A new energy and energy storage collaborative working method

Abstract

The present invention discloses a method for collaborative working of new energy and energy storage, and relates to the field of new energy. The present invention includes the following steps: according to the historical data of new energy prediction and actual power generation, the predicted power generation and predicted deviation power are obtained; a declaration strategy is established: strategy one, the predicted value of the predicted power generation is used as the upper limit of the reported power; strategy two, the predicted value of the power generation after superimposing the deviation power is used as the upper limit of the declared power; combined with the real-time power of energy storage, the deviation power of new energy is reduced; the declaration strategy is selected according to the predicted deviation power vector and the probability of the predicted deviation power; the deviation risk is obtained according to the deviation power; the objective function of maximizing the expected return is established with the goal of maximizing the difference between market return and deviation risk. The present invention can effectively avoid the deviation risk of new energy participating in the market, obtain more opportunities to make profits in the energy market and the auxiliary service market, and improve the power generation efficiency of new energy and its initiative to participate in the market.

Description

Technical Field

[0001] The present invention relates to the field of new energy, and more specifically to a method for coordinated working of new energy and energy storage. Background Art

[0002] The power generation efficiency of new energy depends on costs and benefits. The cost of new energy is different from that of traditional thermal power. Its construction investment and maintenance costs decrease with the increase of new energy installed capacity, and the fuel consumption cost is almost zero. The cost mainly lies in the deviation penalty and power abandonment loss of new energy participating in the market. In the power market, the deviation penalty is generally determined by the difference between the declared power and the actual power of the power generation entity or user, that is, the deviation power. Due to the uncertainty of new energy, its actual power generation power is often not accurately issued as planned, resulting in a large amount of deviation power accumulated in each settlement cycle, resulting in high deviation penalties and direct economic losses. At present, the technical and economic means adopted by new energy to reduce deviation power and reduce deviation penalties mainly include power abandonment, energy storage construction, and purchase of power market auxiliary services. Among them, the essence of power abandonment is the failure to fully utilize resources, which wastes the commodity value of new energy power and causes another economic loss to new energy power plants. The two economic losses caused by deviation penalties and power abandonment constitute the main costs of new energy participating in the market. Summary of the invention

[0003] In view of this, the present invention provides a method for the coordinated working of new energy and energy storage, which can effectively avoid the deviation risk of new energy participating in the market, obtain more profit opportunities in the energy market and ancillary service market, and improve the power generation efficiency of new energy and its initiative in participating in the market.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] A method for collaborative working of new energy and energy storage comprises the following steps:

[0006] According to the historical data of new energy forecast and actual power generation, the predicted power generation and predicted deviation power are obtained;

[0007] Establish a reporting strategy: Strategy 1: Use the predicted power generation value as the upper limit of the reported power; Strategy 2: Use the predicted power generation value after adding the deviation power as the upper limit of the reported power;

[0008] Combined with the real-time power of energy storage, the deviation power of new energy can be reduced;

[0009] Selecting a declaration strategy according to the predicted deviation power vector and the probability of the predicted deviation power;

[0010] Obtain deviation risk based on deviation power;

[0011] With the goal of maximizing the difference between market return and deviation risk, an objective function for maximizing expected return is established.

[0012] Preferably, the expression of the deviation risk is:

[0013]

[0014] Where ΔP t en is the deviation power in period t, which is positive if overestimated; H is the penalty unit price, recorded as the overestimated electricity price; L is the unit cost of abandoned electricity, recorded as the underestimated electricity price.

[0015] Preferably, the predicted prediction deviation power vector and the probability of the predicted deviation power are calculated as follows:

[0016] The predicted deviation power in period t is The probability is Deviation Power Vector and its probability for:

[0017]

[0018] The above formula indicates that the prediction deviation power vector is composed of the permutations and combinations of the prediction deviation powers of each time period, and its corresponding probability is the product of the deviation power probabilities of each time period in the permutation and combination.

[0019] Preferably, the constraints for reducing the deviation power of new energy are as follows:

[0020]

[0021] Among them, P s,t is the output of the new energy deviation power, P ot h er,t is the other initial power, is the charging power, is the discharge power.

[0022] Preferably, the objective function for maximizing expected return is as follows:

[0023]

[0024] is the prediction bias vector The probability of, both are parameters of the objective function F; function I en ,I an , C en , C an is the decision variable of F, where I en ,I anThe benefits of participating in the energy market and the benefits of participating in ancillary services, c en 、c an They are energy trading electricity price and ancillary service electricity price respectively.

[0025] Preferably, strategy 1 is as follows: only the predicted power generation of new energy sources on the day before is used as the power reported on the day before for participating in the energy market and ancillary services:

[0026]

[0027] are the transaction power reported in the energy market and ancillary services during the t period a day ago; P p,t is the power generation power predicted in time period t on the day before.

[0028] Preferably, Strategy 2 is as follows:

[0029] The sum of the new energy day-ahead predicted power generation and the predicted deviation power is used as the day-ahead total declared power for participating in the energy market and ancillary services.

[0030]

[0031] in, are the transaction power reported in the energy market and ancillary services during the t period a day ago; P p,t is the power generation power predicted in time period t, is the deviation power during period t of the next day.

[0032] Preferably, it also includes a constraint that the power reported by the auxiliary service is consistent with the direction of system demand:

[0033]

[0034] in, The trading power in the energy market during the t period reported a few days ago; is the system auxiliary service demand during period t.

[0035] Through the above technical solutions, it can be known that compared with the prior art, the present invention discloses a method for the coordinated working of new energy and energy storage. Compared with new energy power plants without energy storage, power plants with coordinated operation of energy storage and new energy have lower deviation risks and higher market returns. This not only reflects that the coordinated operation of new energy and energy storage can also avoid some deviation risks in the market, but also shows that energy storage can transfer the deviation electricity of new energy in a certain period to a period with market demand for profit. Overall, in this specific scenario, the total expected return of participating in the energy market and ancillary services according to strategy one is higher than that of strategy two, and it can be found that the synergistic effect of energy storage in new energy power plants is mainly:

[0036] 1) In the underestimation (overestimation) of new energy 1 charging (discharging) during the period and overestimating (underestimating) the energy source t 2 Discharge (charge) during a certain period of time, thereby reducing the deviation power of new energy and avoiding the risk of partial deviation of new energy;

[0037] 2) In the underestimation (overestimation) of new energy 1 charging (discharging) during the period, and when the positive (negative) auxiliary service is in short supply, 3 Discharge (charge) during time periods, thereby increasing the revenue from new energy auxiliary services. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 It is a new energy power generation curve diagram of the present invention;

[0040] Figure 2 A power relationship diagram for the operation of the strategy of the present invention;

[0041] Figure 3 is the predicted deviation power diagram of the present invention;

[0042] Figure 4 A relationship diagram between the probability of predicted deviation power and the total expected benefit of the present invention;

[0043] Figure 5 is a relationship diagram between the total expected benefit and the deviation power and probability of the present invention;

[0044] Figure 6 is a relationship diagram between the advantage strategy of the present invention and the deviation power and probability;

[0045] Figure 7 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The embodiment of the present invention discloses a method for cooperating between new energy and energy storage, comprising the following steps: Figure 7 As shown:

[0048] According to the historical data of new energy forecast and actual power generation, the predicted power generation and predicted deviation power are obtained;

[0049] Establish a reporting strategy: Strategy 1: Use the predicted power generation value as the upper limit of the reported power; Strategy 2: Use the predicted power generation value after adding the deviation power as the upper limit of the reported power;

[0050] Combined with the real-time power of energy storage, the deviation power of new energy can be reduced;

[0051] Selecting a declaration strategy based on the predicted deviation power vector and the probability of the predicted deviation power;

[0052] Obtain deviation risk based on deviation power;

[0053] With the goal of maximizing the difference between market return and deviation risk, an objective function for maximizing expected return is established.

[0054] In this embodiment, the details are as follows:

[0055] 1. Deviation risk and deviation power of new energy market participation

[0056] 1.1 Deviation risks of new energy market participation

[0057] 1.1.1 Bias risks in the energy market

[0058] Renewable energy needs to formulate a power generation plan to participate in energy market transactions. When the planned power generation of new energy is lower than the actual power generation (hereinafter referred to as "underestimation"), new energy faces power abandonment, which directly causes economic losses to new energy; when the planned power generation of new energy is higher than the actual power generation (hereinafter referred to as "overestimation"), new energy will be punished by the market. Due to the uncertainty of new energy resources, the actual power generation of new energy on a day deviates from the planned power generation (such as Figure 1 The shaded part shows the deviation between the actual power and the bid power, which is the deviation power. Therefore, the economic loss of new energy abandonment and the penalty cost are regarded as the deviation risk of new energy participating in the energy market. The expression is for:

[0059]

[0060] Where ΔPten is the deviation power during period t, with overestimation being positive; λ H is the penalty unit price, recorded as the overestimated electricity price; L is the unit cost of abandoned electricity, recorded as the underestimated electricity price.

[0061] 1.1.2 Risk of Bias in Auxiliary Services

[0062] Renewable energy can obtain high returns by participating in auxiliary services, but the uncertainty of new energy power generation makes it difficult for it to become a high-quality auxiliary service resource like traditional units, which is inconsistent with the development trend of a high proportion of new energy in the power system. Therefore, by setting a penalty electricity price in the auxiliary market, new energy power plants can share the adverse impact of the uncertainty of the auxiliary services they are called upon to bring to the power grid, and part of the penalty fee can be regarded as the deviation risk of new energy power plants participating in the auxiliary service market. It is expressed as:

[0063]

[0064] Where ΔP t an is the deviation power of new energy in response to auxiliary services. In order to respond to policy calls, encourage and guide new energy to actively participate in auxiliary services, a nonlinear auxiliary market deviation penalty price λ can be set an .

[0065] 1.2 Energy storage reduces the deviation power of new energy

[0066] 1.2.1 New energy deviation power including energy storage regulation

[0067] New energy power plants with energy storage can reduce the absolute value of the new energy deviation power through flexible charging and discharging of energy storage:

[0068] ΔP t b.err =ΔP t err -P s,t (3)

[0069] Where ΔP t err , ΔP t b.err are the sum of the deviation power of the energy market or ancillary services before and after the superposition of energy storage output; P s,t Energy storage is used to reduce the output of deviation power of new energy, and it needs to be restricted to prevent overcharging and over-discharging.

[0070] 1.2.2 Maximum charging and discharging power of energy storage under constant power

[0071] 1) Existing energy storage models

[0072] Existing literature mainly describes the operation constraints of energy storage in terms of success rate and capacity constraints, such as equations (4) and (5):

[0073]

[0074] Et+1 (P bess,t )=E t -η d / c P bess,t Δt (5)

[0075] Among them, D t is the power that can be generated by the energy storage; P bess,t is the total energy storage power; P max is the rated power of the energy storage; α and β are the upper and lower limit coefficients of the charge capacity; E t is the remaining energy stored at the beginning of period t; E max is the rated capacity of energy storage; η d / c It is the charging and discharging efficiency of the energy storage battery, which is greater than 1 during discharge and less than 1 during charging; Δt is the continuous charging and discharging time.

[0076] 2) Energy storage maximum charge and discharge power function

[0077] When the energy storage is continuously charged and discharged at a constant power for a period of time, the energy storage model equations (4) and (5) cannot intuitively describe the value limit of the constant power at the beginning of any period, such as the value limit of Ps,t in equation (3), and thus it is easy to fall into a local optimal solution. Therefore, it is necessary to characterize the maximum power.

[0078] At the beginning of time period t, the charge is E t Maximum charging power of energy storage and maximum discharge power The function expression is:

[0079]

[0080]

[0081] Formula (6) and Formula (7) indicate that the maximum constant power of energy storage is Δt after this moment. Charging or The maximum charge and discharge power function can intuitively describe the constraints on the constant output of the initial energy storage in any period of time.

[0082] 3) Energy storage reduces the constraints of new energy deviation power

[0083] If the energy storage has other output at the beginning of time period t, then the energy storage can reduce the output of the new energy deviation power P in this state. s,t The constraints are:

[0084]

[0085] Among them, P other,tis the other initial power, and the discharge is positive. It is worth pointing out that formula (8) is not only applicable to the research object of this paper, but also applicable to constraining the constant power that the energy storage needs to increase or decrease within the time Δt under any operating state.

[0086] 2. Strategies for new energy and energy storage to participate in the market together

[0087] 2.1 Two day-ahead declaration strategies and operation procedures

[0088] 2.1.1 Prediction bias power and probability

[0089] The historical power generation deviation power can be known from the historical forecast of new energy and the actual power generation data, and then the discrete probability distribution of the deviation power in the next day t period can be predicted, that is, the deviation power in the next day t period is The probability is Assuming that the deviation power of the new energy power plant in 24 periods is independent of each other, the deviation power vector of the new energy power plant for the next 24 periods is predicted to be and its probability for:

[0090]

[0091] Formula (9) indicates that the next day's forecast deviation power vector is composed of the permutations and combinations of the forecast deviation powers of each time period, and its corresponding probability is the product of the deviation power probabilities of each time period in the permutation and combination.

[0092] 2.1.2 Day-ahead reporting strategy considering forecast deviation power

[0093] Strategy 1: The sum of the new energy day-ahead predicted power generation and the predicted deviation power is used as the day-ahead total power reported for participating in the energy market and ancillary services.

[0094]

[0095] in, are the transaction power reported in the energy market and ancillary services during the t period a day ago; P p,t is the power generation power predicted in the day-ahead period t. This strategy takes the predicted deviation power into account in the day-ahead power application, which has a certain probability of avoiding the expected deviation risk and obtaining a greater market return than expected, thereby increasing the total expected return. However, it also has a certain probability of bearing a greater deviation risk caused by uncertainty.

[0096] 2.1.3 Day-ahead reporting strategy without considering forecast deviation power

[0097] Strategy 2: Only use the predicted power generation of renewable energy as the day-ahead power reported for participating in the energy market and ancillary services.

[0098]

[0099] This strategy does not include the predicted deviation power into the range of power reported on the previous day. It is more conservative than Strategy 1 and does not need to bear the risk of unexpected deviations, but at the same time it also does not have the possibility of obtaining greater market returns.

[0100] 2.1.4 Strategy Operation Process

[0101] The operation process of the new energy and energy storage collaborative participation market strategy is as follows: Figure 2 As shown in Figure 1, it mainly includes three processes: day-ahead declaration, simulated intraday operation and simulated settlement. In the day-ahead declaration process, according to the predicted next day deviation power vector and its probability Select reporting strategies and determine reporting of next-day trading power in energy markets and ancillary services During the simulation day operation, according to the actual power generation power P of renewable energy re , real-time adjustment of energy storage output P bess , and distribute the power of the two to the power P of the new energy market transaction 1 en , P 1 an In the simulation settlement process, the power that new energy can benefit from on that day is calculated based on the power reported on the previous day and the power allocated during the day. and deviation power ΔP t en , ΔP t an , and then calculate the corresponding daily returns I en ,I an and risk of bias C en , C an .

[0102] 2.2 Market revenue and deviation risk settlement for energy storage coordinated operation

[0103] 2.2.1 Intraday power allocation and transaction settlement

[0104] The actual power generation of renewable energy on a day plus the real-time power of energy storage is the maximum value of the power that can be allocated by renewable energy power plants in the energy market and auxiliary services on a day:

[0105]

[0106]

[0107] Among them, P re,t P is the actual power generated by renewable energy in the day during period t; bess,t is the real-time power of energy storage during period t; are the intraday trading power of new energy power plants allocated to the energy market and ancillary services during period t, recorded as allocated power; Formula (13) constrains the intraday allocated power of ancillary services to be consistent with the direction of the power reported the day before.

[0108] New energy power plants are Participating in intraday settlement of the two markets can generate income for a part of the power, while the other part of the power is subject to deviation risk. The power that can obtain settlement income is:

[0109]

[0110]

[0111] The power at risk of deviation (deviation power) is:

[0112]

[0113]

[0114] Formula (14) indicates that the energy market only settles positive power, and the power that can obtain settlement income does not exceed the power reported on the day before; Formula (15) indicates that ancillary services can settle positive power and negative power, but the power that can obtain settlement income cannot exceed the power reported on the day before. In formulas (16) and (17), ΔP t en , ΔP t an They are the powers with deviation risks in the energy market and ancillary services during period t, i.e., the deviation powers of the market in which new energy and energy storage participate in collaboration.

[0115] 2.2.2 Market benefits of energy storage-based coordinated operation

[0116] Benefits of participating in the energy market I en and benefits from participation in ancillary services I an for:

[0117]

[0118]

[0119] where c en 、c an They are energy trading electricity price and ancillary service electricity price respectively.

[0120] 2.2.3 Bias risks in coordinated operation with energy storage

[0121] According to equations (1) to (3) and (6) to (8), new energy is The risk of deviation in reporting energy markets and ancillary services is:

[0122]

[0123]

[0124] Among them, C en , C an They are the deviation risks of participating in the energy market and ancillary services; ΔP t en , ΔP t an Calculated by equations (16) and (17), it is the deviation power that still exists after energy storage adjustment. Its absolute value is smaller than the absolute value of the actual power generation deviation of new energy. Therefore, it can effectively avoid the deviation risk of new energy power generation to a certain extent.

[0125] 3 New energy and energy storage collaborative operation model

[0126] 3.1 Maximum expected return objective function with bias risk

[0127] Under the same day-ahead declaration strategy, even when the forecast deviation power and probability are constant, the market returns and deviation risks are different due to the different combinations of transaction power declared by new energy power plants in the energy market and ancillary services, as well as the different combinations of power allocated during intraday operation. Therefore, with the goal of maximizing the difference between market returns and deviation risks, an objective function for maximizing expected returns is established to optimize the power combination declared by new energy power plants on the day-ahead and the intraday power allocation:

[0128]

[0129] in, is the prediction bias vector The probability of, both are parameters of the objective function F; function I en ,I an , C en , C an The independent variables include P bess,t , and these are also the decision variables of F.

[0130] 3.2 Constraints

[0131] The equality constraints for solving the objective function include power balance equations (10) to (12), (16) and (17). In addition to the maximum storage charge and discharge power constraint (8) and the auxiliary service intraday power allocation constraint (13), the inequality constraints also include the constraint that the auxiliary service reported power is consistent with the system demand direction:

[0132]

[0133] in, is the system auxiliary service demand during period t.

[0134] 4 Simulation Analysis

[0135] 4.1 Parameter settings

[0136] In the Matlab development environment, based on the Yalmip platform, Cplex12.0 is called for solution. A wind-storage power generation system is selected as the analysis object, with a wind power installed capacity of 200MW, a storage rated power of 20MW, a rated capacity of 40MWh, an initial power of 20MWh, other operating parameters as shown in Table 1, and electricity price parameters as shown in Table 2. 24 time periods are set in a day, each time period is 1h.

[0137] Table 1 Other parameters of energy storage

[0138]

[0139] Table 2 Electricity price parameters

[0140] parameter Electricity price (yuan / MWh) <![CDATA[Overestimated electricity price λ in the energy market H > 900 <![CDATA[Underestimated electricity price λ in the energy market L > 920 <![CDATA[Ancillary service deviation electricity price λ an > 500 <![CDATA[Electricity price for energy market transactions c en > 560 <![CDATA[Ancillary service trading electricity price c an > 900

[0141] 4.2 Comparison of market returns and bias risk

[0142] 4.2.1 Energy storage and new energy synergistic operation increase expected benefits

[0143] When the wind power plant's next-day forecast deviation power vector is like Figure 3 As shown, the probability is 0.8. Under the day-ahead declaration strategy 1 and strategy 2, the two market returns and deviation risks of the coordinated operation of energy storage and wind power plants are shown in Table 3, and the market returns and deviation risks of wind power plants without energy storage are shown in Table 4.

[0144] Table 3 Simulation results of energy storage and new energy coordinated operation

[0145]

[0146] Table 4 Simulation results of new energy operation without energy storage

[0147]

[0148] The common point between Table 3 and Table 4 is that the returns of the two markets of Strategy 1 are higher than those of Strategy 2, and the two deviation risks of Strategy 1 are lower than those of Strategy 2. This is because in Strategy 1, the day-ahead declarations of the two markets have taken into account a part of the deviation power that may occur the next day, and because the probability of the deviation power occurring is higher Therefore, it not only increases the upper limit of market benefits, but also avoids deviation risks to a certain extent through strategies. The difference is that compared with new energy power plants without energy storage, power plants that operate in conjunction with energy storage and new energy have lower deviation risks and higher market returns. This not only reflects that the coordinated operation of new energy and energy storage can also avoid some of the deviation risks of participating in the market, but also shows that energy storage can transfer the deviation electricity of new energy in a certain period of time to a period of time with market demand for profit. Overall, in this specific scenario, the total expected benefits of participating in the energy market and ancillary services according to strategy one are higher than those of strategy two, and it can be found that the synergistic effects of energy storage in new energy power plants are mainly:

[0149] 1) In the underestimation (overestimation) of new energy 1 charging (discharging) during the period and overestimating (underestimating) the energy source t 2 Discharge (charge) during a certain period of time, thereby reducing the deviation power of new energy and avoiding the risk of partial deviation of new energy;

[0150] 2) In the underestimation (overestimation) of new energy 1 charging (discharging) during the period, and when the positive (negative) auxiliary service is in short supply, 3 Discharge (charge) during time periods, thereby increasing the revenue from new energy auxiliary services.

[0151] 4.2.2 Impact of Deviation Power Probability on Expected Return

[0152] Different from the specific scenario simulation results in Section 4.2.1, this section uses the deviation power vector Comparing the probabilities under fixed conditions The impact of the change on the total expected return is as follows: Figure 4 shown. Figure 4 middle It is positively correlated with the total expected return of strategy 1 and negatively correlated with the total expected return of strategy 2. The probability of the return balance point of the two strategies is between 0.4 and 0.5. This is because as the probability increases, the expectation of additional market returns obtained by strategy 1 due to the consideration of deviation power increases, and the expectation of deviation risk caused by strategy 2 due to failure to consider deviation power also increases, so the total expected return of strategy 1 gradually exceeds that of strategy 2; on the contrary, as the probability decreases, the expectation of deviation risk caused by considering deviation power increases in strategy 1, and the expectation of deviation risk decreases in strategy 2 due to failure to consider deviation power, so the total expected return of strategy 1 gradually decreases than that of strategy 2.

[0153] 4.2.3 Impact of Deviation Power on Expected Return

[0154] 1) Market returns and bias risk results

[0155] When the probability is 0.49, the prediction deviation power is calculated as follows: The market returns and deviation risks under the three scenarios are shown in Tables 5 to 7.

[0156] Comparing the calculation results of Tables 5 to 7, it can be seen that as the forecast deviation power increases, the market benefits of both Strategy 1 and Strategy 2 in the energy market and ancillary services decrease, the deviation risk increases, and the total expected benefits decrease. However, the magnitude of the decrease in total expected benefits of Strategy 1 is greater than that of Strategy 2, resulting in the following phenomenon: When the forecast deviation power is When the total expected return of strategy 1 is higher than that of strategy 2; when the prediction deviation power is When the total expected returns of strategy 1 and strategy 2 are close, when the prediction deviation power is , the total expected return of strategy 2 is higher than that of strategy 1. This is because under this fixed probability, when the deviation power is small, strategy 1 can reasonably share the deviation power by changing the market's declared power, thereby avoiding deviation risks and obtaining additional market benefits, so the total expected return is higher; however, as the deviation power gradually increases, due to limited market demand, the ability to avoid deviation risks by changing the declared power is also limited. At the same time, the overestimation penalty and power abandonment costs are also very high. Continuing to allocate the deviation power to the two markets will cause the marginal cost to exceed the marginal benefit. Therefore, the total expected return of strategy 1 is gradually lower than that of strategy 2. At this time, the advantage of the relatively conservative strategy 2 is revealed.

[0157] Table 5 Market returns and deviation risks in scenario 1

[0158]

[0159] Table 6 Market returns and deviation risks in scenario 2

[0160]

[0161] Table 7 Market returns and deviation risks in scenario 3

[0162]

[0163]

[0164] 2) Using strategies to avoid bias risk characteristics

[0165] Comparing Table 5 with Table 3, we can see that when the predicted deviation power is the same, after the probability increases, the two market returns of Strategy 1 increase by 2,100 yuan and 10,300 yuan respectively, the deviation risk decreases by 5,500 yuan and 10,500 yuan respectively, and the total expected return increases by 28,400 yuan, which verifies that the conclusion of Section 4.2.2 is correct. In addition, the above data show that when using strategies to avoid deviation risks, that is, when a part of the power is to be apportioned and reported, the optimization model will be more inclined to report auxiliary services to obtain higher auxiliary market returns, but it will also face higher deviation risks.

[0166] 4.2.4 The combined impact of deviation power and probability on expected returns

[0167] Without considering extreme special cases, the deviation power between the actual power generation of renewable energy and its forecast and probability Closely related: When larger, The larger the amplitude, the greater the uncertainty of new energy. The smaller the amplitude, the more accurate the renewable energy generation is. When smaller, The larger the amplitude, the more uncertain the new energy is likely to be than predicted. The smaller the amplitude, the worse the uncertainty of new energy may be. In order to explore the impact of new energy uncertainty on the total expected return of the strategy, the objective function F and the parameter variable Some of the relationship surfaces are as follows Figure 5 As shown, its top view is as Figure 6 shown.

[0168] Within a certain range, It is roughly positively correlated with the objective function surface of strategy 1 and roughly negatively correlated with the objective function surface of strategy 2, which is consistent with the analysis in Section 4.2.2. Figure 5 It can be seen that there is an intersection line between the two expected return surfaces, from which we can find that there is no absolute superiority or inferiority relationship between Strategy 1 and Strategy 2.

[0169] from Figure 6 In any prediction deviation power Under this condition, as the probability increases, the total expected return advantage changes from strategy 2 to strategy 1, and as As the value increases, the probability corresponding to the conversion point also gradually increases. From another perspective, when When , the total expected return of strategy 2 is higher; when When , the total expected return of strategy 1 is higher; when When there is power like The total expected return of strategy 1 is higher, and vice versa, the total expected return of strategy 2 is higher, which is consistent with the results of Section 4.2.3. As the probability increases, the strategy conversion point with better total expected return corresponds to Also bigger.

[0170] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for collaborative working of new energy and energy storage, characterized in that: The following steps are involved: According to the historical data of new energy forecast and actual power generation, the predicted power generation and predicted deviation power are obtained; Establish a reporting strategy: Strategy 1: Use the predicted power generation value as the upper limit of the reported power; Strategy 2: Use the predicted power generation value after adding the deviation power as the upper limit of the reported power; Combined with the real-time power of energy storage, the deviation power of new energy can be reduced; Selecting a declaration strategy according to the predicted deviation power vector and the probability of the predicted deviation power; Obtain deviation risk based on deviation power; It also includes establishing an objective function for maximizing expected returns with the goal of maximizing the difference between market returns and deviation risk; The expression of the bias risk is: in, is the deviation power in period t, which is positive if overestimated; H is the penalty unit price, recorded as the overestimated electricity price; L is the unit cost of abandoned electricity, recorded as the underestimated electricity price; The predicted prediction deviation power vector and the probability of the predicted deviation power are calculated as follows: The predicted deviation power in period t is The probability is Deviation Power Vector and its probability for: The above formula indicates that the prediction deviation power vector is composed of the permutations and combinations of the prediction deviation powers of each time period, and its corresponding probability is the product of the deviation power probabilities of each time period in the permutation and combination; The constraints for reducing the deviation power of new energy are as follows: Among them, P s,t is the output of the new energy deviation power, P other,t is the other initial power, is the charging power, is the discharge power; The objective function to maximize expected return is as follows: is the prediction bias vector The probability of, both are parameters of the objective function F; function I en ,I an , C en , C an is the decision variable of F, where I en ,I an The benefits of participating in the energy market and the benefits of participating in ancillary services, c en 、c an They are energy trading electricity price and ancillary service electricity price; Strategy 1 is as follows: Only the predicted power generation of renewable energy sources is used as the power reported for participating in the energy market and ancillary services: are the transaction power reported in the energy market and ancillary services during the t period a day ago; P p,t is the power generation power predicted in period t on the day-ahead; Strategy 2 is as follows: The sum of the new energy day-ahead predicted power generation and the predicted deviation power is used as the day-ahead total power reported for participating in the energy market and ancillary services; in, are the transaction power reported in the energy market and ancillary services during the t period a day ago; P p,t is the power generation power predicted in time period t, is the deviation power during period t of the next day; It also includes the constraint that the power reported by the ancillary services is consistent with the direction of system demand: in, The trading power in the energy market during the t period reported a few days ago; is the system auxiliary service demand during period t.

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