Method for predicting inaccurate energy system capacity configuration based on renewable energy output

Abstract

The invention discloses a method for predicting inaccurate energy system capacity configuration based on renewable energy output. The problems that in the prior art, wind and light power generation calculation is conducted through a fixed output curve, only the uncertainty of renewable energy output is considered, and operation scheduling and installed capacity configuration are different are solved. The method comprises the steps that a renewable energy source output model is established by combining a renewable energy source reference output curve and a renewable energy source output prediction curve; establishing an energy system configuration optimization model by taking the overall operation optimization of the energy system as a target; and solving the energy system configuration optimization model by adopting a rolling window optimization algorithm to obtain an optimal capacity configuration scheme. According to the method, the influence of renewable energy output uncertainty and renewable energy prediction inaccuracy on energy system planning is comprehensively considered, and the system economics is optimal while the operation stability of the energy system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization allocation, and particularly to a method for configuring the capacity of an energy system based on inaccurate prediction of renewable energy output. Background Art

[0002] With the continuous increase in the proportion of renewable energy such as wind energy and solar energy in the energy system, the impact and challenges brought by the uncertainty of its output to the energy system are becoming greater and greater. This makes it necessary to reserve a part of flexible resources to cope with the negative impact brought by the uncertainty of renewable energy output when planning the energy system. Most of the current technologies aim at the overall optimal benefit of the energy system. First, the uncertainty curve of renewable energy output is characterized, and then the installed capacity of each unit and the real-time scheduling of the energy system are obtained through optimization calculation. In addition, the current technology does not analyze how high the prediction accuracy of renewable energy output needs to be to basically meet the requirements.

[0003] For example, the invention patent with the patent number CN202211005903.X and the name of a distributionally robust configuration method for an integrated energy system considering the uncertainty of renewable energy output. The key points of its technical solution are that this configuration method comprehensively considers the uncertainty of renewable energy output, aims at the overall optimal operation of the energy system, adopts a distributionally robust optimization method for the uncertainty of error distribution, and continuously searches for the probability distribution of the most extreme weather conditions within the constraints, ensuring the optimal value of the result of the energy system under the probability distribution of the most extreme climate conditions, and the configuration result is more conservative to achieve a better configuration effect.

[0004] The existing technology still has disadvantages. When the current research conducts optimization of energy system configuration, the wind and solar power generation mostly calculates with a fixed output curve, or only considers the uncertainty of renewable energy output, lacking the consideration of the differences in operation scheduling and installed capacity configuration caused by the prediction accuracy of renewable energy output. Since the prediction of renewable energy output has a very important impact on the scheduling of the energy system, which in turn affects the overall configuration planning of the energy system, it is necessary to study an energy system capacity configuration optimization method that can consider both the uncertainty of renewable energy output and the uncertainty of the short-term output prediction accuracy of renewable energy. Summary of the Invention

[0005] The present invention mainly solves the problems that the existing technology calculates wind and solar power generation with a fixed output curve and only considers the uncertainty of renewable energy output, resulting in differences in operation scheduling and installed capacity configuration, and provides a method for configuring the capacity of an energy system based on inaccurate prediction of renewable energy output.

[0006] The above technical problems of the present invention are mainly solved by the following technical solutions: A method for configuring the capacity of an energy system based on inaccurate prediction of renewable energy output, comprising the following steps: Obtain the historical output data of renewable energy at each moment to construct a renewable energy benchmark output curve, calculate a renewable energy output prediction curve based on the renewable energy benchmark output, and establish a renewable energy output model by combining the renewable energy benchmark output curve and the renewable energy output prediction curve; Establish an energy system configuration optimization model with the goal of overall optimal operation of the energy system; Solve the energy system configuration optimization model by using a rolling window optimization algorithm to obtain an optimal capacity configuration plan.

[0007] The present invention comprehensively considers the impacts of the uncertainty of renewable energy output and the inaccuracy of renewable energy prediction on the energy system planning, achieving the optimal system economics while improving the stability of the energy system operation. By introducing inaccurate prediction accuracy, the impact of short-term weather prediction on the energy system configuration optimization is taken into account, making the model planning process closer to the actual situation, and ultimately improving the adaptability of the energy system to extreme weather conditions or situations with increased errors. By analyzing, the specific requirements for improving the prediction accuracy of renewable energy output in practical applications are clarified, thus avoiding excessive resource investment caused by pursuing too high prediction accuracy.

[0008] As a preferred solution, Renewable energy includes wind power and photovoltaic power generation. Obtain the historical wind power and photovoltaic output data of the research area in the past many years, and conduct sampling at each time node through simple random sampling to construct a renewable energy benchmark output curve.

[0009] The renewable energy explored in the present invention mainly includes wind power and photovoltaic power generation. The renewable energy benchmark output can be established based on the historical data of renewable energy in the research area. Specifically, obtain the historical wind power and photovoltaic output data of the research area in the past N years through satellite remote sensing data or ground monitoring stations. According to the historical output data of wind power and photovoltaic power in each year, conduct sampling at each time node in each year through simple random sampling to construct a renewable energy benchmark output curve. Preferably, it can be in units of years. According to the historical output data of renewable energy in N years, conduct sampling at each time node in each historical year to construct multiple renewable energy benchmark output curves.

[0010] As a preferred solution, Based on the renewable energy benchmark output curve, set the prediction accuracy of renewable energy output, and generate a renewable energy output prediction curve through a stochastic optimization method.

[0011] During the actual operation process, operators generally determine the action amount of the key operation parameters of the energy system through short-term weather prediction values. The additional error caused by the inaccuracy of renewable energy output prediction usually resulting from short-term weather prediction errors is represented as the renewable energy output prediction curve, which follows a normal distribution.

[0012] As an optimal solution, The renewable energy output model is the sum of the renewable energy baseline output curve and the renewable energy output prediction curve.

[0013] The final renewable energy output curve is obtained by combining the uncertainty of renewable energy output with the inaccuracy of renewable energy output prediction.

[0014] As an optimal solution, an energy system configuration optimization model is established with the goal of the overall optimal operation of the energy system, including: A target function is established with the goal of minimizing the sum of the operation and maintenance investment of renewable energy equipment, the purchase investment of renewable energy equipment, the operation and maintenance investment of energy storage equipment, and the purchase investment of energy storage equipment; Constraints existing in the operation of the energy system are established, including the energy supply-demand balance constraint of the energy system, the stored electricity constraint of lithium batteries, and the constraints on the charging and discharging power and installed capacity of energy storage.

[0015] The constructed energy system simulation model, which includes four themes: wind power generation, photovoltaic power generation, electrochemical energy storage, and users, is configured and optimized with the goal of the overall optimal operation of the energy system. The energy system configuration optimization model includes a target function and constraint conditions. The target function is established with the goal of optimal configuration, that is, with the goal of minimizing the sum of the operation and maintenance investment of renewable energy equipment, the purchase investment of renewable energy equipment, the operation and maintenance investment of energy storage equipment, and the purchase investment of energy storage equipment. In addition, there are energy supply-demand balance constraints, stored electricity constraints of lithium batteries, and constraints on charging and discharging power and installed capacity in the operation of the energy system.

[0016] As an optimal solution, the energy supply-demand balance constraint of the energy system includes: The sum of wind power output, photovoltaic power output, and energy storage discharge power at the same moment is equal to the sum of energy storage charging power and load energy consumption power at the same moment; Among them, the wind power output and photovoltaic power output are calculated according to the renewable energy output model.

[0017] The wind power output and photovoltaic power output are calculated according to the renewable energy output model. For the energy supply-demand balance constraint condition of the energy system, it includes that the sum of wind power output, photovoltaic power output, and energy storage discharge power at time t is equal to the sum of energy storage power supply and load energy consumption power at time t.

[0018] As a preferred solution, the storage power constraint of the lithium battery includes: Under the condition of setting the energy storage charging efficiency and discharging efficiency, The energy storage power at the next moment is the difference between the sum of the energy storage power at the previous moment and the product of the energy storage charging efficiency and the energy storage charging power, and the quotient of the energy storage discharging power and the energy storage discharging efficiency.

[0019] Specifically, the energy storage battery power constraint condition is that, considering the energy storage charging and discharging efficiency, the energy stored in the energy storage at time t + 1 is equal to the energy stored in the energy storage at time t, plus the product of the energy storage charging efficiency and the energy storage charging power at time t, minus the ratio of the energy storage discharging power at time t to the energy storage discharging efficiency.

[0020] As a preferred solution, the energy storage charging and discharging power and installed capacity limit constraints include: The energy storage charging power is in the closed interval range of 0 and the product of the maximum energy storage charging and discharging power and the energy storage charging adjustment coefficient; The energy storage discharging power is in the closed interval range of 0 and the product of the maximum energy storage charging and discharging power and the energy storage discharging adjustment coefficient.

[0021] The energy storage charging and discharging power and installed capacity limit constraint condition at time t in this solution is specifically that the energy storage charging power is greater than or equal to 0 and less than or equal to the product of the maximum energy storage charging and discharging power and the energy storage charging adjustment coefficient, and the energy storage discharging power is greater than or equal to 0 and less than or equal to the product of the maximum energy storage charging and discharging power and the energy storage discharging adjustment coefficient. The values of the energy storage charging adjustment coefficient and the energy storage discharging adjustment coefficient are both 0 or 1.

[0022] As a preferred solution, the rolling window optimization algorithm is used to solve the energy system configuration optimization model, including: Based on the historical output data of renewable energy, N renewable energy reference output curves are constructed; Set the prediction accuracy of renewable energy output, and generate M renewable energy output prediction curves for each renewable energy reference output curve based on the stochastic optimization method; Based on each renewable energy reference output curve and renewable energy output prediction curve, according to the constraint conditions, the candidate optimal capacity configuration scheme is obtained by solving the energy system configuration optimization model at each time step; Select the most complex scheme from all candidate optimal capacity configuration schemes as the final optimal capacity configuration scheme.

[0023] The rolling window optimization algorithm of the present invention considers that the output of renewable energy has strong seasonality. To realize the annual cycle simulation, the annual time T is divided into K rolling windows, the length of each window, i.e., the time step, is Δt, and the rolling step size is Δt roll , and the annual time T = K * Δt. The end time t of each rolling windowstart k+1 is the start time t of the previous window start k plus the scroll step, t start k+1 = t start k + Δt roll .

[0024] First, based on the historical output data of renewable energy, obtain the output range of renewable energy at each time node, perform random sampling according to the output range, and construct N baseline output curves of renewable energy. On this basis, set the prediction accuracy of renewable energy output, and correct the N generated baseline output curves of renewable energy based on the stochastic optimization method. Generate M renewable energy output prediction curves for each baseline output curve of renewable energy through Monte Carlo simulation. Based on this, perform the optimal configuration calculation. By initializing the installed capacities of wind power, photovoltaic, and energy storage, under the premise of considering the constraints of the operation of the energy system, run the parallel computing technology for the objective function of the energy system configuration optimization model. After optimization simulation by Yalmip and Gurobi, obtain the optimal scheduling within each time step and the installed capacity data of various required devices, and obtain the stored electricity of the energy storage facility at the end of this simulation period. Use this as the initial value for the calculation of the next time step. During this process, continuously adjust the installed capacities of various devices in the energy system until the simulation completes all time periods. Finally, obtain the candidate optimal capacity configuration of the energy system, and at the same time, the operation conditions of the energy system under various renewable energy output scenarios can be obtained and compared to obtain the final optimal capacity configuration. Since the most complex scenario represents the most extreme weather conditions, it is necessary to ensure that the energy system can operate normally under the most extreme weather conditions. Therefore, the installed capacity configuration plan of the most complex scenario is used as the final optimal capacity configuration plan.

[0025] As an optimal solution, it also includes the steps of calculating the prediction accuracy of the optimal capacity configuration plan, including: Set the capacity configuration change threshold, Judge whether the change amount of the optimal capacity configuration is not less than the capacity configuration change threshold, If not, output the prediction accuracy; If so, increase the prediction accuracy, return to the step of generating M renewable energy output prediction curves, continue to execute the subsequent steps until the final optimal capacity configuration is obtained, and repeat the prediction accuracy calculation until the prediction accuracy is output.

[0026] This solution can determine the optimal demand for the prediction accuracy of renewable energy output in the future energy system. Based on the final optimal capacity configuration obtained by solving the energy system configuration optimization model using the above rolling window optimization algorithm, the prediction accuracy of renewable energy output is improved, and cyclic parallel operations are performed to obtain the operation of the energy system after improving the prediction accuracy of renewable energy. Until the change amount of the optimal operation of the system before and after improving the prediction accuracy is less than the capacity configuration threshold Q, it indicates that the effect of improving the prediction accuracy of renewable energy is very limited, thus proving that the prediction accuracy of renewable energy output in the case of p-1 can meet the daily production planning requirements.

[0027] Therefore, the advantages of the present invention are as follows: comprehensively considering the impact of the uncertainty of renewable energy output and the inaccuracy of renewable energy prediction on energy system planning, while improving the stability of the energy system operation, achieving the best system economics. By introducing the impact of inaccurate prediction accuracy on the energy system configuration optimization considering short-term weather prediction, this makes the model planning process closer to the actual situation, and finally improves the adaptability of the energy system to extreme weather conditions or situations with increased errors. By analyzing and clarifying the specific requirements for improving the prediction accuracy of renewable energy output in practical applications, thus avoiding excessive resource investment caused by pursuing too high prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a schematic flow chart of the rolling window optimization algorithm for solving the energy system configuration optimization model in the present invention.

[0029] Figure 2 is a schematic flow chart of the prediction accuracy calculation of the optimal capacity configuration scheme of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0030] The technical solution of the present invention will be further specifically described below through examples and in combination with the drawings.

[0031] Example 1: A method for configuring the capacity of an energy system based on the inaccurate prediction of renewable energy output in this embodiment includes the following steps: S1. Obtain the historical output data of renewable energy at each moment to construct a renewable energy reference output curve, calculate the renewable energy output prediction curve based on the renewable energy reference output, and establish a renewable energy output model by combining the renewable energy reference output curve and the renewable energy output prediction curve.

[0032] Renewable energy includes wind power and photovoltaic power generation. Obtain the historical wind power and photovoltaic output data in the research area over the past years, and sample at each time node through simple random sampling to construct a renewable energy reference output curve.

[0033] The renewable energy explored in this invention mainly includes wind power and photovoltaic power generation. Since their output forms are different but both are affected by weather and have strong uncertainties, the same method is adopted for the output uncertainty. Assume that the output P of renewable energy at a certain time period t a t is expressed as: P a t =P fore t +ε fore t where P fore t is the reference output of renewable energy, and ε fore t is the predicted value of renewable energy output, which follows a normal distribution: ε fore t ~N(0,σ 2 fore ).

[0034] The reference output of renewable energy can be established based on the historical data of renewable energy in the research area. Specifically, the historical wind power and photovoltaic output data of the research area in the past N years are obtained through satellite remote sensing data or ground monitoring stations. The historical renewable energy output value at the t-th time period in the n-th year is P a t,n ~(δ t1 ,δ t2 ), where δ t1 and δ t2 represent the upper and lower limits of the historical renewable energy output data respectively.

[0035] According to the historical output data of wind power and photovoltaic power generation in each year, simple random sampling is carried out at each time node in each year to construct the reference output curve of renewable energy, P fore t ∈(δ t1 ,δ t2 ),t∈(1,T) where T is the annual time length.

[0036] Since in the actual operation process, operators generally determine the action amount of the key operation parameters of the energy system through short-term weather prediction values, and the additional error caused by the inaccuracy of renewable energy output prediction usually generated by short-term weather prediction errors is represented as ε fore t , which follows a normal distribution: ε fore t ~N(0,σ 2 fore)。

[0037] Among them, σ 2 fore is the prediction accuracy factor, which is used to adjust the prediction error range. By predicting the renewable energy output, the basic value of the renewable energy output is adjusted, and then until the energy system scheduling action is adjusted.

[0038] The specific process of constructing the renewable energy output prediction curve includes initializing the prediction accuracy of the renewable energy output, and the prediction fluctuation range of the renewable energy output can be obtained. Based on the stochastic optimization method, the renewable energy benchmark output curve is corrected, and multiple renewable energy output prediction curves are generated by Monte Carlo simulation.

[0039] The final renewable energy output curve is obtained by combining the uncertainty of the renewable energy output and the inaccuracy of the renewable energy output prediction. Specifically, the renewable energy output model is the sum of the renewable energy benchmark output curve and the renewable energy output prediction curve.

[0040] S2. Establish an energy system configuration optimization model with the goal of the overall optimal operation of the energy system.

[0041] Establish an objective function with the goal of minimizing the sum of the operation and maintenance investment of renewable energy equipment, the purchase investment of renewable energy equipment, the operation and maintenance investment of energy storage equipment, and the purchase investment of energy storage equipment; Establish the constraints existing in the operation of the energy system, including the energy system supply-demand balance constraint, the stored electricity constraint of the lithium battery, and the energy storage charge and discharge power and installed capacity limit constraint.

[0042] Construct an energy system simulation model, which includes four main bodies: wind power generation, photovoltaic power generation, electrochemical energy storage, and users. Conduct configuration optimization design with the goal of the overall optimal operation of the energy system, and the established objective function is: min(F EP )=C om EP + C co EP + C om ES + C co ES Among them, C om EP is the operation and maintenance investment of renewable energy equipment, C co EP is the purchase investment of renewable energy equipment, C om ES is the operation and maintenance investment of energy storage, C co ESEquipment purchase investment for energy storage, with renewable energy being wind power and photovoltaic power.

[0043] The equipment purchase investment for renewable energy is the installed capacity investment C co EP and the equipment operation and maintenance investment C om EP The specific calculation is as follows: C co EP = C w * c w inv + C p * c p inv C om EP =( C w * c w om + C p * c p om ) * ∑ Y t=1 1 / (1 + r)^t P w t = P w_fore t + ε w_fore t P s t = P s_fore t + ε s_fore t Among them, c w inv is the unit wind power installed capacity investment, c p inv is the unit photovoltaic installed capacity investment, C w is the wind power installed capacity, C p is the photovoltaic installed capacity, c w om is the unit installed capacity wind power operation and maintenance investment, c p om is the unit installed capacity photovoltaic operation and maintenance investment, P w t is the wind power output at time t, P w_fore t is the reference output of wind power, ε w_fore t is the predicted value of wind power output, P s t is the photovoltaic output at time t, P s_foret is the reference output of photovoltaic power generation, and ε s_fore t is the predicted value of photovoltaic power generation output, r is the discount rate, and Y is the service life of the equipment.

[0044] The equipment purchase investment of energy storage, that is, the installation investment C co ES and the equipment operation and maintenance investment C om ES The specific calculation is as follows: C co ES = C es * c es inv + C x * c est inv C om ES = C es * c es om ∑ Y t=1 1 / (1 + r)^t Among them, C es is the energy storage installation capacity, c es inv is the unit energy storage installation investment, c es om is the unit installation energy storage operation and maintenance investment, C x is the energy storage charge and discharge power, c est inv is the unit charge and discharge power investment of energy storage.

[0045] There are constraints in the operation of the energy system. Among them, the energy system supply-demand balance constraint means that the sum of wind power output, photovoltaic power generation output, and energy storage discharge power at the same moment is equal to the sum of energy storage charging power and load energy consumption power at the same moment. The formula is expressed as follows: P w t + P s t + P d t = P ch t + P l t Among them, P w t is the output of wind power at time t, P s t is the output of photovoltaic power at time t, P d t$P_{discharge}(t)$ is the discharge power of the energy storage at time $t$. ch t $P_{charge}(t)$ is the charge power of the energy storage at time $t$. l t $P_{load}$ is the power of the load energy consumption.

[0046] The storage capacity constraint of the lithium battery is specifically as follows: Under the condition of setting the charge efficiency and discharge efficiency of the energy storage, The energy storage power at the next moment is the difference between the sum of the energy storage power at the previous moment and the product of the energy storage charge efficiency and the energy storage charge power, and the quotient of the energy storage discharge power and the energy storage discharge efficiency. The formula is expressed as follows: $S$ t+1 $=$ $S$ t $+$ $\eta$ t $P$ ch t $-$ $P$ d t $ / $ $\eta$ f Among them, $S$ t+1 is the energy stored in the energy storage at time $t + 1$, $S$ t is the energy stored in the energy storage at time $t$, $\eta$ t is the charge efficiency of the energy storage, $P$ ch t is the charge power of the energy storage at time $t$, $P$ d t is the discharge power of the energy storage at time $t$, $\eta$ f is the discharge efficiency of the energy storage.

[0047] The constraints on the charge-discharge power and installed capacity of the energy storage are specifically as follows: The charge power of the energy storage is within the closed interval range of 0 and the product of the maximum charge-discharge power of the energy storage and the charge regulation coefficient of the energy storage; The discharge power of the energy storage is within the closed interval range of 0 and the product of the maximum charge-discharge power of the energy storage and the discharge regulation coefficient of the energy storage.

[0048] Among them, the values of the charge regulation coefficient and the discharge regulation coefficient of the energy storage are both 0 or 1. The formula is expressed as follows: $0 \lt P$ ch t $\lt X$ c t $C$ x $0 \leq P$ d t $\leq X$ d t $C$ x $X$ c t , $X$ d t∈ {0, 1} where C x is the maximum charge and discharge power of the energy storage, X c t is the energy storage charging adjustment coefficient, and X d t is the energy storage discharging adjustment coefficient.

[0049] S3. Use the rolling window optimization algorithm to solve the energy system configuration optimization model and obtain the optimal capacity configuration plan.

[0050] For the rolling window optimization algorithm of the present invention, considering that the output of renewable energy has strong seasonality, to achieve annual cycle simulation, the annual time T is divided into K rolling windows, the length of each window, i.e., the time step, is Δt, and the rolling step size is Δt roll , and the annual time T = K * Δt. The end time t start k+1 of each rolling window is the start time t start k of the previous window start k+1 plus the rolling step size, t start k = t roll .

[0051] As Figure 1 shown, it specifically includes the following steps: S31. Based on the historical output data of renewable energy, construct N renewable energy baseline output curves.

[0052] Specifically, based on the historical output data of renewable energy, obtain the output range of renewable energy at each time node, and perform random sampling according to the output range to construct N renewable energy baseline output curves.

[0053] S32. Set the prediction accuracy of renewable energy output, and generate M renewable energy output prediction curves for each renewable energy baseline output curve based on the stochastic optimization method; Specifically, on the basis of step S31, set the prediction accuracy of renewable energy output, and correct the N generated renewable energy baseline output curves based on the stochastic optimization method, and generate M renewable energy output prediction curves for each renewable energy baseline output curve through Monte Carlo simulation.

[0054] S33. Based on each renewable energy baseline output curve and the renewable energy output prediction curve, according to the constraint conditions, the energy system configuration optimization model is solved within each time step to obtain a candidate optimal capacity configuration plan. In the present invention, for each renewable energy baseline output curve, combined with each corresponding generated renewable energy output prediction curve, the candidate optimal capacity configuration plan is calculated within each time step until all the candidate optimal capacity configuration plans obtained by all combinations are acquired.

[0055] Specifically, based on steps S31 and S32, the optimal configuration calculation is carried out. By initializing the installed capacities of wind power, photovoltaic, and energy storage, and considering the constraints of the energy system operation, the parallel computing technology is applied to the objective function of the energy system configuration optimization model. Through the optimization simulation of Yalmip and Gurobi, the optimal scheduling situation within each time step is obtained, and based on this, the calculation of the next time step is carried out. During this process, the installed capacities of various devices in the energy system are continuously adjusted until all time lengths and all scenarios are simulated.

[0056] S34. Select the most complex plan from all the candidate optimal capacity configuration plans as the final optimal capacity configuration plan.

[0057] Since the most complex plan represents the most extreme weather conditions, it is necessary to ensure that the energy system can operate normally under the most extreme weather conditions. Therefore, the installed capacity configuration plan of the most complex plan is used as the final optimal capacity configuration plan.

[0058] The present invention comprehensively considers the impacts of the uncertainty of renewable energy output and the inaccuracy of renewable energy prediction on the energy system planning, achieving the optimal system economics while improving the stability of the energy system operation. By introducing the inaccurate prediction accuracy, the influence of short-term weather prediction on the energy system configuration optimization is taken into account, which makes the model planning process closer to the actual situation, and ultimately improves the adaptability of the energy system to extreme weather conditions or situations with increased errors. By analyzing, the specific requirements for improving the prediction accuracy of renewable energy output in practical applications are clarified, thus avoiding excessive resource investment due to the pursuit of too high prediction accuracy.

[0059] Embodiment 2:

[0060] A method for configuring the capacity of an energy system based on the inaccuracy of renewable energy output prediction in this embodiment includes the following steps: S1. Obtain the historical output data of renewable energy at each moment to construct the renewable energy baseline output curve, calculate the renewable energy output prediction curve based on the renewable energy baseline output, and establish a renewable energy output model by combining the renewable energy baseline output curve and the renewable energy output prediction curve.

[0061] Renewable energy includes wind power and photovoltaic power generation. Historical wind power and photovoltaic output data for the past many years in the study area are obtained, and simple random sampling is carried out at each time node to construct the baseline output curve of renewable energy.

[0062] The renewable energy explored in this invention mainly includes wind power and photovoltaic power generation. Since their output forms are different but both are affected by weather and have strong uncertainty, the same method is adopted for output uncertainty. Assume that the output P of renewable energy at a certain time period t a t is expressed as: P a t =P fore t +ε fore t where P fore t is the baseline output of renewable energy, and ε fore t is the predicted value of renewable energy output, which follows a normal distribution: ε fore t ~N(0,σ 2 fore ).

[0063] The baseline output of renewable energy can be established based on the historical data of renewable energy in the study area. Specifically, the historical wind power and photovoltaic output data for the past N years in the study area are obtained through satellite remote sensing data or ground monitoring stations. The historical renewable energy output value at the t-th time period in the n-th year is P a t,n ~(δ t1 ,δ t2 ), where δ t1 and δ t2 represent the upper and lower limits of the historical output data of renewable energy respectively.

[0064] According to the historical output data of wind power and photovoltaic power generation in each year, simple random sampling is carried out at each time node in each year to construct the baseline output curve of renewable energy, P fore t ∈(δ t1 ,δ t2 ),t∈(1,T) where T is the annual time length.

[0065] During the actual operation process, operators generally determine the action amounts of the key operation parameters of the energy system through short-term weather prediction values. The additional error caused by the inaccuracy of the renewable energy output prediction usually resulting from the short-term weather prediction error is denoted as ε fore t , which follows a normal distribution: ε fore t ~N(0,σ 2 fore ).

[0066] Among them, σ 2 fore is the prediction accuracy factor, which is used to adjust the prediction error range. The renewable energy output basic value is adjusted through the renewable energy output prediction, and then the energy system scheduling action is adjusted until it reaches the desired state.

[0067] The specific process of constructing the renewable energy output prediction curve includes initializing the prediction accuracy of the renewable energy output and obtaining the prediction fluctuation range of the renewable energy output. Based on the stochastic optimization method, the renewable energy benchmark output curve is corrected, and multiple renewable energy output prediction curves are generated through Monte Carlo simulation.

[0068] The final renewable energy output curve is obtained by combining the uncertainty of the renewable energy output with the inaccuracy of the renewable energy output prediction. Specifically, the renewable energy output model is the sum of the renewable energy benchmark output curve and the renewable energy output prediction curve.

[0069] S2. Establish an energy system configuration optimization model with the goal of overall optimal operation of the energy system.

[0070] Establish an objective function with the goal of minimizing the sum of the operation and maintenance investment of renewable energy equipment, the purchase investment of renewable energy equipment, the operation and maintenance investment of energy storage equipment, and the purchase investment of energy storage equipment; Establish the constraints existing in the operation of the energy system, including the energy system supply-demand balance constraint, the stored electricity constraint of the lithium battery, and the energy storage charge-discharge power and installed capacity limit constraint.

[0071] Construct an energy system simulation model, which includes four entities: wind power generation, photovoltaic power generation, electrochemical energy storage, and users. Conduct configuration optimization design with the goal of overall optimal operation of the energy system. The established objective function is: min(F EP )=C om EP + C co EP + C om ES + C co ES Among them, C om EP is the operation and maintenance investment of renewable energy equipment, C co EP is the equipment purchase investment of renewable energy, C om ES is the operation and maintenance investment of energy storage equipment, C co ES is the equipment purchase investment of energy storage. Renewable energy refers to wind power and photovoltaic power.

[0072] The equipment purchase investment of renewable energy is the installed capacity investment C co EP and the operation and maintenance investment of equipment C om EP The specific calculation is as follows: C co EP = C w * c w inv + C p * c p inv C om EP =(C w * c w om + C p * c p om ) * ∑ Y t=1 1 / (1 + r)^t P w t = P w_fore t + ε w_fore t P s t = P s_fore t + ε s_fore t Among them, c w inv is the unit wind power installed capacity investment, c p inv is the unit photovoltaic installed capacity investment, C w is the wind power installed capacity, C p is the photovoltaic installed capacity, c w om is the unit installed wind power operation and maintenance investment, c p om is the unit installed photovoltaic operation and maintenance investment, Pw t is the output of wind power at time t, P w_fore t is the reference output of wind power, ε w_fore t is the predicted value of wind power output, P s t is the output of photovoltaic power at time t, P s_fore t is the reference output of photovoltaic power, ε s_fore t is the predicted value of photovoltaic power output, r is the discount rate, and Y is the equipment life cycle years.

[0073] The equipment purchase investment of energy storage, i.e., the installed capacity investment C co ES and the equipment operation and maintenance investment C om ES The specific calculation is as follows: C co ES = C es * c es inv + C x * c est inv C om ES = C es * c es om ∑ Y t=1 1 / (1 + r)^t where C es is the energy storage installed capacity, c es inv is the unit energy storage installed capacity investment, c es om is the unit installed capacity energy storage operation and maintenance investment, C x is the energy storage charge and discharge power, c est inv is the unit charge and discharge power investment of energy storage.

[0074] Constraints existing in the operation of the energy system. Among them, the energy system supply-demand balance constraint means that the sum of the wind power output, photovoltaic power output, and energy storage discharge power at the same moment is equal to the sum of the energy storage charging power and the load energy consumption power at the same moment. The formula is expressed as follows: P w t + P s t + P d t = P ch t+P l t Among them, P w t is the output of wind power at time t, P s t is the output of photovoltaic power at time t, P d t is the discharge power of energy storage at time t, P ch t is the charging power of energy storage at time t, P l t is the power of load energy consumption.

[0075] The storage capacity constraint of lithium batteries is specifically as follows: Under the set energy storage charging efficiency and discharging efficiency, The energy storage power at the next moment is the difference between the sum of the energy storage power at the previous moment and the product of the energy storage charging efficiency and the energy storage charging power, and the quotient of the energy storage discharging power and the energy storage discharging efficiency. The formula is expressed as follows: S t+1 = S t + η t P ch t - P d t / η f Among them, S t+1 is the energy stored in the energy storage at time t + 1, S t is the energy stored in the energy storage at time t, η t is the energy storage charging efficiency, P ch t is the energy storage charging power at time t, P d t is the energy storage discharging power at time t, η f is the energy storage discharging efficiency.

[0076] The energy storage charging and discharging power and installed capacity limit constraints are specifically as follows: The energy storage charging power is within the closed interval range of 0 and the product of the maximum energy storage charging and discharging power and the energy storage charging adjustment coefficient; The energy storage discharging power is within the closed interval range of 0 and the product of the maximum energy storage charging and discharging power and the energy storage discharging adjustment coefficient.

[0077] Among them, the values of the energy storage charging adjustment coefficient and the energy storage discharging adjustment coefficient are both 0 or 1. The formula is expressed as follows: 0 < P ch t < X c t C x 0 ≤ P d t ≤ X d t C x X c t , X d t ∈ {0, 1} Among them, C x is the maximum charge-discharge power of energy storage, and X c t is the charge regulation coefficient of energy storage, and X d t is the discharge regulation coefficient of energy storage.

[0078] S3. Use the rolling window optimization algorithm to solve the energy system configuration optimization model, obtain the optimal capacity configuration plan, and calculate the prediction accuracy of the optimal capacity configuration plan.

[0079] For the rolling window optimization algorithm of the present invention, considering that the output of renewable energy has strong seasonality, in order to achieve annual cycle simulation, the annual time T is divided into K rolling windows, the length of each window, i.e., the time step, is Δt, and the rolling step size is Δt roll , and the annual time T = K * Δt. The end time t start k+1 of each rolling window is the start time t start k of the previous window plus the rolling step size, t start k+1 = t start k + Δt roll .

[0080] As Figure 2 shown, it specifically includes the following steps: S31. Based on the historical output data of renewable energy, construct N renewable energy reference output curves.

[0081] Specifically, based on the historical output data of renewable energy, obtain the output range of renewable energy at each time node, and perform random sampling according to the output range to construct N renewable energy reference output curves.

[0082] S32. Set the prediction accuracy p of the renewable energy output, and generate M renewable energy output prediction curves for each renewable energy reference output curve based on the random optimization method; Specifically, based on step S31, the prediction accuracy of the renewable energy output is set, and the N generated renewable energy baseline output curves are corrected based on the stochastic optimization method. M renewable energy output prediction curves are generated for each renewable energy baseline output curve through Monte Carlo simulation.

[0083] S33. Based on each renewable energy baseline output curve and the renewable energy output prediction curve, according to the constraint conditions, the energy system configuration optimization model is solved at each time step to obtain the candidate optimal capacity configuration plan.

[0084] Specifically, based on steps S31 and S32, the optimal configuration calculation is carried out. By initializing the installed capacities of wind power, photovoltaic, and energy storage, and considering the constraints of the energy system operation, the parallel computing technology is applied to the objective function of the energy system configuration optimization model. Through the Yalmip and Gurobi optimization simulations, the optimal scheduling situation at each time step is obtained, and based on this, the calculation of the next time step is carried out. During this process, the installed capacities of the various devices in the energy system are continuously adjusted until the simulation is completed for all time lengths and all scenarios.

[0085] S34. Select the most complex plan from all candidate optimal capacity configuration plans as the final optimal capacity configuration plan.

[0086] Since the most complex plan represents the most extreme weather conditions, it is necessary to ensure that the energy system can operate normally under the most extreme weather conditions. Therefore, the installed capacity configuration plan of the most complex plan is used as the final optimal capacity configuration plan.

[0087] S35. Set the capacity configuration change threshold Q. Judge whether the optimal capacity configuration change amount is not less than the capacity configuration change threshold, specifically, judge whether 1 - F(p) / F(p - 1) ≥ Q is satisfied. If not, output the prediction accuracy p - 1. If so, increment the prediction accuracy p = p + 1%, return to the step of generating M renewable energy output prediction curves, that is, step S32, and repeat steps S32 - S35 until the calculated optimal capacity configuration satisfies 1 - F(p) / F(p - 1) ≥ Q to obtain the final optimal capacity configuration, and output the prediction accuracy p - 1.

[0088] The present invention can determine the optimal demand for the prediction accuracy of renewable energy output in future energy systems. Based on the above-mentioned rolling window optimization algorithm to solve the energy system configuration optimization model to obtain the final optimal capacity configuration, the prediction accuracy of renewable energy output is improved, and cyclic parallel operations are carried out to obtain the operation situation of the energy system after improving the prediction accuracy of renewable energy. Until the change amount of the optimal operation situation of the system before and after improving the prediction accuracy is less than the capacity configuration threshold Q, it indicates that the effect of improving the prediction accuracy of renewable energy will be very limited, thus proving that the prediction accuracy of renewable energy output in the case of p - 1 can meet the requirements of daily production planning.

[0089] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Those skilled in the art to which the present invention pertains can make various modifications or supplements to the described specific embodiments or use similar ways of substitution, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0090] Although terms such as renewable energy baseline output curve, renewable energy output prediction curve, renewable energy output model, energy system configuration optimization model, and optimal capacity configuration are used more frequently in this article, the possibility of using other terms is not excluded. The use of these terms is only for more convenient description and explanation of the essence of the present invention; interpreting them as any additional limitation is contrary to the spirit of the present invention.

Claims

1. A method for energy system capacity configuration based on uncertainty in renewable energy output prediction, characterized in that: The following steps are involved: Obtain the historical output data of renewable energy at each moment to construct a renewable energy benchmark output curve, calculate the renewable energy output forecast curve based on the renewable energy benchmark output, and establish a renewable energy output model by combining the renewable energy benchmark output curve and the renewable energy output forecast curve; Establish an energy system configuration optimization model with the goal of optimizing the overall operation of the energy system; The rolling window optimization algorithm is used to solve the energy system configuration optimization model and obtain the optimal capacity configuration plan, including: Based on the historical output data of renewable energy, N benchmark output curves of renewable energy are constructed; Set the prediction accuracy of renewable energy output, and generate M renewable energy output prediction curves based on the random optimization method for each renewable energy benchmark output curve; Based on each renewable energy benchmark output curve and renewable energy output forecast curve, according to the constraints, the energy system configuration optimization model is solved in each time step to obtain the candidate optimal capacity configuration plan; The most complex solution is selected from all candidate optimal capacity configuration solutions as the final optimal capacity configuration solution.

2. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 1 is characterized by: Renewable energy includes wind power and photovoltaic power generation. The historical wind power and photovoltaic output data of the study area in the past years are obtained, and sampling is performed at various time nodes through simple random sampling to construct a renewable energy benchmark output curve.

3. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 2 is characterized by: Based on the renewable energy benchmark output curve, the renewable energy output prediction accuracy is set, and the renewable energy output prediction curve is generated through the random optimization method.

4. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 1, 2 or 3 is characterized by: The renewable energy output model is the sum of the renewable energy benchmark output curve and the renewable energy output forecast curve.

5. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 1 is characterized in that: An energy system configuration optimization model is established with the goal of optimizing the overall operation of the energy system, including: Establish an objective function with the goal of minimizing the sum of investment in renewable energy equipment operation and maintenance, investment in renewable energy equipment purchase, investment in energy storage equipment operation and maintenance, and investment in energy storage equipment purchase; Establish constraints on the operation of the energy system, including energy system supply and demand balance constraints, lithium battery storage capacity constraints, energy storage charging and discharging power and installed capacity limit constraints.

6. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 5 is characterized in that: Energy system supply and demand balance constraints, including: The sum of wind power output, photovoltaic power generation output, and energy storage discharge power at the same time is equal to the sum of energy storage charging power and load energy consumption power at the same time; The wind power output and photovoltaic power output are calculated based on the renewable energy output model.

7. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 5 is characterized in that: Lithium battery storage capacity constraints, including: Under the condition of setting the energy storage charging efficiency and discharging efficiency, The energy storage capacity at the next moment is the difference between the sum of the energy storage capacity at the previous moment and the product of the energy storage charging efficiency and the energy storage charging power, and the energy storage discharge power and the energy storage discharge efficiency.

8. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 5 is characterized in that: Energy storage charging and discharging power and installed capacity restrictions include: The energy storage charging power is within the closed interval of 0 and the product of the maximum energy storage charging and discharging power and the energy storage charging regulation coefficient; The energy storage discharge power is within a closed interval between 0 and the product of the maximum energy storage charge and discharge power and the energy storage discharge regulation coefficient.

9. The energy system capacity configuration method based on renewable energy output forecast inaccuracy according to claim 1 is characterized by: The method also includes the steps of calculating the prediction accuracy of the optimal capacity configuration scheme, including: Set the capacity configuration change threshold, Determine whether the optimal capacity configuration change is not less than the capacity configuration change threshold, If not, output the prediction accuracy; If so, increase the prediction accuracy, return to the step of generating M renewable energy output prediction curves, continue to execute subsequent steps until the final optimal capacity configuration is obtained, and repeat the prediction accuracy calculation until the prediction accuracy is output.

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