Business area type virtual power plant system and optimal scheduling method thereof

Abstract

The invention discloses a business area type virtual power plant system and an optimal scheduling method thereof, and the method comprises the following steps: quantifying unprovided expected energy reserve capacity and wasted expected energy reserve capacity based on a probability density function of a business area net demand prediction error; considering the electricity purchasing cost and the electricity selling income of the virtual power plant, the adjustable resource cost and the penalty term cost due to the fact that the expected energy reserve is not provided and the expected energy reserve is wasted, and establishing an objective function for minimizing the operation cost of the virtual power plant; establishing constraint conditions of operation of the virtual power plant, wherein the constraint conditions comprise a power generation and utilization power balance constraint, an adjustable resource power physical constraint and a reserve capacity constraint; and under the constraint condition, solving the target function to obtain a virtual power plant control decision instruction containing the reserve capacity. The invention proposes that the virtual power plant operator predetermines the reserve capacity by taking into account the economic and technical indicators of the dynamic risk reserve at 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 area type virtual power plant system and an optimal dispatching method thereof. Background Art

[0002] The continuous development of energy utilization technologies has promoted the emergence of a large number of distributed resources with adjustment potential on the distribution network side, such as electric vehicles, distributed energy storage, distributed generation, and data centers and 5G base stations under the background of new infrastructure. This type of load has many points, large quantity, small capacity, low voltage level, and diverse entities, and it is difficult for traditional power grid dispatching to directly control a single load.

[0003] A virtual power plant realizes the aggregation and coordinated optimization of distributed resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles through advanced information and communication technologies and software systems, so as to participate in the power market and the power grid operation as a special power plant for power coordination management system.

[0004] As a basic component of urban power grid electricity consumption, with the development of information technology, intelligent terminals, automation technology, etc., commercial areas are moving towards intelligence and energy conservation. At the same time, emerging elements of the power grid such as electric vehicles and distributed photovoltaics are being increasingly configured in commercial areas, becoming the future development direction of commercial areas.

[0005] In the prior art, Chinese invention patent CN118739437B discloses a method for quantitatively evaluating the adjustable capacity of a virtual power plant; Chinese invention patent CN116542439B discloses an optimal operation method and system for multi-energy response of a virtual power plant; Chinese invention patent CN113794200B discloses a method for aggregating multi-type load resources for a virtual power plant. After aggregating various resources in these existing solutions for safe and stable operation in engineering practice, it is often necessary to use a relatively large static margin to cope with uncertainties. Compared with the dynamically changing uncertainties, the excessive margin results in room for further decline in the operation efficiency and cost of the virtual power plant. Summary of the Invention

[0006] To solve the deficiencies in the prior art, the present invention provides a commercial area type virtual power plant system and an optimal dispatching method thereof, which quantifies the operation reserve capacity of the commercial area type virtual power plant, and proposes that the virtual power plant operator determines the reserve capacity in advance by considering the economic and technical indicators of dynamic risk reserve under a specific safety level, so as to minimize the operation cost of the virtual power plant.

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

[0008] The first aspect of the present invention provides a method for optimizing the scheduling of a commercial area virtual power plant, comprising the following steps: Quantify the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity based on the probability density function of the commercial area net demand forecasting error; Taking into account the virtual power plant's electricity purchase cost and electricity sales revenue, adjustable resource cost, and penalty cost for unprovided expected energy reserve and wasted expected energy reserve, establish an objective function for minimizing the virtual power plant's operating cost; Establish the constraint conditions for the operation of the virtual power plant, including: power balance constraint for power generation and consumption, physical constraint for adjustable resource power, and reserve capacity constraint; Under the constraint conditions, solve the objective function to obtain the virtual power plant control decision instructions including reserve capacity.

[0009] Preferably, the quantification of the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity based on the probability density function of the commercial area net demand forecasting error includes: Based on the probability density function of the commercial area net demand forecasting error, establish a load loss probability and a power curtailment probability model; Based on the load loss probability and power curtailment probability models, calculate and obtain the minimum positive reserve capacity and the minimum negative reserve capacity; Based on the minimum positive reserve capacity and the minimum negative reserve capacity, establish a model for unprovided expected energy reserve and wasted expected energy reserve.

[0010] Preferably, the establishment of the load loss probability and power curtailment probability models based on the probability density function of the commercial area net demand forecasting error includes: Integrate the probability density function of the net demand forecasting error over the interval between the minimum positive reserve capacity required to cover the corresponding forecasting error and the maximum value of the net demand forecasting error to obtain the load loss probability at the time step; Integrate the probability density function of the net demand forecasting error over the interval between the minimum value of the net demand forecasting error and the minimum negative reserve capacity required to cover the corresponding forecasting error to obtain the power curtailment probability at the time step.

[0011] Preferably, the calculation of the minimum positive reserve capacity and the minimum negative reserve capacity based on the load loss probability and power curtailment probability models includes: When the reserve 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 reserve capacity is equal to the minimum negative reserve capacity, the power curtailment probability of the virtual power plant is not greater than the power curtailment probability.

[0012] Preferably, the establishment of the model for unprovided expected energy reserve and wasted expected energy reserve based on the minimum positive reserve capacity and the minimum negative reserve capacity includes: Subtract the net demand forecasting error from the minimum positive reserve capacity required to cover the corresponding forecasting error, then multiply the result by the probability density function of the net demand forecasting error, and integrate the product over the interval between the positive reserve capacity and the maximum value of the net demand forecasting error to obtain the unprovided expected energy reserve capacity; Subtract the net demand forecasting error from the minimum negative reserve capacity required to cover the corresponding forecasting error, then multiply the result by the probability density function of the net demand forecasting error, and integrate the product over the interval between the minimum value of the demand forecasting error and the negative reserve capacity to obtain the wasted expected energy reserve capacity.

[0013] Preferably, the objective function for minimizing the operating cost of the virtual power plant by taking into account the power purchase cost and power sales revenue of the virtual power plant, the adjustable resource cost, and the penalty item costs for unprovided expected energy reserve and wasted expected energy reserve includes: Establish a power exchange cost model between the virtual power plant and the main power grid, including: if purchasing power from the grid, multiplying the purchase electricity price by the electricity quantity; if selling power to the grid, multiplying the selling electricity price by the electricity quantity; Establish an operating cost model for adjustable resources in the commercial area, which is fitted as a quadratic function of the exchanged electricity quantity of the adjustable resources; Multiply the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity by the unit cost as the penalty item costs.

[0014] Preferably, the reserve capacity constraint includes: The sum of the positive and negative reserve capacities of the bi-directional charging piles, virtual energy storage devices, and flexible adjustable loads is not less than the minimum positive and negative reserve capacities required to cover the corresponding forecasting errors.

[0015] Preferably, the reserve capacity constraint includes: For the bi-directional charging pile, calculate the difference between the current power and the maximum and minimum power limits, and take the smaller value between the difference and the electricity quantity fluctuation as the upper limit of the reserve capacity of the bi-directional charging pile; For the virtual energy storage device, calculate the sum of the abandoned light quantity and the discharge margin, and take the smaller value between the sum and the discharge upper limit as the upper limit of the positive reserve capacity of the virtual energy storage device; calculate the sum of the current photovoltaic power and the charging margin, and take the smaller value between the sum and the charging upper limit as the upper limit of the negative reserve capacity of the virtual energy storage device; For the flexible adjustable load, taking into account the flexible adjustable ratio, take the smaller value that satisfies the electricity quantity fluctuation of the flexible adjustable load itself as the upper limit of the reserve capacity of the adjustable flexible adjustable load.

[0016] The second aspect of the present invention provides a commercial area type virtual power plant system, including: A photovoltaic and energy storage device installed in a building, a centralized new energy power generation device, a flexible adjustable load installed in a building, an energy storage device, a bi-directional charging pile, 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 through a first converter, and the first AC bus is connected to the main grid through a first transformer; Each energy storage device and the centralized new energy power generation device are respectively connected to the first DC bus through a DC / DC converter; after the photovoltaic and energy storage devices installed in the building are aggregated through the second DC bus, they are then connected to the first DC bus through a DC / DC converter; The bi-directional charging pile is connected to the first AC bus through 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 through a second transformer.

[0017] Preferably, the energy storage device is at least one of a super capacitor and an electrochemical energy storage, and is equivalent to a virtual energy storage device together with a centralized photovoltaic array; The flexible adjustable load is a heat storage and cold storage air conditioner.

[0018] Compared with the prior art, the beneficial effects of the present invention at least include: quantifying the operating reserve capacity of a commercial area type virtual power plant, and proposing that the virtual power plant operator pre-determine the reserve capacity by considering the economic and technical indicators of dynamic risk reserve under a specific safety level to minimize the operating cost of the virtual power plant. Description of the Drawings

[0019] Figure 1 is a flowchart of an optimization scheduling method for a commercial area type virtual power plant provided according to an embodiment of the present invention; Figure 2 is a schematic diagram of the architecture of a commercial area type virtual power plant system provided according to an embodiment of the present invention. Detailed Embodiments

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0021] As Figure 1 shown, Embodiment 1 of the present invention provides an optimization scheduling method for a commercial area type virtual power plant, including the following steps: Step 1: Quantify the virtual power plant's failure to provide the expected energy reserve capacity and waste of the expected energy reserve capacity based on the probability density function of the net demand forecasting error in the commercial area; the operating reserve capacity of the virtual power plant includes the active power capacity reserved to cope with the output of distributed power sources and load fluctuations.

[0022] Preferably but not limitedly, Step 1 specifically includes: Step 1.1: Establish a load loss probability and power curtailment probability model based on the probability density function of the net demand forecasting error in the commercial area.

[0023] Specifically, the control period contains 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 positive reserve for load loss and negative reserve for power curtailment. Forecast the net demand at each time step t, and use e t to represent the net demand forecasting error, and use f(e t ) to represent the probability density function of the net demand forecasting error. It can be understood that the net demand refers to the net difference between the power generation and power consumption of the virtual power plant in the commercial area.

[0024] Based on the probability density function f(e t ) of the net demand forecasting error, establish a load loss probability and power curtailment probability model, including: Integrate the probability density function of the net demand forecasting error over the interval between the minimum positive reserve capacity required to cover the corresponding forecasting error and the maximum value of the net demand forecasting error, and the load loss probability at the time step can be obtained; similarly, integrate the probability density function of the net demand forecasting error over the interval between the minimum value of the net demand forecasting error and the minimum negative reserve capacity required to cover the corresponding forecasting error, and the power curtailment probability at the time step can be obtained. Specifically, it can be expressed by the following formula:

[0025] In the formula: LOLP t and LOPP t are the load loss probability and power curtailment probability at time step t respectively; e t is the net demand forecasting error, and are the maximum and minimum values of the net demand forecasting error respectively; f(e t ) is the probability density function of the net demand forecasting error; is the minimum positive reserve capacity required to cover the corresponding forecasting error, is the minimum negative reserve capacity required to cover the corresponding forecasting error.

[0026] The probability density function of the net demand forecasting error is preferably but not limited to the normal distribution.

[0027] Step 1.2: Based on the load loss probability and power curtailment probability models, calculate and obtain the minimum positive reserve capacity and the minimum negative reserve capacity.

[0028] Specifically, when the reserve capacity is equal to the minimum positive reserve capacity the load loss probability of the virtual power plant will not be less than the load loss probability LOLP t ; correspondingly, when the reserve capacity is equal to the minimum negative reserve capacity the power curtailment probability of the virtual power plant will not be greater than the power curtailment probability LOPP t . Specifically, the minimum positive reserve capacity and the minimum negative reserve capacity are expressed by the following formula:

[0029] In the formula: and are the positive reserve capacity and the negative reserve capacity respectively; and are the load loss amount or power curtailment amount of the virtual power plant respectively; , are the minimum positive and negative reserve capacities required to cover the corresponding forecasting errors respectively.

[0030] Step 1.3: Based on the minimum positive reserve capacity and the minimum negative reserve capacity, establish the models of the reserve capacity of the unprovided expected energy and the wasted expected energy reserve capacity.

[0031] Specifically, subtract the net demand forecasting error from the minimum positive reserve capacity required to cover the corresponding forecasting error, then multiply the result by the probability density function of the net demand forecasting error, and integrate the product over the interval between the positive reserve capacity and the maximum value of the net demand forecasting error to obtain the reserve capacity of the unprovided expected energy; similarly, subtract the net demand forecasting error from the minimum negative reserve capacity required to cover the corresponding forecasting error, then multiply the result by the probability density function of the net demand forecasting error, and integrate the product over the interval between the minimum value of the demand forecasting error and the negative reserve capacity to obtain the wasted expected energy reserve capacity. Specifically, it can be expressed by the following formula:

[0032] In the formula: EENS t , EEW t are the reserve capacity of the unprovided expected energy and the wasted expected energy reserve capacity at time step t respectively.

[0033] It should be noted that, as one of the prominent substantial features of the present invention, the quantification of the failure to provide the expected energy reserve capacity and the waste of the expected energy reserve capacity enables minimizing the possible reserve shortage or surplus during the decision-making process, dynamically correlating the reserve with uncertainty, and can be used to improve the operation efficiency and reduce the cost of the virtual power plant in subsequent steps.

[0034] Step 2: Considering the power purchase cost and power sales revenue of the virtual power plant, the adjustable resource cost, and the penalty cost for the failure to provide the expected energy reserve and the waste of the expected energy reserve, establish an objective function for minimizing the operation cost of the virtual power plant.

[0035] It can be understood that the optimization problem of the virtual power plant is transformed into meeting the load demand at the minimum cost. To ensure power balance, there is energy exchange between the virtual power plant and the power grid. In engineering practice, for the virtual power plant operator, as long as the power limit of the transmission line is met, the large power grid can be regarded as a power supply source. However, as a user, the virtual power plant must submit a power generation plan for system security operation inspection. It can be understood that if the demand is greater than the power generation, it is negative.

[0036] Specifically, the optimization objective of the virtual power plant is to minimize its total operation cost, which can be expressed by the following formula:

[0037] In the formula: 、 、 、 are the operation cost of the virtual power plant, the relevant electricity charges collected / paid by the power grid at time step t, the two-way charging cost of electric vehicle i, and the operation cost of virtual energy storage device q, respectively; EENS t 、EEW t are the capacity of the failure to provide the expected energy reserve and the capacity of the waste of the expected energy reserve at time step t, respectively; V LL 、V LP are the unit cost of load loss and the unit penalty factor for curtailment of light, respectively; I is the number of electric vehicles participating in the aggregation of the virtual power plant at time step t; Q is the number of virtual energy storage devices; where T is the number of cycles during the scheduling period.

[0038] 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 power grid ; the second part is the operation cost of the adjustable resources in the park ; the third part is the load loss cost and the curtailment of light cost caused by reserve shortage .

[0039] Step 2.1: Establish a power exchange cost model between the virtual power plant and the main power grid, including: if purchasing power from the grid, multiply the purchase electricity price by the electricity quantity; if selling power to the grid, multiply the selling electricity price by the electricity quantity, which can be shown by the following formula:

[0040] In the formula: is the relevant electricity charge collected / paid by the grid; and are the electricity prices for selling power to the grid and purchasing power from the grid at time step t respectively; is the power exchanged between the commercial area and the grid. If purchasing power from the grid or selling power to the grid at time step t, the variable will be positive or negative; is a binary variable used to identify 's direction. When is greater than zero, is 1, otherwise is 0; τ is the length of time step t.

[0041] Step 2.2: Establish an operating cost model for two-way charging piles in the commercial area, which is fitted as a quadratic function of the electricity quantity exchanged between electric vehicles and two-way charging piles, and can be expressed by the following formula:

[0042] In the formula: and and are the two-way 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 respectively; is the electricity quantity exchanged between electric vehicle i and the two-way charging pile at time step t; a i and b i are the fitted quadratic term coefficient and linear term coefficient respectively, which characterize the non-linear part and linear part of the cost varying with the electricity quantity. For example, but not limited to, the non-linear parts such as battery accelerated aging and virtual power plant scheduling, and the linear cost change part caused by the electricity price.

[0043] It can be understood that the electricity quantity exchanged between electric vehicle i and the two-way charging pile at time step t is the product of power and the length of the time step, and is expressed by the following formula:

[0044] In the formula: is the charging and discharging power of electric vehicle i at time step t; τ is the length of time step t.

[0045] Step 2.3: Equivalent the optical storage device or the photovoltaic and energy storage devices connected to the same bus to a virtual energy storage device, including each rooftop photovoltaic and its energy storage device (i.e., rooftop optical storage device) connected to the second DC bus, and the energy storage device and the centralized photovoltaic array connected to the first DC bus. Similar to the operating cost model of the bi-directional charging pile, the cost of the virtual energy storage device is fitted to a quadratic function of the electricity quantity exchanged between the virtual energy storage device and the bus, which can be expressed by the following formula:

[0046] It can be understood that the cost of photovoltaic power generation itself is almost zero. Therefore, the cost of the virtual energy storage device mainly comes from the fixed investment and the energy storage operation cost.

[0047] is the electricity quantity exchanged between the virtual energy storage device q and the bus it is connected to at time step t; c q and d q are the fitted quadratic term coefficient and linear term coefficient respectively, which represent the non-linear part and linear part of the cost varying with the electricity quantity respectively. are the fixed investment and the energy storage operation cost.

[0048] It can be understood that the electricity quantity exchanged between the virtual energy storage device q and the bus it is connected to at time step t is the product of the net sum of the photovoltaic output and the energy storage output and the length of the time step, which is expressed by the following formula:

[0049] In the formula: is the output of the photovoltaic in the virtual energy storage device q at time step t; and are the charging power and discharging power of the energy storage in the virtual energy storage device q at time step t respectively; τ is the length of time step t.

[0050] Step 2.4: Establish a penalty term considering the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity as part of the operating cost of the virtual power plant.

[0051] Specifically, multiply the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity at time step t by the unit cost as the penalty term cost.

[0052] Step 3: Establish the constraints for the operation of the virtual power plant, including: power balance constraint between power generation and consumption, physical constraint on the power of adjustable resources, and reserve capacity constraint.

[0053] Preferably but not restrictively, Step 3 specifically includes: Step 3.1: Establish the power balance constraint between power generation and consumption within the commercial area.

[0054] Specifically, from the perspective of the virtual power plant operator, power balance must be ensured at each time step, which means that the electricity purchased from the power grid and the electricity generated by the internal resources of the virtual power plant must be equal to the electricity consumption of the load demand, and it can be expressed by the following formula:

[0055] In the formula: is the electricity quantity exchanged between the electric vehicle i and the bi-directional charging pile at time step t; I is the number of electric vehicles participating in the aggregation of the virtual power plant at time step t; is the charging and discharging power of the virtual energy storage device q at time step t; Q is the number of virtual energy storage devices; is the power exchanged between the park and the power grid at time step t; τ is the length of time step t; is the electricity quantity consumed by the load j at time step t; J is the number of loads in the park at time step t.

[0056] Step 3.2: Establish the safe operation constraint for the bi-directional charging pile.

[0057] Specifically, consider using a binary variable to judge whether the charging pile unit is in the working state. The charging and discharging power of the electric vehicle cannot be greater than the maximum limit, and it can be expressed by the following formula:

[0058] In the formula: is a binary variable, = 0 or = 1, used to represent whether the charging pile i is in the working state at time step t; is the charging and discharging power of the charging pile i at time step t; , are the minimum and maximum power limits of the charging pile i respectively.

[0059] Step 3.3: Establish the safe operation constraint for the virtual energy storage device.

[0060] Specifically, the virtual energy storage device includes rooftop PV, centralized PV, and the corresponding equipped energy storage devices. To extend the service life of the batteries inside the energy storage system, the charging and discharging powers of the energy storage system must be limited by the upper limits and their SOC limits respectively, and the PV power generation cannot exceed its physical limit, which can be expressed by the following formula:

[0061] In the formula: and are respectively the charging power and the maximum charging power of the energy storage device entity included in the virtual energy storage device q at time step t; and are respectively the discharging power and the maximum discharging power of the energy storage device entity included in the virtual energy storage device q at time step t; and and are respectively the lower SOC limit, the SOC at time step t, and the upper SOC limit of the virtual energy storage device q; and are respectively the power output and the upper limit of the power output of the PV device entity included in the virtual energy storage device q at time step t.

[0062] Step 3.4: Considering the operation reserve capacity limit of adjustable resources, establish the operation reserve capacity constraint conditions.

[0063] Specifically, the sum of the positive and negative reserve capacities of the bi-directional charging piles, virtual energy storage devices, and flexible adjustable loads is not less than the minimum positive and negative reserve capacities required to cover the corresponding prediction errors; in addition, each of the positive and negative reserve capacities of the bi-directional charging piles, virtual energy storage devices, and flexible adjustable loads is between 0 and the smaller value between its single maximum adjustment capacity and single power change. It can be expressed by the following formula:

[0064]

[0065] In the formula: and and are respectively the positive reserve capacities of the bi-directional charging pile i, the virtual energy storage device q, and the flexible adjustable load at time step t; and and are respectively the negative reserve capacities of the bi-directional charging pile i, the virtual energy storage device q, and the flexible adjustable load at time step t; and are the minimum positive and negative reserve capacities required to cover the corresponding prediction errors at time step t, respectively; are the charging and discharging power, maximum power limit, minimum power limit, power increase change amount, and power decrease change amount of charging pile i at time step t; are the output power, light curtailment amount, maximum discharging power, maximum charging power, discharging power, and charging power of virtual energy storage device q at time step t, respectively; are the power, rated power, power decrease change amount, power increase change amount, and flexible adjustable ratio of flexible adjustable load j at time step t, respectively.

[0066] Step 4: Solve the objective function under the constraint conditions to obtain the virtual power plant control decision instruction including the reserve capacity.

[0067] Preferably but not restrictively, a heuristic algorithm is used to solve the objective function, such as but not limited to, genetic algorithm, particle swarm optimization, simulated annealing, or ant colony algorithm, etc.

[0068] As one of the prominent substantial features of the present invention, the energy balance equation and constraints of the foregoing steps mix different variables, and the predicted power output from each stochastic unit depends on weather conditions and can be obtained through weather forecasting. The deviation generated by weather forecasting together with the load forecasting error (net demand uncertainty) will be handled by the minimum positive and negative reserve capacities required to cover the corresponding prediction errors for processing.

[0069] The power output of the internal resources of the virtual power plant is predicted one day in advance and regarded as real-time production because its uncertainty has been covered by the reserve capacity. Therefore, the positive and negative reserve capacities of the bi-directional charging pile i, virtual energy storage device q, and flexible adjustable load at time step t are output decisions based on the predicted electricity price for making decisions.

[0070] Generally, the electricity exchanged with the main grid must be determined according to the day-ahead electricity price forecast. The day-ahead market buy and sell quotes are the most relevant trading revenues in the electricity market and should be submitted before the physical energy delivery. Therefore, is a decision variable that is not independent of the stochastic power output and load demand. In fact, , , and All decisions are made based on electricity prices and optimized according to the specific realization of electricity prices in the day-ahead unit commitment stage. Therefore, the optimization goal of the virtual power plant operator is to find the optimal operating points of each variable while considering the uncertainty of electricity prices.

[0071] As Figure 2 shown, Embodiment 2 of the present invention provides a commercial area type virtual power plant system, including: a photovoltaic and energy storage device, a centralized new energy power generation device, a flexible adjustable load, an energy storage device, a bi-directional charging pile, a first DC bus, a first AC bus, a second DC bus, and a second AC bus, which are arranged in a building; the first DC bus is connected to the main power grid through a first converter, and the first AC bus is connected to the main power grid through a first transformer; Each energy storage device and the centralized new energy power generation device are respectively connected to the first DC bus through a DC / DC converter; after the photovoltaic and energy storage devices arranged in the building are aggregated through the second DC bus, they are then connected to the first DC bus through a DC / DC converter; The bi-directional charging pile is connected to the first AC bus through a converter; the flexible adjustable load arranged in the building is connected to the second AC bus, and the second AC bus is connected to the first AC bus through a second transformer.

[0072] The energy storage device is at least one of a super capacitor and an electrochemical energy storage, and is equivalent to a virtual energy storage device together with the centralized photovoltaic array; The flexible adjustable load is a heat storage and cold storage air conditioner.

[0073] Embodiment 3 of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements a commercial area type virtual power plant optimal scheduling method according to Embodiment 1.

[0074] 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 area type virtual power plant optimal scheduling method according to Embodiment 1.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the scheduling of a commercial regional virtual power plant, characterized in that, It includes the following steps: Quantify the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity based on the probability density function of the net demand prediction error in the commercial area; Establish an objective function for minimizing the operating cost of the virtual power plant by taking into account the power purchase cost and power sale revenue of the virtual power plant, the cost of adjustable resources, and the penalty cost for unproviding the expected energy reserve and wasting the expected energy reserve; Establish the constraint conditions for the operation of the virtual power plant, including: power balance constraint between power generation and consumption, physical constraint of adjustable resource power, and reserve capacity constraint; Under the constraint conditions, solve the objective function to obtain the control decision instruction of the virtual power plant including the reserve capacity.

2. A method for optimizing the dispatching of a commercial area type virtual power plant according to claim 1, wherein: The quantification of the unprovided expected energy reserve capacity and the wasted expected energy reserve capacity based on the probability density function of the net demand prediction error in the commercial area includes: Based on the probability density function of the net demand prediction error in the commercial area, establish a load loss probability and a power curtailment probability model; Based on the load loss probability and the power curtailment probability model, calculate and obtain the minimum positive reserve capacity and the minimum negative reserve capacity; Based on the minimum positive reserve capacity and the minimum negative reserve capacity, establish a model for unproviding the expected energy reserve and wasting the expected energy reserve.

3. A method for optimizing the dispatching of a commercial area type virtual power plant according to claim 2, wherein: The establishment of the load loss probability and the power curtailment probability model based on the probability density function of the net demand prediction error in the commercial area includes: Integrate the probability density function of the net demand prediction error over the interval between the minimum positive reserve capacity required to cover the corresponding prediction error and the maximum value of the net demand prediction error to obtain the load loss probability at the time step; Integrate the probability density function of the net demand prediction error over the interval between the minimum value of the net demand prediction error and the minimum negative reserve capacity required to cover the corresponding prediction error to obtain the power curtailment probability at the time step.

4. A method for optimizing the dispatching of a commercial area type virtual power plant according to claim 2 or 3, wherein: The calculation of the minimum positive reserve capacity and the minimum negative reserve capacity based on the load loss probability and the power curtailment probability model includes: When the reserve 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 reserve capacity is equal to the minimum negative reserve capacity, the power curtailment probability of the virtual power plant is not greater than the power curtailment probability.

5. A method for optimizing the dispatching of a commercial area type virtual power plant according to claim 2 or 3, wherein: The establishment of a model for unproviding the expected energy reserve and wasting the expected energy reserve based on the minimum positive reserve capacity and the minimum negative reserve capacity includes: Subtract the net demand prediction error from the minimum positive reserve capacity required to cover the corresponding prediction error, then multiply the result by the probability density function of the net demand prediction error, and integrate the product over the interval between the positive reserve capacity and the maximum value of the net demand prediction error to obtain the unprovided expected energy reserve capacity; Subtract the minimum negative reserve capacity required to cover the corresponding prediction error from the net demand prediction error, then multiply the result by the probability density function of the net demand prediction error, and integrate the product over the interval between the minimum value of the demand prediction error and the negative reserve capacity to obtain the wasted expected energy reserve capacity.

6. A method for optimizing the dispatching of a commercial area type virtual power plant 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 by taking into account the power purchase cost and power sale revenue of the virtual power plant, the cost of adjustable resources, and the penalty item costs for not providing the expected energy reserve and wasting the expected energy reserve includes: Establish a power exchange cost model between the virtual power plant and the main power grid, including: if purchasing power from the grid, multiplying the purchase electricity price by the electricity quantity; if selling power to the grid, multiplying the selling electricity price by the electricity quantity; Establish an operating cost model for adjustable resources in the commercial area, which is fitted as a quadratic function of the exchanged electricity quantity of the adjustable resources; Multiply the capacity of not providing the expected energy reserve and the wasted expected energy reserve capacity by the unit cost as the penalty item cost.

7. A method for optimizing the dispatching of a commercial area type virtual power plant according to any one of claims 1 to 3, characterized in that: The reserve capacity constraint includes: The sum of the positive and negative reserve capacities of the bi-directional charging piles, virtual energy storage devices, and flexible adjustable loads is not less than the minimum positive and negative reserve capacities required to cover the corresponding prediction errors.

8. A method for optimizing the dispatching of a commercial area type virtual power plant according to any one of claims 1 to 3, characterized in that: The reserve capacity constraint includes: For the bi-directional charging pile, calculate the difference between the current power and the maximum and minimum power limits, and take the smaller value between the difference and the power quantity fluctuation as the upper limit of the reserve capacity of the bi-directional charging pile; For the virtual energy storage device, calculate the sum of the abandoned light quantity and the discharge margin, and take the smaller value between the sum and the discharge upper limit as the upper limit of the positive reserve capacity of the virtual energy storage device; calculate the sum of the current photovoltaic power and the charging margin, and take the smaller value between the sum and the charging upper limit as the upper limit of the negative reserve capacity of the virtual energy storage device; For the flexible adjustable load, taking into account the flexible adjustable ratio, take the smaller value that satisfies the power quantity fluctuation of the flexible adjustable load itself as the upper limit of the reserve capacity of the adjustable flexible adjustable load.

9. A commercial area - type virtual power plant system, characterized in that, Including: Optical storage devices, centralized new energy power generation devices, flexible adjustable loads, energy storage devices, bi-directional charging piles, a first DC bus, a first AC bus, a second DC bus, and a second AC bus arranged in a building; the first DC bus is connected to the main power grid through a first converter, and the first AC bus is connected to the main power grid through a first transformer; Each energy storage device and centralized new energy power generation device are respectively connected to the first DC bus through a DC / DC converter; The optical storage devices arranged in the building are connected to the first DC bus through a DC / DC converter after being aggregated through the second DC bus; The bi-directional charging pile is connected to the first AC bus through a converter; The flexible adjustable loads arranged in the building are connected to the second AC bus, and the second AC bus is connected to the first AC bus through a second transformer.

10. A commercial area - type virtual power plant system according to claim 9, characterized in that: The energy storage device is at least one of a supercapacitor and an electrochemical energy storage, and is equivalent to a virtual energy storage device together with the centralized photovoltaic array; The flexible adjustable load is a heat storage and cold storage air conditioner.

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