Novel power supply and demand balance optimization method and system for power system

Abstract

The invention discloses an electric power supply and demand balance optimization method for a novel electric power system. The method comprises the following steps: acquiring data information of a target novel electric power system; constructing an upper-layer adjustable power supply daily planned output optimization model and a lower-layer energy storage daily planned operation optimization model to obtain a power supply and demand balance optimization model of the target novel power system; and solving the established model based on a two-stage robust optimization method to complete power supply and demand balance optimization of the novel power system. The invention also discloses a system for realizing the power supply and demand balance optimization method of the novel power system. Through the construction and solution of the upper-layer adjustable power supply daily planned output optimization model and the lower-layer energy storage daily planned operation optimization model, the power supply and demand balance optimization of the novel power system is realized on the basis of considering the new energy output characteristics, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to a method and system for optimizing power supply and demand balance in a novel power system. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] At present, environmental problems are becoming more and more serious, so more and more new energy power generation systems are beginning to be integrated into the power system and generate electricity. However, the output volatility, intermittency and randomness of new energy power generation systems have brought great challenges to the optimization of the supply and demand balance of the power system.

[0004] At present, the existing power system supply and demand balance optimization scheme generally does not consider the source and load prediction characteristics and uncertainty information of different types of sources and loads and different power stations; however, this processing method is difficult to apply to the new power system with a large number of new energy power generation systems connected. This situation makes the power supply and demand balance optimization results of the new power system today have poor reliability and accuracy, which has seriously affected the safe and stable operation of the power system. Summary of the invention

[0005] One of the purposes of the present invention is to provide a new type of power system power supply and demand balance optimization method with high reliability and good accuracy.

[0006] A second objective of the present invention is to provide a system for realizing the power supply and demand balance optimization method of the new power system.

[0007] The novel power supply and demand balance optimization method of the power system provided by the present invention comprises the following steps:

[0008] S1. Obtain data information of the target new power system;

[0009] S2. Construct an upper-layer adjustable power source daily planned output optimization model and a lower-layer energy storage daily planned operation optimization model to obtain a power supply and demand balance optimization model for the target new power system;

[0010] S3. Based on the two-stage robust optimization method, the model established in step S2 is solved to complete the optimization of the power supply and demand balance of the new power system.

[0011] The step S2 of constructing the upper-layer adjustable power source daily planned output optimization model and the lower-layer energy storage daily planned operation optimization model to obtain the power supply and demand balance optimization model of the target new power system specifically includes the following steps:

[0012] The objective function is constructed based on the daily operating cost, renewable energy transmission rate, and losses in extreme scenarios. The upper and lower limits of the adjustable power output, the power balance constraints during system operation, and the maximum transmission power constraints of the transmission lines are used as constraints to build an upper-level adjustable power daily planned output optimization model.

[0013] Based on the source-load interval prediction value, the loss objective function under extreme scenarios is established, and constraints including source-load data uncertainty constraints, energy storage operation constraints and power balance constraints are established to construct the lower-level energy storage daily planning operation optimization model.

[0014] The objective function is constructed based on the daily operating cost, renewable energy delivery rate, and losses in extreme scenarios, and specifically includes the following steps:

[0015] First objective function: daily operating cost

[0016] The following formula is used as the first objective function:

[0017]

[0018] Where f1 is the first objective function value; N AP is the number of adjustable power supplies; is the planned output of the ith adjustable power source at time t; c AP is the unit power generation cost of the adjustable power source; N NHT is the number of non-adjustable small hydropower; is the output forecast value of the i-th non-adjustable small hydropower station at time t; c HT is the unit power generation cost of non-adjustable small hydropower; N WT is the number of fans; is the predicted output value of the i-th wind turbine at time t; c WT is the unit power generation cost of the wind turbine; N PV is the number of PV panels; is the predicted output value of the ith photovoltaic power station at time t; c PV is the unit electricity generation cost of photovoltaics;

[0019] Second objective function: renewable energy delivery rate

[0020] The following formula is used as the second objective function:

[0021]

[0022] Where f2 is the second objective function value; Pt out is the power planned to be transmitted by the outer area line at time t; The maximum power allowed to be transmitted by the outer area line;

[0023] The third objective function: loss in extreme scenarios

[0024] The following formula is used as the third objective function:

[0025]

[0026] Where f3 is the third objective function value; is the power shortage power at time t in the scenario with the maximum power shortage; is the power abandonment power at time t in the scenario with maximum power abandonment.

[0027] The above-mentioned constraint conditions of the upper and lower limits of the adjustable power supply output, the power balance constraint during system operation and the maximum transmission power constraint of the external transmission line specifically include the following steps:

[0028] The following formula is used as the constraint condition:

[0029]

[0030] In the formula is the power lower limit constraint of the i-th adjustable power source at time t; is the power upper limit constraint of the i-th adjustable power source at time t; P t L is the system load forecast value; N ES is the amount of energy storage; is the planned discharge power of the i-th energy storage at time t; Plan the charging power for the i-th energy storage at time t.

[0031] The loss objective function under extreme scenarios is established based on the source-load interval prediction value, and specifically includes the following steps:

[0032] The following formula is used as the loss objective function:

[0033]

[0034] Where l is the loss objective function value; lv is the optimization variable; uv is the uncertainty variable; The load under the scenario of maximum power shortage; is the output of the i-th non-adjustable small hydropower station at time t in the scenario with the maximum power shortage; is the output of the i-th wind turbine at time t in the scenario with the maximum power shortage; is the output of the i-th non-PV at time t in the scenario with the maximum power shortage; is the load in the scenario with the maximum amount of power abandonment; is the output of the i-th non-adjustable small hydropower station at time t in the scenario with the maximum amount of power abandonment; is the output of the i-th wind turbine at time t in the scenario with the maximum amount of power abandonment; is the output of the ith photovoltaic at time t in the scenario with the maximum amount of power abandonment; P s P is short of power; c For wasted electricity.

[0035] The establishment of constraints including source-load data uncertainty constraints, energy storage operation constraints and power balance constraints specifically includes the following steps:

[0036] Source load data uncertainty constraints:

[0037]

[0038] Where P t L is the lower limit of the load interval prediction at time t; is the upper limit value of the load interval prediction at time t; is the predicted lower limit of the i-th non-adjustable hydropower output interval at time t; is the predicted upper limit of the i-th non-adjustable hydropower output interval at time t; is the predicted lower limit of the output range of the i-th wind turbine at time t; is the predicted upper limit of the output range of the i-th wind turbine at time t; is the predicted lower limit of the ith photovoltaic output interval at time t; is the predicted upper limit value of the ith photovoltaic output interval at time t;

[0039] Power balance constraints:

[0040]

[0041] Energy storage operation constraints:

[0042]

[0043] In the formula is the planned storage capacity of the i-th energy storage at time t+1; is the loss efficiency of the i-th energy storage; is the charging efficiency of the i-th energy storage; is the discharge efficiency of the i-th energy storage; is the binary variable of the charging state of the i-th energy storage at time t, and if the i-th energy storage is in the charging state at time t, then At time t, the i-th energy storage is in an uncharged state. is the maximum charging power of the energy storage; is the discharge state binary variable of the i-th energy storage at time t, and if the i-th energy storage at time t is in the discharge state, then At time t, the i-th energy storage is in the undischarged state. is the lower limit of the energy storage charge state; is the state of charge of the i-th energy storage at time t; K is the upper limit of the energy storage charge state; ES,i is the initial output coefficient of the i-th energy storage; is the state of charge at the start time of the i-th energy storage; is the state of charge at the end of the i-th energy storage.

[0044] The two-stage robust optimization method described in step S3 is used to solve the model established in step S2, which specifically includes the following steps:

[0045] A. Initialization: Initialize the parameters of the second-generation non-dominated sorting genetic algorithm, generate the initial population and individuals, and each individual represents a set of operation schemes in the upper model;

[0046] B. Optimization of daily planned operation of lower-level energy storage: Based on the daily planned output of the adjustable power source transmitted by the upper-level model and considering the prediction results of the source-load interval, GUROBI is used to solve the optimization model of daily planned operation of the lower-level energy storage;

[0047] C. Optimization of the daily planned output of the upper-layer adjustable power source: Based on the energy storage daily operation plan transmitted by the lower-layer model, the target value of each individual is calculated according to the source-load plan value, i.e., the point prediction result, and the non-dominated sorting level of each individual in the population is determined; the target value includes daily operation cost, renewable energy transmission rate, and extreme scenario loss;

[0048] D. Iterative optimization: Determine whether the target value converges:

[0049] If converged, the Pareto frontier solution set consisting of the final optimization solution is output;

[0050] If it does not converge, a new population is generated through genetic operations, and the new population corresponds to a new set of daily planned output values ​​of the adjustable power source, and the process returns to step C to continue the next round of iteration;

[0051] E. For the Pareto frontier solution set obtained in step D, the optimal solution is selected through multi-dimensional preference analysis linear programming technology to obtain the final daily planned operation plan of the adjustable power supply and energy storage.

[0052] The present invention also provides a system for implementing the power supply and demand balance optimization method of the new power system, comprising a data acquisition module, a model construction module and a balance optimization module; the data acquisition module, the model construction module and the balance optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target new power system, and upload the data information to the model construction module; the model construction module is used to construct an upper-level adjustable power supply daily planned output optimization model and a lower-level energy storage daily planned operation optimization model to obtain a power supply and demand balance optimization model of the target new power system, and upload the data to the balance optimization module; the balance optimization module is used to solve the established model according to the received data information and based on a two-stage robust optimization method to complete the power supply and demand balance optimization of the new power system.

[0053] The power supply and demand balance optimization method and system of the novel power system provided by the present invention, through the construction and solution of the upper-layer adjustable power source daily planned output optimization model and the lower-layer energy storage daily planned operation optimization model, not only realizes the power supply and demand balance optimization of the novel power system on the basis of considering the output characteristics of new energy, but also has higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0055] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0056] like Figure 1 The method flow diagram of the method of the present invention is shown as follows: the power supply and demand balance optimization method of the new power system disclosed by the present invention comprises the following steps:

[0057] S1. Obtain data information of the target new power system;

[0058] S2. Construct an upper-layer adjustable power source daily planned output optimization model and a lower-layer energy storage daily planned operation optimization model to obtain a power supply and demand balance optimization model for the target new power system; specifically, the following steps are included:

[0059] The objective function is constructed based on the daily operating cost, renewable energy transmission rate, and losses in extreme scenarios. The upper and lower limits of the adjustable power output, the power balance constraints during system operation, and the maximum transmission power constraints of the transmission lines are used as constraints to build an upper-level adjustable power daily planned output optimization model.

[0060] Based on the source-load interval prediction value, the loss objective function under extreme scenarios is established, and constraints including source-load data uncertainty constraints, energy storage operation constraints, and power balance constraints are established to build a lower-level energy storage daily plan operation optimization model;

[0061] Taking into account the impact of factors such as source-load volatility, randomness, and intermittency on power supply and new energy consumption, the present invention proposes a new multi-objective optimization method for power supply and demand balance of power system based on two-stage robust optimization based on source-load point prediction and interval prediction results; wherein the first stage optimizes the daily output curve of the adjustable power source to ensure that the power system can achieve the optimal target under severe scenarios when operating with the source-load planned value, i.e., the predicted value; in the second stage, based on the daily output curve of the adjustable power source, the energy storage is used as a means of regulation to resist uncertain risks, and the daily operation curve of the energy storage is optimized to minimize the system loss under the two scenarios of maximum power gap and maximum new energy abandonment;

[0062] The upper layer optimizes the daily planned output of adjustable power sources, taking into account the operating scenarios composed of various types of source-load plan values, taking daily operating costs, renewable energy transmission rates, and extreme scenario losses as optimization variables, and comprehensively considering various constraints such as power output and power balance; for non-adjustable small hydropower, wind turbines, and photovoltaics, the point prediction value is the planned value; the upper layer transmits the results of the adjustable power source plan output to the lower layer; the lower layer optimizes the daily planned operation of energy storage, taking into account various constraints such as source-load uncertainty constraints, system operation constraints, and power balance, to minimize the sum of power shortages and abandoned power in extreme scenarios; the lower layer transmits the daily planned operation of energy storage to the upper layer;

[0063] In specific implementation, the objective function is constructed based on the daily operating cost, renewable energy delivery rate, and loss in extreme scenarios, and specifically includes the following steps:

[0064] The first objective function: daily operating cost; the daily operating cost of the new power system is the power generation cost of various types of power sources, which mainly includes the power generation costs of hydropower, wind power and photovoltaic power in the context of full green power operation;

[0065] The following formula is used as the first objective function:

[0066]

[0067] Where f1 is the first objective function value; N AP is the number of adjustable power supplies; is the planned output of the ith adjustable power source at time t; c AP is the unit power generation cost of the adjustable power source; N NHT is the number of non-adjustable small hydropower; is the output forecast value of the i-th non-adjustable small hydropower station at time t; c HT is the unit power generation cost of non-adjustable small hydropower; N WT is the number of fans; is the predicted output value of the i-th wind turbine at time t; c WTis the unit power generation cost of the wind turbine; N PV is the number of PV panels; is the predicted output value of the ith photovoltaic power station at time t; c PV is the unit electricity generation cost of photovoltaics;

[0068] Second objective function: renewable energy export rate; in addition to meeting its own power supply, the new power system also needs to assume the function of a power source, transmit electricity to nearby power systems, and realize the joint consumption of wide-area new energy while improving the power supply in other areas;

[0069] The following formula is used as the second objective function:

[0070]

[0071] Where f2 is the second objective function value; P t out is the power planned to be transmitted by the outer area line at time t; The maximum power allowed to be transmitted by the outer area line;

[0072] The third objective function: loss in extreme scenarios; considering the impact of source-load uncertainty on the new power system, the present invention defines the loss in extreme scenarios as the sum of the maximum power shortage and the maximum power abandonment;

[0073] The following formula is used as the third objective function:

[0074]

[0075] Where f3 is the third objective function value; is the power shortage power at time t in the scenario with the maximum power shortage; is the power abandonment power at time t in the scenario with the maximum power abandonment;

[0076] The above-mentioned constraint conditions of the upper and lower limits of the adjustable power supply output, the power balance constraint during system operation and the maximum transmission power constraint of the external transmission line specifically include the following steps:

[0077] The following formulas are used as constraints: specifically, the upper and lower limits of the output of the adjustable power source (corresponding to the first formula), the power balance constraint during system operation (corresponding to the second formula), and the maximum transmission power constraint of the external area line (corresponding to the third formula);

[0078]

[0079] In the formula is the power lower limit constraint of the i-th adjustable power source at time t; is the power upper limit constraint of the i-th adjustable power source at time t; P tL is the system load forecast value; N ES is the amount of energy storage; is the planned discharge power of the i-th energy storage at time t; The planned charging power for the i-th energy storage at time t;

[0080] The loss objective function under extreme scenarios is established based on the source-load interval prediction value, and specifically includes the following steps:

[0081] Considering the uncertain information of unadjustable hydropower, wind turbines, and photovoltaics, and taking into account the two possible system operation scenarios of maximum power shortage and maximum power abandonment, the loss objective function under extreme scenarios is established; the following formula is used as the loss objective function:

[0082]

[0083]

[0084] Where l is the loss objective function value; lv is the optimization variable; uv is the uncertainty variable; The load under the scenario of maximum power shortage; is the output of the i-th non-adjustable small hydropower station at time t in the scenario with the maximum power shortage; is the output of the i-th wind turbine at time t in the scenario with the maximum power shortage; is the output of the i-th non-PV at time t in the scenario with the maximum power shortage; is the load in the scenario with the maximum amount of power abandonment; is the output of the i-th non-adjustable small hydropower station at time t in the scenario with the maximum amount of power abandonment; is the output of the i-th wind turbine at time t in the scenario with maximum power abandonment; is the output of the ith photovoltaic at time t in the scenario with the maximum amount of power abandonment; P s P is short of power; c For the amount of wasted electricity;

[0085] The establishment of constraints including source-load data uncertainty constraints, energy storage operation constraints and power balance constraints specifically includes the following steps:

[0086] There is uncertainty in the load and source load data such as non-adjustable hydropower, wind turbines, and photovoltaic output. This part of the uncertainty information can be described by the interval prediction results; source load data uncertainty constraints:

[0087]

[0088] Where P t L is the lower limit of the load interval prediction at time t; is the upper limit of the load interval prediction at time t; is the predicted lower limit of the i-th non-adjustable hydropower output interval at time t; is the predicted upper limit of the i-th non-adjustable hydropower output interval at time t; is the predicted lower limit of the output range of the i-th wind turbine at time t; is the predicted upper limit of the output range of the i-th wind turbine at time t; is the predicted lower limit of the ith photovoltaic output interval at time t; is the predicted upper limit value of the ith photovoltaic output interval at time t;

[0089] Power balance constraints:

[0090]

[0091] Energy storage equipment needs to meet operation constraints; energy storage operation constraints include charge and discharge power constraints, charge and discharge state constraints, energy storage capacity constraints, and initial and final capacity constraints; energy storage operation constraints:

[0092]

[0093] In the formula is the planned storage capacity of the i-th energy storage at time t+1; is the loss efficiency of the i-th energy storage; is the charging efficiency of the i-th energy storage; is the discharge efficiency of the i-th energy storage; is the binary variable of the charging state of the i-th energy storage at time t, and if the i-th energy storage at time t is in the charging state, then At time t, the i-th energy storage is in an uncharged state. is the maximum charging power of the energy storage; is the discharge state binary variable of the i-th energy storage at time t, and if the i-th energy storage at time t is in the discharge state, then At time t, the i-th energy storage is in the undischarged state. is the lower limit of the energy storage charge state; is the state of charge of the i-th energy storage at time t; K is the upper limit of the energy storage charge state; ES,i is the initial output coefficient of the i-th energy storage; is the state of charge at the start time of the i-th energy storage; is the state of charge at the end of the i-th energy storage;

[0094] S3. Based on the two-stage robust optimization method, the model established in step S2 is solved to complete the optimization of the power supply and demand balance of the new power system; specifically, the steps include:

[0095] GUROBI is used for solving. The upper-level planning model is a multi-objective optimization model, which can be solved by the second-generation non-dominated sorting genetic algorithm to obtain the Pareto frontier; on this basis, multi-dimensional preference analysis linear programming technology is used to select the optimal equipment plan operation solution;

[0096] A. Initialization: Initialize the parameters of the second-generation non-dominated sorting genetic algorithm, generate the initial population and individuals, and each individual represents a set of operation schemes in the upper model;

[0097] B. Optimization of daily planned operation of lower-level energy storage: Based on the daily planned output of the adjustable power source transmitted by the upper-level model and considering the prediction results of the source-load interval, GUROBI is used to solve the optimization model of daily planned operation of the lower-level energy storage;

[0098] C. Optimization of the daily planned output of the upper-layer adjustable power source: Based on the energy storage daily operation plan transmitted by the lower-layer model, the target value of each individual is calculated according to the source-load plan value, i.e., the point prediction result, and the non-dominated sorting level of each individual in the population is determined; the target value includes daily operation cost, renewable energy transmission rate, and extreme scenario loss;

[0099] D. Iterative optimization: Determine whether the target value converges:

[0100] If converged, the Pareto frontier solution set consisting of the final optimization solution is output;

[0101] If it does not converge, a new population is generated through genetic operations, and the new population corresponds to a new set of daily planned output values ​​of the adjustable power source, and the process returns to step C to continue the next round of iteration;

[0102] E. For the Pareto frontier solution set obtained in step D, the optimal solution is selected through multi-dimensional preference analysis linear programming technology to obtain the final daily planned operation plan of the adjustable power supply and energy storage.

[0103] The method of the present invention is further described below in conjunction with embodiments:

[0104] A new power system in a certain county in China was selected to carry out the optimization study of power supply and demand balance. The system mainly includes adjustable hydropower, non-adjustable small hydropower, centralized wind turbines, and distributed photovoltaics. Based on the source-load point prediction and interval prediction results in February 2024, the maximum load day and the maximum new energy output day of the new power system in February 2024, namely February 26 and February 21, were selected to analyze the optimization scheme of power supply and demand balance. According to the optimization analysis results, the three indicators of daily operating cost, renewable energy transmission rate, and loss in extreme scenarios of the new power system have certain mutual exclusivity, and all goals cannot be achieved at the same time. In addition, the Pareto frontier shows different trends under the scenarios of maximum load and maximum renewable energy output. On the maximum load day, as the daily operating cost of the system increases, the renewable energy transmission rate also gradually increases, and the loss in extreme scenarios gradually decreases. In the scenario of maximum renewable energy output, as the daily operating cost increases, the renewable energy transmission rate also gradually increases, but the loss in extreme scenarios gradually increases.

[0105] In order to further verify the superiority of the method in this paper, this embodiment selects the three traditional methods shown in Table 1 for comparative analysis with the method in this paper. Among them, methods 1 and 2 are based on the source-load point prediction results, and the system daily operating cost and renewable energy transmission rate are selected as the optimization targets respectively. Method 3 is based on the source-load interval prediction results, and the maximum loss in extreme scenarios is used as the optimization target. Method 4 is the method in this paper, which combines the source-load point prediction and interval prediction results, and comprehensively considers the three objectives of daily operating cost, renewable energy transmission rate, and maximum loss in extreme scenarios. The various indicators of the power supply and demand balance optimization plan of the example system in Methods 1-4 on the maximum load day and the maximum renewable energy output day are shown in Table 2. Among them, the real loss index represents the sum of the green power gap and the abandoned power of the system on that day in the real scenario when the current operation plan is executed.

[0106] Table 1 Schematic diagram of factors and indicators considered in different power supply and demand balance optimization methods

[0107]

[0108] Table 2 Optimization results comparison diagram

[0109]

[0110] The green power gap is the focus of attention on the maximum load day. As shown in Table 2, method 1 blindly pursues the optimal economic performance based on the point prediction results, ignoring the uncertain information of source and load. Since the cost of non-adjustable hydropower, wind turbines, and photovoltaic power generation is mainly determined by the planned value, that is, the point prediction value, and this part of the cost is equal under each scheme, the daily operating cost of the system mainly depends on the power generation cost of adjustable hydropower. Method 1 seeks the minimum adjustable hydropower output and achieves the optimal economic performance on the premise that the power output plan value and the load forecast value are exactly equal. This scheme just meets the regional self-balancing and no longer transmits green power to the outside of the region, so the renewable energy transmission rate is 0. Due to the lack of consideration of uncertainty information, the output of renewable energy may be lower than the planned value, and a power gap will occur at this time, and power needs to be transmitted to the upper power grid. Therefore, the maximum loss index is large in extreme scenarios, and there is also a power gap in real scenarios. Since the charging and discharging losses of energy storage will bring economic losses, the results of method 1 do not consider energy storage operation. In the results of method 2, the adjustable hydropower operates at the maximum output at all times. At this time, the renewable energy transmission rate of the system is the highest, and the green power transmission can still meet the transmission constraints of the tie line, but the daily operation cost of the system increases rapidly. Due to the large output of adjustable hydropower, there will be no green power gap and power abandonment even in extreme scenarios, so the maximum loss and real loss of the system are both 0. Energy storage charging and discharging losses will reduce the amount of power transmitted, so the results of method 2 do not consider energy storage operation. The maximum loss in the maximum load day scenario mainly comes from the green power gap. Adjustable hydropower can effectively reduce this part of the loss in the full-time maximum output scenario, so the optimization results of method 3 and 2 are similar. Method 4 comprehensively considers the three objectives of daily operation cost, renewable energy transmission rate, and maximum loss in extreme scenarios. The adjustable hydropower output fluctuates over time, and the total power generation is between the results of method 1 and 2. The system daily operation cost is also between the results of scheme 1 and 2. Energy storage also plays a peak role in this scheme. Since the output of new energy is less during the noon peak, charging is carried out at night and discharging is carried out at noon. Compared with Method 1, the maximum loss of Method 4 in extreme scenarios was reduced by 76.75%, the renewable energy transmission rate was increased to 0.1000, and the green electricity gap in real scenarios was reduced to 0. Compared with Methods 2 and 3, the daily operating cost of Method 4 was reduced by 23.97%.

[0111] The maximum load day focuses on the consumption of renewable energy. As shown in Table 2, the results of method 1 can meet the situation that the planned output of other power sources is greater than the load at each moment when the adjustable hydropower is not working. Therefore, the daily operating cost of the system in method 1 is the lowest, which is only the operating cost of uncertain renewable energy. Since the adjustable hydropower is not working, the maximum power abandonment loss in the extreme scenario of the system is also reduced. Since the energy storage charging and discharging loss will increase the daily operating cost of the system, the results of method 1 do not consider the energy storage operation. Method 2 is to increase the renewable energy transmission rate so that the planned value of green power transmission at each moment is exactly equal to the upper limit of the tie line. Therefore, the adjustable hydropower output is large, and the daily operating cost of the system is also large. Affected by uncertain factors, if the renewable energy output is greater than the planned value, it will lead to power abandonment. Therefore, the optimization result of method 2 has a large maximum loss in extreme scenarios, and the amount of power abandonment is also large in real scenarios. In order to reduce the risk of power abandonment, the optimization results of method 3 are not only that the adjustable hydropower is 0 at each moment, but also that the energy storage is charged at the time when the renewable energy output is large, and discharged at other times. Since the proportion of wind turbines in this system is large, the wind speed is high at night in winter and low at noon. Therefore, in order to promote the consumption of new energy, energy storage is mainly charged at night and mainly discharged at noon. Due to the loss of energy storage charging and discharging, the new energy transmission rate of the optimization result of method 3 is slightly lower than that of method 2, but the maximum loss in extreme scenarios is much lower than that of method 2, and the amount of abandoned electricity in real scenarios is also much lower than that of method 2. Method 4 considers three indicators at the same time. The adjustable hydropower output is large during the peak hours of the afternoon and evening, and the output is small at other times. Energy storage is mainly charged at night and mainly discharged during the day. Unlike method 3, energy storage is mainly discharged during the peak hours of the afternoon due to the large adjustable hydropower output. Compared with method 1, the renewable energy transmission rate of method 4 results increased by 3.35%. Compared with method 2, the daily operating cost of method 4 results decreased by 9.42%, the maximum loss in extreme scenarios decreased by 73.88%, and the amount of abandoned electricity in real scenarios decreased by 95.42%. Compared with method 3, the renewable energy transmission rate of the system of method 4 results increased by 5.08%.

[0112] like Figure 2 The figure shows a schematic diagram of the functional modules of the system of the present invention: the system disclosed in the present invention for realizing the power supply and demand balance optimization method of the new power system comprises a data acquisition module, a model construction module and a balance optimization module; the data acquisition module, the model construction module and the balance optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target new power system, and upload the data information to the model construction module; the model construction module is used to construct an upper-layer adjustable power supply daily planned output optimization model and a lower-layer energy storage daily planned operation optimization model, to obtain a power supply and demand balance optimization model of the target new power system, and upload the data to the balance optimization module; the balance optimization module is used to solve the established model according to the received data information and based on the two-stage robust optimization method, to complete the power supply and demand balance optimization of the new power system.

Claims

1. A new method for optimizing the balance of power supply and demand in a power system, comprising the following steps: S1. Obtain data information of the target new power system; S2. Construct an upper-layer adjustable power source daily planned output optimization model and a lower-layer energy storage daily planned operation optimization model to obtain a power supply and demand balance optimization model for the target new power system; S3. Based on the two-stage robust optimization method, the model established in step S2 is solved to complete the optimization of the power supply and demand balance of the new power system.

2. The method for optimizing the balance of power supply and demand in a new power system according to claim 1 is characterized in that The step S2 of constructing the upper-layer adjustable power source daily planned output optimization model and the lower-layer energy storage daily planned operation optimization model to obtain the power supply and demand balance optimization model of the target new power system specifically includes the following steps: The objective function is constructed based on the daily operating cost, renewable energy transmission rate, and losses in extreme scenarios. The upper and lower limits of the adjustable power output, the power balance constraints during system operation, and the maximum transmission power constraints of the transmission lines are used as constraints to build an upper-level adjustable power daily planned output optimization model. Based on the source-load interval prediction value, the loss objective function under extreme scenarios is established, and constraints including source-load data uncertainty constraints, energy storage operation constraints and power balance constraints are established to construct the lower-level energy storage daily planning operation optimization model.

3. The method for optimizing the balance of power supply and demand in a new power system according to claim 2 is characterized in that The objective function is constructed based on the daily operating cost, renewable energy delivery rate, and losses in extreme scenarios, and specifically includes the following steps: First objective function: daily operating cost The following formula is used as the first objective function: Where f1 is the first objective function value; N AP is the number of adjustable power supplies; is the planned output of the ith adjustable power source at time t; c AP is the unit power generation cost of the adjustable power source; N NHT is the number of non-adjustable small hydropower; is the output forecast value of the i-th non-adjustable small hydropower station at time t; c HT is the unit power generation cost of non-adjustable small hydropower; N WT is the number of fans; is the predicted output value of the i-th wind turbine at time t; c WT is the unit power generation cost of the wind turbine; N PV is the number of PV panels; is the predicted output value of the ith photovoltaic power station at time t; c PV is the unit electricity generation cost of photovoltaics; Second objective function: renewable energy delivery rate The following formula is used as the second objective function: Where f2 is the second objective function value; P t out is the power planned to be transmitted by the outer area line at time t; The maximum power allowed to be transmitted by the outer area line; The third objective function: loss in extreme scenarios The following formula is used as the third objective function: Where f3 is the third objective function value; is the power shortage power at time t in the scenario with the maximum power shortage; is the power abandonment power at time t in the scenario with maximum power abandonment.

4. The method for optimizing the balance of power supply and demand in a new power system according to claim 3 is characterized in that The above-mentioned constraint conditions of the upper and lower limits of the adjustable power supply output, the power balance constraint when the system is running and the maximum transmission power constraint of the external transmission line specifically include the following steps: The following formula is used as the constraint condition: In the formula is the power lower limit constraint of the i-th adjustable power source at time t; is the power upper limit constraint of the i-th adjustable power source at time t; P t L is the system load forecast value; N ES is the amount of energy storage; is the planned discharge power of the i-th energy storage at time t; Plan the charging power for the i-th energy storage at time t.

5. The method for optimizing the balance of power supply and demand in a new power system according to claim 4 is characterized in that The loss objective function under extreme scenarios is established based on the source-load interval prediction value, and specifically includes the following steps: The following formula is used as the loss objective function: Where l is the loss objective function value; lv is the optimization variable; uv is the uncertainty variable; The load under the scenario of maximum power shortage; is the output of the i-th non-adjustable small hydropower station at time t in the scenario with the maximum power shortage; is the output of the i-th wind turbine at time t in the scenario with the maximum power shortage; is the output of the i-th non-PV at time t in the scenario with the maximum power shortage; is the load in the scenario with the maximum amount of power abandonment; is the output of the i-th non-adjustable small hydropower station at time t in the scenario with the maximum amount of power abandonment; is the output of the i-th wind turbine at time t in the scenario with the maximum amount of power abandonment; is the output of the ith photovoltaic at time t in the scenario with the maximum amount of power abandonment; P s P is short of power; c For wasted electricity.

6. The method for optimizing the balance of power supply and demand in a new power system according to claim 5 is characterized in that The establishment of constraints including source-load data uncertainty constraints, energy storage operation constraints and power balance constraints specifically includes the following steps: Source load data uncertainty constraints: Where P t L is the lower limit of the load interval prediction at time t; is the upper limit of the load interval prediction at time t; is the predicted lower limit of the i-th non-adjustable hydropower output interval at time t; is the predicted upper limit of the i-th non-adjustable hydropower output interval at time t; is the predicted lower limit of the output range of the i-th wind turbine at time t; is the predicted upper limit of the output range of the i-th wind turbine at time t; is the predicted lower limit of the ith photovoltaic output interval at time t; is the predicted upper limit value of the ith photovoltaic output interval at time t; Power balance constraints: Energy storage operation constraints: In the formula is the planned storage capacity of the i-th energy storage at time t+1; is the loss efficiency of the i-th energy storage; is the charging efficiency of the i-th energy storage; is the discharge efficiency of the i-th energy storage; is the binary variable of the charging state of the i-th energy storage at time t, and if the i-th energy storage at time t is in the charging state, then At time t, the i-th energy storage is in an uncharged state. is the maximum charging power of the energy storage; is the discharge state binary variable of the i-th energy storage at time t, and if the i-th energy storage at time t is in the discharge state, then At time t, the i-th energy storage is in the undischarged state. is the lower limit of the energy storage charge state; is the state of charge of the i-th energy storage at time t; K is the upper limit of the energy storage charge state; ES,i is the initial output coefficient of the i-th energy storage; is the state of charge at the start time of the i-th energy storage; is the state of charge at the end of the i-th energy storage.

7. The method for optimizing the balance of power supply and demand in a new power system according to claim 6 is characterized in that The two-stage robust optimization method described in step S3 is used to solve the model established in step S2, which specifically includes the following steps: A. Initialization: Initialize the parameters of the second-generation non-dominated sorting genetic algorithm, generate the initial population and individuals, and each individual represents a set of operation schemes in the upper model; B. Optimization of daily planned operation of lower-level energy storage: Based on the daily planned output of the adjustable power source transmitted by the upper-level model and considering the prediction results of the source-load interval, GUROBI is used to solve the daily planned operation optimization model of the lower-level energy storage; C. Optimization of the daily planned output of the upper-layer adjustable power source: Based on the energy storage daily operation plan transmitted by the lower-layer model, the target value of each individual is calculated according to the source-load plan value, i.e., the point prediction result, and the non-dominated sorting level of each individual in the population is determined; the target value includes the daily operation cost, the renewable energy transmission rate, and the extreme scenario loss; D. Iterative optimization: Determine whether the target value converges: If converged, the Pareto frontier solution set consisting of the final optimization solution is output; If it does not converge, a new population is generated through genetic operations, and the new population corresponds to a new set of daily planned output values ​​of the adjustable power source, and the process returns to step C to continue the next round of iteration; E. For the Pareto frontier solution set obtained in step D, the optimal solution is selected through multi-dimensional preference analysis linear programming technology to obtain the final daily planned operation plan of the adjustable power supply and energy storage.

8. A system for realizing the power supply and demand balance optimization method of the new power system according to any one of claims 1 to 7, characterized in that It includes a data acquisition module, a model building module and a balance optimization module; the data acquisition module, the model building module and the balance optimization module are connected in series in sequence; the data acquisition module is used to acquire data information of the target new power system, and upload the data information to the model building module; the model building module is used to construct an upper-layer adjustable power supply daily planned output optimization model and a lower-layer energy storage daily planned operation optimization model, obtain a power supply and demand balance optimization model of the target new power system, and upload the data to the balance optimization module; The balance optimization module is used to solve the established model according to the received data information and based on the two-stage robust optimization method to complete the power supply and demand balance optimization of the new power system.

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