the complexity of the model and presents a lower cost forecast, it is difficult to accurately cope with the complex and changeable operating environment in reality. In contrast, the planning model under the environment of endogenous and exogenous uncertainty shows higher practical application value and foresight, because it deeply recognizes the importance of endogenous uncertainty in the planning process.

[0151] As shown in Table 2, the data in Table 2 reveal the diversity and dynamics of energy storage decisions made by different models in different years. These data reflect the dynamic changes in energy storage demand and may be related to endogenous uncertainty. Therefore, endogenous uncertainty factors must be fully considered to ensure the rationality and foresight of the configuration.

[0152] Table 2 Energy storage phase planning results

[0153]

[0154]

[0155] There are significant differences in the planning results of energy storage capacity between the planning model under a deterministic environment and the planning model under an environment of exogenous and exogenous uncertainty. Specifically, the overall planning of energy storage capacity in the planning model under a deterministic environment is high. This is because the planning model under a deterministic environment is based on historical data and historical load levels, and fails to fully incorporate factors such as the uncertainty of load fluctuations and future dynamic changes in the power grid. This neglect of uncertainty leads to the need for higher energy storage demand to cope with possible extreme situations, resulting in high energy storage capacity planning results. Compared with the planning model under a deterministic environment, the planning model under an environment of exogenous and exogenous uncertainty shows a trend of decreasing energy storage capacity year by year in planning for many consecutive years. This is because in the planning process, not only load uncertainty is considered, but also the uncertainty of the on-grid electricity price affected by the decision capacity. Based on the optimized expectation of future electricity prices, the required capacity of the energy storage system gradually decreases while ensuring the stable operation of the power grid, achieving optimal allocation and efficient utilization of resources.

[0156] In summary, the above-mentioned planning method of the present invention regards the on-grid electricity price as endogenous uncertainty and the load as exogenous uncertainty. By simulating and analyzing the IEEE 30-node distribution network, the relationship between decision-making ability and the endogenous uncertainty of the on-grid electricity price is considered, and the historical electricity price is corrected to generate a more accurate electricity price uncertainty distribution, which can more accurately reflect the possible fluctuation range of electricity prices in the future. The planning model under the environment of endogenous and exogenous uncertainties can accurately grasp the changing trend of time-of-use electricity prices and the fluctuation law of system power load, and realize the strategy of reserving electricity at low electricity prices and reducing electricity purchases at high electricity prices, thereby improving energy utilization efficiency.

[0157] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0158] A distribution network energy storage planning terminal considering exogenous and exogenous uncertainties comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the distribution network energy storage planning method considering exogenous and exogenous uncertainties in embodiment 1 is implemented.

[0159] In summary, the present invention provides a distribution network energy storage planning method and terminal that considers exogenous and exogenous uncertainties, constructs a reference distribution for the initial load, and constructs a reference distribution for the initial electricity price, takes minimizing the difference between the system investment cost and the grid-connected income within the planning period as the first objective function, constructs a planning model under a deterministic environment, and establishes a planning model under exogenous and exogenous uncertainty environments based on the reference distribution for the initial load, the reference distribution for the initial electricity price, and the planning model under a deterministic environment according to the information gap decision theory, solves the planning model under exogenous and exogenous uncertainty environments, and takes the reference distribution for the initial load as exogenous uncertainty, and takes the reference distribution for the initial electricity price as endogenous uncertainty, thereby not only considering exogenous uncertainty that is irrelevant to the decision, but also considering The inherently relevant endogenous uncertainty caused by the decision itself can still make relatively reasonable planning decisions when data acquisition is limited or uncertainty is difficult to quantify accurately, making the access of distributed photovoltaic power generation to the distribution network safer and more stable, thereby effectively avoiding redundancy in the investment planning of energy storage equipment and avoiding unnecessary cost burdens; in addition, the initial reference distribution of the initial electricity price can be corrected to more accurately reflect the uncertainty of future electricity prices; at the same time, the performance of the energy storage planning scheme under the influence of uncertainty factors is adjusted through the cost avoidance factor, so as to select the appropriate scheme according to the actual situation, which helps to ensure the stable operation of distributed photovoltaic power generation after access to the distribution network, thereby increasing tolerance for uncertainty while maintaining the economy of the system and formulating a more flexible planning strategy.

[0160] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distribution network energy storage planning method considering endogenous and exogenous uncertainties, characterized in that: Includes steps: constructing a reference distribution about initial load and constructing a reference distribution about initial electricity price; Taking minimizing the difference between system investment cost and online benefits within the planning period as the first objective function, a planning model in a deterministic environment is constructed; Establishing a planning model under an environment of endogenous and exogenous uncertainty based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under the deterministic environment according to the information gap decision theory; The planning model under the endogenous and exogenous uncertainty environment is solved to obtain a planning result.

2. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 1, characterized in that: The constructing of a reference distribution about the initial load comprises: The prediction error of the load history data is obtained, and parameter estimation is performed on the prediction error of the load history data to obtain a reference distribution of the initial load.

3. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 1, characterized in that: The constructing of a reference distribution of initial electricity prices includes: Acquiring historical electricity price data, and establishing an initial reference distribution for an initial electricity price based on the historical electricity price data; The initial reference distribution about the initial electricity price is corrected to obtain a reference distribution about the initial electricity price.

4. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 1, characterized in that: The planning model in a deterministic environment is constructed by taking minimizing the difference between the system investment cost and the Internet access benefit within the planning period as the first objective function, and includes: The first objective function is to minimize the difference between system investment cost and grid-connected benefits within the planning period, and establish constraints on the DC power flow model, system power balance, power transaction constraints with the grid, energy storage constraints, and energy storage charging and discharging constraints. A planning model in a deterministic environment is generated according to the first objective function, the DC power flow model constraint, the power balance constraint of the system, the transaction power constraint with the power grid, the energy storage energy constraint, and the energy storage charging and discharging constraint.

5. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 4, characterized in that: The first objective function of minimizing the difference between system investment cost and Internet access benefit within the planning period includes: In the formula, C0 represents the first objective function, v represents the discount rate, and M n represents the fixed investment cost of photovoltaic and energy storage in the system in year n, U n represents the operation and maintenance cost of photovoltaic and energy storage in the system in year n, T n represents the time-of-use electricity fee of the system in year n, O n represents the system's online revenue in the nth year, I represents the total number of nodes, and m Sun Represents the unit investment cost of photovoltaics, P i Sun represents the photovoltaic capacity installed at the i-th node, m ESS Represents the unit investment cost of energy storage, P i ESS represents the energy storage installed capacity of the i-th node, u Sun Represents the unit photovoltaic capacity, u ESS represents the maintenance cost of energy storage, represents the time-of-use electricity price at time t, P t buy represents the purchased power of the system at time t, τ t On-grid represents the on-grid electricity price at time t, P t sell Represents the power sold by the system at time t.

6. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 5, characterized in that: The DC power flow model constraints are: Where P i,t represents the sum of active power of node i at time t, S i represents the set of nodes connected to node i, P ij,t represents the active power flow on line ij at time t, θ i,t represents the voltage phase angle of node i at time t, θ j,t represents the voltage phase angle of node j at time t, x ij Represents the reactance value of line ij; The power balance constraint of the system is: In the formula, represents the photovoltaic output of node i at time t, represents the power purchased by the system at node i at time t, represents the discharge power of the energy storage device at node i at time t, represents the load demand of node i at time t, represents the electricity sales power of node i at time t, represents the charging power of the energy storage device at node i at time t; The transaction power constraint with the power grid is: Where T represents the planning period Indicates the maximum time-sharing power, Indicates the maximum Internet access power; The energy storage energy constraint is: In the formula, represents the maximum capacity of the energy storage device at node i at time t, g i represents the maximum energy multiplier of the energy storage device at node i, represents the maximum power of the energy storage device at node i at time t, E i,t represents the energy in the energy storage device at time t, Δt represents the time interval, E i,t-Δt represents the energy in the energy storage device at time t-Δt, r i ch represents the charging rate of the energy storage device at node i, r i dis represents the discharge rate of the energy storage device at node i, vi represents the lower limit of the energy of the energy storage device at node i, represents the energy upper limit of the energy storage device at node i; The energy storage charge and discharge constraints are: In the formula, Indicates the maximum charging power of the energy storage device, u i Indicates the charging and discharging status of the energy storage device at node i, Indicates the maximum discharge power of the energy storage device.

7. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 4, characterized in that: The step of establishing a planning model under an environment of endogenous and exogenous uncertainty based on the reference distribution of the initial load, the reference distribution of the initial electricity price and the planning model under a deterministic environment according to the information gap decision theory includes: Determining the actual load value based on the reference distribution regarding the initial load according to the information gap decision theory; Determining the actual value of the electricity price based on the reference distribution of the initial electricity price according to the information gap decision theory; Establishing a second objective function based on the reference distribution about the initial load, the actual value of the load, the reference distribution about the initial electricity price, the actual value of the electricity price, and the first objective function; A planning model under endogenous and exogenous uncertainty environments is generated according to the second objective function, the DC power flow model constraints, the system power balance constraints, the trading power constraints with the power grid, the energy storage constraints, and the energy storage charging and discharging constraints.

8. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 7, characterized in that: The determining of the actual load value based on the reference distribution of the initial load according to the information gap decision theory comprises: In the formula, represents the load actual value correlation function, α represents the load uncertainty radius, represents the reference distribution of the initial load at time t, represents the actual load value of node i at time t; The determining of the actual value of the electricity price based on the reference distribution of the initial electricity price according to the information gap decision theory includes: In the formula, U(β,τ t ) represents the actual value correlation function of electricity price, β represents the uncertainty radius of electricity price, τ t represents the actual value of electricity price at time t, Represents the revised value of electricity price at time t.

9. A distribution network energy storage planning method considering endogenous and exogenous uncertainties according to claim 8, characterized in that: The establishing of the second objective function based on the reference distribution about the initial load, the actual value of the load, the reference distribution about the initial electricity price, the actual value of the electricity price and the first objective function comprises: Where C represents the investment cost of the planning model under endogenous and exogenous uncertainty, h represents the cost aversion factor, and C0 represents the first objective function.

10. A distribution network energy storage planning terminal considering endogenous and exogenous uncertainties, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of a distribution network energy storage planning method considering endogenous and exogenous uncertainties as described in any one of claims 1 to 9 is implemented.

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