A commercial regional virtual power plant system and its optimization scheduling method

Abstract

A commercial area virtual power plant system and an optimization scheduling method thereof, the method comprising the following steps: quantifying the failure to provide expected energy reserve capacity and the waste of expected energy reserve capacity based on the probability density function of the net demand forecast error of the commercial area; establishing an objective function that minimizes the operating cost of the virtual power plant by taking into account the virtual power plant's electricity purchase cost and electricity sales revenue, the cost of adjustable resources, and the penalty cost for failing to provide expected energy reserves and wasting expected energy reserves; establishing constraints for the operation of the virtual power plant, including: power balance constraints for power generation and consumption, physical constraints for adjustable resource power, and constraints for spare capacity; solving the objective function under the constraints to obtain virtual power plant control decision instructions including spare capacity. The present invention proposes that the virtual power plant operator predetermine the spare capacity by considering the economic and technical indicators of dynamic risk reserves under a specific safety level to minimize the operating cost of the virtual power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and more specifically, relates to a commercial regional virtual power plant system and an optimization scheduling method thereof. Background Art

[0002] The continuous development of energy utilization technology has promoted the emergence of a large number of distributed resources with regulation potential on the distribution network side, such as electric vehicles, distributed energy storage, distributed power generation, data centers and 5G base stations under the background of new infrastructure. This type of load points are numerous, small in capacity, low in voltage level, and diverse in subjects. Traditional power grid dispatching makes it difficult to achieve direct control of a single load.

[0003] Virtual power plants use advanced information and communication technologies and software systems to aggregate and coordinate distributed resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles, and participate in the power market and power grid operation as a special power plant in a power coordination management system.

[0004] As the foundation of urban power grids, commercial areas are evolving towards intelligent and energy-efficient systems with the advancement of information technology, smart terminals, and automation. Furthermore, emerging grid elements such as electric vehicles and distributed photovoltaics are increasingly being deployed in commercial areas, becoming the future direction of commercial area development.

[0005] In the prior art, Chinese invention patent CN118739437B discloses a method for quantitatively assessing the adjustable capacity of a virtual power plant; Chinese invention patent CN116542439B discloses a method and system for optimizing the multi-energy response of a virtual power plant; and Chinese invention patent CN113794200B discloses a method for aggregating multiple types of load resources for a virtual power plant. In engineering practice, these existing solutions often require a relatively large static margin to address uncertainty after aggregating various resources to achieve safe and stable operation. Compared to the uncertainty of dynamic changes, this excess margin leads to further reductions in the operating efficiency and cost of virtual power plants. Summary of the Invention

[0006] In order to address the deficiencies in the prior art, the present invention provides a commercial regional virtual power plant system and an optimized scheduling method thereof, quantifies the operating reserve capacity of the commercial regional virtual power plant, and proposes that the virtual power plant operator predetermine the reserve capacity by considering the economic and technical indicators of dynamic risk reserves under a specific safety level, so as to minimize the operating costs of the virtual power plant.

[0007] The present invention adopts the following technical solutions.

[0008] A first aspect of the present invention provides a commercial regional virtual power plant optimization scheduling method, comprising the following steps:

[0009] Quantify the failure to provide expected energy reserve capacity and the waste of expected energy reserve capacity based on the probability density function of the net demand forecast error in the commercial area;

[0010] Taking into account the virtual power plant's electricity purchase costs and electricity sales revenue, the cost of adjustable resources, and the penalty costs for not providing expected energy reserves and wasting expected energy reserves, an objective function that minimizes the virtual power plant's operating costs is established;

[0011] Establish constraints for virtual power plant operation, including power balance constraints between power generation and consumption, physical constraints on adjustable resource power, and constraints on reserve capacity;

[0012] Under the constraints, the objective function is solved to obtain the virtual power plant control decision instructions including the spare capacity.

[0013] Preferably, the quantifying the failure to provide expected energy reserve capacity and the wasted expected energy reserve capacity based on the probability density function of the commercial area net demand forecast error comprises:

[0014] Based on the probability density function of the net demand forecast error in the commercial area, a load loss probability model and a power curtailment probability model are established;

[0015] Based on the load loss probability and power reduction probability model, the minimum positive reserve capacity and the minimum negative reserve capacity are calculated;

[0016] Based on the minimum positive reserve capacity and the minimum negative reserve capacity, a model of not providing expected energy reserve and wasting expected energy reserve is established.

[0017] Preferably, establishing a load loss probability and power reduction probability model based on a probability density function of a commercial area net demand forecast error comprises:

[0018] The probability density function of the net demand forecast error is integrated over the interval between the minimum positive reserve capacity required to cover the corresponding forecast error and the maximum value of the net demand forecast error to obtain the load loss probability at the time step;

[0019] The probability density function of the net demand forecast error is integrated over the interval between the minimum value of the net demand forecast error and the minimum negative reserve capacity required to cover the corresponding forecast error to obtain the power curtailment probability at that time step.

[0020] Preferably, the calculating and obtaining the minimum positive reserve capacity and the minimum negative reserve capacity based on the load loss probability and power reduction probability model includes:

[0021] When the spare capacity is equal to the minimum positive reserve capacity, the load loss probability of the virtual power plant is not less than the load loss probability;

[0022] When the spare capacity is equal to the minimum negative reserve capacity, the power reduction probability of the virtual power plant is not greater than the power reduction probability.

[0023] Preferably, establishing the model of not providing expected energy reserve and wasting expected energy reserve based on the minimum positive reserve capacity and the minimum negative reserve capacity includes:

[0024] The difference between the net demand forecast error and the minimum positive reserve capacity required to cover the corresponding forecast error is calculated, and then multiplied by the probability density function of the net demand forecast error. The product is then integrated over the interval between the positive reserve capacity and the maximum value of the net demand forecast error to obtain the unprovided expected energy reserve capacity.

[0025] The difference between the minimum negative reserve capacity required to cover the corresponding forecast error and the net demand forecast error is calculated, and then multiplied by the probability density function of the net demand forecast error. The product is then integrated over the interval between the minimum value of the demand forecast error and the negative reserve capacity to obtain the expected energy reserve capacity.

[0026] Preferably, the objective function for minimizing the operating cost of the virtual power plant, taking into account the power purchase cost and power sales revenue of the virtual power plant, the cost of adjustable resources, and the penalty cost for not providing the expected energy reserve and wasting the expected energy reserve, comprises:

[0027] Establish a cost model for electricity exchange between the virtual power plant and the main grid, including: if purchasing electricity from the grid, multiply the purchase price by the amount of electricity; if selling electricity to the grid, multiply the sales price by the amount of electricity;

[0028] Establish a cost model for the operation of adjustable resources in commercial areas, fitting it as a quadratic function of the amount of electricity exchanged by the adjustable resources;

[0029] The penalty cost is obtained by multiplying the unit cost by the failure to provide the expected energy reserve capacity and the waste of the expected energy reserve capacity.

[0030] Preferably, the spare capacity constraint includes:

[0031] The sum of the positive and negative reserve capacities of the bidirectional charging piles, virtual energy storage devices and flexible adjustable loads shall not be less than the minimum positive and negative reserve capacities required to cover the corresponding prediction errors.

[0032] Preferably, the spare capacity constraint includes:

[0033] For bidirectional charging piles, calculate the difference between the current power and the maximum and minimum power limits. The smaller of the difference and the power fluctuation is used as the upper limit of the bidirectional charging pile's spare capacity.

[0034] For the virtual energy storage device, the sum of the abandoned solar power and the discharge margin is calculated, and the smaller of the two is taken as the upper limit of the discharge capacity of the virtual energy storage device. The sum of the current photovoltaic power and the charging margin is calculated, and the smaller of the two is taken as the upper limit of the negative reserve capacity of the virtual energy storage device.

[0035] For flexible adjustable loads, taking into account the flexible adjustable ratio, the smaller one that satisfies the power fluctuation of the flexible adjustable load itself is taken as the upper limit of the adjustable flexible adjustable load standby capacity.

[0036] A second aspect of the present invention provides a commercial regional virtual power plant system, comprising:

[0037] Solar storage equipment, centralized new energy power generation equipment, flexible adjustable loads, energy storage devices, bidirectional charging piles, a first DC bus, a first AC bus, a second DC bus, and a second AC bus installed in the building; the first DC bus is connected to the main grid via a first converter, and the first AC bus is connected to the main grid via a first transformer;

[0038] Each energy storage device and centralized new energy power generation equipment is connected to the first DC bus via a DC / DC converter; the photovoltaic storage equipment installed in the building is collected via the second DC bus and then connected to the first DC bus via a DC / DC converter;

[0039] The bidirectional charging pile is connected to the first AC bus via a converter; the flexible adjustable load installed in the building is connected to the second AC bus, and the second AC bus is connected to the first AC bus via a second transformer.

[0040] Preferably, the energy storage device is at least one of a supercapacitor and an electrochemical energy storage device, which is equivalent to a virtual energy storage device with a centralized photovoltaic array;

[0041] The flexible adjustable load is a heat storage and cold storage type air conditioner.

[0042] Compared with the existing technology, the beneficial effects of the present invention include at least: quantifying the operating reserve capacity of commercial regional virtual power plants, and proposing that the virtual power plant operator predetermine the backup capacity by considering the economic and technical indicators of dynamic risk reserves under a specific safety level to minimize the operating costs of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a commercial regional virtual power plant optimization scheduling method provided in accordance with an embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of the architecture of a commercial regional virtual power plant system provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, embodiment 1 of the present invention provides a commercial regional virtual power plant optimization scheduling method, comprising the following steps:

[0047] Step 1: Quantify the virtual power plant's failure to provide expected energy reserve capacity and waste of expected energy reserve capacity based on the probability density function of the net demand forecast error in the commercial area; the virtual power plant's operating reserve capacity includes the active power capacity reserved to cope with distributed power generation output and load fluctuations.

[0048] Preferably but not limitedly, step 1 specifically includes:

[0049] Step 1.1: Build load loss probability and power curtailment probability models based on the probability density function of the net demand forecast error in the commercial area.

[0050] Specifically, the control cycle consists of T time periods, each time step t is the decision time, t=1,2,⋯,T, and the operating reserve capacity at each time step t includes the positive reserve for load loss and the negative reserve for power reduction. The net demand at each time step t is predicted using e t Denotes the net demand forecast error, expressed as f(e t ) represents the probability density function of the net demand forecast error. It can be understood that net demand refers to the net difference between the power generated by the virtual power plant and the power consumed in the commercial area.

[0051] The probability density function f(e) based on the net demand forecast error t ), establish a load loss probability and power reduction probability model, including: integrating the probability density function of the net demand forecast error in the interval between the minimum positive reserve capacity required to cover the corresponding forecast error and the maximum value of the net demand forecast error, to obtain the load loss probability at the time step; similarly, integrating the probability density function of the net demand forecast error in the interval between the minimum value of the net demand forecast error and the minimum negative reserve capacity required to cover the corresponding forecast error, to obtain the power reduction probability at the time step. Specifically, it can be expressed as follows:

[0052]

[0053] Where:

[0054] LOLP t and LOPP t are the load loss probability and power reduction probability at time step t, respectively;

[0055] e t is the net demand forecast error, and are the maximum and minimum values ​​of the net demand forecast error, respectively;

[0056] f(e t ) is the probability density function of the net demand forecast error;

[0057] The minimum positive reserve capacity required to cover the corresponding forecast error, The minimum negative reserve capacity required to cover the corresponding forecast error.

[0058] The probability density function of the net demand forecast error is preferably, but not limited to, a normal distribution.

[0059] Step 1.2: Based on the load loss probability and power curtailment probability model, calculate the minimum positive reserve capacity and the minimum negative reserve capacity.

[0060] Specifically, when the spare capacity is equal to the minimum positive reserve capacity When the load loss probability of the virtual power plant is not less than the load loss probability LOLP t ; Correspondingly, when the reserve capacity is equal to the minimum negative reserve capacity When the power reduction probability of the virtual power plant is not greater than the power reduction probability LOPP t Specifically, the minimum positive reserve capacity and the minimum negative reserve capacity are expressed as follows:

[0061]

[0062] Where:

[0063] and are positive reserve capacity and negative reserve capacity respectively;

[0064] and are the load loss or power reduction of the virtual power plant, respectively;

[0065] 、 are the minimum positive and negative reserve capacities required to cover the corresponding forecast errors, respectively.

[0066] Step 1.3: Based on the minimum positive reserve capacity and the minimum negative reserve capacity, establish a model of unprovided expected energy reserve capacity and wasted expected energy reserve capacity.

[0067] Specifically, the expected energy reserve capacity that is not provided is obtained by multiplying the difference between the net demand forecast error and the minimum positive reserve capacity required to cover the corresponding forecast error by the probability density function of the net demand forecast error, and integrating the product over the interval between the positive reserve capacity and the maximum value of the net demand forecast error. Similarly, the expected energy reserve capacity that is wasted is obtained by multiplying the difference between the minimum negative reserve capacity required to cover the corresponding forecast error and the net demand forecast error by the probability density function of the net demand forecast error, and integrating the product over the interval between the minimum value of the demand forecast error and the negative reserve capacity. Specifically, it can be expressed as follows:

[0068]

[0069] Where:

[0070] EENS t EEW t They are the expected energy reserve capacity not provided and the expected energy reserve capacity wasted at time step t, respectively.

[0071] It is worth noting that as one of the outstanding essential features of the present invention, the failure to provide expected energy reserve capacity and the waste of expected energy reserve capacity are quantified. In the decision-making process, the possible reserve shortage or reserve surplus can be minimized as much as possible, and the reserve is dynamically associated with uncertainty, which can be used to improve the operating efficiency of the virtual power plant and reduce costs in subsequent steps.

[0072] Step 2: Taking into account the virtual power plant's electricity purchase cost and electricity sales revenue, the adjustable resource cost, and the penalty cost for not providing expected energy reserves and wasting expected energy reserves, establish an objective function that minimizes the virtual power plant's operating cost.

[0073] As you can understand, the optimization problem for a virtual power plant is to meet load demand at minimum cost. To ensure power balance, virtual power plants exist to exchange energy with the grid. In engineering practice, for virtual power plant operators, the larger grid can be considered a power supply source as long as transmission line power limits are met. However, as a user, the virtual power plant must submit a power generation plan to the system for safe operation checks. As you can understand, if demand exceeds generation, the result is a negative number.

[0074] Specifically, the optimization goal of the virtual power plant is to minimize its total operating cost, which can be expressed as follows:

[0075]

[0076] Where:

[0077] 、 、 、 are the operating cost of the virtual power plant, the relevant electricity charges collected / paid by the grid at time step t, the bidirectional charging cost of electric vehicle i, and the operating cost of the virtual energy storage device q;

[0078] EENS t EEW t are the expected energy reserve capacity not provided and the expected energy reserve capacity wasted at time step t, respectively;

[0079] V LL 、V LP are the unit cost of load loss and the unit penalty factor of abandoned solar power, respectively;

[0080] I is the number of electric vehicles participating in the virtual power plant aggregation at time step t; Q is the number of virtual energy storage devices; where T is the number of cycles during the scheduling time.

[0081] It can be understood that the objective function consists of three parts. The first part is the power exchange cost between the virtual power plant and the main grid. The second part is the operating cost of the park's adjustable resources The third part is the cost of abandoned light caused by load loss and insufficient backup. .

[0082] Step 2.1: Establish a cost model for electricity exchange between the virtual power plant and the main grid. This includes: if purchasing electricity from the grid, multiply the purchase price by the amount of electricity; if selling electricity to the grid, multiply the sales price by the amount of electricity. This can be expressed as follows:

[0083]

[0084] Where:

[0085] Related electricity charges collected / paid for the grid;

[0086] 、 are the electricity prices for selling electricity to the grid and purchasing electricity from the grid at time step t, respectively;

[0087] is the power exchanged between the commercial area and the grid. If electricity is purchased from the grid or sold to the grid at time step t, the variable will be positive or negative;

[0088] is a binary variable used to identify direction, when When greater than zero, is 1, otherwise is 0;

[0089] τ is the length of time step t.

[0090] Step 2.2: Establish a bidirectional charging pile operation cost model in the commercial area, fitting it as a quadratic function of the amount of electricity exchanged between the electric vehicle and the bidirectional charging pile, which can be expressed as follows:

[0091]

[0092] Where:

[0093] 、 and are the bidirectional charging cost of electric vehicle i at time step t, the fixed cost of the user part, and the fixed cost of the park part;

[0094] is the amount of electricity exchanged between electric vehicle i and the bidirectional charging pile at time step t;

[0095] a i 、b i They are the fitted quadratic term coefficient and linear term coefficient, respectively representing the nonlinear part and linear part of the cost change with electricity quantity, such as but not limited to the nonlinear parts such as accelerated battery aging and virtual power plant scheduling, and the linear cost change part caused by electricity price.

[0096] It can be understood that the amount of electricity exchanged between electric vehicle i and the bidirectional charging pile at time step t is the product of power and time step length, which can be expressed as the following formula:

[0097]

[0098] Where:

[0099] is the charging and discharging power of electric vehicle i at time step t;

[0100] τ is the length of time step t.

[0101] Step 2.3: Treat the photovoltaic and energy storage devices, or the photovoltaic and energy storage devices connected to the same bus, as virtual energy storage devices, including the rooftop photovoltaic and energy storage devices connected to the second DC bus (i.e., rooftop photovoltaic and energy storage devices), as well as the energy storage devices and centralized photovoltaic arrays connected to the first DC bus. Similar to the bidirectional charging pile operation cost model, the cost of the virtual energy storage device is fitted as a quadratic function of the amount of electricity exchanged between the virtual energy storage device and the bus, which can be expressed as the following formula:

[0102]

[0103] It is understandable that photovoltaic power generation itself has almost no cost, so the cost of virtual energy storage devices mainly comes from fixed investment and energy storage operating expenses.

[0104] is the amount of electricity exchanged between the virtual energy storage device q and the bus it is connected to at time step t;

[0105] c q d q are the fitted quadratic term coefficient and linear term coefficient, respectively representing the nonlinear and linear parts of the cost changing with electricity.

[0106] It is fixed investment and energy storage operating costs.

[0107] It can be understood that the amount of electricity exchanged between the virtual energy storage device q and the bus to which it is connected at time step t is the product of the net sum of the photovoltaic output and the energy storage output and the time step length, which can be expressed as the following formula:

[0108]

[0109] Where:

[0110] is the output of the PV in the virtual energy storage device q at time step t;

[0111] 、 are the charging power and discharging power of the energy storage in the virtual energy storage device q at time step t, respectively;

[0112] τ is the length of time step t.

[0113] Step 2.4: Establish a penalty term for not providing the expected energy reserve capacity and wasting the expected energy reserve capacity as part of the virtual power plant operating cost.

[0114] Specifically, the failure to provide the expected energy reserve capacity and the wasted expected energy reserve capacity at time step t are multiplied by the unit cost to serve as the penalty cost.

[0115] Step 3: Establish constraints for the operation of the virtual power plant, including power balance constraints for power generation and consumption, physical constraints for adjustable resource power, and spare capacity constraints.

[0116] Preferably but not limiting, step 3 specifically includes:

[0117] Step 3.1: Establish power balance constraints between generation and consumption in the commercial area.

[0118] Specifically, from the perspective of the virtual power plant operator, power balance must be guaranteed at each time step, which means that the power purchased from the grid and the power generated by the virtual power plant's internal resources must be equal to the power consumption required by the load, which can be expressed as follows:

[0119]

[0120] Where:

[0121] is the amount of electricity exchanged between electric vehicle i and the bidirectional charging pile at time step t; I is the number of electric vehicles participating in the virtual power plant aggregation at time step t;

[0122] is the charge and discharge power of the virtual energy storage device q at time step t; Q is the number of virtual energy storage devices;

[0123] is the power of interaction between the park and the grid at time step t;

[0124] τ is the length of time step t;

[0125] is the power consumed by load j at time step t; J is the number of loads in the park at time step t.

[0126] Step 3.2: Establish safe operation constraints for bidirectional charging piles.

[0127] Specifically, consider using binary variables to determine whether the charging pile unit is in working state. The charging and discharging power of electric vehicles cannot exceed the maximum limit, which can be expressed as follows:

[0128]

[0129] Where:

[0130] is a binary variable, =0 or =1, used to indicate whether charging pile i is in working state at time step t;

[0131] is the charging and discharging power of charging pile i at time step t;

[0132] 、 are the minimum and maximum power limits of charging pile i respectively.

[0133] Step 3.3: Establish safe operation constraints for virtual energy storage devices.

[0134] Specifically, the virtual energy storage device includes rooftop photovoltaics, centralized photovoltaics, and corresponding energy storage equipment. To extend the service life of the batteries within the energy storage system, the charging and discharging power of the energy storage system must be limited by the upper limit and its SOC limit, respectively, and photovoltaic power generation cannot exceed its physical limit. This can be expressed as follows:

[0135]

[0136] Where:

[0137] 、 are the charging power and maximum charging power of the energy storage device entity contained in the virtual energy storage device q at time step t respectively;

[0138] 、 are the discharge power and maximum discharge power of the energy storage device entity contained in the virtual energy storage device q at time step t respectively;

[0139] 、 、 are the SOC lower limit, SOC and SOC upper limit of the virtual energy storage device q at time step t, respectively;

[0140] 、 are the output and output upper limit of the photovoltaic equipment entity contained in the virtual energy storage device q at time step t.

[0141] Step 3.4: Taking into account the operating reserve capacity limitations of adjustable resources, establish operating reserve capacity constraints.

[0142] Specifically, the sum of the positive and negative reserve capacities of the bidirectional charging pile, virtual energy storage device, and flexible adjustable load must not be less than the minimum positive and negative reserve capacities required to cover the corresponding prediction errors; in addition, the positive and negative reserve capacities of the bidirectional charging pile, virtual energy storage device, and flexible adjustable load must be between 0 and the smaller of their individual maximum adjustment capabilities and individual power changes. This can be expressed as follows:

[0143]

[0144]

[0145] Where:

[0146] 、 、 are the positive reserve capacities of the bidirectional charging pile i, virtual energy storage device q and flexible adjustable load at time step t respectively;

[0147] 、 、 are the negative reserve capacities of the bidirectional charging pile i, virtual energy storage device q, and flexible adjustable load at time step t, respectively;

[0148] 、 are the minimum positive and negative reserve capacities required to cover the corresponding forecast errors at time step t, respectively;

[0149] is the charging and discharging power, maximum power limit, minimum power limit, power increase change, and power decrease change of charging pile i at time step t;

[0150] are the output, abandoned light amount, maximum discharge power, maximum charging power, discharge power and charging power of the virtual energy storage device q at time step t,

[0151] They are the power, rated power, power reduction change, power increase change, and flexible adjustable ratio of the flexible adjustable load j at time step t.

[0152] Step 4: Under the constraints, solve the objective function and obtain the virtual power plant control decision instructions including the spare capacity.

[0153] Preferably, but not restrictively, a heuristic algorithm is used to solve the objective function, such as, but not limited to, a genetic algorithm, a particle swarm optimization, a simulated annealing algorithm, or an ant colony algorithm.

[0154] As one of the outstanding essential features of the present invention, the energy balance equations and constraints in the above steps are mixed with different variables from each random unit. The predicted power output depends on weather conditions, which can be obtained through weather forecasts. The deviation from the weather forecast together with the load forecast error (net demand uncertainty) will be determined by the minimum positive and negative reserve capacity required to cover the corresponding forecast error. deal with.

[0155] The power output of the resources within the virtual power plant is predicted one day in advance and is considered as real-time production because its uncertainty is covered by the reserve capacity. Therefore, the positive and negative reserve capacities of the bidirectional charging pile i, virtual energy storage device q and flexible adjustable load at time step t are based on the predicted electricity price. output decision.

[0156] Typically, the mains The power to be exchanged must be determined based on the day-ahead price forecast. Day-ahead market bids are the most relevant transaction revenue in the electricity market and should be submitted before the physical energy is delivered. Therefore, is a decision variable that is not independent of random power output and load demand. In fact, , , and All decisions are made based on electricity prices and are optimized based on the specific electricity prices achieved during the day-ahead unit combination phase. Therefore, the optimization goal of the virtual power plant operator is to find the optimal operating point for each variable while taking into account the uncertainty of electricity prices.

[0157] like Figure 2 As shown, embodiment 2 of the present invention provides a commercial regional virtual power plant system, including: photovoltaic storage equipment installed in a building, centralized new energy power generation equipment, flexible adjustable loads installed in the building, energy storage devices, bidirectional charging piles, a first DC bus, a first AC bus, a second DC bus, and a second AC bus; the first DC bus is connected to the main grid via a first converter, and the first AC bus is connected to the main grid via a first transformer;

[0158] Each energy storage device and centralized new energy power generation equipment is connected to the first DC bus via a DC / DC converter; the photovoltaic storage equipment installed in the building is collected via the second DC bus and then connected to the first DC bus via a DC / DC converter;

[0159] The bidirectional charging pile is connected to the first AC bus via a converter; the flexible adjustable load installed in the building is connected to the second AC bus, and the second AC bus is connected to the first AC bus via a second transformer.

[0160] The energy storage device is at least one of a supercapacitor and an electrochemical energy storage device, and is equivalent to a virtual energy storage device with a centralized photovoltaic array;

[0161] The flexible adjustable load is a heat storage and cold storage type air conditioner.

[0162] Embodiment 3 of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements a commercial regional virtual power plant optimization scheduling method according to embodiment 1.

[0163] Embodiment 4 of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a commercial regional virtual power plant optimization scheduling method according to embodiment 1.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A commercial regional virtual power plant optimization scheduling method, characterized in that: The commercial area virtual power plant includes: photovoltaic storage equipment installed in the building, centralized new energy power generation equipment, flexible adjustable loads installed in the building, energy storage devices, bidirectional charging piles, a first DC bus, a first AC bus, a second DC bus and a second AC bus; the first DC bus is connected to the main grid via a first converter, and the first AC bus is connected to the main grid via a first transformer; Each energy storage device and centralized new energy power generation equipment is connected to the first DC bus via a DC / DC converter; the photovoltaic storage equipment installed in the building is collected via the second DC bus and then connected to the first DC bus via a DC / DC converter; The bidirectional charging pile is connected to the first AC bus via a converter; the flexible adjustable load set in the building is connected to the second AC bus, and the second AC bus is connected to the first AC bus via a second transformer; The commercial regional virtual power plant optimization scheduling method includes the following steps: Quantify the failure to provide expected energy reserve capacity and the waste of expected energy reserve capacity based on the probability density function of the net demand forecast error in the commercial area; Taking into account the virtual power plant's electricity purchase costs and electricity sales revenue, the cost of adjustable resources, and the penalty costs for not providing expected energy reserves and wasting expected energy reserves, an objective function that minimizes the virtual power plant's operating costs is established; Establish constraints for virtual power plant operation, including power balance constraints between power generation and consumption, physical constraints on adjustable resource power, and constraints on reserve capacity; Under the constraints, the objective function is solved to obtain the virtual power plant control decision instructions including the spare capacity.

2. The commercial regional virtual power plant optimization scheduling method according to claim 1, characterized in that: The probability density function based on the commercial area net demand forecast error, quantifying the failure to provide expected energy reserve capacity and the waste of expected energy reserve capacity includes: Based on the probability density function of the net demand forecast error in the commercial area, a load loss probability and power curtailment probability model is established; Based on the load loss probability and power reduction probability model, the minimum positive reserve capacity and the minimum negative reserve capacity are calculated; Based on the minimum positive reserve capacity and the minimum negative reserve capacity, a model of not providing expected energy reserve and wasting expected energy reserve is established.

3. The commercial regional virtual power plant optimization scheduling method according to claim 2, characterized in that: The load loss probability and power reduction probability model established based on the probability density function of the commercial area net demand forecast error includes: The probability density function of the net demand forecast error is integrated over the interval between the minimum positive reserve capacity required to cover the corresponding forecast error and the maximum value of the net demand forecast error to obtain the load loss probability at the time step; The probability density function of the net demand forecast error is integrated over the interval between the minimum value of the net demand forecast error and the minimum negative reserve capacity required to cover the corresponding forecast error to obtain the power curtailment probability at that time step.

4. A commercial regional virtual power plant optimization scheduling method according to claim 2 or 3, characterized in that: The calculation of the minimum positive reserve capacity and the minimum negative reserve capacity based on the load loss probability and power reduction probability model includes: When the spare capacity is equal to the minimum positive reserve capacity, the load loss probability of the virtual power plant is not less than the load loss probability; When the spare capacity is equal to the minimum negative reserve capacity, the power reduction probability of the virtual power plant is not greater than the power reduction probability.

5. A commercial regional virtual power plant optimization scheduling method according to claim 2 or 3, characterized in that: The establishment of the model of not providing expected energy reserve and wasting expected energy reserve based on the minimum positive reserve capacity and the minimum negative reserve capacity includes: The difference between the net demand forecast error and the minimum positive reserve capacity required to cover the corresponding forecast error is calculated, and then multiplied by the probability density function of the net demand forecast error. The product is then integrated over the interval between the positive reserve capacity and the maximum value of the net demand forecast error to obtain the unprovided expected energy reserve capacity. The difference between the minimum negative reserve capacity required to cover the corresponding forecast error and the net demand forecast error is calculated, and then multiplied by the probability density function of the net demand forecast error. The product is then integrated over the interval between the minimum value of the demand forecast error and the negative reserve capacity to obtain the expected energy reserve capacity.

6. A commercial regional virtual power plant optimization scheduling method according to any one of claims 1 to 3, characterized in that: The objective function for minimizing the operating cost of the virtual power plant, taking into account the power purchase cost and power sales revenue of the virtual power plant, the adjustable resource cost, and the penalty cost for not providing the expected energy reserve and wasting the expected energy reserve, is established. The objective function includes: Establish a cost model for electricity exchange between the virtual power plant and the main grid, including: if purchasing electricity from the grid, multiply the purchase price by the amount of electricity; if selling electricity to the grid, multiply the sales price by the amount of electricity; Establish a cost model for the operation of adjustable resources in commercial areas, fitting it as a quadratic function of the amount of electricity exchanged by the adjustable resources; The penalty cost is obtained by multiplying the unit cost by the failure to provide the expected energy reserve capacity and the waste of the expected energy reserve capacity.

7. A commercial regional virtual power plant optimization scheduling method according to any one of claims 1 to 3, characterized in that: The spare capacity constraint includes: The sum of the positive and negative reserve capacities of the bidirectional charging piles, virtual energy storage devices and flexible adjustable loads shall not be less than the minimum positive and negative reserve capacities required to cover the corresponding prediction errors.

8. A commercial regional virtual power plant optimization scheduling method according to any one of claims 1 to 3, characterized in that: The spare capacity constraint includes: For bidirectional charging piles, calculate the difference between the current power and the maximum and minimum power limits. The smaller of the difference and the power fluctuation is used as the upper limit of the bidirectional charging pile's spare capacity. For the virtual energy storage device, the sum of the abandoned solar power and the discharge margin is calculated, and the smaller of the two is taken as the upper limit of the discharge capacity of the virtual energy storage device. The sum of the current photovoltaic power and the charging margin is calculated, and the smaller of the two is taken as the upper limit of the negative reserve capacity of the virtual energy storage device. For flexible adjustable loads, taking into account the flexible adjustable ratio, the smaller one that satisfies the power fluctuation of the flexible adjustable load itself is taken as the upper limit of the adjustable flexible adjustable load standby capacity.

9. The commercial regional virtual power plant optimization scheduling method according to claim 1, characterized in that: The energy storage device is at least one of a supercapacitor and an electrochemical energy storage device, and is equivalent to a virtual energy storage device with a centralized photovoltaic array; The flexible adjustable load is a heat storage and cold storage type air conditioner.

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