Aggregation response capability assessment method for distributed resources of virtual power plant

Abstract

The invention discloses a virtual power plant distributed resource aggregation response capability assessment method, which comprises the steps of selecting a resource response capability assessment index, performing response capability assessment on various distributed resources in a virtual power plant, and constructing various distributed resource response capability models; constructing a virtual power plant aggregation response capability evaluation model according to various distributed resource response capability models and aggregation response capability evaluation parameters; and carrying out weight assignment on the resource response capability indexes in the virtual power plant, and obtaining an aggregation response capability score by considering a conditional value-at-risk. According to the method, various distributed resource response capability indexes are defined, and the independent response capability of various distributed resources and the response capability of the aggregated virtual power plant can be evaluated, so that the aggregation response capability of the virtual power plant is evaluated, and participation in a power system regulation and control decision is assisted.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant aggregate response capability assessment, and in particular to an aggregate response capability assessment method for distributed resources of a virtual power plant. Background Art

[0002] In recent years, a large number of distributed resources have appeared in the power grid system. Their randomness, volatility and over-dispersion have increased the complexity of the system and the difficulty of regulation, posing challenges to the safe and reliable operation of the power grid. However, the access of large-scale distributed resources also makes the user side flexible to a certain extent, so that the user side has the potential to participate in power grid regulation. Virtual power plants (VPPs) can aggregate various types of dispersed source-load-storage resources to form resource clusters and conduct unified scheduling, thereby effectively exerting the combined force of dispersed resources such as distributed power sources and energy storage systems, and improving the flexibility, safety and economy of power grid regulation; conditional value at risk (CVaR) can measure the risk impact of the volatility of renewable energy such as photovoltaic and wind power on operating costs. At present, there is a lack of analysis of the response capabilities of virtual power plants such as response time in related studies, and there is also a lack of analysis of the weight of aggregated response capability indicators; therefore, it is necessary to study the aggregated response capabilities of virtual power plants to assist in participating in power system regulation decisions.

[0003] The invention application with application number 202410302566.3 discloses a method and system for evaluating the real-time response capability of a virtual power plant based on cloud-edge collaboration. The application scheme constructs an overall architecture of a virtual power plant cloud-edge collaborative scheduling system based on the characteristics of the virtual power plant and the data processing requirements. By adding edge servers to the edge of the network close to the data terminal, the synergistic advantages of cloud computing and edge computing are combined. This method not only achieves unified scheduling, but also meets the real-time requirements. However, its scheme also has the following problems: lack of analysis of the independent response capabilities of various distributed resources, and lack of analysis of the weights of aggregated response capability indicators. Summary of the invention

[0004] In response to the above-mentioned problems, the purpose of the present invention is to provide a method for evaluating the aggregated response capability of distributed resources of a virtual power plant, to evaluate the independent response capabilities of various distributed resources and the response capabilities of the virtual power plant after aggregation, to evaluate the aggregated response capability of the virtual power plant, and to assist in participating in power system regulation and decision-making.

[0005] An embodiment of the present invention provides a method for evaluating the aggregated response capability of distributed resources of a virtual power plant, comprising the steps of:

[0006] S1. Select resource response capability evaluation indicators, evaluate the response capability of various distributed resources in the virtual power plant, and build response capability models of various distributed resources;

[0007] S2. Construct a virtual power plant aggregate response capability evaluation model based on various distributed resource response capability models and aggregate response capability evaluation parameters;

[0008] S3. Assign weights to the resource responsiveness indicators within the virtual power plant to obtain an aggregated responsiveness score.

[0009] Furthermore, the resource response capability evaluation indicators are: adjustment direction D, adjustment amplitude A, response time T R Duration T D and climbing rate R.

[0010] Furthermore, the various types of distributed resources include: energy storage systems and temperature control load clusters.

[0011] Furthermore, according to the resource response capability evaluation index, the energy storage system response capability model is constructed as follows:

[0012] Adjustment direction: bidirectional adjustment;

[0013] Adjustment range: The formula is:

[0014]

[0015] Among them, P b (t) is the current output, P b,max , P b,min The upper and lower limits of output;

[0016] Response time: unit time is Δt;

[0017] Duration: The formula is:

[0018]

[0019] Among them, E b (t) is the electric energy stored in the energy storage system at time t; E b,max , E b,min are the upper and lower limits of the energy stored in the energy storage system; η b is the charge and discharge power, is the charging duration, is the discharge duration;

[0020] Climbing rate: The constraint formula is:

[0021]

[0022] Where: R b (t) is the climbing rate of the energy storage system at time t; They are respectively the maximum downward and upward adjustment values ​​of the output within unit time Δt.

[0023] Furthermore, based on the resource response capability evaluation index, the temperature control load cluster response capability model is constructed as follows:

[0024] Adjustment direction: bidirectional adjustment;

[0025] Adjustment range: The formula is:

[0026]

[0027] Among them, P c (t) is the current output, P c,max , P c,min The upper and lower limits of output;

[0028] Response time: vs. ΔT in Related to, ΔT in The larger it is, the longer the response time will be;

[0029] Duration: The unit time is Δt;

[0030] Climbing rate: The constraint formula is:

[0031]

[0032]

[0033] in, They are the upper and lower limits of output change per unit time Δt; ΔT out and ΔT cr T out and T in Change per unit time; ΔT cr There is a constraint |ΔT cr |≤ΔT cr，max , where ΔT cr，max is the maximum room temperature change.

[0034] Furthermore, the aggregate response capability evaluation parameters are: tie line power baseline, tie line power upper and lower limits, virtual power plant ramp rate upper and lower limits, and virtual power plant response time;

[0035] According to the aggregate response capability evaluation parameters, the virtual power plant aggregate response capability evaluation model is constructed as follows:

[0036] Tie line power baseline, objective function formula is:

[0037]

[0038] Where: ψ is the set of running times; S is the set of all devices; i is the device number; p i(t) is the power emitted by device i at time t; p tie (t) is the interconnection line power at time t; c i (p i (t)) is the cost function of device i at time t; c tie (t) is the electricity purchase price of the interconnection line at time t;

[0039] The upper and lower limits of the tie line power, the objective function formula is:

[0040] maxp tie (t)t∈ψ(9)

[0041] minp tie (t)t∈ψ(10)

[0042] Among them, maxp tie (t), maxp tie (t) refers to the maximum and minimum values ​​that the tie line power can reach at time t;

[0043] The upper and lower limits of the virtual power plant ramp rate and the objective function formula are:

[0044]

[0045] in, The maximum value of the output power increase or decrease from the current time t to the next time t+1 when running at the power baseline;

[0046] Virtual power plant response time When operating at the power baseline, the adjustment command is received at time t, and the adjustment is completed in the shortest time at time The total power of all resources reaches the change ΔP required by the regulation instruction, and the objective function formula is:

[0047]

[0048] Where: S ΔP is a set of ΔP values ​​at equal intervals; m is the time traversed in the summation equation; v ΔP,m It is a 0-1 variable.

[0049] Furthermore, it also includes: obtaining the probability distribution of the adjustable capacity potential of various distributed resources at different times according to the aggregated response capability evaluation model, including the steps of:

[0050] S41. Obtain the historical response data set of various distributed resources at each time node. The formula is:

[0051] d ij =[η ij ,c ij ] (14)

[0052] Among them, η ij is the historical load reduction rate, c ij Incentive price for participating in the response;

[0053] S42, extracting the power consumption feature sets of various distributed resources by integrating empirical mode decomposition and stacked autoencoders;

[0054] S43, performing dimensionality reduction processing on the electricity consumption feature set, and using the least squares method to fit and mine key parameters of the adjustable potential model;

[0055] S44, performing point estimation on the response history data set to obtain estimated values ​​at each time node;

[0056] S45. Perform rolling analysis on the time series data of each time node of each type of distributed resources to obtain the probability distribution of adjustable capacity potential at different times.

[0057] Furthermore, in S3, weights are assigned to resource response capability indicators within the virtual power plant to perform aggregate response capability scoring, wherein the weighted response capability indicators include: upward adjustment range, downward adjustment range, upward climbing rate, downward climbing rate, and average response time. The steps include:

[0058] S31. Standardize the weighted response capability index, the formula is:

[0059]

[0060] Where: x m,n,t is the value of the nth weighted response capability index of the mth virtual power plant at time t; x' m,n,t is the standardized value of the corresponding weighted response capability index; formula (15) is applicable to positive indicators; formula (16) is applicable to negative indicators.

[0061] S32. Calculate the aggregate response capability score F of the mth virtual power plant m , the formula is:

[0062]

[0063] Among them, w m,n is the weight of the nth weighted response capability indicator of the mth virtual power plant; Γ is the sequence set of weighted response capability indicators; e m,n is the entropy value of the nth weighted response capability index of the mth virtual power plant; r m,n,t It is the proportion of the nth weighted response capability index of the mth virtual power plant at time t.

[0064] Furthermore, when obtaining the virtual power plant in S3 for aggregate response capability scoring, the conditional risk value of wind power and photovoltaic power is also considered:

[0065] Among them, the wind power conditional risk value is expressed as follows:

[0066]

[0067] Among them, C S is the spinning reserve price per unit of electricity, P Wjt is the planned power generation of the wind turbine in period j, P Rjt is the actual power generation of the wind turbine in period j, C P is the loss price per unit of electricity, α is the threshold;

[0068] Among them, the photoelectric conditional risk value is expressed as follows:

[0069]

[0070] Among them: C S1 is the spinning reserve price per unit of electricity, P Sjt is the planned power generation of photovoltaic power generation in period t, P Rjt1 is the actual power generation of photovoltaic power generation in period j, C P1 is the loss price per unit of electricity, and α is the threshold.

[0071] Beneficial effects of the present invention:

[0072] 1. The method of the present invention defines a response capability index, which can be used to evaluate the independent response capability of various distributed resources and the response capability of virtual power plants after aggregation. For virtual power plants, the above index can be obtained by solving the optimization model, thereby evaluating the aggregated response capability and assisting in participating in power system regulation and control decisions.

[0073] 2. The present invention is easy to use and has strong versatility in evaluating the aggregate response capability of virtual power plants. The aggregate response capability obtained by solving the problem can be reported as the dispatching information of the power grid and participate in the regulation of the power system and the formulation of power market transaction decisions.

[0074] 3. The present invention takes into account the instability of the response capability of the virtual power plant within a day, and uses integrated empirical mode decomposition and stacked autoencoders to extract the electricity consumption characteristics of various distributed resources. The electricity consumption characteristics are used to obtain the key parameters in the consumer psychology model, and the probability distribution of the response potential of various distributed resources at each time node is formed. Then, the entropy method is used to obtain the indicator weights and obtain the aggregated response capability score of the virtual power plant, so as to intuitively evaluate the response capability through the score.

[0075] 4. When scoring the aggregate responsiveness, the present invention takes into account the conditional value at risk (CVaR) of photovoltaics, wind power, etc., which can measure the risk impact of the volatility of renewable energy sources such as photovoltaics and wind power on operating costs; the conditional risk of wind power and photovoltaic output deviation not only takes into account the impact of random factors on the economic efficiency of power system operation, but also can specifically quantify the size of the loss level in each case and its corresponding probability, which can comprehensively and effectively reflect the potential risks of economic indicators in system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to the present invention;

[0077] Figure 2 It is an evaluation index diagram of the response capability of each distributed resource in the virtual power plant of the present invention;

[0078] Figure 3 It is an evaluation index diagram of the aggregated response capability of the virtual power plant of the present invention;

[0079] Figure 4 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0080] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0081] At present, there is a lack of analysis on the response capabilities of virtual power plants, such as response time, and analysis on the weights of aggregated response capability indicators. The existing analysis on the aggregated response capabilities of virtual power plants is not conducive to accurate power system regulation and control decisions.

[0082] In view of the above problems, the present invention provides a method for evaluating the aggregated response capability of distributed resources in a virtual power plant. Figure 1 A flow chart of a method for evaluating the aggregated response capability of distributed resources of a virtual power plant provided in an embodiment of the present invention, the method comprising:

[0083] S1. Select resource response capability evaluation indicators, evaluate the response capability of various distributed resources in the virtual power plant, and build various distributed resource response capability models. The resource response capability evaluation indicators include:

[0084] The adjustment direction D, i.e. the relative position between the operating point and the operating base point after adjustment, includes two directions: upward and downward.

[0085] The adjustment amplitude A refers to the maximum deviation value that can be achieved relative to the current operating point, including the upward adjustment amplitude A + and downward adjustment amplitude A - .

[0086] Response time T R , refers to the shortest time required from receiving the adjustment instruction to fully meeting the adjustment requirements.

[0087] Duration T D , refers to the longest time that the state can be maintained after the adjustment requirements are fully met.

[0088] The ramp rate R refers to the change in the operating point per unit time Δt during the regulation process. The operating point in distributed resources refers to the resource output during operation.

[0089] The distributed resources of the embodiment of the present invention are described by selecting an energy storage system and a temperature control load cluster.

[0090] According to the response capability of the energy storage system, a response capability model of the energy storage system is constructed, including:

[0091] Adjustment direction: can be adjusted in both directions.

[0092] Adjustment range: Available current output P b (t) and output upper and lower limits P b,max , P b,min Said, the formula is:

[0093]

[0094] in, To adjust the energy storage system upwards, Adjust the amplitude downward for the energy storage system.

[0095] Response time: The response time of the energy storage system is very short, and can be as fast as seconds. Therefore, when the response capability is subsequently aggregated, the response time is considered to be the unit time Δt.

[0096] Duration: If the regulation requirements are met when the state is charging or discharging, the charging duration and discharge duration As shown below:

[0097]

[0098] Climbing rate: The constraints are:

[0099]

[0100] Where: R b (t) is the climbing rate of the energy storage system at time t; They are respectively the maximum downward and upward adjustment values ​​of the output within unit time Δt.

[0101] According to the temperature control load cluster response capability, a temperature control load cluster response capability model is constructed, including:

[0102] Adjustment direction: can be adjusted in both directions.

[0103] Adjustment range: includes the upward and downward adjustment range of the temperature control load cluster, and the formula is expressed as:

[0104]

[0105] Response time: For the proposed response method, the response time is about 0.5 to 1 hour, which is mainly related to ΔT in Related to, ΔT in The larger it is, the longer the response time will be.

[0106] Duration: For the temperature control load cluster, the expected indoor temperature after adjustment is E(T in ) can remain constant, thus maintaining the response throughout the day, so the duration is 24h.

[0107] Climbing rate: T out and T in The change per unit time is ΔT out and ΔT cr , and ΔT cr There is a constraint |ΔT cr |≤ΔT cr，max , where ΔT cr，max is the maximum value of room temperature change. Then the climbing rate of equivalent output power of temperature control load cluster is R c The constraints are as follows:

[0108]

[0109] in: They are respectively the upper and lower limits of output change per unit time Δt.

[0110] S2. Based on various distributed resource response capability models and aggregate response capability evaluation parameters, a virtual power plant aggregate response capability evaluation model is constructed.

[0111] Specifically, in order to effectively score the aggregate response capability of virtual power plants, the aggregate response capability evaluation parameters of virtual power plants are selected, including: tie line power baseline, tie line power upper and lower limits, virtual power plant ramp rate upper and lower limits, virtual power plant response time, etc., among which:

[0112] The power baseline of the tie line, that is, the power curve of the tie line when there is no response demand, is calculated based on the day-ahead optimization scheduling results with the goal of minimizing the total cost; the objective function formula is:

[0113]

[0114] Where: ψ is the set of operating moments, which can be 1d, i.e. 24 moments; S is the set of all devices (including temperature control load); i is the device number; p i (t) is the power emitted by device i at time t; p tie (t) is the interconnection line power at time t, with the positive direction being the power flowing from the upper power grid to the virtual power plant; c i (p i (t)) is the cost function of device i at time t; c tie (t) is the electricity purchase price of the interconnection line at time t.

[0115] The upper and lower limits of the tie line power, the objective function formula is:

[0116] maxp tie (t)t∈ψ(9)

[0117] minp tie (t)t∈ψ(10)

[0118] Among them, maxp tie (t), maxp tie (t) refers to the maximum and minimum values ​​that the tie line power can reach at time t;

[0119] The upper and lower limits of the virtual power plant ramp rate and the objective function formula are:

[0120]

[0121] in, It is the maximum value of output power increase or decrease from the current time t to the next time t+1 when running at the power baseline.

[0122] At the same time, this objective function has the following constraints:

[0123] X(t)=X base (t)(11.1)

[0124] Where: X(t) is the decision variable matrix at time t, including the output of each device, the energy stored by the energy storage system, etc.; X base (t) is the matrix of the values ​​of the decision variables on the baseline at time t. The above formula means that the state of each device at time t is the same as the state on the baseline, that is, the virtual power plant is operating on the baseline at time t.

[0125] Virtual power plant response time When operating at the power baseline, the adjustment command is received at time t, and the adjustment is completed in the shortest time at time The total power of all resources reaches the change ΔP required by the regulation instruction, and the objective function formula is:

[0126]

[0127] Where: S ΔP is a set of ΔP values ​​at equal intervals; m is the time traversed in the summation equation; v ΔP,m It is a 0-1 variable, which represents whether ΔP satisfies the regulation instruction requirement at a certain moment m. If not, it is 1, otherwise it is 0.

[0128] At the same time, this objective function also has the following constraints:

[0129] X(t)=X base (t)(13.1)

[0130] Based on the aggregated response capability evaluation model, integrated empirical mode decomposition and stacked autoencoders are used to extract the electricity consumption characteristics of various distributed resources. These characteristics are used to obtain the key parameters in the consumer psychology model, forming the probability distribution of the response potential of various distributed resource clusters at each time node.

[0131] Specifically, a day is divided into 24 time nodes at 1h intervals. The input data is the time series data of the power consumption of various distributed resources in the cluster on different dates at each time node. The output is the historical response data of n various distributed resources at each time node, namely:

[0132] d ij =[η ij ,c ij ] (14)

[0133] Among them, η ij is the historical load reduction rate, c ij The incentive price for participating in the response.

[0134] The data is first decomposed using the ensemble empirical mode decomposition (EEMD) method, and then the decomposed intrinsic mode functions (IMFs) are used as the input of the stacked autoencoder (SAE) network to extract the power consumption feature sets of various distributed resources in the cluster.

[0135] Assume that the m-dimensional feature set h is obtained by the above feature extraction method iOn this basis, the principal component analysis method is used to reduce the dimension of the feature set of the cluster, and the least squares fitting is used to mine the key parameters of the user-adjustable potential model, and finally the following relationship is obtained:

[0136]

[0137] Where: b 1j (t), b 2j (t), b 3j (t) is the characteristic coefficient of the key parameter at a single time node t; U j is the principal component of the jth characteristic index of electricity consumption of various distributed resources extracted by principal component analysis; a ij (t) is the coefficient of the i-th characteristic index in the j-th principal component; s i is the value of the i-th characteristic index.

[0138] Similarly, by performing point estimation on the response history data set, the estimated value at each time node is obtained, and the estimated value is regarded as the normal distribution parameter satisfied by r4(t), so that Below are:

[0139] η(t) δ＝δ ~N(μ(t),σ 2 (t))(14.2)

[0140]

[0141] Among them: mean reference value μ(t), variance reference value σ 2 (t), mean fuzzy value μ e (t) and variance fuzzy value σ e (t) is a model indicator that describes the uncertainty parameters of the response potential of various distributed resources at specific time nodes.

[0142] Furthermore, by conducting a rolling analysis of the time series data of each time node of various distributed resource clusters, the probability distribution of the adjustable capacity potential of the cluster at different times can be obtained, which helps to facilitate the hierarchical and classified regulation of various distributed resources in the region.

[0143] S3. Assign weights to the resource responsiveness indicators within the virtual power plant to obtain an aggregated responsiveness score.

[0144] In order to score the responsiveness of the virtual power plant, each responsiveness indicator is weighted. When setting the weight, the stability of the responsiveness of the virtual power plant in a day is considered. The more unstable the responsiveness, the smaller the weight of the corresponding indicator. Therefore, the entropy value is used to represent the instability of the responsiveness, and the entropy method is used for objective weighting.

[0145] When calculating the weight, the response capability index includes five indicators: upward adjustment range, downward adjustment range, upward climbing rate, downward climbing rate, and average response time. The average response time is the average response time within the determined adjustment range.

[0146] First, standardize the indicators:

[0147]

[0148] Where: x m,n,t is the value of the nth indicator of the mth virtual power plant at time t; x' m,n,t is the normalized value of the corresponding indicator. Expression (15) is applicable to the positive indicator, i.e., the first four indicators mentioned above; Expression (16) is applicable to the negative indicator, i.e., the average response time.

[0149] The aggregate responsiveness score F of the mth virtual power plant m The calculation is as follows:

[0150]

[0151] in:

[0152]

[0153] Where: w m,n is the weight of the nth indicator of the mth virtual power plant; Γ is the sequence set of indicators; e m,n is the entropy value of the nth indicator of the mth virtual power plant; r m,n,t is the weight of the nth indicator of the mth virtual power plant at time t. m The value range is [0,1], and the larger the value, the better the responsiveness.

[0154] Furthermore, when obtaining the aggregated response capacity score of virtual power plants, the conditional risk value of wind power and photovoltaic power is also considered.

[0155] Conditional risk (CVaR) is an improvement on the value at risk, which is defined as the average value of the risk loss faced by the portfolio during a certain investment period when it is greater than the value at risk at a known confidence level. Its mathematical description is:

[0156] QUR β (X)=E[f(X,ξ)|f(X,ξ)>VaR β ](19.1)

[0157] Where: f(X,ξ) is the loss function of portfolio X, and ξ represents a continuous random variable that may affect the loss function.

[0158] Compared with risk value, conditional risk can effectively consider tail risk, but the risk value calculation formula is more complicated and cannot be solved by analytical methods. Therefore, a function is introduced to simplify the calculation of conditional risk value, as shown in the following formula:

[0159]

[0160] Among them, [t] = max{t,0}, and it can be proved that the following formula holds:

[0161]

[0162] The corresponding calculated η is the risk value.

[0163] The calculation of conditional risk can be equivalent to a linear constraint containing discrete variables, which has a simpler calculation method. The calculation method of conditional risk in a discrete scenario is as follows:

[0164]

[0165] Where: M is the number of all discrete scenarios; y m is the value of the random variable under the mth discrete scenario. This formula assumes that the probability of all discrete scenarios occurring is the same.

[0166] Wind power output is uncertain, and there will be deviations in wind power output forecasts. When the wind power output capacity is overestimated during the forecast, its actual output does not meet the load balance requirements. At this time, additional generators need to be invested to supplement this part of the load demand. The system needs to increase the spinning reserve capacity. This part of the cost can be expressed as:

[0167] F S =C S (P Wjt -P Rjt )(19.5)

[0168] Where: C S is the spinning reserve price per unit of electricity, P Wjt is the planned power generation of the wind turbine in period j, P Rjt is the actual power generation of the wind turbine in period j.

[0169] Also, due to the deviation of wind power output forecast, wind power output is sometimes underestimated, and the actual wind power output will exceed the load demand. In order to meet the safety constraints of the power grid, the excess should be abandoned, which will lead to waste of wind power resources and corresponding economic losses, which is equivalent to a fine for underestimating wind power output. This part of the cost can be expressed as:

[0170] F P =C P (PRjt -P Wjt )(19.6)

[0171] Where: C P is the loss price per unit of electricity.

[0172] Due to the uncertainty of wind power output, there is a certain deviation between the actual wind power output and the planned output. The deviation has the greatest impact on the system. Therefore, we should not only pay attention to the average level of the deviation, but also the probability of the deviation. Conditional risk can just describe these characteristics. It can not only give the size of the output deviation, but also reflect the probability of a certain deviation through the confidence level. The deviation of wind power output is now defined as:

[0173] f(P Wjt ,P Rjt )=|P Wjt -P Rjt |(19.7)

[0174] Where: P Wjt Contribute to wind power planning, P Rjt is the actual output of wind power. The actual output of wind power is a random variable, while the planned output is a controlled variable. The wind power output deviation can accurately characterize the uncertainty of wind power output when a certain confidence level is known. The probability that the deviation function does not exceed a threshold α is:

[0175]

[0176] Then, at a given confidence level β, the risk value of wind power output deviation can be expressed as follows:

[0177] V VaR (P Wjt )=min{α∈R:ψ(P Wjt ,α)≥β}(19.9)

[0178] Using the conditional risk calculation formula, the conditional risk calculation formula for wind power output deviation is:

[0179]

[0180] The meaning of wind power output deviation conditional risk is: the average level under certain conditions when the wind power output deviation value is greater than the risk value, that is, the average level of excess deviation. The deviation it reflects is more comprehensive and can accurately portray potential risks.

[0181] According to the above calculation analysis, the conditional risk contains the risk value function term, and the analytical expression of the risk value function is very complex and difficult to calculate directly. Therefore, an auxiliary function is introduced to simplify the calculation of conditional risk:

[0182]

[0183] Where: [t] + =max{t,0}.

[0184] Because the actual value of wind power output is a random variable, the analytical expression of its probability density function is usually very complex and difficult to obtain directly. Therefore, it will be difficult to directly use the analytical method to calculate the expected value of the above formula. Here, the Monte Carlo random sampling method is used to extract M sample points of the random variable for estimation. The calculation formula for the estimated value of the auxiliary function is:

[0185]

[0186] Introduce auxiliary variables:

[0187]

[0188] So the above problem becomes:

[0189]

[0190] The above problem is transformed into the optimization problem of the auxiliary function. The minimum value of the auxiliary function is the conditional risk value of wind power output deviation. At the same time, the calculation formula of the cost of wind power due to output deviation can also be unified into the piecewise function:

[0191]

[0192] Where: C S is the spinning reserve price per unit of electricity, P Wjt is the planned power generation of the wind turbine in period j, P Rjt is the actual power generation of the wind turbine in period j, C P is the loss price per unit of electricity, and α is the threshold.

[0193] Similarly, photovoltaic output is uncertain, and photovoltaic output prediction may be biased. When the photovoltaic output capacity is overestimated during prediction, its actual output does not meet the load balance requirements. At this time, additional generators are needed to supplement this part of the load demand. The system needs to increase the spinning reserve capacity. This part of the cost can be expressed as:

[0194] F S1 =C S1 (P Sjt -P Rjt1 )(20.1)

[0195] Where: C S1 is the spinning reserve price per unit of electricity, P Sjt is the planned power generation of photovoltaic power generation in period j, PRjt1 is the actual power generation of photovoltaic power generation in period j.

[0196] Also, due to the deviation of photovoltaic output prediction, photovoltaic output is sometimes underestimated, and the actual photovoltaic output will exceed the load demand. In order to meet the safety constraints of the power grid, the excess should be abandoned, which will lead to waste of photovoltaic resources and corresponding economic losses, which is equivalent to a fine for underestimating photovoltaic output. This part of the cost can be expressed as:

[0197] F P1 =C P1 (P Rjt1 -P Sjt )(20.2)

[0198] Where: C P1 is the loss price per unit of electricity.

[0199] Due to the uncertainty of photovoltaic output, there is a certain deviation between the actual photovoltaic output and the planned output. The deviation of photovoltaic output is also defined as:

[0200] f(P Sjt ,P Rjt1 )=|P Sjt -P Rjt1 |(20.3)

[0201] Where: P Sjt Contribute to the optoelectronics project, P Rjt1 is the actual output of photovoltaic power. The actual output of photovoltaic power is a random variable, while the planned output is a controlled variable. The deviation of photovoltaic output can accurately represent the uncertainty of photovoltaic output when a certain confidence level is known. The probability that the deviation function does not exceed a threshold α is:

[0202]

[0203] Then, at a given confidence level β, the risk value of photovoltaic output deviation can be expressed as follows:

[0204] V VaR (P Sjt )=min{α∈R:ψ(P Sjt ,α)≥β}(20.5)

[0205] Using the conditional risk calculation formula, the conditional risk calculation formula for photoelectric output deviation is:

[0206]

[0207] According to the above calculation analysis, the conditional risk contains the risk value function term, and the analytical expression of the risk value function is very complex and difficult to calculate directly. Therefore, an auxiliary function is introduced to simplify the calculation of conditional risk:

[0208]

[0209] Where: [t] + =max{t,0}.

[0210] Because the actual value of photovoltaic output is a random variable, the analytical expression of its probability density function is usually very complex and difficult to obtain directly. Therefore, it will be difficult to directly use the analytical method to calculate the expected value of the above formula. Here, the Monte Carlo random sampling method is used to extract N sample points of the random variable for estimation. The calculation formula for the estimated value of the auxiliary function is:

[0211]

[0212] Introduce auxiliary variables:

[0213]

[0214] So the above problem becomes:

[0215]

[0216] The above problem is transformed into the optimization problem of the auxiliary function. The minimum value of the auxiliary function is the conditional risk value of the photovoltaic output deviation. At the same time, the calculation formula of the cost of photovoltaic output deviation can also be unified into the piecewise function:

[0217]

[0218] Where: C S1 is the spinning reserve price per unit of electricity, P Sjt is the planned power generation of photovoltaic power generation in period t, P Rjt1 is the actual power generation of photovoltaic power generation in period j, C P1 is the loss price per unit of electricity, and α is the threshold.

[0219] The conditional risk of wind power and photovoltaic power output deviation not only takes into account the impact of random factors on the economic operation of the power system, but also can specifically quantify the size of the loss level and its corresponding probability in each case, which can comprehensively and effectively reflect the potential risks of economic indicators in system operation. According to the conditional risk calculation formula of wind power and photovoltaic power, it can be seen that when the deviation between the planned output and the actual output of distributed power sources is greater, the value of the conditional risk is greater, indicating that the risk faced by the system during operation is greater, and the penalty cost generated is higher, the economic efficiency of the system operation is worse, and the safe and stable operation of the system is more unfavorable, which is consistent with the actual situation.

[0220] like Figure 2 As shown, Figure 2 This is a schematic diagram of the distributed resource response capability evaluation index. Taking the process of a VPP internal resource accepting the upper-level scheduling as an example, t ss The command is issued at this moment, and the resource output is P init , and starts to descend according to the scheduling instruction, at t ds The power reaches the minimum value P at the moment min At this time, the adjustment direction D is downward, T R is the response time, T D For duration.

[0221] like Figure 3 As shown, Figure 3 The value of the aggregate response capability evaluation index is determined for VPP according to the output power variation curve. The output power variation in the figure is the difference between the planned output power and the output power during the adjustment process, and ΔP is the total power variation of VPP required by the adjustment instruction.

[0222] In order to establish a more comprehensive response capability evaluation index system and evaluate the overall response capability of a virtual power plant, the present invention first evaluates the response capability of various distributed resources in the virtual power plant. The response capability of distributed resources mainly includes the upward adjustment amplitude, the downward adjustment amplitude, the upward climbing rate, the downward climbing rate, and the average response time.

[0223] On the basis of the independent response capability model of distributed resources, two types of internal resources of virtual power plants, namely energy storage system and temperature control load cluster, are selected. Their respective response capability models are established based on the power generation, power consumption mechanism or demand response model. As the basis for the evaluation of aggregated response capability, an aggregated response capability evaluation model of virtual power plants is established on the day-ahead, and the multi-dimensional quantitative indicators of response capability are calculated to obtain the feasible regulation domain after the aggregated resources.

[0224] Considering the instability of response capability after aggregation, integrated empirical mode decomposition and stacked autoencoder are used to extract the electricity consumption characteristics of various distributed resources. The characteristics are used to obtain the key parameters in the consumer psychology model, and the probability distribution of the response potential of various distributed resource clusters at each time node is formed. The entropy method is used to obtain the indicator weights, and the indicator weights are analyzed to obtain the aggregated response capability score. The feasible regulation domain after aggregation is obtained, which provides decision-making assistance for power grid dispatching and control. The aggregated response capability score can be used as a reference indicator for other problems such as the optimal configuration of virtual power plants.

[0225] The Monte Carlo simulation method is used to construct uncertain scenarios of distributed wind power and photovoltaics, and a virtual power plant optimization model considering conditional risk value is established to quantify the probability of risk caused by the uncertainty of renewable energy as a reference for operation optimization cost risk.

[0226] The present invention also provides an electronic device, Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 4 As shown, the electronic device may include: a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory communicate with each other via the communications bus. The processor may call the logic instructions in the memory, for example, to execute the following method:

[0227] S1. Select resource response capability evaluation indicators, evaluate the response capability of various distributed resources in the virtual power plant, and build response capability models of various distributed resources;

[0228] S2. Construct a virtual power plant aggregate response capability evaluation model based on various distributed resource response capability models and aggregate response capability evaluation parameters;

[0229] S3. Assign weights to the resource responsiveness indicators within the virtual power plant to obtain an aggregated responsiveness score.

[0230] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0231] An embodiment of the present invention further provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method provided in each of the above embodiments is implemented, for example, including:

[0232] S1. Select resource response capability evaluation indicators, evaluate the response capability of various distributed resources in the virtual power plant, and build response capability models of various distributed resources;

[0233] S2. Construct a virtual power plant aggregate response capability evaluation model based on various distributed resource response capability models and aggregate response capability evaluation parameters;

[0234] S3. Assign weights to the resource responsiveness indicators within the virtual power plant to obtain an aggregated responsiveness score.

[0235] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0236] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the aggregate response capability of distributed resources of a virtual power plant, characterized in that: include: S1. Select resource response capability evaluation indicators, evaluate the response capability of various distributed resources in the virtual power plant, and build response capability models of various distributed resources; S2. Construct a virtual power plant aggregate response capability evaluation model based on various distributed resource response capability models and aggregate response capability evaluation parameters; S3. Assign weights to the resource responsiveness indicators within the virtual power plant to obtain an aggregated responsiveness score.

2. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 1, characterized in that: The resource response capability evaluation indicators are: adjustment direction D, adjustment amplitude A, response time T R Duration T D and climbing rate R.

3. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 2, characterized in that: The various types of distributed resources include: energy storage systems and temperature control load clusters.

4. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 3, characterized in that: According to the resource response capability evaluation index, the energy storage system response capability model is constructed as follows: Adjustment direction: bidirectional adjustment; Adjustment range: The formula is: Among them, P b (t) is the current output, P b,max , P b,min The upper and lower limits of output; Response time: unit time is Δt; Duration: The formula is: Among them, E b (t) is the electric energy stored in the energy storage system at time t; E b,max , E b,min are the upper and lower limits of the energy stored in the energy storage system; η b is the charge and discharge power, is the charging duration, is the discharge duration; Climbing rate: The constraint formula is: Where: R b (t) is the climbing rate of the energy storage system at time t; They are respectively the maximum downward and upward adjustment values ​​of the output within unit time Δt.

5. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 3, characterized in that: According to the resource response capability evaluation index, the temperature control load cluster response capability model is constructed as follows: Adjustment direction: bidirectional adjustment; Adjustment range: The formula is: Among them, P c (t) is the current output, P c,max , P c,min The upper and lower limits of output; Response time: vs. ΔT in Related to, ΔT in The larger it is, the longer the response time will be; Duration: The unit time is Δt; Climbing rate: The constraint formula is: in, They are the upper and lower limits of output change per unit time Δt; ΔT out and ΔT cr T out and T in Change per unit time; ΔT cr There is a constraint |ΔT cr |≤ΔT cr，max , where ΔT cr，max is the maximum room temperature change.

6. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 1, characterized in that: The aggregate response capability evaluation parameters are: tie line power baseline, tie line power upper and lower limits, virtual power plant ramp rate upper and lower limits, and virtual power plant response time; According to the aggregate response capability evaluation parameters, the virtual power plant aggregate response capability evaluation model is constructed as follows: Tie line power baseline, objective function formula is: Where: ψ is the set of running times; S is the set of all devices; i is the device number; p i (t) is the power emitted by device i at time t; p tie (t) is the interconnection line power at time t; c i (p i (t)) is the cost function of device i at time t; c tie (t) is the electricity purchase price of the interconnection line at time t; The upper and lower limits of the tie line power, the objective function formula is: maxp tie (t)t∈ψ(9)minp tie (t)t∈ψ(10)where maxp tie (t), maxp tie (t) is the maximum and minimum value that the tie line power can reach at time t; The upper and lower limits of the virtual power plant ramp rate and the objective function formula are: in, The maximum value of the output power increase or decrease from the current time t to the next time t+1 when running at the power baseline; Virtual power plant response time When operating at the power baseline, the adjustment command is received at time t, and the adjustment is completed in the shortest time at time The total power of all resources reaches the change ΔP required by the regulation instruction, and the objective function formula is: Where: S ΔP is a set of ΔP values ​​at equal intervals; m is the time traversed in the summation equation; v ΔP,m It is a 0-1 variable.

7. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 1, characterized in that: Also includes: According to the aggregated response capability evaluation model, the probability distribution of the adjustable capacity potential of various distributed resources at different times is obtained, including the following steps: S41. Obtain the historical response data set of various distributed resources at each time node. The formula is expressed as: d ij =[the ij ,c ij ] (14) Among them, η ij is the historical load reduction rate, c ij Incentive price for participating in the response; S42, extracting the power consumption feature sets of various distributed resources by integrating empirical mode decomposition and stacked autoencoders; S43, performing dimensionality reduction processing on the electricity consumption feature set, and using the least squares method to fit and mine key parameters of the adjustable potential model; S44, performing point estimation on the response history data set to obtain estimated values ​​at each time node; S45. Perform rolling analysis on the time series data of each time node of each type of distributed resources to obtain the probability distribution of adjustable capacity potential at different times.

8. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 1, characterized in that: In S3, weights are assigned to resource response capability indicators within the virtual power plant to perform aggregate response capability scoring, wherein the weighted response capability indicators include: upward adjustment range, downward adjustment range, upward climbing rate, downward climbing rate, and average response time. The steps include: S31. Standardize the weighted response capability index, the formula is: Where: x m,n,t is the value of the nth weighted response capability index of the mth virtual power plant at time t; x' m,n,t is the standardized value of the corresponding weighted response capability index; formula (15) is applicable to positive indicators; formula (16) is applicable to negative indicators; S32. Calculate the aggregate response capability score F of the mth virtual power plant m , the formula is: Among them, w m,n is the weight of the nth weighted response capability indicator of the mth virtual power plant; Γ is the sequence set of weighted response capability indicators; e m,n is the entropy value of the nth weighted response capability index of the mth virtual power plant; r m,n,t It is the proportion of the nth weighted response capability index of the mth virtual power plant at time t.

9. The method for evaluating the aggregated response capability of distributed resources of a virtual power plant according to claim 1, characterized in that: When obtaining the aggregated response capability score of the virtual power plant in S3, the conditional risk value of wind power and photovoltaic power is also considered: Among them, the wind power conditional risk value is expressed as follows: Among them, C S is the spinning reserve price per unit of electricity, P Wjt is the planned power generation of the wind turbine in period j, P Rjt is the actual power generation of the wind turbine in period j, C P is the loss price per unit of electricity, α is the threshold; Among them, the photoelectric conditional risk value is expressed as follows: Among them: C S1 is the spinning reserve price per unit of electricity, P Sjt is the planned power generation of photovoltaic power generation in period t, P Rjt1 is the actual power generation of photovoltaic power generation in period j, C P1 is the loss price per unit of electricity, and α is the threshold.

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