Scheduling method for energy system containing controllable air conditioner based on scene model predictive control

Abstract

The invention discloses a scene model predictive control-based scheduling method for an energy system containing a controllable air conditioner, and belongs to the field of power system operation optimization. The method comprises the following steps: constructing a target function containing a controllable air-conditioning load energy system multi-stage random collaborative scheduling model; establishing constraint conditions of the multi-stage random collaborative scheduling model of the energy system containing the controllable air-conditioning load; generating a large number of scenes based on Monte Carlo simulation, and performing scene reduction by applying a fast forward selection algorithm; for an energy system containing a controllable air-conditioning load, correspondingly converting an objective function and a constraint condition of a multi-stage random collaborative scheduling model of a mapping scene set of the energy system; and solving the scene model predictive control-based multi-stage random collaborative scheduling model of the controllable air-conditioning load energy system to obtain a scene model predictive control-based multi-stage random collaborative scheduling scheme of the controllable air-conditioning load energy system.

Description

Technical Field

[0001] The present invention belongs to the field of power system operation optimization, and in particular relates to a scheduling method for a controllable air-conditioning energy system based on scenario model predictive control. Background Art

[0002] A microgrid is a small energy system that can operate independently or in conjunction with the main grid to produce, distribute and manage electricity. It is usually composed of various energy sources, including renewable energy (such as solar and wind power) and traditional energy (such as diesel generators), and is equipped with storage systems (such as batteries) to improve flexibility and reliability. Microgrids can achieve self-sufficiency in local areas, thereby improving energy reliability and security, especially in remote areas or during natural disasters.

[0003] However, due to the limited number of controllable devices within a single microgrid, such as air conditioners and electric vehicles (EVs), there is an upper limit to the flexibility and reliability that a single microgrid can provide. When uncontrollable factors increase significantly, such as large fluctuations in the output of distributed renewable energy or a sudden surge in charging loads within a microgrid, the microgrid's ability to cope with these unforeseen situations may be insufficient, which may lead to power outages or frequency imbalances. Due to the geographical proximity of multiple microgrids, such as several building microgrids that may be located in the same area, these microgrids can form a multi-microgrid. In addition to achieving self-sufficiency within each microgrid, energy can be exchanged between microgrids and between microgrids and the main grid, further improving the operational flexibility of the microgrid. Through team collaborative operation, the reliability of each microgrid's operation will also be significantly improved.

[0004] Due to the above reasons, in recent years, the collaborative optimization and scheduling of multi-microgrids has become more and more popular. From the perspective of modeling, the collaborative optimization and scheduling models of multi-microgrids can be divided into deterministic scheduling models and stochastic scheduling models. The deterministic scheduling model mainly assumes that all entities on both the supply and demand sides of the multi-microgrid are deterministic, and the optimization objectives usually consider minimizing operating costs, maximizing social benefits, and minimizing carbon emissions. Compared with the deterministic scheduling model, the stochastic scheduling model for optimizing multi-microgrids is more complex.

[0005] In general, for the problem of coordinated operation optimization of multiple microgrids including air conditioners and electric vehicles, there is the following trade-off. On the one hand, there is a trade-off between dispatch optimization accuracy and dispatch duration. Longer dispatch duration can more comprehensively consider future supply and demand uncertainties, but it also increases the difficulty of predicting these uncertainties. On the other hand, there is a trade-off between dispatch optimization accuracy and the number of simulations of uncertain variables. By performing multiple Monte Carlo simulations on uncertain variables, various possible future values ​​can be generated for these uncertain variables. The more simulations are performed, the higher the optimization accuracy of the corresponding stochastic dispatch model, but this also leads to a greater computational burden. Summary of the invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to propose a scheduling method for a controllable air-conditioning energy system based on scenario model predictive control, which solves the problems in the prior art.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] S1 is based on the energy system structure, and the objective function of the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load is the total operating cost;

[0009] S2 considers the constraints of multi-microgrid system, charging pile constraints, distributed renewable energy generation operation constraints, air conditioning operation constraints and energy storage constraints, and determines the constraints of the multi-stage random collaborative scheduling model of the energy system with controllable air conditioning loads;

[0010] S3 generates a large number of scenarios based on Monte Carlo simulation and applies the fast forward selection algorithm to reduce the scenarios;

[0011] S4 is the objective function and constraint conditions of the multi-stage stochastic collaborative scheduling model for energy systems with controllable air-conditioning loads, corresponding to the transformation of their mapping scenario sets;

[0012] S5 considers multi-microgrid system constraints, charging pile constraints, distributed renewable energy generation operation constraints, air conditioning operation constraints, and energy storage constraints. For energy systems with controllable air conditioning loads, it transforms the constraints of the multi-stage random collaborative scheduling model corresponding to its mapping scenario set.

[0013] S6 linearizes the converted energy storage constraints;

[0014] S7 solves the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load based on scenario model predictive control, and obtains the multi-stage random collaborative scheduling scheme of the energy system with controllable air-conditioning load based on scenario model predictive control.

[0015] Furthermore, the objective function of the multi-stage random collaborative scheduling model of the energy system containing controllable air-conditioning load is:

[0016]

[0017] Where J represents the total operating cost of the multi-microgrid, T represents the window length of the rolling optimization, E represents the expected value, K represents the number of microgrids in the multi-microgrid system, represents the operating cost of the kth microgrid at time τ, represents the decision variables in the multi-microgrid stochastic cooperative scheduling model. Specifically, represents the power exchange vector, where represents the power exchange between the kth microgrid and the k'th microgrid. represents the power obtained by the kth microgrid from the grid. represents the charge vector, where It means that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ. Indicates the operating power of the air conditioner. Represents the charging and discharging power of the energy storage in the kth microgrid. τ represents the electricity price provided by the power grid, The value of depends on the remaining decision variables and

[0018] Furthermore, the constraints of the multi-stage random collaborative scheduling model of the energy system containing controllable air-conditioning loads are:

[0019]

[0020] Among them, N k represents the number of charging piles in the kth microgrid, indicates that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ, Q represents the charging power of the charging pile, Indicates the operating power of the air conditioner. represents the base load of the kth microgrid, represents the power exchange between the kth microgrid and the jth microgrid, represents the charging and discharging power of energy storage in the kth microgrid, represents distributed wind power generation, represents distributed solar power generation, represents the power obtained by the kth microgrid from the grid. and They are all uncertain variables. and They represent the upper and lower limits of the exchange power respectively. When power is transferred from the kth microgrid to the jth microgrid, Otherwise there is Obviously, there are represents the charging energy required by the i-th charging pile in the k-th microgrid from time t to t+T, Indicates that the i-th charging pile is occupied at time τ, at which time Otherwise and It is also an uncertain variable. represents the capacity of distributed wind power generation, represents the wind speed of the kth microgrid distributed wind power generation, v cutin Indicates the minimum wind speed, v cutout Indicates the maximum wind speed, v rated Indicates rated wind speed. represents the capacity of distributed solar energy, η s represents the power generation efficiency, represents the solar radiation of the kth microgrid at time τ, I s Represents standard solar radiation. and is also an uncertain variable. k Indicates the maximum operating power of the air conditioning system. Represents the energy required to ensure user comfort during sliding window. Indicates the charging status of the energy storage device at the current moment. Indicates the charging state of the energy storage device at the next moment, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device. Otherwise

[0021] Furthermore, for the energy system containing controllable air-conditioning loads, the objective function of the multi-stage random collaborative scheduling model corresponding to the transformation of its mapping scenario set is:

[0022]

[0023] Where M represents the number of scenarios, J represents the total operating cost of multiple microgrids, and S redu represents the set of scenarios reduced by the fast forward selection algorithm, T represents the window length of the rolling optimization, K represents the number of microgrids in the multi-microgrid system, represents the operating cost of the kth microgrid at time τ, represents the decision variable corresponding to the s-th scenario in the multi-microgrid random cooperative scheduling model. Specifically, represents the power exchange vector of the sth scenario, represents the power obtained from the grid by the kth microgrid in the sth scenario. represents the charging vector of the s-th scenario, represents the operating power of the air conditioner in the sth scenario, Represents the charging and discharging power of the energy storage in the kth microgrid in the sth scenario. τ represents the electricity price provided by the power grid, The value of depends on the remaining decision variables and

[0024] Furthermore, for the energy system with controllable air-conditioning load, the constraints of the multi-stage random collaborative scheduling model corresponding to the transformation of its mapping scenario set are:

[0025]

[0026]

[0027] Among them, N k represents the number of charging piles in the kth microgrid, Indicates that the i-th charging pile in the k-th microgrid in the s-th scenario is selected to provide charging service at time τ, Q represents the charging power of the charging pile, represents the operating power of the air conditioner in the sth scenario, represents the basic load of the kth microgrid corresponding to the simulated sth scenario, represents the power exchange between the kth microgrid and the jth microgrid in the sth scenario, represents the charging and discharging power of the energy storage in the kth microgrid in the sth scenario, represents the distributed wind power generation corresponding to the simulated s-th scenario, represents the distributed solar power generation corresponding to the simulated s-th scenario, represents the power obtained from the grid by the kth microgrid in the sth scenario. and Respectively represent the upper and lower limits of the exchange power. represents the charging energy required for the ith charging pile in the kth microgrid to simulate the sth scenario from time t to t+T, It means that the simulated s-th scene corresponding to the i-th charging pile is occupied at time τ. represents the capacity of distributed solar energy, η s represents the power generation efficiency, represents the solar radiation of the simulated sth scenario corresponding to the kth microgrid at time τ, I s Represents standard solar radiation. k Indicates the maximum operating power of the air conditioning system. represents the energy required to ensure user comfort during the sliding window of the corresponding simulated scenario s. Indicates the charging state of the energy storage device at the current moment of the corresponding simulation scenario s, represents the charging state of the energy storage device at the next moment of the corresponding simulation scenario s, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device. represents the capacity of distributed wind power generation, represents the wind speed of distributed wind power generation corresponding to the kth microgrid simulating the sth scenario, v cutin Indicates the minimum wind speed, v cutout Indicates the maximum wind speed, v rated Indicates the rated wind speed. S redu Represents the set of scenes reduced by applying the fast forward selection algorithm.

[0028] Furthermore, the converted energy storage constraint condition is linearized as follows:

[0029]

[0030] in, Indicates the charging state of the energy storage device at the current moment of the corresponding simulation scenario s, represents the charging state of the energy storage device at the next moment of the corresponding simulation scenario s, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device. and Indicates a temporary variable, h cap Indicates the maximum charge / discharge power of the energy storage device.

[0031] Furthermore, a multi-stage random collaborative scheduling model of an energy system with controllable air-conditioning load based on scenario model predictive control is solved, and a multi-stage random collaborative scheduling strategy of the energy system is obtained under the objective function of minimizing the total operating cost.

[0032] A dispatching system for a controllable air conditioning energy system based on scenario model predictive control, comprising:

[0033] The objective function construction module of the energy system model with controllable air-conditioning load: Based on the energy system structure, the objective function of the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load is the total operating cost;

[0034] The module for building constraints of the energy system model with controllable air-conditioning loads: considering the constraints of multi-microgrid systems, charging pile constraints, distributed renewable energy generation operation constraints, air-conditioning operation constraints, and energy storage constraints, the constraints of the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning loads are determined;

[0035] Scenario selection module: Generates a large number of scenarios based on Monte Carlo simulation and applies fast forward selection algorithm to reduce the scenarios;

[0036] Energy system model conversion module based on scenario model predictive control: for energy systems with controllable air-conditioning loads, it converts the objective function and constraints of the multi-stage stochastic collaborative scheduling model of its mapping scenario set;

[0037] Constraint linearization module: linearizes the converted energy storage constraints;

[0038] And, the model solving module: solves the multi-stage random collaborative scheduling model of the energy system containing controllable air-conditioning load based on scenario model predictive control, and obtains the multi-stage random collaborative scheduling scheme of the energy system containing controllable air-conditioning load based on scenario model predictive control.

[0039] A computer storage medium stores a readable program, characterized in that when the program is run, it can execute the above-mentioned scheduling method for a controllable air-conditioning energy system based on scenario model predictive control.

[0040] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0041] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for scheduling a controllable air-conditioning energy system based on scenario model predictive control.

[0042] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned method for scheduling a controllable air-conditioning energy system based on scenario model predictive control.

[0043] Beneficial effects of the present invention:

[0044] 1. This paper proposes a scheduling method for an energy system with controllable air conditioners based on scenario model predictive control, which takes into account uncertain supply and demand as well as controllable air conditioners and partially controllable electric vehicle loads. In order to improve the solution efficiency, the above model is linearized, and the scenario simplification method is applied to solve the trade-off between optimization accuracy and computational complexity.

[0045] 2. The present invention can reduce operating costs and operation costs, and at the same time, by reducing the maximum power purchase, it can alleviate the impact of the multi-microgrid system on the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 is the energy system dispatching flow chart of the present invention;

[0048] Figure 2 is a structural diagram of the energy scheduling problem used in Example 1;

[0049] Figure 3 is the basic load diagram of the three building microgrids used in Example 2 and the comparative example;

[0050] Figure 4 is a total charging power diagram of each microgrid in the multi-microgrid system used in Example 2 and the comparative example;

[0051] Figure 5 Graph 1 is a graph of power obtained from the grid by each microgrid used in Example 2 and the comparative example. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Example 1

[0054] like Figure 1 As shown, the scheduling method of a controllable air-conditioning energy system based on scenario model predictive control includes the following steps:

[0055] S1, based on Figure 2 The structure diagram of the energy scheduling problem is shown in the figure. The main optimization goal of the multi-microgrid operator is to reduce the total operating cost of all subordinate microgrids. Due to the uncertainty of electric vehicle drive and distributed renewable energy generation, the idea of ​​model predictive control (MPC) is adopted, and a sliding window is used to calculate the total operating cost of the multi-microgrid system in the future period of time;

[0056] The objective function of the multi-stage random coordinated scheduling model of the energy system with controllable air-conditioning load is:

[0057]

[0058] Where J represents the total operating cost of the multi-microgrid, T represents the window length of the rolling optimization, E represents the expected value, K represents the number of microgrids in the multi-microgrid system, represents the operating cost of the kth microgrid at time τ, represents the decision variables in the multi-microgrid stochastic cooperative scheduling model. Specifically, represents the power exchange vector, where represents the power exchange between the kth microgrid and the k'th microgrid. represents the power obtained by the kth microgrid from the grid. represents the charge vector, where It indicates that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ. Indicates the operating power of the air conditioner. Represents the charging and discharging power of the energy storage in the kth microgrid. τ represents the electricity price provided by the power grid, The value of depends on the remaining decision variables and

[0059] S2, considering the constraints of multi-microgrid system, charging pile constraints, distributed renewable energy generation operation constraints, air conditioning operation constraints and energy storage constraints, determine the constraints of the multi-stage random coordinated scheduling model of the energy system with controllable air conditioning load;

[0060] a) Multi-microgrid system constraints:

[0061]

[0062] Among them, N k represents the number of charging piles in the kth microgrid, K represents the number of microgrids in the multi-microgrid system, indicates that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ, Q represents the charging power of the charging pile, Indicates the operating power of the air conditioner. represents the base load of the kth microgrid, represents the power exchange between the kth microgrid and the jth microgrid, represents the charging and discharging power of energy storage in the kth microgrid, represents distributed wind power generation, represents distributed solar power generation, represents the power obtained by the kth microgrid from the grid. and They are all uncertain variables. and They represent the upper and lower limits of the exchange power respectively. When power is transferred from the kth microgrid to the jth microgrid, Otherwise there is Obviously, there are

[0063] b) Operation constraints of charging piles:

[0064]

[0065] Where T represents the window length of rolling optimization. indicates that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ, Q represents the charging power of the charging pile, represents the charging energy required by the i-th charging pile in the k-th microgrid from time t to t+T, Indicates that the i-th charging pile is occupied at time τ, at which time Otherwise and It is also an uncertain variable.

[0066] c) Distributed renewable energy generation operation constraints:

[0067]

[0068] in, represents distributed wind power generation, represents the capacity of distributed wind power generation, represents the wind speed of the kth microgrid distributed wind power generation, v cutin Indicates the minimum wind speed, v cutout Indicates the maximum wind speed, v rated Indicates rated wind speed. represents distributed solar power generation, represents the capacity of distributed solar energy, η s represents the power generation efficiency, represents the solar radiation of the kth microgrid at time τ, I s Represents standard solar radiation. and It is also an uncertain variable.

[0069] d) Air conditioning operation constraints:

[0070]

[0071] Where T represents the window length of rolling optimization. Indicates the operating power of the air conditioner, A k Indicates the maximum operating power of the air conditioning system. Represents the energy required to ensure user comfort during sliding window.

[0072] e) Energy storage constraints:

[0073]

[0074] in, Indicates the charging status of the energy storage device at the current moment. Indicates the charging state of the energy storage device at the next moment, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device. Otherwise

[0075] S3, Since there are many uncertain variables in each microgrid, using Monte Carlo simulation to generate scenarios for each uncertain variable will significantly increase the number of decision variables in the model. This will increase the difficulty of solving the model. Therefore, in order to strike a balance between computational complexity and the accuracy of stochastic optimization, a fast forward selection algorithm is applied to reduce the above situations. This can obtain the most representative scenarios and incorporate them into the solution model. S init represents the preliminary scenario set generated based on Monte Carlo simulation, S redu represents the set of scenes reduced by the fast forward selection algorithm;

[0076] The fast forward selection algorithm can be summarized as follows:

[0077] 1) Calculate the distances of all scene pairs;

[0078] 2) Select the scene with the smallest expected distance to other scenes;

[0079] 3) Delete this scene s and add the probability of scene s to its nearest neighbor scene;

[0080] 4) If the number of remaining scenes is acceptable, stop scene reduction, otherwise go to step 1) and repeat.

[0081] Note that the above scenario reduction does not need to be performed on the controller of the multi-microgrid system. Instead, each microgrid can analyze the uncertain characteristics of supply and demand based on historical data. Then, the scenario reduction algorithm can be implemented independently in a distributed manner. This process produces a reduced set of scenarios that represent the uncertain characteristics of supply and demand of each microgrid. Finally, this reduced set of scenarios is sent to the multi-microgrid system to perform scenario-based model predictive control, thereby providing a collaborative scheduling strategy for multiple microgrids.

[0082] S4, for the energy system with controllable air-conditioning load, the objective function of the multi-stage stochastic collaborative scheduling model corresponding to the transformation of its mapping scenario set;

[0083] The objective function after the transformation is:

[0084]

[0085] Where M represents the number of scenarios, J represents the total operating cost of multiple microgrids, and S redu represents the set of scenarios reduced by the fast forward selection algorithm, T represents the window length of the rolling optimization, K represents the number of microgrids in the multi-microgrid system, represents the operating cost of the kth microgrid at time τ, represents the decision variable corresponding to the s-th scenario in the multi-microgrid random cooperative scheduling model. Specifically, represents the power exchange vector of the sth scenario, represents the power obtained from the grid by the kth microgrid in the sth scenario. represents the charging vector of the s-th scenario, represents the operating power of the air conditioner in the sth scenario, Represents the charging and discharging power of the energy storage in the kth microgrid in the sth scenario. τ represents the electricity price provided by the power grid, The value of depends on the remaining decision variables and

[0086] S5, considering the constraints of multi-microgrid system, charging pile constraints, distributed renewable energy generation operation constraints, air conditioning operation constraints and energy storage constraints, for the energy system with controllable air conditioning load, the corresponding transformation of the constraints of the multi-stage random collaborative scheduling model of its mapping scenario set. Specifically, for each scenario, its objective function and constraints are clarified and specified, and finally the objective function values ​​under each scenario are summed up.

[0087] The constraints after the transformation are:

[0088]

[0089]

[0090] Among them, N k represents the number of charging piles in the kth microgrid, Indicates that the i-th charging pile in the k-th microgrid in the s-th scenario is selected to provide charging service at time τ, Q represents the charging power of the charging pile, represents the operating power of the air conditioner in the sth scenario, represents the basic load of the kth microgrid corresponding to the simulated sth scenario, represents the power exchange between the kth microgrid and the jth microgrid in the sth scenario, represents the charging and discharging power of the energy storage in the kth microgrid in the sth scenario, represents the distributed wind power generation corresponding to the simulated s-th scenario, represents the distributed solar power generation corresponding to the simulated s-th scenario, represents the power obtained from the grid by the kth microgrid in the sth scenario. and Respectively represent the upper and lower limits of the exchange power. represents the charging energy required for the ith charging pile in the kth microgrid to simulate the sth scenario from time t to t+T, It means that the simulated s-th scene corresponding to the i-th charging pile is occupied at time τ. represents the capacity of distributed solar energy, η s represents the power generation efficiency, represents the solar radiation of the simulated sth scenario corresponding to the kth microgrid at time τ, I s Represents standard solar radiation. k Indicates the maximum operating power of the air conditioning system. represents the energy required to ensure user comfort during the sliding window of the corresponding simulated scenario s. Indicates the charging state of the energy storage device at the current moment of the corresponding simulation scenario s, represents the charging state of the energy storage device at the next moment of the corresponding simulation scenario s, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device. represents the capacity of distributed wind power generation, represents the wind speed of distributed wind power generation corresponding to the kth microgrid simulating the sth scenario, v cutin Indicates the minimum wind speed, v cutout Indicates the maximum wind speed, v rated Indicates the rated wind speed. S redu represents the set of scenarios reduced by applying the fast forward selection algorithm. The last constraint is used to ensure the unique output of the current decision variable at time t. Future decision variables can have multiple outputs because these variables will not be realized in the current optimization at time t.

[0091] S6, linearize the converted energy storage constraints;

[0092] The linearization constraints are:

[0093]

[0094] in, Indicates the charging state of the energy storage device at the current moment of the corresponding simulation scenario s, represents the charging state of the energy storage device at the next moment of the corresponding simulation scenario s, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device. and Indicates a temporary variable, h cap represents the maximum charging / discharging power of the energy storage. The last constraint is used to avoid the simultaneous occurrence of charging and discharging behaviors. By using the above model, the multi-stage stochastic cooperative scheduling model of the energy system with controllable air-conditioning load based on scenario model predictive control can finally be transformed into a mixed integer linear programming model, which can be solved by many solvers.

[0095] S7, solving a multi-stage random collaborative scheduling model of an energy system containing controllable air-conditioning loads based on scenario model predictive control, and obtaining a multi-stage random collaborative scheduling scheme of an energy system based on scenario model predictive control;

[0096] Use solvers (such as gurobi, cplex, etc.) to solve the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load based on scenario model predictive control, and obtain the multi-stage random collaborative scheduling strategy of the energy system under the objective function of minimizing the total operating cost.

[0097] Example 2

[0098] In this embodiment, a typical multi-microgrid system consisting of three building microgrids is taken as an example to introduce a coordinated scheduling method of an energy system containing controllable air-conditioning loads based on scenario model predictive control; specifically, the following steps are included:

[0099] S1, consider a typical multi-microgrid system consisting of three building microgrids. The base loads of these building microgrids are as follows: Figure 3 As shown in Figure 1. Building A and Building B are residential buildings, and their peak loads occur at night. Building C is a commercial building, and its load is high during the day.

[0100] S2, wind speed and solar radiation data are from relevant literature. The uncertainty of distributed solar and wind energy is assumed to follow a normal distribution. The variation is set to 10% of the actual output. Since most electric vehicles are parked, we assume that the idle state of commercial building charging piles follows a normal distribution norm (20:00, 1 hour) and norm (5:00, 1 hour). Similarly, it is assumed that the idle state of residential building charging piles follows a normal distribution norm (12:00, 2 hours). The charging demand is assumed to follow a random distribution with a mean of 40% of its battery capacity and a variance of 10% of its battery capacity.

[0101] S3, the electricity price of the main grid is from the relevant literature. Other parameters are shown in Table 1. After applying the fast-forward selection algorithm, the number of reduced scenario sets is reduced from 1000 to 50.

[0102] Table 1 Parameter settings

[0103]

[0104] S4. In this embodiment, a commercial solver is used to solve a multi-stage random collaborative scheduling model of an energy system containing controllable air-conditioning loads based on scenario model predictive control, and a multi-stage random collaborative scheduling scheme of an energy system containing controllable air-conditioning loads based on scenario model predictive control is obtained.

[0105] Comparative Example

[0106] In order to verify the scheduling performance of the proposed method, the proposed method is compared with two other benchmark scheduling strategies. The first benchmark strategy is the greedy strategy. In this greedy strategy, the electric vehicle will start charging as soon as it is connected to the charging pile. The remaining decision variables are optimized by the proposed multi-stage stochastic collaborative scheduling model of energy systems based on scenario model predictive control. In the delay strategy, the electric vehicle will postpone charging as late as possible. The remaining decision variables are also optimized similar to the greedy strategy.

[0107] The operation results of these strategies are shown in Table 2. It can be observed that the proposed method has the lowest operating cost compared to the greedy and delay strategies. The delay strategy has a lower operating cost compared to the greedy strategy due to the non-aggressive charging control. In terms of power purchase cost, the proposed method has the smallest maximum power purchase among the three strategies, while the greedy strategy has the largest maximum power purchase. The reduction in maximum power purchase can mitigate the impact of the multi-microgrid system on the power grid.

[0108] Table 2 Operation comparison of different scheduling strategies

[0109]

[0110] The total charging power of microgrid A under the above scheduling strategy is as follows: Figure 4 As shown. It can be observed that the greedy strategy usually reaches the peak of charging power around 8 pm. This is because microgrid A is a residential building, so its electric vehicle users usually go home to charge their vehicles at night. The greedy strategy adopts an instant charging method, which causes the peak charging power to appear in the evening when the electricity price is higher. In contrast, due to the delayed charging characteristics, the delayed strategy usually reaches the peak charging power around 6:00 in the morning. The proposed strategy dynamically adjusts the charging power according to the power source within the microgrid and the electricity price in the grid. Therefore, the greedy strategy has a higher operating cost, while the proposed strategy has a lower operating cost.

[0111] The purchased power of the three building microgrids in the multi-microgrid system is as follows: Figure 5 As shown. It can be seen that for residential buildings A and B, the maximum purchase occurs around 7:00 p.m., coinciding with the peak load period. For commercial building C, since its peak load occurs around noon, this causes the maximum purchase to occur around 12:00 a.m. In addition, due to the number of partially controllable electric vehicles in commercial building C and the reduction of controllable air-conditioning load at night, another purchase peak occurs around 20:00 p.m.

[0112] The present invention proposes a multi-stage random collaborative scheduling model for an energy system with controllable air-conditioning load based on scenario model predictive control, which takes into account uncertain supply and demand as well as controllable air-conditioning and partially controllable electric vehicle loads. In order to improve the solution efficiency, the above model is linearized, and the scenario simplification method is applied to solve the trade-off between optimization accuracy and computational complexity. The above comparative examples prove the effectiveness of this method.

[0113] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0114] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for scheduling a controllable air conditioning energy system based on scenario model predictive control, characterized in that: The following steps are involved: S1 is based on the energy system structure, and the objective function of the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load is the total operating cost; S2 considers the constraints of multi-microgrid system, charging pile constraints, distributed renewable energy generation operation constraints, air conditioning operation constraints and energy storage constraints, and determines the constraints of the multi-stage random collaborative scheduling model of the energy system with controllable air conditioning loads; S3 generates a large number of scenarios based on Monte Carlo simulation and applies the fast forward selection algorithm to reduce the scenarios; S4 is the objective function and constraint conditions of the multi-stage stochastic collaborative scheduling model for energy systems with controllable air-conditioning loads, corresponding to the transformation of their mapping scenario sets; S5 considers multi-microgrid system constraints, charging pile constraints, distributed renewable energy generation operation constraints, air conditioning operation constraints, and energy storage constraints. For energy systems with controllable air conditioning loads, it transforms the constraints of the multi-stage random collaborative scheduling model corresponding to its mapping scenario set. S6 linearizes the converted energy storage constraints; S7 solves the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load based on scenario model predictive control, and obtains the multi-stage random collaborative scheduling scheme of the energy system with controllable air-conditioning load based on scenario model predictive control.

2. According to claim 1, a method for scheduling a controllable air conditioning energy system based on scenario model predictive control is characterized in that: The objective function of the multi-stage random coordinated scheduling model of the energy system with controllable air-conditioning load is: Where J represents the total operating cost of the multi-microgrid, T represents the window length of the rolling optimization, E represents the expected value, K represents the number of microgrids in the multi-microgrid system, represents the operating cost of the kth microgrid at time τ, represents the decision variables in the multi-microgrid stochastic cooperative dispatch model; specifically, represents the power exchange vector, where represents the power exchange between the kth microgrid and the k'th microgrid; represents the power obtained by the kth microgrid from the grid; represents the charge vector, where It means that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ; Indicates the operating power of the air conditioner; represents the charging and discharging power of energy storage in the kth microgrid; τ represents the electricity price provided by the power grid, The value of depends on the remaining decision variables and 3. The method for scheduling a controllable air conditioning energy system based on scenario model predictive control according to claim 2, characterized in that: The constraints of the multi-stage random coordinated scheduling model of the energy system with controllable air-conditioning load are: Among them, N k represents the number of charging piles in the kth microgrid, indicates that the i-th charging pile in the k-th microgrid is selected to provide charging service at time τ, Q represents the charging power of the charging pile, Indicates the operating power of the air conditioner. represents the base load of the kth microgrid, represents the power exchange between the kth microgrid and the jth microgrid, represents the charging and discharging power of energy storage in the kth microgrid, represents distributed wind power generation, represents distributed solar power generation, represents the power obtained by the kth microgrid from the grid; Note and They are all uncertain variables. and represent the upper and lower limits of the exchange power respectively; when power is transferred from the kth microgrid to the jth microgrid, Otherwise there is Obviously, there are represents the charging energy required by the i-th charging pile in the k-th microgrid from time t to t+T, Indicates that the i-th charging pile is occupied at time τ, at which time Otherwise and It is also an uncertain variable; represents the capacity of distributed wind power generation, represents the wind speed of the kth microgrid distributed wind power generation, v cutin Indicates the minimum wind speed, v cutout Indicates the maximum wind speed, v rated Indicates rated wind speed; represents the capacity of distributed solar energy, η s represents the power generation efficiency, represents the solar radiation of the kth microgrid at time τ, I s represents the standard solar radiation; and is also an uncertain variable; k Indicates the maximum operating power of the air conditioning system. represents the energy required to ensure user comfort during sliding window; Indicates the charging status of the energy storage device at the current moment. Indicates the charging state of the energy storage device at the next moment, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device; when the energy storage device is discharged Otherwise 4. The method for scheduling a controllable air conditioning energy system based on scenario model predictive control according to claim 2, characterized in that: For the energy system with controllable air-conditioning load, the objective function of the multi-stage random collaborative scheduling model corresponding to the transformation of its mapping scenario set is: Where M represents the number of scenarios, J represents the total operating cost of multiple microgrids, and S redu represents the set of scenarios reduced by the fast forward selection algorithm, T represents the window length of the rolling optimization, K represents the number of microgrids in the multi-microgrid system, represents the operating cost of the kth microgrid at time τ, represents the decision variable corresponding to the simulated s-th scenario in the multi-microgrid random cooperative dispatch model; specifically, represents the power exchange vector of the sth scenario, represents the power obtained from the grid by the kth microgrid in the sth scenario; represents the charging vector of the s-th scenario, represents the operating power of the air conditioner in the sth scenario, represents the charging and discharging power of the energy storage in the kth microgrid in the sth scenario; τ represents the electricity price provided by the power grid, The value of depends on the remaining decision variables and 5. The method for scheduling a controllable air conditioning energy system based on scenario model predictive control according to claim 2, characterized in that: For the energy system with controllable air-conditioning load, the constraints of the multi-stage random collaborative scheduling model corresponding to the transformation of its mapping scenario set are: Among them, N k represents the number of charging piles in the kth microgrid, Indicates that the i-th charging pile in the k-th microgrid in the s-th scenario is selected to provide charging service at time τ, Q represents the charging power of the charging pile, represents the operating power of the air conditioner in the sth scenario, represents the basic load of the kth microgrid corresponding to the simulated sth scenario, represents the power exchange between the kth microgrid and the jth microgrid in the sth scenario, represents the charging and discharging power of the energy storage in the kth microgrid in the sth scenario, represents the distributed wind power generation corresponding to the simulated s-th scenario, represents the distributed solar power generation corresponding to the simulated s-th scenario, represents the power obtained from the grid by the kth microgrid in the sth scenario; and Respectively represent the upper and lower limits of the exchange power; represents the charging energy required for the ith charging pile in the kth microgrid to simulate the sth scenario from time t to t+T, It means that the simulated s-th scene corresponding to the i-th charging pile is occupied at time τ. represents the capacity of distributed solar energy, η s represents the power generation efficiency, represents the solar radiation of the simulated sth scenario corresponding to the kth microgrid at time τ, I s Represents standard solar radiation; A k Indicates the maximum operating power of the air conditioning system. represents the energy required to ensure user comfort during the sliding window of the corresponding simulated scenario s; Indicates the charging state of the energy storage device at the current moment of the corresponding simulation scenario s, represents the charging state of the energy storage device at the next moment of the corresponding simulation scenario s, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device; represents the capacity of distributed wind power generation, represents the wind speed of distributed wind power generation corresponding to the kth microgrid simulating the sth scenario, v cutin Indicates the minimum wind speed, v cutout Indicates the maximum wind speed, v rated Indicates rated wind speed; S redu Represents the set of scenes reduced by applying the fast forward selection algorithm.

6. The method for scheduling a controllable air conditioning energy system based on scenario model predictive control according to claim 2, characterized in that: The converted energy storage constraint condition is linearized as follows: in, Indicates the charging state of the energy storage device at the current moment of the corresponding simulation scenario s, represents the charging state of the energy storage device at the next moment of the corresponding simulation scenario s, η dc represents the discharge efficiency, η c Indicates charging efficiency, K cap Indicates the capacity of the energy storage device; and Indicates a temporary variable, h cap Indicates the maximum charge / discharge power of the energy storage device.

7. The method for scheduling a controllable air conditioning energy system based on scenario model predictive control according to claim 1, characterized in that: The multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load based on scenario model predictive control is solved, and the multi-stage random collaborative scheduling strategy of the energy system is obtained under the objective function of minimizing the total operating cost.

8. A coordinated dispatching system for energy systems containing controllable air-conditioning loads based on scenario model predictive control, which uses a dispatching method for energy systems containing controllable air-conditioning loads based on scenario model predictive control as described in any one of claims 1 to 7 to realize coordinated dispatching of energy systems containing controllable air-conditioning loads based on scenario model predictive control, characterized in that: include: The objective function construction module of the energy system model with controllable air-conditioning load: Based on the energy system structure, the objective function of the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning load is the total operating cost; The module for building constraints of the energy system model with controllable air-conditioning loads: considering the constraints of multi-microgrid systems, charging pile constraints, distributed renewable energy generation operation constraints, air-conditioning operation constraints, and energy storage constraints, the constraints of the multi-stage random collaborative scheduling model of the energy system with controllable air-conditioning loads are determined; Scenario selection module: Generates a large number of scenarios based on Monte Carlo simulation and applies fast forward selection algorithm to reduce the scenarios; Energy system model conversion module based on scenario model predictive control: for energy systems with controllable air-conditioning loads, it converts the objective function and constraints of the multi-stage stochastic collaborative scheduling model of its mapping scenario set; Constraint linearization module: linearizes the converted energy storage constraints; And, the model solving module: solves the multi-stage random collaborative scheduling model of the energy system containing controllable air-conditioning load based on scenario model predictive control, and obtains the multi-stage random collaborative scheduling scheme of the energy system containing controllable air-conditioning load based on scenario model predictive control.

9. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the method for scheduling a controllable air-conditioning energy system based on scenario model predictive control as described in any one of claims 1 to 7.

10. An electronic device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to a method for scheduling a controllable air-conditioning energy system based on scenario model predictive control as described in any one of claims 1-7.

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